<![CDATA[Marketing Seeds]]>https://seeds.bixamedia.comhttps://substackcdn.com/image/fetch/$s_!K03m!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F4c35c3b1-18ae-4de2-a77b-bad33a11c1e6_500x500.pngMarketing Seedshttps://seeds.bixamedia.comSubstackThu, 07 May 2026 20:41:36 GMT<![CDATA[What's Changed in AI (for Marketers)]]>https://seeds.bixamedia.com/p/whats-changed-05-04-26https://seeds.bixamedia.com/p/whats-changed-05-04-26Mon, 04 May 2026 23:57:38 GMT

AI capabilities do not wait for organizational readiness. They ship, they integrate, and they start affecting how your tools work whether your team has planned for them or not.

That gap - between what the stack can now do and how most marketing teams actually operate - is the story of 2026 so far. And it is getting harder to ignore.

Thanks for reading Marketing Seeds! Subscribe for free to receive new posts and support my work.

This issue covers where the cracks are showing up and what to do about them.

🔥 High Impact

The Ad Market Voted. AI-Optimized Infrastructure Won.

What’s Changed

For the first time in digital advertising history, Meta is projected to overtake Google in global ad revenue. According to Emarketer’s latest forecast, Meta will generate $243.46 billion in net worldwide ad revenue in 2026, edging past Google’s $239.54 billion. In share terms, Meta is expected to capture 26.8% of global digital ad spend compared to Google’s 26.4% - the first time Google has lost the top spot since digital advertising became an industry.

Q1 2026 earnings confirmed the momentum. Meta’s ad revenue jumped 33% year over year to $55 billion. Google grew 15% to $77.25 billion. Both numbers are strong, but the growth rate gap tells the real story. Meta is accelerating at 24.1% for the full year. Google is holding at 11.9%.

In the last issue, we covered Muse Spark - Meta’s first proprietary model from Superintelligence Labs, now running across Facebook, Instagram, WhatsApp, Messenger, and Ray-Ban glasses. The ad revenue story is the market’s verdict on that infrastructure investment. Meta built Muse Spark to improve how its ads perform across three billion users. The revenue growth says it’s working.

Why It Matters

Google built its dominance on search intent. When someone searches, they tell you exactly what they want, and you pay to show up at that moment. That model worked for two decades and shaped how most marketing teams think about paid media.

Meta built its dominance on behavior. It knows what people do, what they respond to, and how to optimize delivery in real time across a unified intelligence layer. Muse Spark tightened that loop further. The result is an ad platform that doesn’t wait for someone to express intent… it predicts it.

The market is rewarding prediction over declaration. That’s the shift many media plans haven’t accounted for.

What This Means

  1. AI-optimized ad delivery is now the performance benchmark, not the exception. Meta’s growth isn’t happening because more advertisers showed up. It’s happening because the platform is converting existing spend more efficiently. Advantage+ campaigns, AI-driven creative optimization, and Muse Spark-powered targeting are compounding. Teams running manual campaign structures against this infrastructure are competing on uneven ground.

  2. The relationship between intent and performance is being redefined. Search captured intent at the moment of expression. Meta’s AI infrastructure captures intent before it’s expressed, by modeling behavior across three billion users. That changes where high-value attention lives… and where budgets should follow.

  3. This is a structural shift, not a quarterly blip. Meta’s growth rate is more than double Google’s. Emarketer’s forecast covers the full year. Morgan Stanley projected Meta’s quarterly ad revenue would surpass Google’s search ads specifically by Q2 2026. One of these trajectories is accelerating. The other is holding steady. Ad budgets built on 2024 assumptions are already misaligned.

What To Do

  • If your paid media budget is still heavily weighted toward search, this data warrants a closer look. Not necessarily a permanent reallocation, but a structured experiment. Run a controlled test that shifts a portion of search budget toward Meta’s AI-driven formats, specifically Advantage+ campaigns where the platform’s optimization has the most room to work. Set a clear measurement window, define what success looks like before you start, and let the data make the case rather than the headline.

  • If you are already running Meta campaigns, revisit how much of your spend is in manual versus AI-optimized structures. The performance gap between the two is widening as Muse Spark improves the platform’s ability to match creative to context. Teams still running legacy campaign structures are leaving performance on the table.

  • If you manage budgets for clients, this is a conversation worth having proactively. The shift is documented, the numbers are public, and waiting for clients to raise it puts you in a reactive position on a story that is only going to get louder.

Ignore This If

Paid media is not part of your marketing mix and you have no plans to test it.

Sources

Search Didn't Die. It Just Stopped Sending People to Your Site.

What’s Changed

In the April 6 issue, we covered the directional shift: AI Overviews were reducing clicks, zero-click searches were becoming the norm, and search budgets were starting to move toward GEO. Since then, the data has gotten significantly harder.

B2B organic traffic is down 33.6% year over year, according to Cognism’s Inside Inbound 2026 report. In Google’s AI Mode specifically, the zero-click rate reaches 93%. The overlap between pages that rank in the top 10 and pages that get cited in AI Overviews has collapsed from 75% in mid-2025 to between 17% and 38% by early 2026. Organic CTR drops 61% on queries where an AI Overview appears, according to Seer Interactive’s analysis of 25 million impressions across 42 organizations.

The overlap between pages that rank in the top 10 and pages that get cited in AI Overviews has collapsed from 75% in mid-2025 to between 17% and 38% by early 2026. That means ranking well no longer predicts whether your content gets cited in AI answers. They have become two separate problems.

One additional data point worth flagging: publishers who blocked AI crawlers to protect their content saw an average 7% decline in human browsing traffic within weeks of implementation. Blocking AI access didn’t protect traffic. It reduced it.

Why It Matters

The mechanism is simple and it’s not going to reverse. AI Overviews answer the query before the user has any reason to click. Your content informed the answer. You didn’t get the visit.

What makes this particularly difficult for marketing teams is that the standard signals of success are still green. Rankings are holding. Impressions are flat or rising. The traffic isn’t there. Teams that are waiting for a clear signal that something is wrong may already be six to twelve months into a decline they haven’t connected to this cause yet.

And because ranking and citation are now largely separate problems, teams that have invested heavily in SEO are not automatically protected. The search rankings playbook and the AI search visibility playbook are diverging fast.

What This Means

  1. Don’t block AI crawlers, the data shows it hurts human traffic too. Publishers who restricted access to bots like GPTBot and ClaudeBot saw human browsing traffic drop 7% within weeks. The instinct to protect content by blocking AI access is backfiring. If AI systems cannot read your content, they cannot cite it. Reduced citation is now correlating with reduced human traffic, not just bot traffic.

  2. Owned channels are becoming more valuable, not less. As search delivers less reliable traffic, the channels you control - email and SMS lists, direct audiences, community - absorb more of the acquisition burden. Teams that have been treating owned channels as secondary to search are now in a more exposed position.

  3. Generic content is the most exposed. AI systems cite sources that demonstrate real expertise, original data, and clear structure. Content that ranked because of volume and backlinks - thin informational pages built around keyword density rather than genuine depth - is losing citation ground fast. The sites that built their traffic on surface-level coverage of broad topics are seeing the steepest declines.

What To Do

  • Start with your highest-traffic informational pages - the ones built to capture top-of-funnel search queries. These are the pages most likely to be summarized by AI Overviews rather than clicked through. Restructure them to be citable: lead with a direct, quotable answer in the first paragraph, use clear subheadings that match user questions, and include original data or specific expertise signals that give AI systems a reason to attribute the source.

  • Audit which channels are currently carrying your acquisition load and how much of that depends on organic search. If search is carrying significant acquisition weight for your business, the exposure is real enough to warrant accelerating investment in owned channels now rather than waiting for the traffic decline to force the conversation.

Ignore This If

Organic search is not a meaningful source of traffic or leads for your business.

Sources

GPT-5.5 Didn't Just Get Smarter. It Got More Independent.

What’s Changed

On April 23, 2026, OpenAI released GPT-5.5 - internally codenamed “Spud” - six weeks after GPT-5.4. If you read our coverage of GPT-5.4, you can think of this as the moment the model stopped being a better assistant and started being a capable worker. OpenAI’s own framing makes the shift explicit: you can now give GPT-5.5 a messy, multi-part task and trust it to plan, use tools, check its work, navigate through ambiguity, and keep going… without managing every step.

The model launched with significant capability gains in agentic coding, computer use, knowledge work, and multi-step task execution. It handles a one million token context window, matches GPT-5.4’s per-token latency, and runs more efficiently, completing tasks with fewer tokens and fewer retries. GPT-5.5 and GPT-5.5 Pro are available to Plus, Pro, Business, and Enterprise users in ChatGPT and Codex. API access opened April 24.

OpenAI’s internal teams are already using it in real workflows. OpenAI’s Finance team used GPT-5.5 to review 24,771 K-1 tax forms totaling 71,637 pages, accelerating the task by two weeks compared to the prior year. A Go-to-Market employee automated generating weekly business reports, saving five to ten hours a week. More than 85% of OpenAI’s company uses Codex weekly across functions including marketing, finance, communications, and product management.

Why It Matters

GPT-5.5 is the first OpenAI flagship model positioned primarily as an agent runtime rather than a chat model. Previous releases led with quality metrics - reasoning scores, benchmark results, token-level evaluations. GPT-5.5 leads with outcomes: it completes the task, it uses your tools, it doesn’t need you to manage every step.

That is a different product than what most marketing teams are currently using. The majority of teams interacting with ChatGPT today are still in prompt-and-response mode - asking questions, reviewing outputs, adjusting and asking again. GPT-5.5 is designed for something different: give it a goal, connect it to your tools, and let it work.

The gap between teams using AI transactionally and teams using it operationally just got more expensive to ignore. Teams with repeatable workflows and connected systems get compounding leverage from a model like this. Teams still using it for one-off tasks get incrementally better outputs from a tool that is now capable of significantly more.

What This Means

  1. The prompt-and-response model of working with AI is becoming the floor, not the ceiling. GPT-5.5 is designed to interpret intent and execute across multiple steps without constant direction. Teams that have optimized their prompts but haven’t built workflows around AI are getting diminishing returns from their effort. The leverage has moved upstream to system design, not prompt craft.

  2. The tools you already use are about to get significantly more autonomous. GPT-5.5 flows downstream into ad platforms, CRMs, and content tools built on OpenAI’s infrastructure. The automation those tools offer isn’t static - it improves as the underlying model does. Teams don’t need to do anything differently to benefit, but they do need to understand what’s changing underneath them.

  3. Computer use and multi-step task execution are now mainstream capabilities. GPT-5.5 can operate software, navigate interfaces, and execute tasks across applications without human handholding at each step. This is no longer a research feature. It’s available to paid ChatGPT subscribers today, starting at the Plus tier.

What To Do

  • Start by auditing which workflows in your marketing stack involve repetitive, multi-step tasks that currently require a human to move between tools or stages. Campaign briefing, performance reporting, competitive monitoring, and content QA are strong starting candidates - they have clear inputs, defined outputs, and enough repetition to make automation worthwhile.

  • Pick one and run a structured pilot with GPT-5.5. The goal is not to automate everything at once…it’s to build operational experience with a model that works differently than what most teams are used to. The teams building that experience now will have a meaningful advantage when agentic workflows become standard practice rather than early adoption.

  • If your team is still using ChatGPT primarily for drafting and ideation, spend time exploring what Workspace Agents and Codex now support. The gap between how most marketing teams use the platform and what it can currently do is significant, and it is only going to widen as GPT-5.5 becomes the default intelligence layer across OpenAI’s product suite.

Ignore This If

You are not currently using ChatGPT or OpenAI-powered tools in any part of your marketing workflow.

Sources

⚠️ Emerging Shifts

The Roles Being Cut First Tell You Everything About What Comes Next.

What’s Changed

The tech layoff wave of 2026 is the largest concentrated workforce reduction in a decade. Oracle eliminated an estimated 20,000 to 30,000 positions. Amazon cut approximately 16,000 corporate roles in Q1. Meta announced 8,000 cuts - 10% of its workforce - effective May 20, with recruiting and HR absorbing 35 to 40% of those reductions. Microsoft offered voluntary retirement to 8,750 U.S. employees. Salesforce eliminated 4,000 customer support roles. By mid-April, trackers counted over 150,000 tech jobs eliminated in 2026 alone.

AI has been cited as a factor in roughly 27,600 of those cuts - about 13% of total job cut plans, up from 5% in 2025. But the headline number undersells the story. Both things are true simultaneously: AI is already replacing certain roles directly, and companies are using that narrative to fund a much larger infrastructure bet. Google, Amazon, Microsoft, and Meta collectively plan to spend $725 billion on AI infrastructure in 2026 - up 77% from last year. Human salaries are the only cost flexible enough to be cut fast enough to partially offset that build-out. The result is a transfer: headcount budgets becoming AI budgets, with the infrastructure those budgets fund eventually doing more of the work the headcount used to do.

One detail worth noting: Oracle employees reported being asked to document their workflows to train AI systems before being laid off. That sequence - capture the knowledge, cut the role - is the clearest illustration of how the transfer actually works in practice.

Why It Matters

The roles being eliminated are not random. Customer support, recruiting, HR, marketing ops, campaign reporting, and revenue operations are absorbing disproportionate cuts across every company in this wave. These are not coincidentally the same functions where agent deployment is most mature and most documented.

The pattern is legible: companies are cutting where agents can already do the work reliably, and investing the savings into infrastructure that will expand that capability further.

For marketers, this is not an abstract labor market story. It is a signal about where the technology is, what it can currently do reliably, and which functions are next in line as capability improves.

What This Means

  1. Vendor support is getting thinner. When the platforms you depend on are cutting customer support headcount and replacing it with agents, the quality and responsiveness of human help you get when something breaks is changing. That has direct operational implications for marketing teams managing complex stacks.

  2. The roles closest to agent capability are the most exposed. Customer support, marketing ops, campaign reporting, content QA, lead enrichment - these are not hypothetical future targets. They are where agent deployment is most mature right now, and the layoff data confirms it. If these functions exist in your org in their current form, the question is not whether they will be affected but when and how.

  3. The governance conversation is coming whether marketing is ready or not. Leadership is making headcount decisions informed by AI capability claims. Marketing teams that haven’t formed a clear view on where agents can and can’t replace human judgment will have that decision made for them by someone who has.

  4. Your own role is not exempt. If your primary value is executing tasks that agents can now do reliably - pulling reports, briefing campaigns, managing workflows, scheduling and coordinating - that is worth confronting honestly. The marketers who will be most valuable in the next two years are not the ones who do the most work. They are the ones who direct AI to do the most work while adding judgment, strategy, and accountability that agents cannot replicate.

What To Do

  • Audit which functions in your marketing operation map most closely to what agents can currently do reliably. Campaign reporting, content QA, competitive monitoring, lead enrichment, and briefing are strong candidates. This is not an exercise in identifying what to eliminate, it is an exercise in understanding where your team’s time is going and whether that time is being spent on work that compounds or work that agents are already doing elsewhere.

  • Get ahead of the governance conversation internally. Form a point of view on where AI augments your team versus where it replaces specific functions, and bring that perspective to leadership before they develop one without you. The teams that will fare best in this environment are the ones that proactively redesign their workflows rather than defending existing ones.

  • Factor vendor support degradation into your stack decisions. If a platform is cutting significant support headcount, that changes the risk calculus around depending on it for mission-critical workflows. Ask your vendors directly how AI is changing their support model before you expand your reliance on their tools.

  • Assess your own role with the same honesty you would apply to your team. Where do you add value that AI cannot replicate - strategic judgment, client relationships, creative direction, accountability for outcomes? Where are you spending time on execution that agents are already doing reliably elsewhere? The marketers who make themselves demonstrably AI-powered are the ones who make that question easy to answer.

Ignore This If

AI agent deployment is not a factor in your industry and your organization has no plans to evaluate it in the near term.

Sources

If You Want Help With This

If this story landed close to home, this is exactly the problem AI-Powered Marketing Department is built for.

It helps marketers build practical, repeatable AI workflows so the work that used to define your role gets done faster… and the time that frees up goes toward the strategic, judgment-driven work that agents cannot do.

The marketers who build this now won’t be explaining their value later.

Learn more about AI-Powered Marketing Department here.

Or start with a free preview here.

You Can Now Pick AI Tools Based on What They Do, Not Where They Live.

What’s Changed

On April 27, 2026, Microsoft and OpenAI restructured their partnership in a way that changes how enterprises access AI tools. For nearly three years, OpenAI’s models were exclusively available through Microsoft Azure - meaning if your organization ran on AWS or Google Cloud, accessing OpenAI’s tools required working around your existing infrastructure or switching providers. That exclusivity is now gone.

OpenAI models, including GPT-5.5, are now available natively on Amazon Web Services through AWS Bedrock. Google Cloud access is expected to follow later in 2026. Microsoft remains OpenAI’s primary cloud partner and retains a non-exclusive license to OpenAI’s technology through 2032, but the lock-in that defined enterprise AI procurement since 2022 is over.

OpenAI’s own revenue chief framed the shift directly in an internal memo: the Microsoft exclusivity had “limited our ability to meet enterprises where they are.” The inbound demand from enterprises wanting OpenAI tools on AWS was, in her words, “frankly staggering.”

Why It Matters

For two years, the AI vendor map was shaped by exclusive relationships that had nothing to do with which tools were best for your team. If your organization was AWS-native, OpenAI tools came with friction. If your IT and procurement teams were committed to a particular cloud, that commitment quietly determined which AI capabilities were available to your marketing stack.

That constraint shaped tool decisions that are still in place at most organizations today. Teams evaluated, selected, and passed on AI tools based on infrastructure compatibility as much as actual capability. The map those decisions were made against no longer exists.

What This Means

  1. Tool selection can now be based on fit, not on inherited infrastructure. The cloud your organization runs on no longer determines which frontier AI models are available to you. If OpenAI tools were off the table because of AWS or Google Cloud constraints, that conversation is worth reopening with a different starting point.

  2. Vendor lock-in is a smaller risk in AI tool decisions than it was six months ago. The exclusive era that made choosing an AI tool feel like choosing a cloud provider is ending. OpenAI models are available on AWS today and Google Cloud later this year. Anthropic models are available across multiple clouds. The market is moving toward model portability, which changes the risk calculus around committing to any single platform.

  3. The competition among AI platforms is shifting. When model access was tied to cloud relationships, the question was which vendor your org was aligned with. Now that the same models are available across providers, the competition moves to platform quality - which provider offers the best performance, pricing, governance, and integration for the work your team actually does. That is a better question for marketing teams to be asking.

What To Do

  • Revisit any AI tool decisions that were made or avoided based on cloud compatibility. If OpenAI tools were evaluated and deprioritized because of infrastructure constraints rather than capability concerns, that evaluation is worth reopening now that those constraints have changed.

  • If your team is in the early stages of building or expanding an AI stack, use this moment to establish evaluation criteria that are cloud-agnostic. Assess tools on what they do, how well they integrate with your workflows, and what governance they support… not on which cloud partnership they were born from.

  • If you manage vendor relationships or are involved in procurement decisions, flag this shift to the stakeholders who made earlier tool decisions based on cloud alignment. The constraints they were working around may no longer apply.

Ignore This If

Your AI tool decisions have never been shaped by cloud infrastructure constraints and you evaluate tools purely on capability.

Sources

👀 Keep An Eye On

AI Content Is Flooding the Feed. Platforms Are Starting to Push Back.

What’s Changed

Music streaming platforms are the first content distribution channel to hit an AI saturation problem at scale, and the numbers make the issue concrete. Deezer reports that 44% of daily uploads to its platform are now AI-generated tracks. Suno, one of the largest AI music generators, produces seven million tracks per day. Sony Music has requested the removal of more than 135,000 AI-generated songs impersonating its artists.

The volume is enormous. The engagement is not. AI-generated tracks account for less than 3% of total streams on Deezer, and the majority of those streams have been identified as fraudulent - driven by bots rather than human listeners. Platforms are responding. Spotify launched Artist Profile Protection, giving artists the ability to review releases before they go live. Deezer implemented AI detection tools to track and label synthetic content and demonetizes those streams.

Consumer sentiment is moving in the same direction. According to Luminate’s Generative AI in Entertainment 2026 report, overall listener comfort with AI music dropped from -13% to -20% between May and November 2025. Across the board, consumers are more likely to feel uncomfortable with AI-generated music than comfortable with it.

Why It Matters

Music is the leading indicator. The pattern playing out on streaming platforms - massive AI content volume, negligible human engagement, platform policy responses, declining consumer trust - will play out on every content distribution channel as AI generation tools become cheaper and more accessible.

Warner Music Group framed the dynamic precisely in a recent filing: in a world of near-infinite content, what becomes scarce is trust. That is not a music industry observation. It is a content marketing observation. When any feed can be flooded with AI-generated content at effectively zero marginal cost, the channels and creators that audiences trust become more valuable, not less.

What This Means

  1. Volume is no longer a content advantage. AI has made content production effectively free and infinitely scalable. The platforms dealing with this first - music streaming - are showing that volume without authenticity doesn’t drive engagement. That dynamic will reach every content channel.

  2. Platform policy will shape what content competes against yours. The decisions Spotify, Deezer, and Apple Music are making right now about how to handle AI content - labeling, demonetization, distribution limits - will likely be replicated by social platforms and content networks. Those decisions will determine what your content has to compete against and how platforms weigh authenticity signals in their algorithms.

  3. Audience trust is becoming a content asset. In a saturated feed, the brands and creators audiences already trust get more attention, not less. That makes trust-building a strategic priority, not just a brand value.

What To Do

  • Watch how streaming platforms structure their policy responses to AI content saturation - labeling requirements, demonetization rules, distribution limits - because those decisions will inform how social platforms, content networks, and search engines handle the same problem.

  • If your content strategy has been focused primarily on volume, more posts, more articles, more output, this is the moment to audit whether that approach is building trust or contributing to the noise your audience is already tuning out.

Ignore This If

Your content strategy is already focused on depth, specificity, and audience trust rather than volume, and you have no concerns about AI-generated content affecting your distribution channels.

Sources

The Bottom Line

A map doesn’t stop working all at once. It becomes unreliable gradually… a road that moved, a landmark that disappeared, a route that no longer takes you where it used to. You don’t know it’s wrong until you’re somewhere you didn’t expect to be.

That’s where a lot of marketing teams are right now. The assumptions underlying how they allocate budget, evaluate tools, staff their teams, and create content were accurate enough twelve months ago. They’re less accurate today. And the gap between the map and the territory is widening faster than most teams are updating their assumptions.

The advantage in this environment doesn’t go to the teams that saw everything coming. It goes to the teams that update fastest when the ground shifts, and that treat each new data point not as a headline to bookmark but as a reason to ask whether their current approach still makes sense.

Which of these changes is already showing up in how your team operates? And which ones are you still treating as someone else's problem? I'd love to hear in the comments.

If you want more issues like this, subscribe to Marketing Seeds and share this newsletter with a colleague or team member who's still navigating with last year's map.

Thanks for reading Marketing Seeds! Subscribe for free to receive new posts and support my work.

]]><![CDATA[What's Changed in AI (for Marketers)]]>https://seeds.bixamedia.com/p/whats-changed-04-20-26https://seeds.bixamedia.com/p/whats-changed-04-20-26Wed, 22 Apr 2026 13:10:07 GMT

AI capabilities do not wait for organizational readiness. They ship, they integrate, and they start affecting how your tools work whether your team has planned for them or not.

That gap - between what the stack can now do and how most marketing teams actually operate - is the story of 2026 so far. And it is getting harder to ignore.

Thanks for reading Marketing Seeds! Subscribe for free to receive new posts and support my work.

This issue covers where the cracks are showing up and what to do about them.

🔥 High Impact

Meta Just Rebuilt the Brain Behind Its Ad Platform

What’s Changed

On April 8, Meta launched Muse Spark, the first major model from its newly formed Meta Superintelligence Labs, led by Alexandr Wang, who joined via a $14.3 billion Scale AI acquisition. Built in nine months, Muse Spark is closed-source - a deliberate break from Meta’s open-source Llama tradition - and runs in two modes: Instant for fast responses and Thinking for multi-step reasoning. It is natively multimodal, handling text, image, and audio.

The model is already live inside Meta AI and deploying across WhatsApp, Instagram, Facebook, Messenger, and Ray-Ban smart glasses. That is over three billion monthly active users on a single upgraded intelligence layer. Meta’s stock jumped roughly 6.5% on the announcement.

The business logic is straightforward. Advertising accounts for 98% of Meta’s roughly $200 billion in annual revenue. With $115–135 billion in 2026 capital expenditure, the pressure to show returns is significant. Analysts at Morningstar and Citizens both point to ad targeting, not developer adoption, as the real prize.

Why It Matters

In the last issue, we flagged the frontier model race as something to watch. Muse Spark is the moment that race becomes a marketing operations story.

The benchmarks are not the point. Meta was never going to win on raw capability against OpenAI or Anthropic. What Meta has that no other AI lab comes close to is distribution: three billion people already inside its platforms, already generating behavioral data, already seeing ads. Muse Spark gives Meta a model purpose-built to make that distribution more valuable.

For marketers, the implication is concrete. The targeting logic behind every Meta campaign just changed. The content surfaces where your ads appear are being re-ranked by a new model. The recommendation systems that determine organic reach are running on different intelligence. Most teams have not adjusted how they operate on Meta to account for any of it.

What This Means

  1. Ad targeting on Meta is entering a new optimization regime. Muse Spark was built with advertising as the primary revenue case, not a secondary feature. When the model powering your ad platform is rebuilt from scratch with tighter integration to user behavior across text, image, and audio, the variables that determined performance in 2025 are not guaranteed to hold in 2026.

  2. The creative and content bar on Meta platforms is rising. A more capable model means the platform’s ability to assess content relevance, quality, and engagement signals is improving. Content that worked because it gamed older ranking signals will lose ground to content that genuinely matches user intent and context.

  3. Meta’s closed-source shift has competitive implications. By keeping Muse Spark proprietary, Meta controls how and when its most capable intelligence shows up in its products. Advertisers and marketers have less visibility into how decisions are being made, and less ability to reverse-engineer what works.

What To Do

  • If you are running Meta campaigns, treat Q2 2026 as a recalibration period. Pull your last 90 days of performance data and look for signals that targeting efficiency or creative performance is shifting. Do not assume that what worked before Muse Spark will hold.

  • Review your creative strategy against the multimodal reality of where Muse Spark is deployed. Content that appears across Instagram, WhatsApp, and Ray-Ban glasses is being evaluated by the same model across very different contexts. Consistency of message matters more when the intelligence layer is unified.

  • If your team has not revisited its Meta strategy since the Llama era, that gap is now measurable in performance terms, not just theoretical ones.

Ignore This If

Meta platforms are not part of your media mix and you have no plans to test them.

Sources

Microsoft Is Building Its Own AI Stack, and It's Already in Your Tools

What’s Changed

On April 2, Microsoft launched three in-house AI models - MAI-Transcribe-1, MAI-Voice-1, and MAI-Image-2 - available immediately through Microsoft Foundry and a new MAI Playground. They were built by Microsoft’s Superintelligence team, formed in October 2025 and led by Mustafa Suleyman, Microsoft AI’s CEO, under an explicit mission he calls “AI self-sufficiency.”

The models cover three commercially significant capabilities. MAI-Transcribe-1 handles speech-to-text across 25 languages at 2.5x the speed of Microsoft’s previous Azure Fast offering, at $0.36 per hour. MAI-Voice-1 generates 60 seconds of natural-sounding audio in one second and can clone a custom voice from just a few seconds of audio input. MAI-Image-2 is Microsoft’s most capable image generation model, already ranking top 3 on the Arena.ai leaderboard, with rollouts underway in Copilot, Bing, and PowerPoint.

These are not models in testing. MAI-Transcribe-1 is already running inside Copilot Voice Mode and Microsoft Teams. MAI-Image-2 is live in Copilot and Bing Image Creator. PowerPoint is next.

In a separate announcement the same week, Microsoft also moved Agent Evaluation in Copilot Studio to general availability, giving enterprise teams automated tools to assess AI agent behavior at scale without manual testing.

Why It Matters

For most of the past two years, Microsoft’s AI story was straightforward: partner with OpenAI, embed Copilot everywhere, let the model layer sit with someone else. That story just changed.

Suleyman was unambiguous in his public comments: Microsoft is building toward complete independence at the model layer across every modality. The MAI launch is the opening move in a multi-year roadmap, not a one-off product release.

If your team uses Teams for calls, Copilot for drafts, or PowerPoint for presentations, the AI underneath those tools has already been replaced. The question is whether the way you use them has changed at all.

What This Means

  1. The Microsoft productivity stack is being rebuilt on first-party AI. Copilot, Teams, PowerPoint, Bing - each of these products is already running or phasing in MAI models. The AI capabilities your team relies on inside Microsoft’s suite are no longer third-party features; they are first-party infrastructure that Microsoft controls end-to-end.

  2. Custom voice and transcription workflows just got significantly cheaper. MAI-Transcribe-1 at $0.36 per hour undercuts leading alternatives by roughly 50%. MAI-Voice-1’s ability to generate a custom brand voice from seconds of audio removes a production bottleneck that previously required specialized vendors. Teams running these workflows on legacy tooling now have a direct cost and quality comparison to make.

  3. Microsoft’s move signals a broader platform independence trend. Meta rebuilt its ad intelligence in-house. Microsoft is rebuilding its productivity intelligence in-house. The era of large platforms depending on outside AI providers for core features is ending. For marketers, that means the tools you rely on will increasingly reflect their platform owners’ priorities, not a shared model layer.

What To Do

  • Audit which Microsoft tools your team uses for transcription, voice, and image generation. If you are paying for third-party vendors to handle these workflows inside a Microsoft environment, the calculus on those contracts has changed.

  • If you use Copilot in Teams or PowerPoint, check what features have updated in the last 30 days. MAI model rollouts are phased and not always announced loudly. The capability upgrade may already be live in your environment.

  • If you have AI agents running inside Copilot Studio, you previously had to review what they were doing manually, which does not scale once agents handle hundreds of interactions a day. Microsoft just shipped a built-in tool that does that review automatically. For teams that have been hesitant to move agents into live use, this removes one of the last practical blockers.

Ignore This If

Your team does not use Microsoft products and has no plans to evaluate AI transcription, voice, or image generation workflows.

Sources

Your Martech Stack Just Changed Its Architecture. Both Salesforce and Adobe Did It on the Same Day.

What’s Changed

On April 15, Salesforce and Adobe each announced a fundamental shift in how their platforms work, and both moved in the same direction on the same day.

Salesforce launched Headless 360 at its annual TrailblazerDX developer conference. The announcement is straightforward in its implications: everything on Salesforce is now an API, an MCP tool, or a CLI command. Customer 360, Data 360, Agentforce, your data, your workflows, your business logic - all of it is now accessible directly by AI agents without anyone logging into a Salesforce interface. The platform also introduced the Agentforce Experience Layer, which decouples what an agent does from where it appears. Build an agent interaction once and it deploys natively across Slack, Teams, WhatsApp, ChatGPT, Claude, and Gemini without writing separate code for each surface. Salesforce also announced it is moving from per-seat licensing to consumption-based pricing for Agentforce - a direct acknowledgment that agents, not humans, are becoming the primary users of the platform.

Adobe announced Firefly AI Assistant the same day. The assistant brings Photoshop, Premiere, Lightroom, Express, Illustrator, and Firefly into a single conversational interface where creators describe the outcome they want and the assistant orchestrates and executes the multi-step workflow across apps. Adobe’s Experience Platform Agent Orchestrator has been live in enterprise marketing stacks since September 2025. Most teams are still not fully using it. Firefly AI Assistant extends that same agentic model into the creative workflow layer, adding to a capability set that is already further along than most teams realize.

One real-world data point worth noting: Engine, a B2B travel company featured in the Salesforce keynote, built its customer service agent in 12 days using Agentforce and now handles 50% of customer cases autonomously.

Why It Matters

When two of the most widely used platforms in marketing make the same architectural shift on the same day, the direction of the category is no longer ambiguous.

The shift is from platforms you log into to platforms agents operate inside of. The dashboard is not disappearing, but it is no longer the primary interface. Data, workflows, and business logic are being exposed as infrastructure that agents can call directly. For marketing teams, that changes what the job actually is. If the platform handles execution, the work that remains is strategy, oversight, and the quality of the instructions you give.

Most teams using Salesforce and Adobe today are still operating with a dashboard-first workflow. The architecture underneath them has changed. The workflows have not.

What This Means

  1. The martech stack is becoming an orchestration layer, not just a set of dashboards. When Salesforce says everything is now an API and Adobe builds a single conversational interface across its entire creative suite, both companies are making the same bet: the next primary interface for marketing work is natural language and agent orchestration, not click-through UI. Teams that still think of these platforms as software to log into are already behind the architectural curve.

  • Salesforce’s pricing model change is the most underreported signal. Moving from per-seat to consumption-based pricing means Salesforce is explicitly designing for a world where agents - not humans - are the primary platform users. That is a business model bet with direct implications for how marketing teams budget and staff around these tools going forward.

  • The creative workflow is the last frontier for agentic marketing, and Adobe just opened it. Audience segmentation and campaign analytics have been getting the agentic treatment for months. Firefly AI Assistant brings that same model to creative production, the part of the workflow that has remained most stubbornly human. The gap between strategic direction and finished creative output is about to compress significantly.

What To Do

  • If your team uses Salesforce, find out whether Agentforce is already part of your contract. Many Salesforce customers have access to Agentforce features they have not activated. The Headless 360 launch is the right moment to evaluate what is already available in your instance before scoping new tooling.

  • If your team uses Adobe Creative Cloud or Experience Platform, pay attention to the Firefly AI Assistant public beta rollout in the coming weeks. The teams that figure out how to direct the assistant effectively, not just use it, will build a workflow advantage that compounds over time.

  • Use both announcements as a forcing function to audit your current martech workflows against the architecture that now exists. The gap worth closing is between what these platforms can already do and how your team currently uses them.

Ignore This If

You do not use Salesforce or Adobe products and your martech stack is not moving in an agentic direction.

Sources

⚠️ Emerging Shifts

AI Didn't Break Your Marketing Workflows. It Just Made the Cracks Visible.

What’s Changed

A SmartBrief piece published in late March, written by Christine Royston, CMO of Wrike, puts hard numbers on something marketing leaders are quietly running into: 82% of knowledge workers already use AI on the job, and nearly 40% rely on three to five AI tools weekly. Yet 96% say AI would be significantly more valuable if their tools could automatically share context and work together. Only 23% of employees feel aligned with leadership on AI strategy. Fewer than half of companies have rolled out company-wide AI training or policies. And 42% of workers are using shadow AI - tools their organizations have not sanctioned - to fill the gaps.

The diagnosis is straightforward: two years of AI tool adoption layered on top of workflows that were already broken. The tools accelerated output. The dysfunction scaled with it. More AI did not mean more clarity - it meant the same fragmentation moving faster, across more channels, with higher stakes attached.

Why It Matters

This story lands differently when read alongside the Salesforce and Adobe announcements from April 15. Those platforms just restructured themselves around the assumption that marketing workflows are connected, governed, and agent-ready. The SmartBrief data shows most teams are nowhere close.

The problem predates the tools. Most marketing workflows were already fragmented - disconnected hand-offs, siloed data, no single source of truth. AI didn’t create those conditions. It just removed the slack that was absorbing them. AI is now making that gap expensive: in duplicated work, misaligned output, and campaigns that move fast in the wrong direction.

What This Means

  1. Tool adoption without workflow redesign is the most common AI mistake marketing teams are making right now. The Wrike research makes it concrete: 96% of workers say AI would be more useful if tools shared context automatically. Most teams asking for better AI connectivity never had connected workflows to begin with.

  2. Shadow AI is a workflow signal, not a compliance problem. When 42% of employees are using unsanctioned AI tools, it means the official workflow is not meeting their actual needs. The instinct to crack down misses the real question: what is broken in the approved process that is pushing people toward workarounds?

  3. Agentic platforms accelerate what is already there, good or bad. Salesforce and Adobe can now orchestrate across data, content, and campaigns autonomously. But that capability only delivers value if the workflows feeding those systems are clean, connected, and governed. Fragmented input produces fragmented output, faster.

What To Do

  • Before adding more AI tools, map how work actually moves through your marketing team today - from brief to approval to launch. Not how it is supposed to move. How it actually moves. The gaps in that map are where AI will create the most friction, not the most value.

  • Pick one workflow to redesign before you automate it. Campaign briefing, creative review, and performance reporting are the three places most teams have the most undocumented, inconsistent hand-offs. Fix the process first, then apply AI to accelerate it.

  • If shadow AI usage is visible on your team, treat it as a diagnostic. Ask which approved tools or processes people are working around and why, before restricting access.

Ignore This If

Your marketing workflows are already documented, connected across tools, and operating with a clear system of record for how work moves from brief to launch.

Sources

SmartBrief - 2026 will expose broken marketing workflows being masked by AI (March 2026)

If You Want Help With This

If your team is adding AI tools faster than you are redesigning the workflows underneath them, this is exactly the problem AI-Powered Marketing Department is built for.

It helps marketing teams build practical, repeatable AI workflows, so the tools you already have start working together instead of creating more fragmentation.

Learn more about AI-Powered Marketing Department here.

Or start with a free preview here.

AI Agents Can Now Complete Two Thirds of Real Computer Tasks. A Year Ago It Was One in Eight.

What’s Changed

Stanford’s 2026 AI Index Report, released this month, tracks AI performance across capability domains with independent, data-driven sourcing. One number stands out for anyone thinking about marketing operations: on OSWorld - a benchmark that tests AI agents on real computer tasks across operating systems - task success jumped from 12% to approximately 66% in a single year.

That is a fivefold improvement in twelve months. The same report documents that on SWE-bench Verified, which tests AI on real software engineering tasks, performance rose from 60% to near 100% of the human baseline in one year. Organizational AI adoption has reached 88% of surveyed companies. And the estimated value of generative AI tools to U.S. consumers reached $172 billion annually by early 2026 - up from $112 billion a year prior, with the median value per user tripling over that period.

The counterweight is worth noting. Agents still fail roughly one in three tasks on structured benchmarks. Agent deployment sits in single digits across nearly all business functions. And the same models that solve PhD-level science problems read analog clocks correctly just 50.1% of the time.

Why It Matters

The 12% to 66% jump is the number that changes the conversation about agents from theoretical to operational. At 12%, agents were curiosities - useful in narrow conditions, unreliable enough to require constant supervision. At 66%, they are viable for a meaningful range of real marketing tasks: research, reporting, content QA, campaign monitoring, data summarization, and workflow execution.

The failure rate still matters. One in three tasks failing is not acceptable for anything customer-facing or brand-critical without a human review layer. But for internal operations - the work that happens before anything reaches a customer - that threshold is workable and improving fast.

What This Means

  1. The operational case for agents in marketing is no longer speculative. A 66% task success rate on real computer tasks means agents can reliably handle a meaningful portion of the repetitive, high-volume operational work that consumes marketing teams. The question has shifted from whether agents can do the work to which work is worth delegating first.

  2. The failure rate defines where human oversight is non-negotiable. One in three tasks still fails. Any agent-driven workflow touching customer communications, brand assets, paid media, or public-facing content needs a human checkpoint. The teams that will get burned are the ones treating 66% as good enough across the board rather than as a starting point for scoping where agents are safe to run.

  3. Deployment is still early, which means the window for advantage is open. Agent deployment sits in single digits across nearly all business functions. Most organizations that report using AI are not yet running agents in production. The gap between adoption and deployment is where the competitive opportunity currently lives.

What To Do

  • Use the 66% benchmark as a scoping tool, not a green light. Map your marketing tasks by two criteria: repetition and stakes. High repetition, lower stakes - performance reporting, content tagging, briefing summaries, competitive monitoring - are where agents are most viable right now. High stakes, customer-facing work stays under human oversight until the reliability curve improves.

  • If you have not started testing agents on any internal workflow, pick one task this quarter and run a structured pilot. The teams building operational experience with agents now will have a meaningful head start when deployment becomes standard practice.

Ignore This If

You are not evaluating or using AI agents in any part of your marketing workflow and have no plans to in the near term.

Sources

👀 Keep An Eye On

The AI Workspace Is Consolidating. OpenAI Is Merging Its Tools Into One Platform.

What’s Changed

In March, OpenAI confirmed plans to merge ChatGPT, its Codex coding agent, and its Atlas browser into a single unified desktop application. The full merger has no official launch date yet, but the pieces are already in motion. Codex launched on Windows on March 4. Atlas has been in beta since March. ChatGPT 5.5 launched April 6 with improved memory and task continuity. The three products - previously separate tools requiring separate sessions and context - are being brought into one interface where you can move between conversation, research, and autonomous task execution without switching windows or rebuilding context.

Codex handles coding and background task execution. Atlas browses, understands, and acts on web pages on your behalf. ChatGPT coordinates everything. The planned unified app includes multi-agent parallel execution, reusable agent workflows, scheduled background tasks, and AI action tracking for enterprise teams. OpenAI’s CEO of Applications, Fidji Simo, framed the move as a product focus decision: consolidating bets that are working rather than maintaining a fragmented product surface.

ChatGPT now has 900 million weekly users. Over one million companies use OpenAI’s agents in daily operations. The super app is OpenAI’s move to make that usage stickier, deeper, and harder to replace.

Why It Matters

The pattern playing out across this issue - Meta rebuilding its intelligence layer, Microsoft embedding first-party models into its productivity suite, Salesforce and Adobe restructuring around agents - is showing up inside the tool many marketing teams already use daily.

The shift from ChatGPT as a prompt-and-response tool to ChatGPT as a persistent workspace changes how teams should think about using it. Research, drafting, competitive monitoring, and task execution that currently happen across multiple tools and require manual context transfer between them can increasingly happen inside one session. Most teams using ChatGPT today are using a fraction of what the platform now supports.

What This Means

  1. The AI interface is becoming a work hub, and the tools you use separately today may consolidate inside it. ChatGPT already handles conversation, research, image generation, and code. The super app adds autonomous browsing and background task execution. Teams that treat it as a chat tool are underusing a platform that is now closer to an operating layer for knowledge work.

  2. Context continuity is the real unlock. The friction in most AI-assisted workflows is not the individual task, it is rebuilding context every time you switch tools or start a new session. A unified workspace that maintains context across conversation, research, and execution removes the most common bottleneck in how marketing teams currently work with AI.

  3. Enterprise adoption signals where this is heading. Over a million companies are already using OpenAI agents in daily operations, and 40% of OpenAI’s revenue now comes from enterprise. The super app is being built with IT governance, action tracking, and access controls designed for business use.

What To Do

  • If your team uses ChatGPT primarily as a writing or research tool, spend 30 minutes exploring what agent mode and the current workspace already support. The gap between how most teams use it and what it can now do is significant - and the super app has not fully launched yet, meaning the capability floor is still rising.

  • Watch how your team’s workflow across research, briefing, drafting, and review could be compressed into fewer tools and fewer context switches. The value is in reducing how much time and effort surrounds the decisions, not in making the decisions themselves.

Ignore This If

Your team does not use ChatGPT in any regular workflow and has no plans to evaluate it.

Sources

OpenClaw Is Shipping Fast. The Security Picture Is Getting Clearer, and It's Not Comfortable.

What’s Changed

In the last issue, we covered OpenClaw’s explosive growth and the security warnings that followed. Both have continued accelerating.

On the capability side: OpenClaw released two major updates in April alone - versions 2026.4.12 and 2026.4.14 - adding cloud-backed memory, active memory sub-agents, GPT-5.4 support, Gemini text-to-speech, improved multi-agent coordination, and broader plugin integrations across Slack, Telegram, and messaging platforms. The project now has 347,000 GitHub stars, up from 247,000 in early March.

On the security side: researchers have now tracked 138 CVEs (Common Vulnerabilities and Exposures) against OpenClaw since February, including 7 critical and 49 high severity. The April 2026 batch alone covered 13 vulnerabilities, two at critical severity, patched in version 2026.4.5. Over 135,000 OpenClaw instances are currently exposed on the public internet across 82 countries. Of those, 63% are running without any authentication. Security researchers tracking ClawHub - OpenClaw’s plugin marketplace - have confirmed over 824 actively distributed malicious skills, including keyloggers and credential stealers targeting OAuth tokens and API keys.

The recommended posture from security experts is now unambiguous: if your organization has employees or contractors running OpenClaw, assume compromise. Audit what the tool has accessed, rotate credentials for every connected service, and treat every session it has touched as potentially compromised until you can verify otherwise.

Why It Matters

The pace of OpenClaw’s shipping is the signal worth tracking, not any individual release. Two major updates in two weeks, model integrations keeping pace with frontier releases, memory and multi-agent capabilities maturing rapidly - this is a project moving faster than most enterprise software teams can evaluate it.

The security picture is the counterweight. Every capability OpenClaw adds - deeper memory, broader platform integrations, more autonomous agent coordination - also expands the attack surface. The architectural problem has not changed: OpenClaw operates with the same permissions as the user running it. A single compromised plugin does not isolate to one app. It touches everything the user has connected.

For marketing teams, the practical question is not whether to use OpenClaw specifically. Most won’t. The question is whether the same pattern - rapid capability expansion, lagging security governance - is showing up in the agentic tools your team is already using.

What This Means

  1. Rapid capability growth and rapid vulnerability growth are happening simultaneously. OpenClaw is not slowing down on either front. Every new integration - memory, multi-agent coordination, platform connections - also creates new attack surface.

  • The architectural risk is not a bug. It is a design reality. OpenClaw works by operating with the same permissions as the user running it. That is what makes it powerful. It is also what makes a compromise so costly - a single vulnerable plugin does not expose one app, it exposes everything the user has connected. That design tradeoff exists in varying degrees across most agentic tools, not just OpenClaw.

  • The pattern matters more than the tool. Most marketing teams will never run OpenClaw. But the same dynamic - autonomous tools with broad permissions, security governance lagging behind capability rollout - is playing out across the agentic tools category. OpenClaw is just the most visible case study.

What To Do

  • If anyone on your team is running OpenClaw, update to version 2026.4.5 immediately and audit connected credentials. If you cannot confirm they are on a current version, treat the environment as compromised and rotate API keys and OAuth tokens for every connected service.

  • More broadly, use OpenClaw as a reference case when evaluating any agentic tool. Before deployment, ask: what permissions does this tool have, what happens if it is compromised, and what is the process for auditing what it has done?

Ignore This If

No one on your team is using OpenClaw or evaluating open-source agent frameworks.

Sources

The Bottom Line

Most teams are one workflow audit away from getting significantly more out of the tools they already pay for.

The platforms are ready. The question is whether your team is operating in a way that takes advantage of them.

The advantage in 2026 will go to the teams who changed their workflows and drove measurable performance gains because of it.

Which of these changes is already showing up in your stack, and which ones still feel like someone else's problem? I'd love to hear where your team is in the comments.

If you want more issues like this, subscribe to Marketing Seeds and share this newsletter with a friend, colleague, or team member who is trying to close the gap between what AI can do and how their marketing actually runs.

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]]><![CDATA[What's Changed in AI (for Marketers)]]>https://seeds.bixamedia.com/p/whats-changed-04-06-26https://seeds.bixamedia.com/p/whats-changed-04-06-26Mon, 06 Apr 2026 13:10:42 GMT

Agentic AI went mainstream this quarter.

Thanks for reading Marketing Seeds! Subscribe for free to receive new posts and support my work.

Open-source agents hit millions of users. Google standardized protocols for agent-driven commerce. ChatGPT became an ad platform.

And in every case, the security, governance, and measurement systems around these tools are trailing behind the capabilities themselves.

This issue covers where that gap is showing up and what it means for marketers paying attention.

🔥 High Impact

The AI Agent Era Is Here. Security and Control Will Decide Who Trusts It.

What’s Changed

OpenClaw, an open-source AI agent framework built by Austrian developer Peter Steinberger, has become the fastest-growing open-source project in history. Originally a side project created in November 2025, it hit 247,000 GitHub stars by early March 2026. Steinberger has since joined OpenAI to work on personal agents, and the project is moving to an independent foundation.

Unlike a chatbot, OpenClaw does not just answer questions. It acts. It can manage calendars, check into flights, write and execute code, browse the web, and coordinate tasks across messaging apps like WhatsApp, Telegram, and Signal. It runs locally and connects to whichever AI model the user chooses.

That capability has attracted serious institutional interest. At GTC 2026, NVIDIA CEO Jensen Huang called OpenClaw “the operating system for personal AI” and announced NemoClaw, an enterprise stack that adds security sandboxing, privacy controls, and local model support to OpenClaw in a single command. NVIDIA built NemoClaw in collaboration with Steinberger and designed it to run on any hardware.

At the same time, adoption has outpaced governance. China saw a rapid wave of consumer and enterprise adoption, with Tencent, Alibaba Cloud, and ByteDance building services on top of OpenClaw and local governments subsidizing installation. That enthusiasm triggered an equally fast security backlash. China’s National Cyber Security Emergency Response Team published warnings about severe default configuration risks, prompt injection vulnerabilities, and malicious plugins. Chinese authorities restricted state-owned enterprises and government agencies from running OpenClaw on office devices. Gartner separately described it as an “unacceptable cybersecurity risk” for business users and recommended running it only in isolated environments with throwaway credentials.

By the end of March, over 63,000 live OpenClaw instances were confirmed on the public internet, many with open SSH ports, exposed databases, and minimal authentication.

Why It Matters

Agentic AI is not new. Enterprise platforms have been shipping agent features for months, and we covered Salesforce’s Agentforce rollout in the last issue. What OpenClaw changed is who has access.

By making a fully autonomous agent available to anyone with a messaging app, running locally and for free, it compressed the adoption curve that enterprise tools would have taken years to play out. Millions of people tried it. Security researchers immediately found real vulnerabilities.

The pattern playing out around OpenClaw will repeat. Consumer enthusiasm moves first. Enterprise interest follows. Security and governance scramble to catch up. Marketers do not need to use OpenClaw specifically, but they need to understand what it signals, because the tools they already use are heading in the same direction.

What This Means

  1. Demand for AI that acts, not just responds, is massive and mainstream. OpenClaw proved this is not just an enterprise category. Its creator describes 2026 as ‘the year of the general agent.’ When NVIDIA builds an enterprise wrapper and OpenAI hires the creator, the infrastructure is being built around a category that now has consumer-scale pull, not just vendor push.

  2. Enterprise adoption requires a security and permissions layer that does not exist yet. NVIDIA’s NemoClaw is an early attempt to close the gap between what agents can do and what organizations can safely allow. But it launched as an alpha release. The security story is still being written.

  3. The trust gap is real and growing. Over 63,000 exposed instances. Prompt injection attacks. Malicious plugins that exfiltrate data. A maintainer warning that if you cannot understand the command line, the tool is too dangerous for you to use safely. Enthusiasm is running well ahead of readiness.

  4. This is a global story, not just a Silicon Valley one. China’s adoption wave, followed by government restrictions and published best practices, is the first large-scale case study in how a country navigates the gap between agent capability and agent governance. Other markets will face the same tension.

What To Do

  • If your team is already using AI tools with any level of autonomous action, audit what permissions those tools have today. The gap OpenClaw exposed is not unique to OpenClaw. It applies to any tool that can access your data, take actions, or connect to other systems on your behalf.

  • Establish basic internal standards before you need them: what systems can an agent access, what actions require human approval, and how do you audit what it did.

  • Pay attention to where agent capabilities are showing up inside platforms you already use. CRMs, ad platforms, and content tools are all adding autonomous features. Each one introduces the same trust and permissions questions OpenClaw surfaced at scale.

Ignore This If

Your team has no exposure to AI tools that take autonomous actions and you are not evaluating any for the near future.

Sources

ChatGPT Is Now an Ad Platform, and It's Scaling Fast

What’s Changed

OpenAI began testing ads inside ChatGPT in February 2026 for US users on its Free and Go subscription tiers. By late March, the ad business had surpassed $100 million in annualized revenue with more than 600 advertisers participating. OpenAI is now expanding the pilot to Canada, Australia, and New Zealand, with more markets planned later this year.

Ads appear at the bottom of ChatGPT responses, clearly labeled as sponsored. They are matched to the topic of the conversation, past chats, and prior ad interactions. Advertisers do not have access to user conversations or personal data, only aggregate performance metrics. Paid tiers (Plus, Pro, Business, Enterprise, Education) remain ad-free.

Three of the world’s largest ad agency groups, WPP, Omnicom, and Dentsu, are part of the testing program. OpenAI has also hired former Meta advertising executive David Dugan to lead ad sales. A recent Truist analyst note called 2026 an “inflection year” for LLM-powered ads, estimating OpenAI could generate under $1 billion in ad revenue this year, growing to over $30 billion by 2030.

Why It Matters

This is a new advertising surface materializing faster than most teams expected.

ChatGPT users are often in research mode: comparing options, exploring decisions, working through problems. That is high-intent attention. Placing ads inside that context is fundamentally different from a banner alongside search results or a sponsored post in a social feed. The user’s intent is already deeply qualified by the conversation itself.

For marketers, this raises a practical question: is this a channel worth testing, or is it too early? The honest answer is that it depends on your category. The current rollout is conservative. Some advertisers have committed $200,000-$250,000 to the test, and the volume of data coming back is still limited. But the trajectory is clear.

What This Means

This makes AI more usable across everyday marketing work, not just isolated tasks:

  1. AI conversations are becoming monetized surfaces. It is live, generating real revenue, and expanding internationally. The pattern that played out with search ads and social ads is beginning again inside AI interfaces.

  2. Intent signals inside AI conversations may be more valuable than traditional search signals. When someone asks ChatGPT to compare three project management tools for a 10-person team, the context is richer than a keyword search. If ad targeting can use that conversational depth effectively, the format could outperform traditional placements for high-consideration purchases.

  3. The separation between answers and ads is being tested at scale for the first time. OpenAI says ads do not influence ChatGPT’s responses. That claim is now being pressure-tested with hundreds of millions of users and 600+ advertisers. Whether that separation holds as the program scales will shape how much trust users and marketers place in the platform.

What To Do

  • If you run paid media, start tracking ChatGPT as an emerging channel. Understand how ad eligibility works (currently through Performance Max-style contextual matching, not keyword targeting).

  • If your category involves high-consideration decisions where users are likely researching through AI, this format may be worth testing early. Early movers will have competitive data that latecomers cannot replicate.

  • If you are primarily a content creator or publisher, recognize that this further fragments the discovery landscape. Another surface where users may find answers, products, and recommendations without visiting your site.

Ignore This If

You do not run paid media and AI-driven discovery is not relevant to your business model.

Sources

⚠️ Emerging Shifts

Google Is Building the Operating System for Agent-Driven Commerce

What’s Changed

In the last issue, we covered AI shopping becoming an acquisition channel as platforms began enabling purchases inside AI interfaces. Since then, the infrastructure layer beneath that shift has moved fast.

The protocols that let AI agents browse, compare, buy, and negotiate on behalf of users are now production-ready and backed by an industry coalition. Google’s Agent-to-Agent protocol (A2A) reached v1.0 in early 2026. It sits alongside Anthropic’s Model Context Protocol (MCP), Google’s Universal Commerce Protocol (UCP), and others, each handling a different layer: MCP connects agents to tools and data, A2A lets agents communicate with other agents, and UCP standardizes how agents handle shopping, checkout, and transactions.

In December 2025, Anthropic, Google, OpenAI, and Block founded the Agentic AI Foundation under the Linux Foundation to govern these protocols collectively. As of March 2026, that foundation has 146 members including AWS, Microsoft, Salesforce, Shopify, and JPMorgan Chase.

On the commerce side, UCP-powered checkout is now live inside AI Mode, with Shopify, Target, and Walmart coming next. Two new ad formats surface sponsored retailer listings and travel placements inside AI Mode conversations. And AI Max for Search, which uses keywordless targeting to match ads to user intent, is now available to all advertisers.

Why It Matters

The March 23rd story was about AI shopping arriving as a channel. This is about the plumbing becoming standardized.

When an industry coalition of this size agrees on how agents interact with tools, with each other, and with commerce systems, the shift stops being experimental. It becomes infrastructure that the rest of the ecosystem builds on.

For marketers, the new development is not the advice to clean up product data. That stands. The new development is that Google is now running ads inside these agent-mediated conversations, and the protocols that power agent commerce are no longer controlled by any single company. Both of those change the timeline from “prepare for this” to “this is live.”

What This Means

  1. Ads inside AI Mode are no longer experimental. Google has made it clear that 2026 is the year these placements become a primary format. The user’s intent is already deeply qualified by the conversational context before the ad appears, which changes how relevance and performance should be evaluated.

  2. Agent protocols are now industry-governed infrastructure. 146 organizations backing a shared standard means this is not a single platform’s bet. The protocols are designed to work across vendors and frameworks, which accelerates how quickly agent-driven interactions will show up in the tools marketers already use.

  3. Marketing may need to account for machine-to-machine interactions. When agents can negotiate, compare, and transact autonomously, the competitive surface shifts from persuading a human to being selected by a system. That is a fundamentally different optimization problem, and it is arriving faster than most teams are planning for.

What To Do

  • Start monitoring AI Mode as a separate channel in Google Ads reporting.

  • If you took action on product data quality after the last issue, pressure-test whether your feeds are consistent across platforms. Google is now enforcing multi-channel product ID consistency in Merchant Center.

  • Watch for agent protocol support inside the platforms you already use. When your CRM, ad platform, or commerce tools start referencing MCP, A2A, or UCP, that is the signal to pay closer attention.

Ignore This If

You do not sell products or services online and are not dependent on search-driven discovery.

Sources

Search Budgets Are Shifting Toward AI Visibility

What’s Changed

Marketers are reallocating within their search budgets. A growing share of search spending is moving toward optimizing for AI-generated discovery surfaces, a practice the industry is calling generative engine optimization (GEO) or answer engine optimization (AEO), depending on who you ask.

According to Digiday, 55% of marketers surveyed by Scribewise now have dedicated budgets for AI search visibility. Brands are carving this spending out of existing SEO allocations, with some expecting at least half of their SEO budget to also cover AI discoverability. Pet food brand Pawco increased its AI discovery spend by 10% in Q1 to test LLM optimization, and agency PMG is recommending pilot budgets at 1.5 to two times existing search spend.

Why It Matters

The underlying behavior driving this shift is now well documented. AI Overviews reduce clicks to the top-ranking organic result by up to 58%. Roughly 60% of searches on traditional engines end without a click. ChatGPT now accounts for an estimated 20% of search-related traffic worldwide. When users get answers inside an AI interface without clicking through, the metrics that justified traditional SEO spending start to break down.

At the same time, there is a counterweight worth noting. AI-referred traffic converts at 4.4x the rate of traditional search traffic, and those visitors spend 45% more time per visit. The volume is lower, but the quality is higher. That tradeoff is part of what is pushing budget decisions forward.

Measurement remains the biggest unsolved problem. There is no standard equivalent of Google Search Console for tracking how AI models cite your content. But teams waiting for perfect measurement before allocating any budget will be building visibility from scratch while competitors have months of learning.

What This Means

  1. Search budgets are being recomposed, not cut. Brands are maintaining traditional SEO while adding a new layer of work: making content structured, citable, and trustworthy enough for AI systems to reference. The companies investing in both now are building visibility across two discovery systems simultaneously.

  2. Measurement is the biggest unsolved problem. Clicks are giving way to citation frequency and brand mention rates as the relevant metrics, but the tools to track these reliably are still emerging. Most budgets are in pilot territory for that reason.

  3. The quality bar is converging across both systems. What works for AI citation - clear structure, demonstrated expertise, accurate sourcing, comprehensive coverage - also works for traditional search. The investment is not wasted even if AI discovery takes longer to mature than expected.

What To Do

If your search budget is still 100% traditional SEO with no allocation toward AI discoverability, review whether that split still reflects where your audience is actually finding you.

Ignore This If

AI-driven search is not a meaningful discovery channel for your audience.

Sources

👀 Keep An Eye On

The Next Wave of AI Models Is Arriving, and the Stakes Are Higher

What’s Changed

The frontier model race is intensifying. Anthropic accidentally revealed it is testing Claude Mythos, a new model tier above its current flagship Opus line, which the company described as a “step change” and “the most capable we’ve ever built.” The model is currently available only to early access customers, and Anthropic has flagged it internally as posing unprecedented cybersecurity risks.

Google released Gemini 3.1 Flash-Lite, an efficiency-focused model delivering 2.5x faster response times at a fraction of previous costs.

Open-weight models continue narrowing the gap with frontier systems, putting more capable AI within reach of smaller teams.

Why It Matters

Model releases matter to marketers for two reasons.

First, capability improvements flow downstream into the tools you use every day. When the underlying models get better, your CRM automation gets smarter, your ad platform optimizes more effectively, and your content tools produce more usable output. Knowing what is changing at the model layer helps you anticipate what is about to change in your stack.

Second, cheaper inference accelerates the agentic shift covered earlier in this issue. Agents that plan, execute, and iterate across multiple steps consume far more compute than a single chat response. The economics of continuous autonomous operation only work when inference costs drop far enough to sustain it. Every efficiency gain at the model layer makes agent-driven workflows more viable at scale.

The Mythos leak also highlights a new dynamic: the most capable models are now arriving with explicit safety concerns from their own creators. Anthropic is privately briefing government officials about the cybersecurity implications. That tension between capability and caution will increasingly shape how and when new models reach the tools marketers rely on.

What This Means

  1. The gap between what frontier models can do and what is considered safe to deploy is widening. Anthropic is privately briefing government officials about the cybersecurity implications of Mythos. When a company warns that its own model poses unprecedented risks, that tension between capability and caution will increasingly shape how and when new features reach the tools marketers rely on.

  2. Cost is becoming less of a barrier to embedded AI. Gemini 3.1 Flash-Lite at $0.25 per million input tokens makes it viable to run AI on high-volume, low-margin tasks that were previously too expensive. That opens the door for AI to show up in more places across your workflow, not just the high-value use cases that justified premium pricing.

  3. The model layer is fragmenting in useful ways. Not every task needs a frontier model. The emergence of capable, cheap, fast options alongside powerful but expensive ones means teams can match the right model to the right job. That flexibility will matter as AI usage moves from occasional to continuous.

What To Do

  • Follow model releases for what they signal about where your tools are heading.

  • When your existing platforms announce AI feature updates, trace them back to the model improvements driving them. That context helps you evaluate whether a new capability is a genuine step forward or a marginal change.

  • Pay attention to the growing gap between what frontier models can do and what is considered safe to deploy broadly. That gap will affect rollout timelines and the features that reach your tools.

Ignore This If

You are not currently using AI tools in your marketing workflow and have no plans to in the near term.

Sources

The Bottom Line

The pressure right now is to move fast. Adopt agents. Test new ad formats. Shift budget toward AI visibility. And that pressure is justified. The window to build early advantage in these channels is real.

But speed without governance is how you end up with 63,000 exposed instances on the public internet. The teams that will benefit most are the ones treating trust, permissions, and measurement as part of the strategy, not something to figure out later.

What surprised you most in this issue? What are you already acting on, and what still feels premature? I’d love to hear your take in the comments.

If you want more issues like this, subscribe to Marketing Seeds and share this newsletter with a friend, colleague, or team member who wants a clearer view of where AI is creating real change in marketing.

Thanks for reading Marketing Seeds! Subscribe for free to receive new posts and support my work.

]]><![CDATA[What's Changed in AI (for Marketers)]]>https://seeds.bixamedia.com/p/whats-changed-03-23-26https://seeds.bixamedia.com/p/whats-changed-03-23-26Mon, 23 Mar 2026 13:10:43 GMT

AI is moving out of the sandbox and deeper into the system.

In this issue, the biggest shifts are not just better models. They’re changes in where discovery happens, how work gets done, and what marketers may need to measure differently. New channels are emerging, workflows are getting rebuilt, and the pressure to perform is not letting up.

This issue focuses on what actually changed, what it means for marketers, and where I’d pay attention next.

🔥 High Impact

AI Shopping Is Now an Acquisition Channel

What’s Changed

Platforms including OpenAI, Shopify, Amazon, and Walmart are rolling out AI-powered shopping experiences that allow users to discover, compare, and purchase products directly inside AI interfaces.

Why It Matters

The buying journey is compressing.

Instead of moving across multiple steps and sites, users can now go from intent to purchase within a single interaction.

This reduces the number of opportunities brands have to influence decisions through traditional touchpoints.

What This Means

AI is becoming a distribution layer for commerce.

That changes three things:

1. Discovery is shifting further away from owned properties.

Users can discover, evaluate, and purchase products inside AI interfaces without following a traditional browsing flow.

2. Product data becomes your primary marketing surface.

Titles, attributes, reviews, pricing, and availability matter more than page design.

3. There’s a split between discovery and transaction.

AI platforms are positioning themselves to control discovery, while Shopify is positioning itself as the infrastructure layer for transactions.

What To Do

  • Audit your product catalog for structure, not just copy

  • Improve titles, attributes, and metadata for machine readability

  • Treat AI shopping surfaces as an emerging acquisition channel

  • Prepare for fewer traditional browsing journeys

Ignore This If

You don’t sell products or rely on online transactions.

Sources

ChatGPT Gets More Controllable and Practical for Marketing Work

What’s Changed

OpenAI released GPT-5.4, improving real-world task performance, adding steerable “Thinking” that can be adjusted mid-response, expanding context length for larger inputs, and introducing clearer model tiers for balancing cost and quality.

Why It Matters

The biggest limitation of AI in marketing hasn’t been capability, it’s been friction in real use.

When outputs are hard to guide, lack context, or require constant restarting, AI stays limited to low-stakes tasks.

GPT-5.4 reduces that friction:

  • Outputs are easier to refine instead of restart

  • Larger context allows more complete inputs

  • Performance is more consistent across real tasks

  • Cost can be managed across different levels of work

The result isn’t just better outputs, it’s more consistent use, the shift that turns AI from a tool into infrastructure.

What This Means

This makes AI more usable across everyday marketing work, not just isolated tasks:

1. More work can happen inside a single flow.

Research, drafting, and refinement can happen without constantly resetting context.

2. Outputs are closer to usable on the first pass.

Less cleanup, fewer retries, more predictable results.

3. Teams can rely on AI for higher-stakes work.

Not just ideation, but analysis, structured outputs, and repeatable tasks.

4. Usage shifts from occasional to embedded.

AI becomes part of how work gets done, not something used on the side.

What To Do

  • Start working with larger inputs (full briefs, reports, campaign data)

  • Treat outputs as iterative, refine instead of restarting

  • Identify tasks where editing time is the bottleneck and test higher-quality models

  • Compare cost vs output quality across different types of work

Ignore This If

You’re still using AI for small, isolated tasks instead of end-to-end work.

Sources

Getting Found and Getting Clicked Are Starting to Split

What’s Changed

Search platforms are expanding AI-native experiences that answer more questions inside the platform itself.

Google continues to push AI Mode and AI-generated responses inside Search, while Microsoft has introduced AI citation reporting in Bing Webmaster Tools so site owners can see when their content is being surfaced in AI answers.

Why It Matters

For years, marketers could treat search visibility and website traffic as closely linked. That relationship is getting weaker.

Your content can now influence discovery, appear in AI-generated answers, and be cited by search platforms without driving the same volume of visits back to your site.

Microsoft is telling marketers that being cited by AI is now something to measure, even when it doesn’t lead to a click.

What This Means

1. Search visibility and site traffic need to be measured separately.

Being included in AI answers does not guarantee a visit.

2. Content strategy has to optimize for inclusion, not just ranking.

Content now needs to be clear, structured, and credible enough to be cited or summarized inside AI experiences.

3. Top-of-funnel search traffic may become less dependable.

More informational queries can now be answered without a user ever clicking through.

4. Search value is getting harder to measure with old metrics alone.

Rankings, impressions, and sessions no longer tell the full story when platforms are using your content inside AI-generated answers.

What To Do

  • Start tracking search visibility beyond clicks, including AI citations where available

  • Update key pages so they are easier to summarize, quote, and trust

  • Put more pressure on conversion once visitors do reach your site

  • Strengthen channels you own, like email and direct audience relationships, and invest in additional discovery channels so search is not carrying the full acquisition burden.

Ignore This If

Organic search is not an important source of discovery or demand for your business.

Sources

If You Want to Track This

I recommend SegMetrics to many of my clients, and they just launched AI Search Tracking, which identifies traffic from platforms like ChatGPT, Claude, Gemini, and Perplexity as its own channel instead of burying it in referrals. More importantly, it ties that traffic to leads, customers, revenue, and LTV, not just visits.

If you’re trying to understand whether AI discovery is actually driving business results, this is worth a look.

Note: If you sign up for Segmetrics through this link, I may earn a commission at no extra cost to you. (Think of it as helping fund the caffeine supply behind this newsletter.)

You can also use coupon code BIXA to get 10% off your plan.

⚠️ Emerging Shifts

The Agent Conversation Is Turning Into Product Rollouts

What’s Changed

Salesforce launched Agentforce Sales, packaging agentic AI into a product designed for everyday sales workflows like prospecting, account research, follow-up, and pipeline management.

The significance isn’t just the feature set, it’s that a major enterprise platform is turning the agent conversation into a mainstream product rollout.

Why It Matters

Marketers have been hearing about AI agents for a while, but a lot of that conversation has lived in demos, predictions, and vague promises.

Once major platforms start shipping agent features inside real products, the category gets harder to dismiss as hype. The conversation moves from abstract potential to practical implementation, especially in areas tied to pipeline and revenue.

What This Means

1. Agentic AI is getting easier to test in the tools teams already use.

Adoption becomes less dependent on custom builds or early-adopter behavior once these features show up inside mainstream platforms.

2. Pressure will grow to automate more of the revenue workflow.

As platforms position agents as a way to handle research, follow-up, and CRM upkeep, teams will need to decide where automation actually helps and where human involvement still matters.

3. Marketing, sales, and ops workflows may need to be designed together.

If agents start taking on more of the coordination work between lead capture and sales follow-through, the handoffs between teams become even more important.

4. Workflow design will matter more than access.

The differentiator won’t be whether a team has agent features. It will be whether they know how and where to use them well.

What To Do

  • Watch for agent features inside the platforms you already use

  • Identify repetitive follow-up, research, and admin tasks that could be good candidates for agent support

  • Tighten handoff logic and data quality now, before layering in more automation

  • Treat these rollouts as signals of where broader workflow change is heading

Ignore This If

Your business doesn’t rely on a sales-assisted funnel, CRM workflows, or multi-step lead handoffs.

Sources

The Current Demand for AI Looks More Practical Than Creative

What’s Changed

Anthropic published findings from 80,508 Claude user interviews across 159 countries and 70 languages, asking people what they want AI to do for them, what they fear, and whether it has already helped. The strongest patterns were not centered on creative experimentation. They were much more grounded in doing better work, managing life more effectively, reclaiming time, and reducing mental burden.

Why It Matters

1. Free up capacity before pushing creativity.

The best starting point for your team may be using AI to reduce workload around repetitive tasks, coordination, and day-to-day execution. Once that capacity is freed up, it becomes easier to use AI more creatively and strategically.

2. Practical value may be the strongest adoption wedge.

AI use cases tied to clarity, speed, organization, and reduced effort may gain traction faster than ones framed primarily around creativity.

3. This is also a signal about the environment your team is operating in.

If people are turning to AI for help with workload, time, and mental overhead, that points to a broader reality of overload. Your team is not operating in a vacuum, and that should shape how you prioritize AI internally.

What To Do

  • Start by identifying repetitive, mentally draining work your team wants relief from

  • Position AI internally as a way to reduce friction and free up capacity, not just generate more output

  • Use execution-focused wins to build trust before introducing more creative or experimental use cases

  • Revisit where your team is spending time now, especially in planning, reporting, coordination, and production, and look for places where AI can lighten the load first

Ignore This If

You’re only looking for platform news, not clues about how teams may need to adopt and position AI in practice.

Sources

If You Want Help With This

If this story hit a little too close to home, this is the kind of problem I built AI-Powered Marketing Department to solve.

It helps marketing teams move beyond one-off prompts and start using AI in more practical, repeatable ways, especially when the real need is reducing friction, saving time, and creating more capacity for the work that matters most.

That foundation usually comes before the more creative and advanced use cases.

Learn more about AI-Powered Marketing Department here.

Or start with a free preview here.

👀 Keep An Eye On

AI Regulation Is Starting to Take Shape

What’s Changed

The White House released a national AI legislative framework on March 20, 2026, positioning it as a unified federal approach and continuing its push against a fragmented patchwork of state AI rules. At the same time, state-level AI legislation is still moving, which means marketers may be dealing with both federal direction and uneven state requirements for a while.

Why It Matters

This is less about laws you need to react to today and more about where AI regulation is headed. As policy starts to take shape, marketers should expect more scrutiny around disclosure, data use, consumer protection, child safety, and how AI-generated content is presented. The White House framework specifically calls for privacy-protective age assurance, limits on data collection for model training and targeted advertising when minors are involved, and a more unified national approach.

What This Means

1. The era of loose AI norms will not last forever.

Even if enforcement remains uneven in the short term, expectations around transparency and responsible use are getting more defined.

2. Marketing teams may need cleaner internal rules before the law forces them to.

Disclosure, synthetic content policies, and data-use standards are likely to become more important over time.

3. Regulatory complexity could become a practical operating issue.

A federal framework may reduce some uncertainty eventually, but the path there may still involve overlapping state and national expectations.

What To Do

  • Review how your team is using AI in customer-facing content

  • Start documenting internal standards for disclosure, review, and acceptable use

  • Pay closer attention if you work in regulated, sensitive, or youth-adjacent categories

  • Treat this as a signal to tighten governance before you are forced to

Ignore This If

Your team has minimal AI usage and very little public-facing marketing activity.

Sources

AI May Start Showing Up in More Customer Interactions

What’s Changed

The current AI infrastructure push is not only about making models bigger. It is also about making AI faster and easier to embed into products, services, and the moments where customers interact with brands.

Why It Matters

This is not an immediate priority for every marketing team. But it is a signal that AI may increasingly show up in the actual customer experience, not just behind the scenes in planning, writing, reporting, and analysis.

What This Means

1. AI may move closer to the moments customers actually experience.

That could include support, sales conversations, guided buying, onboarding, recommendations, and other interactions where speed and relevance matter.

2. The experience may become part of the advantage.

As AI gets embedded more directly into customer interactions, the differentiator may be less about having AI and more about where and how it shows up.

3. Marketing may need a broader lens.

Over time, marketers may need to think more about how AI shapes the full buyer and customer journey, not just internal workflow efficiency.

What To Do

  • Watch where your existing platforms are adding AI into customer interactions

  • Pay attention to moments in the journey where faster, more adaptive experiences could matter

  • Treat this as an early signal, not an immediate overhaul

Ignore This If

Your team is focused purely on internal execution and has little influence over the buyer or customer experience.

Sources

The Bottom Line

Marketing isn’t getting easier, it’s getting faster.

AI is compressing the path from discovery to purchase, reducing friction inside workflows, and changing how visibility turns into traffic. But none of that lowers the bar. Teams still have to drive performance, protect margin, and make smart bets about where to focus.

The advantage will come from applying AI deliberately in the areas where it can save time, reduce friction, or improve performance.

Which of these developments feels the most actionable for you and/or your team right now? And which one are you still watching from a distance? I’d love to hear your take in the comments.

If you want more issues like this, subscribe to Marketing Seeds and share this newsletter with a friend, colleague, or team member who wants a clearer view of where AI is creating real change in marketing.

]]><![CDATA[Pricing Power in Volatile Times: Lessons from Costco’s BNPL Play]]>https://seeds.bixamedia.com/p/costcoxaffirmhttps://seeds.bixamedia.com/p/costcoxaffirmFri, 16 May 2025 13:15:35 GMTThis article builds on my original piece on BNPL psychology — featuring Coachella as a case study. If you missed that one, you can read it here.

Thanks for reading Marketing Seeds! Subscribe for free to receive new posts and support my work.

When Costco added Affirm as a Buy Now, Pay Later option online last week, I wasn't surprised. I was intrigued.

Costco isn't in the habit of chasing every trend. Their brand is built on simplicity, loyalty, and pricing discipline. So when they make a move like this, especially with a targeted, online-only rollout, it signals something deeper.

Even the most trusted, value-focused brands are adjusting to the new psychology of buying in uncertain times.

Why Costco's BNPL Move Matters

What caught my attention wasn't just the partnership itself. It was the strategic intention behind it.

Costco's official announcement highlighted "helping members confidently manage their spending on larger purchases." That's not just marketing fluff.

It reflects the pressure even reliable buyers are feeling right now, and shows that Costco is trying to secure revenue without compromising their pricing structure or brand promise.

This isn't about affordability in the traditional sense. It's about maintaining buyer momentum in a financially cautious market.

BNPL lets them do that by reducing friction, not by lowering prices.

From Fringe to Infrastructure

Early BNPL adopters like DTC brands leaned into it as a growth hack…a way to boost conversions or ease sticker shock, often aimed at younger, more cost-sensitive consumers.

By contrast, Costco doesn't need to experiment recklessly. Their brand is built on trust, consistency, and value.

When they adopt BNPL, it signals that this model is no longer fringe. It's infrastructure. It's normalized. And it's not just for the cash-strapped. It's for the value-savvy.

The timing is strategic too. With consumer uncertainty rising, this isn't about chasing new customers. It's about preserving momentum with loyal ones.

Online-Only: Strategic Discipline, Not Hesitation

Costco's selective, online-only rollout makes total strategic sense. It's not hesitation. It's discipline.

Online purchases tend to skew toward higher-ticket items: electronics, furniture, travel packages. These are exactly the kinds of purchases where BNPL removes friction and drives conversion without requiring price cuts.

It's also easier to control and measure the impact online. Costco can test messaging, analyze opt-in rates, and monitor cart behavior without disrupting the consistency of the in-store experience.

There's a margin consideration, too. In-store, they often operate on razor-thin margins and impulse-driven bulk buys. Adding BNPL there could overcomplicate the checkout experience or eat into operational simplicity.

This online-first move is Costco doing what they do best: testing strategically, prioritizing where it makes the most business sense, and only expanding when the data tells them it's worth it.

Impact on Average Order Value

When you reduce the emotional weight of the initial purchase decision, you don't just increase conversions. You expand the mental budget of the buyer.

That's the real power of BNPL.

Once the buyer is thinking in monthly terms instead of total cost, the question shifts from "Can I afford this overall?" to "What's a few extra dollars a month?" That's where AOV tends to rise.

It's the same principle behind bundling or freemium-to-premium upgrades: if the big decision already feels handled, bonus products or add-ons feel like smaller, lower-stakes choices.

Research published in the Harvard Business Review confirms this effect, showing that BNPL drives both increased purchase likelihood and 10 percent larger basket sizes, with these effects especially pronounced for consumers who historically made smaller purchases.

I've seen this play out especially in service-based businesses and digital offers. When BNPL is available, buyers are more likely to say yes to things like bonus modules, strategy calls, or extended access.

It's not about tricking anyone. It's about helping them feel more in control of how they say yes, and that often opens the door for deeper, more valuable engagement.

Why Now? Market Conditions Driving BNPL Adoption

We're in a moment where economic uncertainty is high, but overt discounting can still damage brand equity, especially for retailers like Costco that pride themselves on everyday value.

So BNPL becomes a strategic middle ground: a way to meet consumer hesitation without lowering the price.

Several forces are converging:

Rising Costs of Living with Stagnant Wages

Consumers are feeling squeezed. Essentials like groceries, gas, housing, and insurance have all gone up, but most paychecks haven't kept pace. That creates more scrutiny at checkout, even among loyal buyers.

Increased Financial Anxiety

Between inflation, interest rates, and geopolitical instability, people are more hesitant to part with large sums, even if they can technically afford it. They're looking for ways to maintain their lifestyle without feeling reckless.

Normalization of Installment Behavior

Thanks to streaming services, subscriptions, and tech payment plans, we're all used to thinking in monthly terms. BNPL just extends that logic to everyday purchases and now feels familiar, not risky.

Retailers Protecting Margin Without Losing Momentum

In a volatile economy, companies want to keep revenue flowing without slashing prices or signaling panic. BNPL gives them a conversion lever that doesn't damage pricing integrity.

Research from the Filene Institute indicates that around 55 percent of the U.S. adult population has tried a BNPL service, showing its widespread adoption and growing normalization as a payment method across demographic groups.

So when even brands like Costco make this move, it reflects a broader shift: BNPL isn't about financial need anymore. It's about psychological reassurance in a time of uncertainty.

BNPL as Strategic Infrastructure

As BNPL becomes infrastructure, it's no longer just a feature at checkout. It's a strategic layer embedded across the entire customer journey.

We're already seeing this shift ripple through retail in three major ways:

Marketing and Positioning Strategy

BNPL is influencing how products are priced, framed, and promoted. Instead of leading with discounts, brands are leading with accessibility.

Product and Offer Structuring

Brands are rethinking how they package value. They're bundling more, upselling differently, and using BNPL to stretch what a customer is willing to consider, all without touching base prices.

Customer Experience and Trust Building

As BNPL normalizes, the expectation is no longer just "Can I split this up?" It becomes "Will this brand help me buy in a way that aligns with how I manage money?" That’s a trust signal.

So this isn't just about how people check out. It's about how they commit, how they upgrade, and how they feel about a brand after the purchase.

BNPL vs. Discounting: Preserving Brand Integrity

The biggest difference between BNPL and discounting is who's absorbing the cost and what message it sends.

With traditional discounting, the brand takes the hit, not just on margin, but often on perception. You're signaling, "This isn't worth full price." That undermines your positioning.

BNPL, on the other hand, doesn't require lowering the price. Instead, it reframes the buyer's experience of that price by breaking it into psychologically manageable pieces without diminishing perceived value.

It's the difference between asking someone to pay less versus making it easier to say yes.

In short:

Discounts trade long-term positioning for short-term volume.

BNPL trades short-term friction for long-term growth while keeping your brand intact.

I’ve personally implemented BNPL for my own AI-Powered Marketing Department training—not because we lowered our prices, but because we understand how decision-making works under financial pressure. The price stayed the same. The path to yes got easier.

Which Businesses Benefit Most from Soft-Pricing?

The sweet spot for BNPL—or what I call "soft-pricing"—is anywhere there's a meaningful purchase that requires emotional or financial readiness.

That includes:

High-consideration Products

Furniture, tech, fitness equipment, wellness devices, premium apparel.

Transformation-driven Services

Coaching, education, therapy, career development.

Boutique Travel and Experiences

Experiences that create meaningful memories and require upfront commitment.

Digital Programs

Courses, certifications, or training programs where upfront price creates hesitation.

Home Services

From remodeling to repairs, more homeowners are staying put and investing in their spaces. BNPL makes those high-ticket jobs feel more accessible.

In all of these categories, the customer isn't saying "This isn't worth it." They're saying, "I need a better way to say yes."

Beyond Transactions: BNPL's Impact on Loyalty

When a customer feels like they made a decision on their own terms, they're far more likely to feel good about it.

BNPL reduces post-purchase regret. It builds trust. It creates a more empowered buyer who is often more loyal and more likely to return.

Instead of feeling financially stretched, the customer feels respected. That emotional imprint leads to better experiences, fewer refund requests, and stronger lifetime value.

BNPL doesn't just change how people buy. It changes how they feel about the brand that helped them say yes.

Applying Soft-Pricing to Service and B2B

The psychology behind soft-pricing is just as powerful in service-based and B2B contexts, but the execution looks a little different.

Here's how it applies:

Strategic Framing Before Checkout You need to introduce pricing flexibility early in the sales conversation.

Support for Cash-flow-conscious Buyers B2B buyers often can afford it, but need payment options that align with their budgeting cycles.

Perception of Flexibility Equals Trust Offering payment flexibility shows awareness of your client’s context. It makes you easier to work with.

More Momentum, Less Negotiation Soft-pricing reduces the need to discount or create one-off payment plans, preserving offer integrity.

So yes, soft-pricing works in service and B2B. It just shows up earlier and more conversationally than it does in ecommerce.

Strategic Implementation: Learning from Costco

BNPL is a powerful tool, but like any tool, it has to be used with intention.

That's where Costco's approach is so smart. They started online, where purchases tend to be higher-ticket and where they could test adoption, monitor performance, and minimize disruption.

According to retail tech sources, Costco's implementation is online-only and applies specifically to larger purchases, with approved members able to choose from Affirm's personalized monthly payment plans for purchases ranging from $500 to $17,500.

That kind of discipline is a great model for other businesses. Here are a few key implementation principles:

Know Your Audience BNPL isn’t right for every customer segment. Be thoughtful about rollout.

Clear Messaging Explain it upfront. Show the monthly option. Reiterate it during the journey.

Address Real Friction Use BNPL to reduce hesitation, not to mask a weak offer or confused positioning.

When used selectively and strategically, BNPL can drive more conversions and strengthen brand trust.

The Future of Pricing Psychology

What Costco's move into BNPL reveals is a broader evolution in how brands think about pricing strategy.

The old model was binary: either you maintained your price or you discounted it. Today, brands are finding a third way. Psychological pricing tools like BNPL allow you to preserve margin and value while increasing access.

BNPL is one such lever. But it's not the last. As consumer behavior continues to evolve, expect more innovations that separate the experience of cost from the sticker price.

The brands that thrive will be the ones that understand this distinction.

They'll maintain their pricing power while still making it easier for customers to say yes.

And that's a win for everyone: customers get more flexibility, and brands protect their margins and their message.

Key Takeaways for Strategic Marketers

  1. Reduce friction without reducing price.

  2. Reframe large purchases into small mental commitments.

  3. Test BNPL where it makes the biggest impact.

  4. Make it part of the sales journey, not just checkout.

  5. Use soft-pricing to grow AOV and expand access.

  6. Remember: customer satisfaction starts at the decision point.

The most important question to ask yourself: Are you offering optionality without eroding your brand?

In a market where buyers are increasingly cautious but still want to move forward, that balance might be the key to sustainable growth.

This article originally appeared in my Marketing Seeds newsletter. If you found it helpful, consider subscribing to receive weekly insights on strategic marketing and emerging AI capabilities—sent first to subscribers, before they’re published anywhere else.

Thanks for reading Marketing Seeds! Subscribe for free to receive new posts and support my work.

]]><![CDATA[BNPL Isn’t Optional Anymore]]>https://seeds.bixamedia.com/p/bnplhttps://seeds.bixamedia.com/p/bnplFri, 09 May 2025 13:16:16 GMT

A couple of years ago, I started seeing Buy Now, Pay Later shift from a “nice-to-have” to a revenue-driving must-have for my clients.

What began as a tool primarily for eCommerce brands to nudge hesitant buyers over the finish line has transformed into a strategic lever that boosts average order values, expands customer segments, and reduces the need for discounting.

Thanks for reading Marketing Seeds! Subscribe for free to receive new posts and support my work.

The numbers tell a compelling story. The BNPL industry exploded from $2 billion in 2019 to nearly $34 billion by 2022.

Look at Coachella as a case study: what started as a niche payment option in 2009 with just 18% adoption is now embraced by approximately 60% of festival-goers in 2025 (source). That pattern is repeating across industries at an accelerating pace.

Why BNPL Works So Well

At its core, BNPL eases purchase anxiety.

When customers see a large upfront price, their brain kicks into risk assessment mode: Can I afford this? Is this a smart choice right now?

BNPL sidesteps that stress by breaking the decision into smaller, low-stakes commitments.

This taps into two key psychological levers:

  • Immediate Gratification vs. Deferred Pain – BNPL lets buyers enjoy the product now, while the “pain” of paying is spread out over time. That separation softens the impact and makes the decision feel easier.

  • Cognitive Load Reduction – Large purchases often trigger mental math. BNPL simplifies that process by showing a smaller number, which makes the cost feel more approachable and reduces decision fatigue.

The result? People move from “maybe later” to “why not now?” much faster.

This psychological shift is powerful enough that I’ve seen meaningful revenue lifts even on lower-priced items—simply because installment payments remove mental resistance.

It’s not always about affordability. It’s about emotional ease.

The Accessibility Factor—Without Discounting

Payment fragmentation reframes the value-to-cost equation without touching the actual price.

Instead of lowering the price through a discount, you reduce the perceived financial friction by splitting the cost into more digestible chunks.

It’s the difference between asking someone to pay $200 today vs. $50 over four months. Same price—but the latter feels more doable.

That psychological relief expands your customer base, especially for higher-ticket items, without eroding margins.

For many businesses, that’s a win-win: accessibility without undercutting brand value or profitability.

BNPL has also given businesses permission to hold the line on price—especially for premium offers. Instead of defaulting to discounts or “lite” versions to make something feel affordable, brands can now present the full value and simply offer payment flexibility.

It’s also opened the door for upselling and bundling. When customers view a purchase in monthly terms, they’re more open to add-ons or upgrades they might’ve passed on if paying all at once.

In effect, BNPL shifts the pricing conversation from “Is this too expensive?” to “Can I manage this monthly amount?”—and that unlocks a lot more freedom in how businesses position and price their offers.

Beyond Retail: Where BNPL Delivers Surprising Results

One of the most interesting shifts in BNPL adoption? It’s no longer limited to big-ticket retail or tech.

It’s showing up in wellness, education, boutique travel, and service-based businesses—anywhere there’s an emotionally significant purchase that requires a financial leap.

For example, service providers have used BNPL to open doors for clients who were emotionally ready to invest but financially hesitant. Similarly, local businesses like home improvement services have seen a boost just by adding payment flexibility at checkout.

(If you're reading this and have an example to contribute, I'd love to hear it.)

The common thread? Wherever there’s a meaningful decision that requires trust and timing, BNPL offers a softer landing—and better conversions.

Implementation Mistakes to Avoid

The biggest mistake? Treating BNPL like a checkbox instead of a strategic lever.

Too often, businesses just add the option at checkout and call it a day.

But to really see results, you have to integrate it into the entire customer journey—on sales pages, in email campaigns, and during launches—where it can actually influence decision-making before someone hits checkout.

Another common miss: not using BNPL to reframe price.

Businesses hesitate to lead with “$500,” when they could be saying “Just $125 today.” That simple shift can dramatically change perception and urgency.

And finally, some teams don’t train their sales staff or customer support on BNPL at all. If someone’s asking about affordability or payment options, that’s a prime opportunity to highlight it—but it gets missed because no one’s thinking holistically.

The Customer Relationship Impact

BNPL doesn’t just change how people buy—it changes how they feel about their purchase after the fact.

When customers feel like a brand helped them access something meaningful without financial strain, there’s a stronger sense of trust and loyalty.

I’ve seen clients receive more thank-you messages, fewer refund requests, and even repeat purchases because BNPL made the first experience possible.

It also gives businesses more flexibility post-purchase. For example, it’s easier to offer upgrade paths, subscriptions, or add-ons when the initial purchase didn’t stretch the buyer’s budget.

In short: BNPL doesn’t just increase revenue. It deepens relationships. And that’s where the real long-term value kicks in.

Demographic Insights That Might Surprise You

Gen Z and Millennials are the most active adopters—but not necessarily because they’re cash-strapped. It’s more about control and convenience. They’ve grown up managing money digitally and expect optionality at checkout. For them, BNPL feels modern and empowering—not like a last resort.

Surprisingly, I’ve also seen strong uptake among higher-income B2B buyers—especially entrepreneurs and freelancers—who use BNPL to manage cash flow more efficiently. It’s not just about whether they can pay in full, but how they want to manage their money.

That said, transparency matters across the board. No one wants to feel tricked or upsold into debt. The clearer the terms—and the more trust you’ve built—the better the response, regardless of demographic.

Balancing Revenue Benefits with Responsibility

It starts with intentionality.

Just because BNPL can boost revenue doesn’t mean it should be used indiscriminately. Responsible implementation means thinking about your audience, your messaging, and the structure of your offers.

I advise against using BNPL to push unnecessary urgency or inflate pricing. Instead, use it to make genuinely valuable products or services more accessible—especially when those purchases support transformation, education, or well-being.

It’s also important to partner with BNPL providers that offer clear terms, transparent fees, and soft credit checks. Some even offer tools to help customers manage repayments responsibly.

Ultimately, it’s about long-term trust. When customers feel empowered—not pressured—they come back.

So the question isn’t just “How can BNPL help you sell more?” but “How can it help you serve more people well?”

Looking Forward

As BNPL continues to evolve, we’ll see more innovation in how businesses integrate these options into the customer journey. The most successful implementations will balance immediate revenue gains with long-term customer well-being.

For businesses considering BNPL, the data is clear: when implemented strategically, it drives significant growth while making your offerings more accessible to a broader audience.

But here’s the catch—it’s not just a payment method. It’s a positioning tool. A pricing strategy. A trust-builder.

The businesses that win with BNPL won’t be the ones chasing quick conversions. They’ll be the ones using it to deliver more value, build deeper relationships, and create an experience that buyers want to come back to.

Have you implemented BNPL in your business—or considered it? What results have you seen? What questions or hesitations are still on your mind? I’d love to hear your perspective—feel free to share your thoughts in the comments.

This article originally appeared in my Marketing Seeds newsletter. If you found it helpful, consider subscribing to receive weekly insights on strategic marketing and emerging AI capabilities—sent first to subscribers, before they’re published anywhere else.

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]]><![CDATA[The GPT-4.1 Threshold: A Strategic Leap for API-Ready Marketers]]>https://seeds.bixamedia.com/p/gpt41https://seeds.bixamedia.com/p/gpt41Fri, 25 Apr 2025 13:15:29 GMT

As a marketing strategist who’s both deeply data-driven and always up for experimentation, I was immediately intrigued when GPT-4.1 was announced. The standout feature? A million-token context window—a scale of input that could finally keep up with how strategists actually think.

But there’s a catch:

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GPT-4.1 is only accessible through the API.

There’s no button for it in ChatGPT. There’s no toggle. If you want access to its full power — including long-document synthesis — you have to use the API.

And that’s not a limitation. It’s a signal: the next wave of marketing advantage will belong to those willing to integrate, automate, and build. And for marketers who can partner with technical teammates, or are willing to explore the API themselves, this unlocks new strategic depth.

A Short Recap of What's New in GPT-4.1

GPT-4.1 is the latest generation model in OpenAI’s GPT-4 family, and it delivers meaningful improvements in performance, usability, and power—especially for those working via the API. Here are some of the major upgrades:

  • 1 million-token context window – 8x larger than GPT-4's 128k max, GPT-4.1’s expanded window allows for ingestion of huge document sets, codebases, or datasets in a single interaction.

  • Improved instruction following – The model performs better across a wide range of reasoning, generation, and conversational tasks.

  • Better coding and tool use – GPT-4.1 excels at using tools, solving coding problems, and performing logic-based operations.

  • Lower latency and higher reliability – Performance improvements across the board make it more usable in production-grade environments.

  • API-only access – GPT-4.1 is not accessible in ChatGPT. If you want to leverage its power—especially the million-token context—you’ll need to access it through OpenAI’s API.

Together, these updates position GPT-4.1 as a powerful foundation for anyone interested in deeper insight synthesis, automation, or technical innovation through the API.

To explore all the details, you can read the full GPT-4.1 release from OpenAI here: https://openai.com/index/gpt-4-1

Why the Million-Token Context Window Changes the Game

GPT-4.1’s million-token context window gives marketers access to long-form, nuanced analysis like never before. The capacity to analyze multiple large files — simultaneously — means AI can now help you synthesize massive volumes of data and content.

One area I’m especially curious about is whether GPT-4.1’s expanded context window might allow marketers to synthesize insight from large, messy qualitative sources like support tickets, customer interviews, or sales transcripts—sources that have historically been difficult to analyze in full.

If GPT-4.1 can effectively process that volume, it may be able to:

  • Surface patterns in customer sentiment and objections

  • Highlight language cues tied to emotional motivators or decision triggers

  • Reveal strategic opportunities grounded in actual customer language

This is one of several emerging possibilities worth exploring. If realized, it could represent a shift in how we extract and apply insight across the marketing lifecycle—especially when working with qualitative data at scale.

Imagining Use Cases for API-Enabled Marketing Teams

While it's still early, I'm starting to imagine some compelling possibilities for how marketers might use GPT-4.1 via the API. These aren't case studies or established playbooks yet—but they hint at what's now within reach.

1. Surfacing What Customers Really Mean

Upload: Customer interviews, review data, support logs
Analyze: Emotional drivers, objections, patterns by persona

2. Spotting Signals That Drive Sales

Upload: Quarterly sales call transcripts
Explore: What signals of conversion, hesitation, or trust-building emerge most consistently?

3. Distilling Brand Voice from Deep Content

Upload: Books, keynote transcripts, long-form assets
Generate: Brand voice guides with tone, pacing, vocabulary, and sample rewrites—potentially reducing brand drift and simplifying onboarding for future collaborators.

4. Exploring the Customer Journey from Existing Data

Upload: CRM logs, email sequences, chat transcripts
Explore: Whether GPT-4.1 can surface behavioral patterns across the customer journey—especially when fed lightly structured communication data.

These imagined use cases represent a shift in how marketers might begin engaging with large-scale, qualitative insight. Whether these are implemented exactly as described or serve as a starting point, the opportunity lies in exploring how GPT-4.1 can reshape strategic analysis at scale.

A Flexible Framework for Understanding GPT-4.1 Tiers

While it's still early days, OpenAI has released GPT-4.1 in multiple configurations that lend themselves to different types of use cases. For those starting to experiment with API access, it may be helpful to think of the model tiers as forming a tiered support system—where each model plays a role depending on complexity and cost-efficiency.

While OpenAI has released GPT-4.1 in multiple configurations—Full, Mini, and Nano—I'm still in the early stages of exploring what each tier might be best suited for. Rather than speculate, I’m holding space for future clarity as more real-world use cases and experiences with the API emerge.

This mental model is not about locking into specific tools—but rather seeing the opportunity to distribute effort across AI capabilities, just like you would across a human team. Use each tier to support your bandwidth, speed, and insight needs with intent.

What Comes Next for Marketers Ready to Build

GPT-4.1 marks a pivotal shift. It’s not just a bigger model—it’s a model that invites you to build differently. To structure your content, organize your customer data, and create workflows that learn and adapt over time.

For marketers willing to meet it on its terms—via API integration—it becomes more than a tool. It becomes a force multiplier.

The next frontier in marketing won’t be won by teams that simply create faster. It will be won by teams who listen more deeply, analyze more holistically, and make decisions powered by the full spectrum of customer and brand intelligence.

And that starts by laying the groundwork now.

What are you curious about as GPT-4.1 rolls out? What questions or early use cases are on your mind? I’d love to hear your thoughts—feel free to share them in the comments.

This article originally appeared in my Marketing Seeds newsletter. If you found it helpful, consider subscribing to receive weekly insights on strategic marketing and emerging AI capabilities—sent first to subscribers, before they’re published anywhere else.

Thanks for reading Marketing Seeds! Subscribe for free to receive new posts and support my work.

]]><![CDATA[The Future-Proof Marketer: How To Build a Skill Set That Thrives Alongside AI]]>https://seeds.bixamedia.com/p/futureproof-marketerhttps://seeds.bixamedia.com/p/futureproof-marketerFri, 18 Apr 2025 13:15:32 GMT

Last week, I shared my predictions about how AI is reshaping marketing. (If you missed it, you can read that article here.)

But understanding what's changing is only half the battle. The real question is: How do we adapt?

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I've spent months mapping out the essential skills marketers need to develop to thrive in this new landscape, both for my clients and my own team.

Today, I want to break down the specific skills that will future-proof your marketing career as AI continues to transform our industry.

The New Marketing Skills Hierarchy

The marketing skills pyramid has been completely reshuffled by AI.

Technical execution skills that were once highly valued are becoming increasingly automated, while strategic thinking and business acumen are becoming more valuable than ever.

Here's how I see the new hierarchy of marketing skills shaping up:

1. Double Down on Strategy

Why it matters: AI can help execute, but it can't (yet) set big-picture direction. As execution becomes more automated, strategy becomes your most valuable contribution.

What to focus on:

  • Learning how to connect marketing to business goals

  • Getting fluent in customer journey mapping, funnel strategy, and campaign planning

  • Becoming the person who decides what to do, not just how to do it

Where to level up:

  • Business strategy courses

  • Revenue-driven marketing frameworks

  • Cross-functional business understanding

2. Keep Your Creative Edge Sharp

Why it matters: AI is great at first drafts — but still needs human polish. The ability to infuse content with emotional resonance, brand personality, and creative insight remains distinctly human.

What to focus on:

  • Honing your storytelling and brand voice chops

  • Studying human psychology, emotion, and what really moves people

  • Learning to spot when AI sounds too generic or off-brand

Where to level up:

  • Copywriting

  • Behavioral psychology for marketing

  • Brand messaging and tone development

3. Get AI-Literate

Why it matters: You don't need to become an engineer — but you do need to understand the tools. AI literacy is becoming as fundamental as digital literacy was a decade ago.

What to focus on:

  • Understanding what AI can and can't do

  • Learning how to write strong prompts

  • Using AI tools to speed up your workflow without losing quality

Where to level up:

  • Experiment with tools

  • Take AI training (like AI-Powered Marketing Department)

  • Try using AI in your actual work: emails, content plans, briefs, research

4. Become a Data-Driven Marketer

Why it matters: AI thrives on data — and so should you. The ability to interpret data and translate it into actionable insights is becoming non-negotiable.

What to focus on:

  • Understanding key metrics and what they actually mean for the business

  • Using insights to inform creative direction

  • Translating analytics into action

Where to level up:

  • Web analytics fundamentals

  • Data visualization and reporting

  • Basic data analysis skills

5. Master Marketing Tech

Why it matters: Marketing automation + AI = dream combo. The marketers who can connect these systems will create exponential results.

What to focus on:

  • Learning your way around marketing automation platforms

  • Understanding integrations, customer journeys, tagging, and trigger-based workflows

  • Setting up systems where AI and automation work together

Where to level up:

  • CRM fundamentals

  • Marketing operations principles

  • Experimenting with automation workflows

6. Focus on Human Skills

Why it matters: Soft skills are becoming power skills. As AI handles more technical tasks, your uniquely human abilities become your greatest differentiator.

What to focus on:

  • Emotional intelligence and empathy in marketing

  • Leadership and cross-functional collaboration

  • Client communication and stakeholder alignment

Where to level up:

  • Communication training

  • Leadership development

  • Collaborative project management

7. Keep Iterating

Why it matters: Tech is moving faster than ever before. The marketers who thrive will be those who make continuous learning a non-negotiable habit.

What to focus on:

  • Following smart voices online (not just the loudest ones)

  • Implementing 1 new tool or technique every month (not just reading about them)

  • Joining a mastermind or learning community where you're accountable for growth

Where to level up:

  • Structured learning programs

  • Peer accountability groups

  • Experimental marketing projects

The Balanced Marketing Professional of 2025

Here's the truth: You don't need to master all of these areas at once. But you do need to be intentional about which ones you're developing.

I recommend a balanced approach:

  • Core focus: Choose 1-2 areas to develop deep expertise

  • Supporting skills: Develop working knowledge in 2-3 complementary areas

  • Awareness: Maintain basic literacy in the remaining areas

For example, you might focus deeply on strategy and data analysis, develop working knowledge of AI tools and marketing tech, and maintain awareness of creative best practices and human skills development.

Where Should You Start?

The best place to begin depends on your current role and career aspirations:

For business owners: Focus first on strategy and human skills, then gradually build AI literacy.

For marketing leaders: Prioritize AI literacy and data skills to lead implementation, while developing strategic thinking.

For specialists: Double down on your core expertise while developing strategic thinking to elevate your contribution.

For generalists: Develop AI literacy and marketing tech skills to increase your efficiency, then focus on strategic thinking to increase your value.

The marketers who will thrive in this AI-powered future are those who view these changes as opportunities rather than threats. They're the ones who are actively upskilling, experimenting, and adapting.

Your Action Plan

  1. Assess your current skills across these seven areas

  2. Identify your biggest gaps based on your career goals

  3. Choose one skill area to focus on for the next 30 days

  4. Commit to specific learning actions (courses, projects, mentorship)

  5. Schedule regular reassessment to track your progress

Where are you focusing your development efforts? Which of these skills are you prioritizing? I'd love to hear your thoughts in the comments.

This post was originally sent to subscribers of my Marketing Seeds newsletter. If you enjoyed this article, consider subscribing to receive these insights directly in your inbox each week, where I share strategic marketing and AI implementation guidance before it appears anywhere else.

Thanks for reading Marketing Seeds! Subscribe for free to receive new posts and support my work.

]]><![CDATA[9 Predictions That Will Transform How You Market in 2025 and Beyond]]>https://seeds.bixamedia.com/p/9-predictionshttps://seeds.bixamedia.com/p/9-predictionsFri, 11 Apr 2025 13:15:54 GMT

The marketing landscape is shifting beneath our feet. AI isn't just changing our tools—it's fundamentally reshaping what marketers do day-to-day and how we deliver value.

After spending the last year deeply immersed in AI implementation with clients across industries, I've observed clear patterns emerging that point to where marketing is headed. These aren't distant future predictions—they're transformations happening right now that will accelerate throughout 2025.

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Let me share what I'm seeing on the frontlines and how you can position yourself to thrive in this new reality.

1. Speed and Efficiency Are Skyrocketing

Remember spending hours drafting copy, brainstorming campaign ideas, and writing blog posts? Those days are numbered.

AI is now handling that heavy lifting—generating drafts, headlines, even full emails in seconds. And with the right prompting, the quality is impressive.

What this means for marketers: We're shifting from creating everything from scratch to curating, refining, and directing AI outputs. Think of yourself as the creative director, not the production assistant.

The marketers who thrive will be those who master the art of prompt engineering and develop systems for efficiently reviewing and refining AI-generated content. They'll produce 5x the output in the same amount of time, allowing them to either take on more projects or focus more deeply on strategy.

The marketers who struggle will be those who either resist using AI tools entirely or use them ineffectively, spending as much time fixing AI outputs as they would have spent creating from scratch.

2. Strategic Thinking Becomes More Valuable

Here's the thing—AI can handle a lot of the "doing," but it still needs us humans to decide what to do and why.

If you can set creative direction, align campaigns with business goals, and use data to make strategic decisions...you're going to be more valuable than ever.

The marketers who thrive will be those who develop frameworks for connecting marketing activities to business outcomes. They'll spend more time understanding customer psychology, competitive positioning, and market trends—areas where AI still can't match human insight.

The marketers who get replaced won't be the strategic thinkers. They'll be the ones who only execute without adding strategic value.

3. Hyper-Personalization at Scale

Remember when we used to segment audiences into maybe 3-5 groups if we were being fancy? Those days are GONE.

AI now enables real-time personalization that would've been impossible to scale manually.

We're talking dynamic content tailored to individuals, predictive behavior modeling, and conversational marketing that actually feels human.

The marketers who thrive will be those who build sophisticated audience models and leverage AI to deliver the right message to the right person at the right time. They'll create content systems that can adapt to individual user behavior without requiring manual intervention for each variation.

The marketers who struggle will be those still using broad-brush approaches and static segmentation. The spray-and-pray approach to marketing is officially dead. Good riddance.

4. Data and Analytics Are Front and Center

AI lives and breathes data. So guess what? We need to get better with data too.

This doesn't mean you need to become a data scientist overnight. But you do need to know how to work with data, ask the right questions, and interpret AI insights to make decisions.

The marketers who thrive will develop at least intermediate data literacy. They'll understand how to set up proper tracking, identify meaningful patterns, and translate data insights into actionable marketing strategies.

The marketers who struggle will be those who remain intimidated by data or who collect data but don't know how to extract meaningful insights from it.

Tools like predictive analytics and sentiment analysis aren't just nice-to-haves anymore—they're becoming standard equipment in our marketing toolbox.

5. Content Creation Is Evolving

The future isn't just AI-generated content or just human-created content. It's a hybrid approach.

As human marketers, we'll set the tone, voice, and brand identity. We'll fine-tune what AI generates. Most importantly, we'll ensure the authenticity and emotional resonance that AI still struggles with.

The marketers who thrive will develop efficient workflows that combine AI generation with human refinement. They'll know exactly where human creativity adds the most value and focus their energy there.

The marketers who struggle will either rely too heavily on unrefined AI outputs (resulting in generic content) or insist on creating everything manually (resulting in much lower output volume).

The best content will have both human creativity and AI efficiency baked in.

6. SEO, SEM, and Paid Ads Are Getting Smarter

Remember manually adjusting bids and obsessing over individual keywords? (I don't miss those days!)

AI can now auto-optimize campaigns, run simulations, and adjust budgets based on performance in real time. Our job is shifting toward understanding these systems and guiding strategy instead of getting lost in the weeds.

The marketers who thrive will focus on setting clear campaign objectives and understanding the customer journey, then letting AI handle the tactical optimizations. They'll spend more time on creative differentiation and less on manual bid adjustments.

The marketers who struggle will be those who can't let go of manual control or who don't understand the AI systems well enough to set them up properly in the first place.

The marketers who thrive will be the ones who can see the forest AND the trees.

7. Roles Will Blend and New Ones Will Emerge

The lines between marketing, product, customer success, and data science are blurring. We're already seeing new roles emerge:

  • AI content strategist

  • Prompt engineer

  • Marketing automation manager

  • AI implementation specialist

  • Marketing solutions architect

The marketers who thrive will be those who can speak multiple "languages"—creative, technical, data, and business. They'll position themselves at the intersection of these disciplines, making them invaluable connectors.

The marketers who struggle will be those who remain siloed in traditional marketing functions without developing adjacent skills.

8. The Human Touch Becomes a Premium Differentiator

As AI-generated content becomes ubiquitous, truly human-crafted experiences will stand out more. Brands will need to decide where the human touch adds enough value to justify the additional time and cost.

I'm already seeing this with clients who are using AI for most of their content but reserving certain high-touch points for purely human creation. It's all about knowing where that human spark makes the biggest difference.

The marketers who thrive will develop a keen sense for where human creativity and empathy create disproportionate value. They'll focus their energy on these high-impact touchpoints while efficiently automating the rest.

The marketers who struggle will either try to maintain 100% human creation (and fall behind on volume) or use AI indiscriminately (and lose the emotional connection with their audience).

9. Marketing Becomes More Experimental and Agile

With AI reducing the cost and time of content creation, marketers can test more ideas faster. This will lead to more experimental approaches and rapid iteration based on real-time feedback.

The days of spending weeks perfecting a single campaign before launch are fading. Now we can test multiple approaches simultaneously and double down on what works.

The marketers who thrive will build systems for rapid experimentation and learning. They'll develop frameworks for quickly evaluating results and applying insights to future campaigns.

The marketers who struggle will be those who remain perfectionists, unwilling to launch until everything is "just right," or who experiment without systematic learning processes.

The Bottom Line

AI isn't replacing us—it's reshaping how we work. The most successful marketers will learn to use AI tools effectively, double down on creative and strategic thinking, and develop enough tech fluency to collaborate across teams.

This is exactly why I created the AI-Powered Marketing Department training—to help marketers and business owners navigate this transformation and come out ahead.

Whether you're a marketing leader, business owner, freelance marketer, solopreneur, or virtual assistant—AI can help elevate your specific role and multiply your marketing results.

What This Means For You

The path forward depends on your specific role in marketing:

For business owners: Focus on building AI-powered systems that scale your marketing without adding headcount. Invest in upskilling your current team rather than hiring specialists for every new channel.

For marketing leaders: Position yourself as the AI implementation expert in your organization. Focus on strategic integration rather than just tactical application of AI tools.

For freelance marketers: Evolve from production work to strategic advisory. Use AI to handle the heavy lifting while you focus on the high-value strategy work clients will pay premium rates for.

For solopreneurs: Build consistent marketing systems that run even when you're focused on delivery work. Use AI to maintain marketing momentum without sacrificing client time.

For marketing team members: Develop both strategic thinking and AI implementation skills. Position yourself as the bridge between business objectives and technical execution.

Ready to position yourself for success in this AI-powered future? Click here to see how AI can transform your specific marketing role.

What are your thoughts on these predictions? Which one resonates most with you? What changes are you already seeing in your marketing work? I'd love to hear your perspective in the comments.

This post was originally sent to subscribers of my Marketing Seeds newsletter. If you enjoyed this article, consider subscribing to receive these insights directly in your inbox each week, where I share strategic marketing and AI implementation guidance before it appears anywhere else.

Thanks for reading Marketing Seeds! Subscribe for free to receive new posts and support my work.

]]><![CDATA[Marketing Asset: Visual Brand Assets]]>https://seeds.bixamedia.com/p/visual-brand-assetshttps://seeds.bixamedia.com/p/visual-brand-assetsFri, 01 Jul 2022 12:00:05 GMTNow that you have the ever-important Brand Style Guide in place, in this article I’m going to focus on the next marketing assets you need for your business…

…Visual Brand Assets.

Yes, this is exactly what you think it is – all the visual components you need to market your business.

Like it or not, we live in a very visual world. And making sure your business looks professional creates a strong first impression.

Let me ask you: 

Have you ever visited any sort of website for a business that looks really outdated and subconsciously thought:

“Ummm, yeah….this looks like a hot mess. I’m going to look elsewhere.”

This is pretty much my experience every time I visit any sort of government website. 

It’s also the experience I have every time I visit the website of our property management company.

Don’t get me wrong, they’re nice people and do a halfway decent job. But if I were starting from scratch and looking for a new provider, their visual brand assets would NOT convince me to take the next step with them.

Now, do I think you need to spend oodles of money on your brand assets in order to generate sales for your business?

Absolutely not.

Can it help to invest more? 

Sure. 

But, there are so many amazing resources these days to help you create great-looking visual brand assets on a budget.

And that’s what I’m going to share with you below.

You’ll learn:

  • The visual brand assets you need for specific channels

  • Vendors and service providers we recommend

  • Where you can cut corners and save big bucks

All the visuals you need by online channel

Yup, that’s right. Scroll through the list, identify the online channels you’re on and you’ll have a checklist of everything you need to create.

Just consider me your friendly Fractional CMO trying to make your marketing easier. It’s like Christmas has come early. 🎅

Do note: I’ve kept this asset list focused on digital visuals, not print visuals, so keep that in mind.

Website

  • Favicon

  • Page layouts + images for Home, About, Contact, Work With Us, What We Do

  • Page layouts + images for sales page, checkout page, purchase confirmation page (only if you’re asking for a sale online)

  • Page layouts + images for Blog + Blog Posts

  • Page layouts, images, videos + PDFs for testimonials + case studies

Lead Magnets

  • PDF for educational content

  • Page layouts for opt-in and confirmation pages

Email

  • Email header (optional) – Honestly, for email deliverability purposes we recommend using very few images in your marketing emails. But we know this visual asset is one many businesses feel is important which is why we’ve included it here.

  • Email signature for entire team

SMS

  • Contact Profile Image

  • Images to accompany campaigns

Webinars + Digital Presentations

  • Page layouts for registration, confirmation and replay pages

  • Slide deck

Facebook Page

  • Cover Image

  • Profile Image

  • Newsfeed Images

  • Event Images

  • Highlight Images

  • Story Images

  • Reels Cover Images

  • Reels Videos

  • Social Ad Images / Videos

Facebook Personal Profile

  • Cover Image

  • Profile Image

  • Newsfeed Images

  • Highlight Images

  • Story Images

  • Reels Cover Images

  • Reels Videos

Facebook Group

  • Cover Image

  • Profile Image

  • Unit Images

  • Feed Images

  • Event Images

Instagram Profile

  • Cover Image

  • Profile Image

  • Feed Images / Videos

  • Guide Images / Videos

  • Highlight Images

  • Story Images

  • Reels Cover Images

  • Reels Videos

  • Social Ad Images / Videos

TikTok

  • Profile Image

  • Feed Videos + Cover Images

  • Story Videos + Images

  • Social Ad Images / Videos

Twitter

  • Profile Image

  • Header Image

  • Background Image

  • In-Stream Images / GIFS

  • Social Ad Images / Videos

LinkedIn Profile

  • Profile Image

  • Cover Image

  • Feed Images / Videos

LinkedIn Page

  • Profile Image

  • Cover Image

  • Social Ad Images / Videos

Pinterest

  • Profile Image

  • Pin Images

  • Expanded Pin Images

  • Board Cover Images

  • Social Ad Images / Videos

Google My Business

  • Profile Image

  • Cover Image

  • Feed Images / Videos

Google AdWords

  • Display Ad Images / Videos

YouTube

  • Cover Photo

  • Profile Image

  • Video Cover Images

  • Social Ad Images / Videos

Podcast

  • Cover Image

  • Show Notes Images

  • Episode Promotional Images / Videos

Press

  • Media Kit

  • Bio Images

  • Lifestyle Images

Resources & Vendors To Help You Create These Assets

Phew! Now that we’re through that non-exhaustive list of visual brand assets you need, it’s time to cover vendors and resources who can help you create these assets.

Let’s get to it, shall we?

First and foremost, if you haven’t already created your Brand Style Guide, start there. It will make creating the rest of these assets so much easier and faster.

Beckie Hermans - An uber-talented designer, we’ve collaborated with Beckie on many projects throughout the years. She can help with everything from branding to execution across all your visual assets.

Nicole Yang Design - Nicole and her team have created some impressive branding for many companies. Plus, once she’s set the branding direction for you, you can hire the team for Design Half or Full Days and get your new assets quickly. Listen to our podcast episode with Nicole to learn more about her business and services.

Cooper & Heart - Christi has a fabulous service where she’ll build out landing pages, sales pages and other web pages in a day. Talk about fast! She also has a template shop if you’re not yet ready for her done-for-you services. You can learn more about her business by listening to this podcast episode.

Marketplaces To Source Designers - Looking for a specific style of aesthetic? Dribbble and Behance are great places to find designers. Upwork can be a wonderful place to find cheaper, overseas help. However, I’d recommend being very specific in your creative brief to have a successful outcome. And make sure you have that Brand Style Guide in place first!

Scalable, Outsourced Design Services - There are so many places that offer outsourced, scalable design teams. A few to consider include: DesignPickle, Penji, Superside and more.

Templates - One way to cut corners and reduce your investment in brand visuals is to start from a template. A few places you can find templates include: Canva and Envato Market (formerly known as Themeforest).

Welp, that’s a wrap on our Brand Visual Asset educational series!

Have questions about anything I’ve shared in this article? Comment below.

]]><![CDATA[Marketing Asset: Brand Style Guide]]>https://seeds.bixamedia.com/p/brand-style-guidehttps://seeds.bixamedia.com/p/brand-style-guideWed, 01 Jun 2022 13:00:37 GMTIn this article, I focus on a key marketing asset that I see many businesses forgo, much to their detriment.

What is it?

A Brand Style Guide.

In short, this important document codifies all the visual aspects of your brand.

Its sole purpose is to maintain a consistent look and feel for any marketing visuals you create, including but certainly not limited to:

  • Your website

  • Landing pages

  • Social media graphics

  • Slide decks

  • Business cards

  • Advertisements (both offline and online)

  • Letterhead

  • And more!

A Brand Style Guide ensures that any time anyone on the team (including you) creates something visual, there will be far fewer revisions required.

Have you ever had an experience where you hired someone to help you and their first draft was so far off what you envisioned / hoped for / wanted?

Yeah, this should help prevent that by giving the creator of the visuals some important guidelines.

(By the way, too many revisions are everyone’s nightmare. They typically result in a delay in the project timeline and an increase in its cost. They’re also a surefire way to drive the executor nuts and can result in high team turnover.) 

Now, there are many ways to go about creating a Brand Style Guide. And you can enlist the help of a branding expert or graphic designer. But, if you’re on a budget, you can also DIY it until you’re ready to level up.

Below, I’m going to share with you what you absolutely need to define in your Brand Style Guide. And, I’m going to show you the Ferrari version of a Brand Style Guide and the DIY version. That way, you can pick the path that makes the most sense for your business right now.

The whole point of a Brand Style Guide is to define the common elements that make up your brand visuals, ensuring that any time anyone on the team (including you) creates something, there will be far fewer revisions required.

Even if you haven’t been through a formal branding process for your business, it’s still important to have a Brand Style Guide to document the guidelines that do exist now.

And remember, a Brand Style Guide will evolve as your business evolves…that is the normal part of the process.

Here’s what your Brand Style Guide should absolutely define:

Fonts

You need to write stuff in your marketing assets, right?

Therefore, defining which fonts your brand uses and how to use them is important. 

This will not only preserve a consistent look, but it should make it easier for someone to read and digest what you’ve written on your marketing asset…whether it’s your website, a slide deck, or your business card.

Ideally, you are only using a couple of different fonts…too much variation means the eye and brain will be distracted from the content you want them to consume. 

Imagine a hoarder's house…too many items inside means you can’t appreciate the home’s beauty and space. The same goes for your fonts.

You should not only specify which fonts your brand uses in the Brand Style Guide but how they are used.

Which fonts are used in headlines? In Sub-headlines? In body font? In buttons? And how are they styled in these various elements?

When do you use bold? Italic? All caps? Sentence case?

I know this sounds perhaps overly detailed but when you think about the amount of visuals you have to create to market your business, the fewer BRAND decisions a designer has to make, the better the creative will turn out.

(Trust me, we’ve worked with a lot of designers on behalf of our clients and know how to ensure the visual part of a marketing asset is executed well, quickly and stands out.)

Color Palette

Defining the color palette that your brand uses makes creating visuals SOOOO much easier.

It also helps with brand recognition, meaning you want people to see your colors and immediately think of your business.

Your Brand Style Guide should lay out the colors your brand uses, ideally including the Hex codes and RGB values so it’s easy for a designer to quickly pull the exact color when designing a creative.

It should also define where and how each color is used. Is the bright accent color only used sparingly and to draw attention to key parts of a marketing asset? Or is it typically the background color?

Are there different tones and/or hues used when more colors are needed that the primary palette?

Laying all of this out in your Brand Style Guide is crucial.

By setting your fonts and color palette, you’ll immediately be a leg up over any business that doesn’t have a Brand Style Guide. It’s barebones, but it will do the job.

When you’re ready to level up, here are a few “nice-to-have but not absolutely necessary” elements in your Brand Style Guide:

Logo Use

What are all the variations of your logo that exist? Color, black and white, wordmarks, icons, etc.

How much space needs to exist around the logo any time you’re using it?

Sounds nitpicky, but you’ll get a much better final product from a designer if you explicitly define this.

Decorative Elements

What kind of graphics, icons, textures, shapes, and illustrations does your brand use? 

How and where are they employed? How do you combine them?

You’ll notice on this page on our site, Bixa has a very distinctive illustration style. This is an important decorative element for our brand.

Photography

Does your brand use photographs in its visuals? If yes, define the common elements of photos that align with your brand.

You’ll ensure that whoever is shooting custom photos for the business has a clear direction. You’ll also save anyone searching for stock photos an enormous amount of time by creating these guidelines for them.

To tie it all together, now I’m going to show you a few examples of Brand Style Guides so you see how to put all of these elements together.

There are many ways to approach the creation of your Brand Style Guide – you can go with the Ferrari version, DIY version or somewhere in between. 

It largely depends on your budget and the level of expertise is needed.

I’m going to show you 3 different examples so you can pick the version that makes the most sense for your business right now.

Example #1: Barebones Style Guide in Canva

Here, I walk you through Bixa’s barebones Brand Style Guide that exists in Canva and allows our team to create graphics without having to lean on our graphic designer.

It basically defines the color palette, fonts and includes some decorative elements, that’s it.

(By the way, it’s not that our graphic designer isn’t fabulous – she is. But for visuals that have a short shelf-life, like social graphics, we don’t want to invest a ton of time or money but still want these to look professional. Clear brand guidelines in Canva allows us to DIY when we want to save.)

Example #2: Quick & Dirty Brand Style Guide created by a professional graphic designer

Here, you get to see an example of a quick and dirty Brand Style Guide created by a professional graphic designer for my husband’s new business.

I say “quick and dirty” because that was the brief I provided the designer. Why? Because we wanted the branding to give a great first impression but my husband didn’t want to invest all his marketing budget in the brand creation process.

(This is a common mistake I see with startup businesses – thinking if you create the beautiful branding and build a website, new customers will be lining up around the block. In my experience, most of the marketing budget should go towards the promotion of your business, not the branding of your business. And remember, your brand can absolutely evolve and you can invest more later as the business becomes more profitable.)

This Brand Style Guide is perfect for what his business needs right now. There’s a clear visual direction, it’s laid out for any designer to 

Example #3: Full Shebang Brand Style Guide created by a professional branding agency

Follow us on Instagram to see this comprehensive Brand Style Guide created by a branding agency for one of our clients. (We’ll be sharing it next week!)

This includes all of the elements mentioned in my last email and more, making it SOOOO easy for the design team to create standout visuals.

This Brand Style Guide was created during a rebrand, which we are still in the process of helping the client roll out.

(Remember how I mentioned above that you can invest in comprehensive branding as your business evolves? This is a great example of that.)

Hopefully, I’ve answered a LOT of your questions about this important marketing asset in this article.

If I missed anything or you have questions, comment below.

Or, if you want to enlist help in creating your Brand Style Guide and want a vendor recommendation, reach out to support@bixamedia.com.

]]><![CDATA[Marketing Asset: Automated Welcome Series]]>https://seeds.bixamedia.com/p/automated-welcome-serieshttps://seeds.bixamedia.com/p/automated-welcome-seriesSun, 01 May 2022 13:00:47 GMTThanks to our recent educational article on lead magnets, you now understand the why, the what and the where…

…and you may even have your lead magnet live and be generating leads as a result.

Now what?

Great question.

Well, now it’s time to nurture those leads, baby!

They want to hear from you and it’s a great opportunity to introduce them to your business, who you help and how you help them.

Even better, you can do this lead nurturing all on autopilot using an automated email campaign.

An automated welcome series for new leads is a fabulous marketing asset to build for your business.

And, it’s often a HUGE missed opportunity I see in businesses I work with.

Take for example, this automated welcome series we set up for a client that generated $57,739.54 in just 92 days.

Yup, that’s right. Mid-five-figures from ONE automated welcome series in just 3 months.

Can you expect the same revenue results?

Honestly, it just depends on what you sell and how you sell it. That’s a question I can better answer in a complimentary Strategy Session.

In the meantime, this article is going to provide you with a ton of details about automated welcome series, including:

  • How to create them,

  • What goes into them,

  • Why you need them, 

  • The different types you can create,

  • Which tech you need (and what you can forgo),

  • And more!

Below I share insights and helpful tips you can apply to the automated welcome series in your own business.

So often I see businesses accidentally fall into the “Buy Now or Bye Forever” trap.

That is, if a lead isn’t ready to buy immediately, they forget about them.

When in reality, the lead just needs more time and information to move through the stages of awareness before they’re ready to buy.

“Companies who send automated emails are 133% more likely to send relevant messages that correspond with a customer’s purchase cycle.” - Lenskold and Pedowitz Groups

This is where an automated welcome series (delivered by email) comes in. 

Its purpose is to onboard leads (new subscribers) by:

  • Introducing them to your brand and making them feel a part of the family

  • Educating them about your product(s) and/or service(s)

  • Answering one or some of their frequently asked questions

  • Giving them a list of actionable tips so they associate a win with your business (not the competition)

  • Telling them a story about one of your customers for social proof

  • Setting expectations about future emails

This welcome series will be one of your lead’s first interactions with you and your company and the first impression in what should hopefully be a long relationship.

So make that first impression count!

By creating a great welcome series, your leads will feel connected and engaged with your company, permitting them to know, life and gasp, even trust you.

By the time they are ready to buy, they’ll be more apt to purchase from you than from your competitors.

“Companies using email to nurture leads generate 50% more sales-ready leads and at a 33% lower cost. And nurtured leads, on average, produce a 20% increase in sales opportunities compared to non-nurtured leads.” - Hubspot

So, in order to help you create a great welcome series, I want you to put some pen and paper to work and do some planning.

By answering the questions below, you’ll find that you have some great material to work with when it comes time to write your automated welcomes series.

(By the way, the writing and “what to say” is where everyone gets stuck when it comes to creating an automated welcome series. These questions are designed to get you unstuck.)

  • First, take some time to envision your first impression. What do you want a new lead to feel / think after receiving your welcome series?

  • Now, write down the primary problem your typical customer is looking to solve.

  • Next, make note of the pain points your customers (and leads) face when trying to solve this problem.

  • Then, what motivation is driving their need / desire to solve this problem? What do they want to achieve?

  • Brainstorming time: when someone first reaches out to your company, what’s one paid offer you can make to them? This doesn’t need to be your primary offer, just a small service or product that would get them in the door, with their wallet in hand.

  • Why did you start your business?

  • Why do you love serving your customers?

  • Do you have any testimonials from existing or past customers? Make note of the best ones here.

  • If you were to sell leads on your offer by educating them about your industry, what might you teach them?

Once you’ve written down the answers to those questions, set them aside for 1-2 days.

Then come back to them and start drafting your automated welcome series.

Now, I want to answer some common questions I always get about this marketing asset and how to deliver the automated welcome series.

Which email marketing platform should I use to deliver this series?

Lots of options here, and the short answer is that it depends on the other email marketing needs of your business. But, offhand, here are a few platforms that will allow you to send automated campaigns:

  • Drip

  • ConvertKit

  • ActiveCampaign

  • Mailchimp

  • FloDesk

How many emails should I include in the automated welcome series?

Well there about 5,231 ways you can create an automated welcome series, and yes that is a scientific number. 😉

It really depends on who your target audience is, what you’re selling, and more. So it’s not a question I can easily answer without having a consultation.

As a starting point, I’d start with 5 emails:

  1. Deliver your lead magnet and introduce your company

  2. Dive deeper into an FAQ and educate the lead

  3. Explain who you serve and why your company exists. Link to your paid, get-in-the-door offer.

  4. Give actionable tips designed to get the new lead quick wins.

  5. Tell a story about one of your past customers and set expectations about future emails. Again, link to your paid, get-in-the-door offer.

How far apart should I send each email?

Again, it depends. Am I starting to sound like a broken record yet?

I’d always err on the side of delivering this welcome series more quickly than spacing it out over weeks or months.

Two frameworks I like to use are:

  • 1 a day until the series is complete - so Day 1, Day 2, Day 3, Day 4, Day 5

  • The Fibonacci sequence (credit goes to Jo Wiebe of Copyhackers for this) - so Day 1, Day 2, Day 3, Day 5, Day 8

What should the lead receive after the automated welcome series is completed?

Ahhh. I really see two options 85% of the time. (Yes, there are exceptions.)

Option #1: Add to your newsletter segment and start sending your regular newsletter.

Option #2: Send the lead to your next automated sequence, likely one that does more hard selling into a specific offer.

I’m working with a client right now that has a list of 127,000+ subscribers and we employ Option #1 after their automated welcome sequence ends.

I share this to illustrate that it is not necessarily the size of your list that dictates which approach you choose.

Hopefully, I’ve answered a LOT of your questions about this important marketing asset in this educational series.

If I missed anything and you have questions, please comment below.

Or, you want to enlist our help in creating your automated welcome series, book a complimentary consultation here.

]]><![CDATA[Marketing Asset: Lead Magnet]]>https://seeds.bixamedia.com/p/lead-magnethttps://seeds.bixamedia.com/p/lead-magnetFri, 01 Apr 2022 13:00:00 GMTOne of the marketing initiatives I always work on with clients is lead generation…

…meaning how do we get contact information for people who are potentially interested in our products or services, so we can keep following up with them until they’re ready to make a buying decision?

Read that again because it’s important.

Then, let me clarify here.

From my standpoint, a lead = contact information for someone who’s interested in what a business has to offer.

And contact information could be a name, email, phone number and more.

So in short, when my clients are looking to generate more leads – and they always are – we have to figure out ways to:

  1. collect contact information from interested people, and 

  2. attract more people to the business who are interested in their offers or services.

I always start with #1 above.

Why?

Well, we can work on #2 all day long, but if we have no way to identify interested people and nurture them until they’re ready to buy…

…then it doesn’t matter if we have 1,000,000 visitors checking out the business online. Because they’re all anonymous, we don’t know who they are and have no way to follow up.

Have you ever heard the saying that’s popular in sales?

“The fortune is in the follow-up.”

It’s true! If you want more revenue in your business, you’ll need leads that you can follow up with.

The marketing asset you’ll need to create if you’re also starting with #1 right now is a lead magnet.

Not sure what a lead magnet is?

Essentially it’s fancy marketing speak for a free incentive you give an interested person in exchange for their contact information.

Lead magnets come in a variety of forms:

  • Discounts

  • Ebooks

  • Videos

  • Automated email courses dripped out over time

  • Cheatsheets

  • Toolkits or resource lists

  • Free trials

  • Contests

  • And more!

Pay attention as you browse the Internet and visit some of your favorite online sites. I guarantee you’ll be asked AT LEAST once to enter your email or phone number in exchange for some sort of free incentive.

Now, if you already have a lead magnet in your business, you might think that what I’m sharing here is rather basic.

While it is foundational, I cannot overstate the impact an amazing lead magnet can have on your business.

It can unearth sales opportunities you didn’t know existed.

It can open doors for your business that you never thought possible.

It can fuel your business growth, week after week, month after month, and year after year.

Yes, that’s right. ONE amazing lead magnet can be all your business needs. I’ve seen it happen time and time again with our clients.

It just might take the creation of a few different lead magnets before you land on that home run lead magnet.

Now, I’m going to share a little more with you about the types of lead magnets you can create and which one I’d recommend for your type of business and why.

And, I’m going to share some common mistakes that I want you to avoid…or fix if you’ve already made them. (No judgment here, by the way.)

Let’s get straight to it!


Here are the biggest lead magnet mistakes I frequently see:

Mistake #1: Creating TOO many lead magnets. 

In most cases, I find ONE strong lead magnet at the top of the funnel to be enough. 

The challenge is that sometimes it takes creating a few different lead magnets to really figure out which one converts the best and is going to perform well for you.

When you have too many lead magnets or too many paths for leads to go down, it can confuse them.

Plus, it frequently means needing to create far too many follow-up sequences than most teams have the capacity or expertise to do.

That leads me to the next mistake…

Mistake #2: Not having a strong follow-up email sequence.

It’s not enough to generate the lead.

You also have to nurture them and create a clear path for them to move to the next step, whether it’s a sales call, a demo, making a purchase, etc.

It takes a ton of work to create and optimize a follow-up sequence.

I find too many businesses disproportionately focus their efforts on creating more lead magnets instead of optimizing their follow-up email sequence.

Mistake #3: Giving too much away in your lead magnet.

The goal of a lead magnet, in my opinion, is to identify who is a potential prospect for your products or services.

You don’t need a lengthy report to do this. Not to mention, this takes a long time to produce the copy and design

A quick checklist, resource guide or coupon is frequently more than enough to generate leads.

When you give too much away, you risk the lead getting overwhelmed or feeling like there is no reason to open the emails in your follow-up sequence because you’re already delivered more than enough value to get started on their own.

Mistake #4: Not having a dedicated landing page to promote your lead magnet. 

You want to be able to send traffic directly to a landing page to promote your lead magnet, not just to your website where there’s a mention in the footer or a popup.

Why?

First, then you truly understand how well traffic converts into leads because you’re only giving them ONE choice. Opt-in to this lead magnet. Or not.

If only 0.05% of your traffic is converting, there’s a problem. 

If you’re looking at a 30-50% conversion rate of your landing page traffic, then you know you have a winner!

(By the way, while this would be a high conversion rate, this is frequently what most of my clients see…even on cold traffic. This is why I say ONE strong lead magnet can be enough.)

Also when you have a landing page dedicated to your lead magnet, you then have a system in place to eventually scale up with more advanced sources of traffic that you don’t own like paid ads and partner traffic.

Mistake #5: Not promoting the lead magnet on all channels.

There are SO many places you should be promoting your lead magnet, including but not limited to:

  • Popup on your website (no it’s not spammy if the lead magnet is a stellar one)

  • Link in your social media bios

  • In dedicated social media posts

  • In your email marketing

  • In the signature of your emails (and your team’s emails)

  • In paid ads

In other words, there’s no channel that isn’t a good candidate for promoting your lead magnet.

Mistake #6: Overcomplicating the heck out of your lead magnet.

Oh my gosh, I could write a novel on this, but I promise I’ll spare you and keep it short.

I see too many businesses choose THE MOST complicated types of lead magnets and therefore it takes forever to produce and therefore delays promotion. 

Which then delays your understanding of how well the lead magnet will or won’t perform.

This means you’ve spent all your time producing what may end up falling flat, and then you’re back to square one. And it’s taken months to run this experiment instead of days or weeks.

Getting leads to raise their hands doesn’t require complication.

Which then brings me to the next part of this post…

What types of lead magnets exist and which is the best for my type of business?

Below is a short, non-exhaustive list as well as the pros and cons of each.

Discounts

10% off your first order! Free shipping! $5 off when you buy $60 or more worth of product!

If you’ve done any sort of online shopping, you’ll be familiar with this type of lead magnet.

It’s easy (and quick) to create. And there is major purchase intent…meaning someone who opts in is likely to purchase.

If you have any sort of product or e-commerce site where the purchasing happens online, this would be my top lead magnet recommendation.

Ebooks

Definitely valuable and the right content can get high-quality leads to raise their hand.

But these are not quick to produce both from a copy and a design standpoint.

And if you’ve guessed wrong on the topic and opt-in rates are low, well you just did a whole lot of work for nothing.

I personally would prefer you not waste your precious resources on this type of lead magnet until you know it’s a for sure thing.

And, I’d consider using this as a qualifier lead magnet, middle of funnel, after someone’s already identified themselves as a lead and you need to see if they’re ready to take the next step to purchase your products or services.

Webinars

Like an ebook, I see a free webinar as a great qualifier lead magnet middle of funnel.

It wouldn’t be where I’d start.

And, if ebooks are complicated, webinars are 10x more complex!

I was just outlining for a client how many steps are involved in creating all assets for a webinar. (Short answer, it’s 17. That doesn’t include all the sub-tasks involved in each of those steps.)

A webinar is an advanced marketing asset. And yes it can absolutely be a very powerful sales mechanism in your business.

But, like any lead magnet, it requires optimization to get it to convert at an optimal rate. Which means you need to run it over and over again.

In short, it’s not a small job and not the first lead magnet I recommend creating.

Cheatsheats / Checklists / Toolkits / Resource Lists

These are frequently the easiest to create, get great opt-in rates and don’t require a ton of production time or resources.

Plus, many people believe that tools solve their problems. Ie: “Which tools do I need to post video on social media?”

When you know they’re really looking for: “How do I use video to generate more business through my social media channels?”

It’s a great opportunity to hook them in with what they want and then position your products or services as what they really need.

If you have a service business, this is absolutely where I’d start with your lead magnet.

Free Audit / Consult

If you’re looking for 1:1 work, this is a great way to identify potential prospects.

There’s not a ton of production time upfront, which means it’s easy to get up and promote this lead magnet quickly.

There is, however, a lot of work on the backend in order to deliver the audit, so this isn’t necessarily easily scalable.

Use this lead magnet wisely, when your biggest challenge is getting prospects to raise their hand…not when you’re booked solid and have no extra time to deliver on this promise.

Pricing Guide

This is also a fabulous way to get potential prospects to raise their hand, especially if you run a service-based business.

Someone who’s inquiring about your prices is likely considering hiring you.

And you have a way of identifying who that is.

Plus, you also get the added bonus of qualifying them by only asking them to take the next step if your services are within their budget.

Contests

In my opinion, the name of the game here is lead volume.

You want to generate a ton of leads with a contest…because you’re then going to have to cull the leads that are only there for the deal and not actually qualified to take the next steps with you.

I personally tell my clients to use contests in conjunction with another lead magnet, so they are attracting both a high volume of leads AND the right type of leads.

Quizzes

These are an advanced type of lead magnet that can be very effective. 

I find that quizzes generate great quality leads, typically at a lower cost.

And, you can send leads down different paths based on their responses.

But, quizzes are not easy to create, especially because you want to deliver the response and personalized next step to the quiz taker. This means setting up quiz software and ongoing optimizations.

So again, I might use this as a second type of lead magnet in the business. For example, have a coupon and a quiz or a resource list and a quiz.

In summary…

If you’re looking to generate more leads, creating a lead magnet will be a powerful marketing asset for the business.

Have questions about lead magnets? Comment below and I’ll do my best to answer.


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