INBOX INSIGHTS: People Are The Problem, Scaffolding AI (2026-01-21) - Trust Insights Marketing Analytics Consulting

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INBOX INSIGHTS: People Are The Problem, Scaffolding AI (2026-01-21)

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People ARE the problem with AI Adoption

I wrote about this last week on LinkedIn, but I was restricted by character count. I figured I would do a deeper dive into the topic of “People and AI Adoption.”

I keep seeing articles, experts, and “thought leaders” lamenting that their AI initiatives are stuck. They can’t get out of pilot mode. They’re no further along than they were two years ago. And their conclusion? People are the problem.

People are getting in the way of innovation. People are the reason AI adoption isn’t happening. People are why we can’t meet our goals.

Here’s the thing: People are a BIG part of the problem. But not in the way that you think they are.

This isn’t a new problem

Companies trying to force new technology on their teams without a real plan? That’s not an AI problem. That’s a leadership problem. And it’s been around forever.

I’ve worked at a lot of different companies throughout my career. I’ve done my best to advocate for myself, to speak up when something isn’t working. And despite the so-called “open door policies” and “cultures of change,” I was met with resistance.

“That’s just how it is.” “You’re going to have to deal with it.” “Stay in your lane.” “Keep your head down.”

Basically anything except: make waves, ask questions, become what they see as “problematic.”

There’s a good reason I struggled to move up and get promoted at previous companies. I’m not one to sit back and just accept what’s given to me. I’m going to look at it, start poking holes, ask questions, and say “I don’t understand why we’re doing it this way.”

And many times, the only answer was “because that’s how it’s done.”

I was prevented from moving up because the more authority I’d be given, the more ability I’d have to see where things weren’t working—and to actually try to change them. Leadership wanted to stay the course with the plans they’d decided on, right or wrong. They didn’t want anyone making waves.

If you know me, you know that’s just not how I roll.

Sound familiar?

This is exactly what’s happening now with AI.

Companies are forcing AI on their teams. It’s positioned as “our way forward, our mission, our disruptor, our differentiator.” But there’s no real plan for what that means for the people actually using it. And leadership says they want feedback—but only if that feedback is agreement.

The people being told they have to use these tools? They’re frustrated. They don’t understand what they’re supposed to be doing. And when they speak up, they get shut down.

This is why AI isn’t getting out of pilot mode. This is why companies can’t get past where they were two years ago.

No kidding.

People not understanding what they’re supposed to do with technology has never been a new problem. And it falls squarely on the shoulders of whoever is setting the course.

The real issue is trust

I know you want to say people are difficult. I know you’re thinking “I just want them to do what I want them to do and not ask questions.”

That’s not how it works. That’s never how it’s going to work.

One of the things I learned very early in my career as a manager: It doesn’t matter what level somebody is at—they just want to be heard. Even if the answer is no, they want to know their voice has been heard. They want some kind of justification for why the answer is no.

A lot of leaders struggle with this because they don’t want that level of transparency. But when you skip it, you’re not building trust.

And that’s the core of what we’re talking about. If your team does not trust you, they will not do what you’re asking them to do. Full stop.

You can have all the open door policies and all-hands meetings you want. But if your team doesn’t trust you because you’re not actually listening? It’s all a waste of time.

Trust is fixable—but it takes work

It’s not as simple as sitting back, hearing people out, and then saying “I heard you. The answer is still no.”

Trust is showing up consistently. Trust is doing what you say you’re going to do. Trust is follow-through.

Here’s what that actually looks like:

Acknowledge what people tell you. Let them know you’ve actually heard it. Not performatively—genuinely.

Come back to them with what you did with the information. Not in a “you told us this and now you’re fired” way. In a “this was helpful feedback—here’s why we can’t implement it right now” way. Or “here’s what we’re going to do with this moving forward.”

Be transparent about constraints. The answer can still be no. But explain the limitations. You don’t have to share every line of your P&L, but you can say something like: “If we move forward with this idea, here’s what it would cost—and we don’t have that budget right now. But if we land three more clients, we’ll have some wiggle room.” That’s a totally reasonable conversation. There’s no reason to keep your team in the dark about why decisions are being made.

Stop shutting people down before they finish. Not every idea is going to be a good idea. But if you shut people down before they even get a chance to finish their thought, you’re never going to get to the good ideas. You have to sift through some not-so-great ones to get there.

Get input from everywhere. Ideas don’t just come from your leadership team—that’s a huge mistake companies make. You need people from all over your organization, from different backgrounds and experiences. That’s how you get a really good idea, a really good plan, a really good product.

People aren’t the problem—they’re the starting point

This is why I get so frustrated with the “people are blocking our AI progress” narrative. It completely misses the point.

If you’ve ever used the 5P framework, you know that purpose and performance are the bookends. But the first P you have to figure out in the middle? People.

Because if you don’t figure out people—if you don’t give them the information they need, the resources they need, the support they need, and if you don’t actually listen to what they’re telling you—the rest of it is wasted. It doesn’t matter what your goal is. It doesn’t matter how much you’ve invested in the platform.

It’s never going to happen.

So if you’re just coming around in 2026 to the idea that “people are the problem”? You’re asking the wrong question. People aren’t the problem. They’re where you start.

That’s my rant.

And if you want help figuring this out in your organization? One of my strengths—my superpower—is understanding people. It’s having those hard conversations. But more than that, it’s listening. I can do the listening for you. I can be that neutral party who gets the information and brokers those conversations between you and your team to help you move forward.

That is what I am exceptionally good at. I’m not even humble about it.

Let me help you. Reach out, and we can talk.

How are you managing change with your people? 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 week’s Data Diaries, let’s talk about a concept called scaffolding. Scaffolding as it relates to generative AI refers to breaking a task up into several stages so that generative AI tools are more likely to successfully complete the task.

For example, let’s say you wanted to write a book about go-to-market strategy. You could, and people do, give your large language model tool of choice like ChatGPT a prompt like, “let’s write a 50,000 word book about go-to-market strategy”. This works about as well as you would expect, which is to say not at all. At best, you’ll get a big pile of slop, assuming the language model is even capable of completing the task at all; most are not.

Even the most capable agentic AI tools will still fail at this task because it’s too broad an ask. I would hope you’d never ask an intern to go write a book about go-to-market strategy. You should not ask AI to do that either.

So if you wanted to write a book about go-to-market strategy, how would you approach it? Scaffolding is the answer. Your first step would be to develop the overall thesis and concept of the book. A simple one-page document that explains what the book is about and in particular why your perspective on it is different from anyone else’s. In architecture terms, think of this like a project concept.

Once you’ve sat down and decided what your book is going to be about, then you would work with your AI tools to develop an overall book outline of what each chapter is going to be about. In the same way that an architect would go from a project concept to a blueprint, you are building scaffolding.

And then as an architect would go from a blueprint to a building rendering, you would then work with your AI tools to flesh out what each chapter is about, building chapter by chapter individual outlines, based on the overall book outline.

Finally, once you’ve got each chapter outlined, you then work with your AI tool to create the actual chapters. You’ve gone from concept to blueprint to building rendering to the actual construction.

Here’s a smaller project to practice with, scaffolding a 3-email welcome sequence. Start with your concept: “This welcome sequence introduces new customers to our top 3 features, with the goal of reaching ‘first value’ within 48 hours. Different from competitors because we focus on quick wins, not feature overload.” Then ask your AI tool to create a 3-email outline showing the subject line, key message, and call-to-action for each email based on that concept.

Finally, for each email in your outline, ask the AI to write the full email in your brand voice, keeping it under 200 words. Total time: 30-45 minutes vs. 3+ hours of staring at the cosmic horror that is a blank page.

Watch out for common scaffolding mistakes. Being too vague at the concept level defeats the purpose – “write marketing content” isn’t a scaffold, but “write a case study proving 40% efficiency gains for mid-market SaaS companies” gives AI the specificity it needs. Skipping validation between layers wastes effort; review each scaffold layer before moving to the next, because fixing a bad outline is easier than fixing a bad draft, and fixing a bad draft is easier and less damaging than fixing a bad final product.

Conversely, don’t over-scaffold simple tasks. A single social post doesn’t need four layers. Match the complexity of your scaffold to the complexity of your project.

That scaffolding process of going from a seed all the way to a large project’s completion is ideally suited for how AI works. AI can’t remember huge amounts of text, but if we give it outlines and guidance, it can accomplish a large number of small tasks. Agentic AI in particular thrives on this kind of small, chunked up work.

Scaffolding applies well beyond content creation. You can scaffold a 12-month integrated marketing campaign by starting with quarterly themes, then monthly initiatives, then weekly tactics. You can scaffold a customer segmentation project by defining segments first, then criteria, then queries. You can scaffold a team workshop by outlining learning objectives, then modules, then exercises. Any complex project benefits from this layered approach.

If you want to maximize the chances of success in your AI projects, ensure that you’re using scaffolding to decompose tasks and make it easy for the machines to do a large number of small tasks that add up to one big task.

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 new 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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