INBOX INSIGHTS: AI Strategy Commonalities, B2B Marketing Writing and AI Part 2 (2026-03-18)

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INBOX INSIGHTS: AI Strategy Commonalities, B2B Marketing Writing and AI Part 2 (2026-03-18)

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The One Thing Every AI Strategy Has in Common

A bartender told me a story recently that I haven’t been able to shake. The parking system in her building is run by an AI startup — a buddy of the building owner. It scans your plate, logs your entry, tracks your space, monitors duration, calculates what you owe, processes the data, generates a record, and… never sends you a bill. Eight steps of sophisticated data collection. And the one thing it was built to do? It doesn’t do that.

I keep thinking about that parking system because it’s the most honest picture of where we are with AI right now. Not the conference keynote version. The real one.

Every failed AI initiative I’ve looked at — every stalled pilot, every abandoned project, every training program that produced certificates but not capability — has the same root cause. Leadership bought the shiny thing to avoid doing the hard thing.

That’s it. That’s the whole pattern.

It shows up everywhere once you start looking. Companies are running AI pilots that collect data, process inputs, generate outputs, and produce beautiful dashboards — but nobody can tell you what business outcome the pilot delivered this quarter. They’re measuring activity, not results. Meetings held, models trained, dashboards built. Check, check, check. Revenue generated? Decisions improved? Crickets.

It shows up in training. The instinct was right — people need to learn how to use AI. The execution was wrong. Most organizations rolled out the same 101 course for everyone: the intern, the VP, the data scientist, and the operations manager all sat through identical training on prompt engineering basics. I say this as someone whose company built generic AI training too. We did it. And then we realized that completion rates tell you nothing about capability. A workshop where the CMO learns to audit AI outputs for brand risk is fundamentally different from one where a junior analyst learns to build a workflow. The hard work is assessing where each person actually is and what they specifically need. Most organizations skipped that step because assessment is slow and messy.

It shows up in infrastructure. Companies want the electric train — the cutting-edge AI, the automation, the intelligent agents — but their data lives in seventeen systems that don’t talk to each other. Their CRM hasn’t been cleaned since 2019. Half their analytics run on spreadsheets one person maintains and nobody else understands. The AI budget got approved. The data infrastructure budget didn’t. And in many organizations, the data team has been raising this flag for years. Leadership just didn’t fund the fix.

And it shows up in talent. AI has automated the entry-level work that used to train future leaders — the research, the data pulls, the first drafts. All the unglamorous tasks that taught junior employees how the business actually works. That pipeline is gone, and nobody has replaced it. We celebrated the efficiency gains without asking where the next generation of leaders comes from.

Four different symptoms. Same disease. Leadership keeps buying solutions to avoid doing the work that makes those solutions actually function.

So before you approve your next AI initiative, answer four questions:

Who’s building judgment? How are you developing the next generation of leaders now that AI handles their old proving ground?
Where is each person today? Have you assessed your team’s actual AI proficiency before buying training?
What’s under the hood? When was the last time someone audited the data infrastructure your AI is supposed to run on?
What does done look like? Can you articulate the specific business outcome this pilot produces, in one sentence?

If you can’t answer all four, that’s your to-do list. Not another platform evaluation. Not another vendor demo. The work that makes everything else work.

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– Katie Robbert, CEO

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In this week’s In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss balancing authenticity in an AI forward world. You will uncover the major flaw of automated social media accounts. You will learn the secrets to spot robotic replies. You will explore techniques to transform artificial intelligence into a helpful companion. You will master the balance between speed and true personality.

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Data Diaries: Interesting Data We Found Last week, we talked at a high level about how B2B writing quality has changed before and after generative AI at a high level. This week, let’s dig deeper into the data. If you missed last week’s issue, a colleague on LinkedIn posited that generative AI has made writing more bland and worse in B2B marketing, and we wanted to understand whether that was a provably true or false statement.

TLDR: Writing has generally improved with generative AI.

Let’s take a look at a few different perspectives on the data. In terms of how to read these charts, pay special attention to the solid colored boxes; they represent the lower quartile, median, and upper quartile. In particular, we pay attention to the top of the colored box, which represents the top 25% of writing in each category, the upper quartile. When we talk about high quality content, it’s content in this upper quartile that we want to measure most.

First, the overall data:

We see in general readability has improved, especially in the top 25% of content, vocabulary richness has improved, structural writing has improved, passive voice has decreased, descriptives have improved, writing complexity and uniqueness has improved. We do see that convergence signals and stylometry have decreased, meaning that there’s more writing that may share similar authorship (i.e. AI).

What’s interesting is when we dig down into the topics. Using those same metrics, here’s what the legacy B2B market of angular ball bearings – which shows no indication of heavy AI usage – looks like:

We see similar improvements in most categories, but a massive drop in stylistic differences; this generally indicates authorship uniqueness is declining. In the case of angular ball bearings, this may mean a convergence of writing styles with or without AI.

On the other hand, when we look at good ol’ digital transformation as a topic, we see marked increases in most metrics and something unusual: for the top 25% of content, uniqueness of authorship remains the same for pre and post AI. This means that while AI is in heavy use (as seen by the sheer volume increases last week), it’s not damaging the top 25% of content in the space even as the amount of content has gone up 10x.

What can we conclude from these metrics and measures? First, while writing style and writing quality are notoriously difficult to measure, there are decades of research on what constitutes better vs. worse writing, from uniqueness to richness of diction, complexity, etc. The 49 metrics we analyzed in this represent an ensemble of the different measures to mitigate the strengths and weaknesses of any one metric.

Second, the hypothesis that writing quality has declined in the post AI world is disproven. Writing quality in general has improved since the advent of generative AI, and that’s a good thing. Better quality writing improves the value of the craft of writing, whether you use AI or not – the more people become accustomed to improved writing quality, the more they’ll value it.

Third, these writing quality measures are something you can implement in your own work. When you work with AI to write, instead of just saying “help me improve my writing”, you can give AI the list of the 48 metrics we’ve compiled, ask your ideal customer profile (ICP) which metrics are most important to them in terms of writing quality, and then use the metrics to assess your own writing – and have AI help you improve it. If, for example, your ICP says that readability is vitally important to them, and you have a readability target of something like grade 8 in Flesch-Kincaid readability, AI can help you achieve that target by taking your original human-led writing and adjusting it.

Writing quality is important. If, as it seems, people are using AI to improve it across all categories but especially the highest quality quartile of writing, that benefits everyone.

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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