Friday, November 21, 2025
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Hi, it’s Chad. Every Friday, I serve as your AI guide to help you navigate a rapidly evolving landscape, discern signals from noise and transform cutting-edge insights into practical leadership wisdom. Here’s what you need to know:
1. Sound Waves: Podcast Highlights
This past Monday, my conversation with Oren Michels (founder of Barndoor AI) dropped with his contrarian take on why most companies are building agents wrong. Spoiler: It’s not about the technology. This coming Monday, my next solo episode tackles your internal AI policy – specifically, why most policies actually discourage the adoption you’re trying to encourage. (Yes, this connects to the research below.) Hit one of the below links to check it out:
Subscribe for free today on your listening platform of choice to ensure you never miss a beat. New episodes release every two weeks.
2. Algorithmic Musings. The Content Bottleneck Is Broken… Now What?
For the past decade, your marketing team has been stuck in a frustrating paradox. You’ve invested in sophisticated martech (CDPs, journey orchestration platforms, analytics suites, personalization engines) that promises to deliver the right message at the right time. Yet you’ve never had enough content to actually make those promises real.
That constraint has collapsed.
Generative AI has eliminated the traditional content bottleneck. And while that sounds like an unambiguous win, here’s the uncomfortable truth: breaking a bottleneck always exposes the next one.
The new bottleneck? Leadership.
When Content Creation Becomes Infinite
At MIT Technology Review’s recent EmTech conference, Adobe’s Hannah Elsakr described a world where creative cycle time has shrunk from seasons to sprints. Ideation, prototyping, and finished assets now happen in hours instead of weeks. Entire campaigns can be localized, personalized, and refreshed continuously. Brand-trained private models generate on-brand imagery with a button click.
The constraint is no longer creation. It’s curation, governance, and coherence.
Most organizations aren’t built for that reality. Your approval processes were designed for monthly campaigns, not real-time content flow. Your brand guidelines were written for designers, not AI models churning out 10,000 variations in an afternoon. Your teams are structured around “projects,” not continuous narrative management.
AI solved the speed problem. Your challenge is solving the structure problem.
Three Shifts Leaders Must Make
If your marketing team produces content at machine-speed while your decisions crawl at human-speed, you’ll experience friction, not growth. Here’s how to adapt:
1. Formalize ContentOps as strategic infrastructure. Someone must own workflow design, model governance, quality control, and measurement. Think DevOps for brand storytelling, except if DevOps crashes, your website goes down for an hour. If ContentOps breaks, you dilute your brand across 10,000 touchpoints.
2. Treat brand-trained private models as core assets. Your brand’s visual and narrative identity increasingly lives inside a model, not just in a PDF guidelines document. That means someone on your team needs to own model training, output auditing, and version control. This isn’t experimental anymore. It’s foundational (like your ERP system).
3. Shift from campaigns to continuous narrative management. Marketing no longer ends at launch. It evolves in real time. Empower your teams with more autonomy and shorter approval loops.
The Hidden Opportunity
The collapse of the content bottleneck doesn’t just threaten old workflows. It flattens the competitive landscape. Mid-market organizations, typically outspent by larger competitors, suddenly have access to enterprise-grade creative scale. The winners will govern content better, not create more of it. Picture this: Your mid-market competitor just launched a campaign with personalized landing pages for 50 different customer segments, localized creative across 15 markets, and A/B tests running continuously. Three years ago, that would’ve required an agency budget you couldn’t match. Today, it requires smart governance, not deep pockets.
That’s a leadership challenge, not a technical one.
3. Research Roundup: What the Data Tells Us
Your Employees are Sabotaging AI ROI… and They Don’t Even Realize It.
A field experiment with 450 workers just revealed something that should worry every CEO rolling out AI tools: Workers deliberately reduced AI usage by 14% when they knew managers were watching—even when explicitly told they’d be evaluated only on accuracy.
The numbers that matter: Workers abandon one in four successful human-AI collaborations when evaluators can see their AI usage. Workers more than doubled their focus on signaling “confidence in their own judgment” when they thought managers were watching. The really sobering part: 69 out of 70 evaluators in the study penalized AI reliance – even after experiencing the worker role themselves.
What this means for your Monday morning: That AI tool you just spent six figures implementing? Your people are underusing it right now because they think leaning on AI makes them look weak or indecisive. And your managers (despite any training) are likely reinforcing this by subtly penalizing the very behavior you want to encourage.
The catch: You can’t simply tell people “it’s okay to use AI” and expect behavior change. Information interventions failed in this study. The fear of appearing over-reliant on AI is deeply embedded, and it persists even under ideal conditions designed to minimize it.
Your next move: Open your performance review template right now. If managers can see how much employees use AI versus just seeing outcome quality, you’ve built the problem into your system.
Concrete fix: Strip AI usage metrics from manager dashboards entirely. Replace them with outcome scores—document accuracy, project completion time, stakeholder satisfaction. Make the how invisible and the what unmissable.
Real example: Using Copilot for 365? Don’t give managers the dashboard showing who’s typing with AI assistance. Give them quality metrics on document accuracy, time-to-completion, and stakeholder satisfaction. Measure results, not methods.
Read our full analysis of this and 100+ other analyzed research papers at AI for the C Suite.
4. Radar Hits: What’s Worth Your Attention
Claude now available in Microsoft Foundry and Azure AI. This matters more than it looks. If you’re a Microsoft shop, you just got procurement-friendly access to best-in-class AI models. No separate vendor approvals, no IT battles. But here’s the bigger play: Microsoft is positioning itself as a model-agnostic clearinghouse, not just an OpenAI distributor. Translation: Your frustrated teams can now pilot alternatives your IT department will actually approve.
OpenAI’s primer on evals shows how to measure what matters. Most AI pilots fail because nobody can articulate success beyond ‘make it work better.’ OpenAI’s evaluation framework forces you to define your AI system’s purpose in plain terms, measure concrete outcomes, and improve systematically. If your team can’t show AI ROI, this methodology turns fuzzy goals into measurable results. Start with one team, one clear objective.
5. Elevate Your Leadership with AI for the C Suite
That research finding about workers hiding AI usage? I’ve watched this exact pattern unfold in three different client organizations this quarter alone—each time in ways the leadership team never saw coming.
If you’re rolling out AI tools and seeing disappointing adoption, I can tell you in 30 minutes exactly where your systems are working against you and what to change first.
Just hit reply.
Worth your time? Forward this to another executive wrestling with AI adoption.
Chad