INBOX INSIGHTS: Land Before You Scale, Enterprise AI Part 3 (2026-06-03)

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INBOX INSIGHTS: Land Before You Scale, Enterprise AI Part 3 (2026-06-03)

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Before You Scale, Land It Once

The fastest path to everywhere goes through one place.

Your AI strategy looks ambitious. That is the problem.

I say that to almost every leader I talk to right now, because the roadmap is impressive on paper and producing very little in reality. The plan covers six departments. The tracker lists twelve workflows. The slide deck mentions every function from Marketing to Finance. By every measure of ambition, the strategy is exactly what a strategy is supposed to look like.

That is also why nothing is landing.

Here is what happens when you spread the investment that thin. Every team gets a piece. Every team also gets less than they need. There is not enough budget, time, or attention concentrated anywhere to make a single workflow actually finish. The result is a portfolio of half-finished attempts that all look like progress in a status update and produce no usable outcome anyone could hand to the next team.

That is the real definition of scattered investment. It is not that the wrong things were chosen. It is that nothing was chosen narrowly enough to finish.

The leaders running these strategies are not being lazy. They are responding to legitimate pressure. The CEO wants to know what AI is doing for the whole business. The board wants a roadmap. Finance wants the cost spread across multiple budgets. Every one of those asks pushes you toward breadth. Breadth feels safe. Breadth looks responsible.

Breadth is the trap.

The fastest path to organization-wide AI adoption is paradoxically narrow. One team. One workflow. All the way through. Until what they are doing is boring, repeatable, and documented well enough that you could hand it to a different team and they could run it without coaching.

That first team is not a small bet. It is the foundation for everything that comes after. They become the case study. They become the blueprint. They become the internal trainers. They are the reason the second team has a path to follow, and the third team after that, and so on. The teams that try to scale to ten places at once never get one place to work well enough for anyone else to copy. That is why the strategies that look most ambitious produce the least.

The framework keeps doing the work

The 5P Framework by Trust Insights™ is built for exactly this kind of decision. Purpose, People, Process, Platform, Performance. The trap we talked about in pilot purgatory last week was committing to Platform. The trap in this one is skipping over People.

The right People, on the right team, working a real Process, are what lets Platform actually produce something useful. If you spread Platform across ten teams without doing the People and Process work for any of them, you do not have an AI strategy. You have a license bill.

Concentration is also how Performance starts to mean something. You cannot measure what you have not contained. One team’s workflow has a measurable outcome. Twelve teams using AI in twelve different ways do not.

Why the pressure to spread has gotten worse

The pressure has always been there. It has gotten worse in the last eighteen months because every new AI tool has its own internal evangelists. Sales says they need it. Finance says they need it. Marketing says they need it. HR and Operations are right behind them. All five departments are right that AI could help them. They are also all asking at once.

The honest answer is that you cannot help five departments well at the same time. You can help one of them well, and then the next, and then the next. The order matters. The discipline of saying “not yet” to four out of five is what makes the one that does get help actually work.

That is also the conversation nobody wants to have. The leader who runs an AI strategy with one team in scope and four departments waiting in queue is going to have a more uncomfortable conversation with the CEO than the leader with a twelve-workflow roadmap. The uncomfortable conversation is the right one. The roadmap is a placeholder for the work.

Your Next Move

This week. Same shape as last time.

Pick one team. Pick them on purpose, not by who asked first. Three criteria: 1) The team with the biggest pain that AI can actually address. 2) The leader who is willing to own the outcome and is not just curious. 3) The workflow that is well-defined enough that you can explain it in two sentences.

Concentrate all your AI investment on that team for one quarter. Budget. Tooling. Time. Coaching. Documentation. Refuse to add a second team until the first one is running the workflow without you. That is the test. If they cannot run it without coaching, the workflow is not ready, and a second team will only multiply the problem.

When the first team is running clean, write down what they are doing. Specifically. Step by step. That document is your scaling plan. Hand it to the second team. See if they can run it. If they can, repeat. If they cannot, the document is the problem, not the AI.

I will keep saying this one, too. Get good at one thing before you get bad at five things. The teams I see make real progress are not more ambitious than everyone else. They are less ambitious, more often. They pick one place. They finish there. They earn the second place by finishing the first.

If your AI strategy has too many teams in it, the most useful move you can make this quarter is to remove teams from the plan until what is left can actually finish.

Pick the team. Concentrate the work. Finish.

Are you spreading your AI too thin? Reply to this email or join the conversation in our Free Slack community, Analytics for Marketers!

– Katie Robbert, CEO

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the evolution of autonomous AI agents and the promise of Level 5 systems. You’ll discover the core differences between simple AI tools and complex agent swarms with persistent memory. You’ll learn how to apply the 5P framework to prevent your AI projects from spiraling out of control. You’ll explore the real-world benefits and risks of offloading high-level executive decision-making to software. You’ll see how these emerging technologies change the way you manage workflows and team communication.

Watch/listen to this episode of In-Ear Insights here »

Last time on So What? The Marketing Analytics and Insights Livestream, we explored the arcane world of data prepping. Catch the episode replay here!

This week on So What? we’ll be taking a tour of Paperclip, the agentic AI system. Are you following our YouTube channel? If not, click/tap here to follow us!

Here’s some of our content from recent days that you might have missed. If you read something and enjoy it, please share it with a friend or colleague!

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In this week’s Data Diaries, last week’s governance-illusion frame leads straight here: the audit trail you owe the regulator starts with data you can actually account for. The cheapest piece of your 2026 AI stack is the model. The expensive piece — the one that shows up in audits, board meetings, and procurement reviews — is proving where your training data came from.

California AB 2013 now anchors the disclosure floor. After Bartz reframed unlicensed training data as a balance-sheet item, AB 2013 requires any generative AI developer that makes its system available to Californians, and substantially modified it after January 1, 2022, to publish a high-level summary of the datasets that trained it. The compliance date is January 1, 2026. Without legal and compliance review, there are no assurances your prompts and documents stay out of someone else’s training corpus.

Here’s why this lands on the board agenda, not just the data team’s. If you cannot produce an AB 2013-compliant disclosure today, you carry active exposure, not theoretical risk. Models commoditize. Data compounds — and so does the liability attached to data you cannot account for.

So what does a defensible data strategy actually look like? Treat your data as the asset that compounds — and treat AI like the untrustworthy contractor it is, gullible, extremely fast, and willing to route your documents wherever the prompt sends them. The risk is exfiltration to a vendor’s training corpus: your client files, your specs, your strategy decks quietly feeding the next model release.

If your team cannot name who reviewed the SLA on every AI tool your people touch, then your data has already left the perimeter and you simply have not measured it yet. The corpus you trained on is the asymmetric advantage no competitor can copy. The enterprise that keeps it under its control retains that advantage; the one that hands it to a free chatbot funds a competitor’s next training run.

Now what should you do this quarter? Bring back a concept from the 2010s called data clean rooms — sanitized data that lets an untrustworthy system see only the parts it needs. You put out replicas. You put out Swarovski fakes, and the real crown jewels never leave your control.

The contractual primitive is the SLA your legal and compliance teams have actually reviewed. The architectural primitive is a routing system that runs locally and detects the use of protected data, then forces traffic only to vendors covered by a data agreement. The action looks different at each scale:

Agency or small team: discipline vendor selection. List every dataset, image library, and client feed that touches an AI tool, and assign role-based access so the intern’s account cannot upload a client’s master file to a free chatbot.
Mid-market leaders: stand up strong SLAs across every AI vendor first. Route every renewal through legal and compliance review, and add RAG index deletion to your regular ops cadence.
Enterprise and regulated: publish your AB 2013 summary before the next 10-K, then build the local model router on top of it. Tie vector-store retrieval to your existing Identity and Access Management (IAM), and treat shared service accounts as the security-side anti-pattern they are.

Bring one question to your next leadership meeting: who owns our training-data license register, and when did they last touch it? If no name surfaces in ten seconds, that gap is your work.

Next week, we follow the data thread into privacy and automated decisions — what your obligations look like when a model “remembers” a person it should forget, and who carries the liability when an automated system decides something material about a real human being.

Almost every AI course is the same, conceptually. They show you how to prompt, how to set things up – the cooking equivalents of how to use a blender or how to cook a dish. These are foundation skills, and while they’re good and important, you know what’s missing from all of them? How to run a restaurant successfully. That’s the big miss. We’re so focused on the how that we completely lose sight of the why and the what.

This is why our capstone course, the AI-Ready Strategist, is different. It’s not a collection of prompting techniques or a set of recipes; it’s about why we do things with AI. AI strategy has nothing to do with prompting or the shiny object of the day — it has everything to do with extracting value from AI and avoiding preventable disasters. This course is for everyone in a decision-making capacity because it answers the questions almost every AI hype artist ignores: Why are you even considering AI in the first place? What will you do with it? If your AI strategy is the equivalent of obsessing over blenders while your steakhouse goes out of business, this is the course to get you back on course.

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