INBOX INSIGHTS: 5P Framework by Trust Insights™, B2B Marketing Writing and AI Part 1 (2026-03-11)

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INBOX INSIGHTS: 5P Framework by Trust Insights™, B2B Marketing Writing and AI Part 1 (2026-03-11)

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(Re)Introducing the 5P Framework by Trust Insights™

I have a confession: I used to think the problem with change management was that people didn’t follow the framework closely enough.

ADKAR. Kotter’s 8 Steps. Lewin’s Unfreeze-Change-Refreeze. I’ve tried all of them. They’re well-researched, well-intentioned, and — in many cases — they work. I’m not here to trash them.

But after years of leading digital transformations and AI adoption projects, I kept seeing the same thing happen: a team would pick a framework, follow it faithfully, execute every step — and still end up six months later with no way to tell whether the project actually worked.

Not because the framework failed. Because the framework never asked the right question in the first place.

Here’s what I mean. Most change management models assume you’ve already figured out why you’re doing this before you start. They assume someone, somewhere, has defined what success looks like. In practice? That almost never happens. People jump straight to the tool, the coalition, the 8-step plan — and the measurable question they were supposed to answer gets lost.

The gap nobody talks about

I went through the five most commonly used change management frameworks — ADKAR, Kotter, Lewin, McKinsey 7-S, and Bridges — and evaluated them across nine dimensions that actually matter when you’re running a real project. Things like: does this framework force you to define a measurable purpose? Does it address your people’s skills and willingness and psychological safety? Does it help you choose the right tools? Does it measure whether the change actually worked?

The pattern was striking. Every single model had at least one critical blind spot — and most of them shared the same two.

First: none of them require you to start with a measurable purpose. ADKAR starts with Awareness. Kotter starts with Urgency. Lewin starts with Unfreeze. Bridges starts with Letting Go. All valid starting points for managing change — but none of them ask, “What specific, measurable question are we trying to answer?” Without that, you can execute the framework perfectly and still have no idea whether the initiative was worth the disruption.

Second: none of them close the loop. Not one of the five major frameworks includes a built-in step for measuring performance against your original goal. Kotter’s final step is “Institute Change” — which is about embedding the change into culture, not measuring whether it produced the outcome you set out to achieve. ADKAR ends with Reinforcement — making the change stick, not proving it worked. There’s a difference.

This is the gap the 5P Framework by Trust Insights™ was built to fill. It bookends the traditional “People, Process, Technology” model with Purpose at the beginning and Performance at the end. Purpose forces you to state a measurable question before you touch anything else. Performance forces you to go back and answer it. Everything in between — People, Process, Platform — serves the Purpose and gets validated by Performance. It’s a simple structural change, but it eliminates the two most common reasons projects fail: unclear goals and unmeasured outcomes.

The frameworks aren’t wrong. They’re incomplete. And the missing pieces are the ones that matter most when someone asks, “Did this actually work?”

I wrote a full comparison — an honest look at what each model does well, where it stops short, and a side-by-side table across all nine dimensions so you can see exactly where the gaps are.

If you’ve ever followed a framework to the letter and still couldn’t prove the project was worth it, this is the piece that explains why.

Read the full comparison: The 5P Framework by Trust Insights vs. Other Change Management Models

And if you’re new to the 5P Framework entirely — or you want to see how it applies beyond change management to AI strategy, marketing audits, and more — we’ve built a full resource hub:

Explore the 5P Framework by Trust Insights™

How are you managing change in 2026? 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 data, or the lack thereof. I routinely check LinkedIn every morning to see what’s happening among my network, and I saw a post recently speculating on how B2B writing has changed since the advent of generative AI.

In it, the author posited an interesting conclusion, that mediocre writing is now the norm and high quality writing has diminished. But the only proof point was an AI-generated chart with no citations or data labels.

This intrigued me. We covered the citizen analyst in an AI world in the last few issues of the newsletter, so this week I decided I would put those skills to the test. How could we prove or disprove this hypothesis, that generative AI has made B2B content more generic and less compelling?

Following the process from the past few weeks of extract, transform, and load, we first need to find B2B data. I chose three very distinct topics, one in an industry typically thought of as a lagging industry, industrial ball bearings (specifically the topic of angular ball bearings). The second topic was the consultant’s favorite: digital transformation. The third was financial services, the topic of debt service coverage ratio.

Why these three? Because they represent what I’d describe as the three phases of B2B content creation – industrial B2B tends to lag, financial services tends to be reasonably current, and folks creating content around digital transformation are fighting tooth and nail for any level of visibility, so they’re likely to use “cutting edge” tech like AI early on.

We need content to assess, so I grabbed 71,000 URLs from the AHREFS SEO software from 2011-present day, using the Crawl4AI Python package to extract the pages’ text to a central database. This took several days. Then, using Claude Code to process the data, our code scanned each article for 48 different writing metrics in 9 broad categories:

Readability: how easily readable is the content?
Vocabulary Richness: how linguistically rich is the content?
Structural / Syntactic: how well structured are things like sentences?
Tone: how emotional is the content?
Passive Voice: how much passive voice?
Convergence Signals: how predictable is the language?
Descriptives: how rich is the language?
Complexity: how unique is the vocabulary?
Stylometry: how similar are pieces to each other?

What we would expect in an AI-forward world is that content would be more readable but more bland, a “Sea of Same” as our friend Jay Baer likes to quip. If the stereotypes about AI writing hold up, we should see incredibly predictable language, shallow, bland language, a drop in uniqueness, and high degrees of similarity. That’s essentially what AI slop is.

What do we actually see? Browse the results for yourself on our website!

Broadly speaking, we see for all 3 topics:

Over the past 10 years, using median as our measure of centrality, we see that writing quality broadly among these three topics has remained largely the same, but in the post-AI era has smoothed out and slightly improved.

For ball bearings, we see almost no change. This might indicate that AI simply isn’t being used in this B2B sector, which is a reasonable assumption. Without investing in heavy fingerprinting of the content itself, we can’t determine from metrics alone whether or not AI is in heavy use. That said, we do not see a significant volume increase, either; one of the hallmarks of AI use is a difference in the volume of content created.

In financial services, we see the smoothing and quality increase, but more important, we see the volume increase substantially in the post-AI world.

For digital transformation? There’s no question AI is in use; there’s a massive jump in content creation in 2025, far above what the industry used to produce. We also see the quality smooth out and increase.

From this initial read, do we see that AI has made B2B writing worse? Not necessarily. Over 48 different metrics, we see that writing quality has generally increased in the AI age, not decreased.

In next week’s issue, we’ll dig a bit deeper into the specific metrics and what we can glean from them about B2B writing quality and what specific things we can do with this data to improve our own writing quality. Stay tuned!

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.

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Here’s a roundup of who’s hiring, based on positions shared in the Analytics for Marketers Slack group and other communities.

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