INBOX INSIGHTS: Stop Hiding in AI Pilot Purgatory, Enterprise AI Part 2 (2026-05-27)

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INBOX INSIGHTS: Stop Hiding in AI Pilot Purgatory, Enterprise AI Part 2 (2026-05-27)

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You’re Not Stuck in Pilot Purgatory. You’re Hiding In It.

A pilot is not a commitment, and that is the whole problem.

You haven’t been stuck in pilot purgatory. You have been hiding in it.

I want to say that as plainly as I can. The pilots are not the problem. Most of them actually work. They produce a result, a deck, a happy quote from somebody on the team. And then they end, and nothing changes, and a few months later somebody starts scoping a new pilot. The loop has been running for two years in some companies. It is going to run for two more if nobody names what is actually happening.

What is actually happening is this: a pilot is reversible. You can run a pilot, get a result, and walk away with no cost to your reputation, no money committed past the original scope, no team forced to change how it works, and no number you have to move. A pilot is safe.

A commitment is not safe. A commitment has a person’s name on it. A commitment has a budget that is gone if you are wrong. A commitment has a deadline and a number that has to move. Most importantly, a commitment forces somebody to say, in writing, “I am betting on this.” That means somebody might be wrong. And it might be visible. That risk is what nobody wants on the table.

So instead of committing, we scope the next pilot. The next pilot is always a way to not have to decide yet. And when the underlying technology is changing every week, “let’s wait and see” sounds like wisdom. It is not wisdom. It is hiding.

I want to be careful here. The people running these pilots are usually doing the work right. They have actually moved on AI personally. They use it daily, they are good at it, and they are trying to bring their organizations along. They are not the problem. They are doing what their organization will allow them to do. The problem is structural. The problem is that nobody above them, beside them, or below them is willing to step up and own what comes after the pilot.

That is the actual definition of pilot purgatory. Not “the pilots don’t scale.” It is “no one will take the pilot and turn it into a commitment they would be willing to defend.” Different problem entirely. And it has nothing to do with the technology.

Why this is worse right now than it has ever been

The fear of being wrong is rational. Models are changing constantly. Vendors are launching every week. You can name three AI tools today that did not exist eighteen months ago and that you are now using daily.

In that environment, the rational person waits. The rational person runs another pilot. The rational person says, “We should evaluate the new tool before we commit.” All those moves feel responsible.

They are not. They are the reason your organization keeps making the same loop. The technology will keep changing. If you wait until the change stops, you will never commit to anything. You will scope pilots until the budget runs out or somebody else gets the job.

This is also where I will say what I always say, and then I will keep saying it. The way out of this is the frameworks.

A framework is what gives you something to commit to when the tools are not stable. The 5P Framework by Trust Insights™ is the one I use: Purpose, People, Process, Platform, Performance, in that order. The reason the order matters is exactly this conversation. If you commit to a Platform, you are committing to a thing that might be obsolete in six months, and you are right to be nervous. If you commit to a Purpose, the tool becomes interchangeable. A defined outcome with the right People and Process behind it does not depend on the model release calendar.

Without the framework, every commitment feels like guessing. With it, commitment becomes a position you can stand behind. That is the difference between a program and another pilot.

Your Next Move

This week. Not next quarter.

Pick the workflow you have already piloted that worked best. Write a one-page commitment memo. Five lines is enough.

What we are doing. The specific outcome we are betting it will produce. Who owns it. What business number it has to move and by when. What happens if it does not.

Take that memo to the person who can sign it. If they sign it, you have left pilot purgatory. You are in production. You’re welcome.

If they will not sign it, then you do not have a pilot problem. You have a commitment problem. And the most useful thing you can do, the bravest thing actually, is to stop running pilots until that is resolved. Either the organization will sign the memo or the organization will say in writing that it is not ready to do this for real. Both of those answers are forward motion. Running another pilot is not.

I will keep saying this until people hear me. The pilot is not the work. The commitment is the work. The pilots have been working. The question is whether anybody in your organization is willing to step out from behind them.

Pick the workflow. Write the memo. Find out where your organization actually is.

Are you struggling with commitment? 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 critical definition and requirements for navigating Enterprise AI. You’ll learn how to distinguish between consumer-grade tools and the strict standards required in regulated industries. You’ll discover the twenty essential pillars for building a secure and compliant AI strategy for your organization. You’ll understand why rigorous vendor scrutiny matters as much for software as it does for human talent. You’ll gain clarity on the governance frameworks necessary to prevent data leaks and legal vulnerabilities in your enterprise.

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

Last time on So What? The Marketing Analytics and Insights Livestream, we got to know knowledge graphs. Catch the episode replay here!

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In this week’s Data Diaries, we move from measurement discipline to the governance layer that makes that discipline defensible to a regulator. Discipline alone will not satisfy the auditor, and enforcement turned operational.

The Senate killed the proposed 10-year state-AI moratorium on July 1, 2025, which retired “wait for Washington” as a strategy. State legislatures filled the gap.

California’s AB 2013 takes effect January 1, 2026, and SB 942’s operative date moved to August 2, 2026 after Governor Newsom signed AB 853 on October 13, 2025. Texas TRAIGA reaches up to $200,000 per uncurable violation and $2,000 to $40,000 per day for continuing violations. Illinois HB 3773 treats AI-driven employment discrimination — zip-code proxies included — as a civil rights violation.

The EU AI Act stacks on top: General-Purpose AI obligations have applied since August 2, 2025, the fines turn on August 2, 2026, and penalties climb to €35M or 7% of turnover for prohibited practices, €15M or 3% for most violations, and €7.5M or 1% for misleading information. Judge Alsup preliminarily approved Bartz v. Anthropic on September 25, 2025 — roughly $1.5 billion across 465,000-500,000 pirated works at $3,000 per book, with final approval landing May 14, 2026. The SEC’s Presto Automation order on January 14, 2025 cemented Section 17(a)(2) for AI-washing cases.

So what does this mean? Every one of those laws assumes a real human-in-the-loop, and human-in-the-loop may become one of the biggest governance illusions in enterprise AI. Modern AI systems do not just generate answers anymore; they increasingly classify risk, which means the system you govern also decides when governance should begin.

If your harness lets the model self-escalate, and your reviewer rubber-stamps the escalation, and your audit log only captures what the model chose to log, then you do not have oversight — you have theater. Would it be acceptable to have a junior employee who does not have a manager, who does not report to anyone? Absolutely hard pass no. And yet most enterprises ship agentic systems that do exactly that, right?

The Bartz outcome shows the cost runs operational. Unlicensed training data became a balance-sheet item overnight, and your indemnity clause now decides whether your vendor or your CFO eats the next one.

Now what should you do this quarter? Treat every agent like a junior employee with a manager, checkpoints, and reporting lines. If you run an agency, build ISO-aligned governance documentation as a service line. Your mid-market and enterprise clients need that dossier, and most of them cannot produce it themselves.

Mid-market leaders should push governance through procurement. Rewrite RFPs to require model cards, training-data provenance, structured logging, and customer audit rights, and wire the same requirements into your renewal cycle so the next contract closes the gap before a regulator opens it.

At the enterprise level, stand up the AI Council with a named owner, a documented escalation path, and a regulator-ready dossier that maps every production agent to a human accountable for its outputs. Appoint a Chief AI Officer if you have not, give the role budget authority, and connect it to legal, security, and the business units that ship the agents.

Wire structured logging into a database the agent updates in near real time, then audit those logs the way you audit a junior employee’s work product. My CEO and co-founder Katie Robbert says, and is correct in saying, that new technology does not solve old problems. The old problem here is familiar: a worker without a manager.

The new problem is that this worker runs 24/7 at machine speed, so the failure mode compounds in hours, not quarters. Real governance comes from good processes, treating machines like junior employees, and strong mechanisms for reporting and clear outcomes.

Next week, we walk the data-boundary layer that makes any of this auditable — where your training data and prompts actually live, and who else sees them.

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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