INBOX INSIGHTS: What Happens When You’re Too Good, Practical Responsible AI Part 5 (2026-10-07)

INBOX INSIGHTS: What Happens When You’re Too Good, Practical Responsible AI Part 5 (2026-10-07) :: View in browser

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What happens when you’re too good?

I’ve spent the last five weeks documenting my process for moving some of my work to local AI, only for it to come to a screeching halt. Last week I laid out the plan: one 30-minute step every Wednesday morning, before any client work. The very first task was a one-minute check, and I didn’t do it. (You probably saw that coming. I did too, honestly.) Since I’m traveling for the next couple of weeks, I’m going to park this initiative and revisit it when I’m back and can focus. I’d rather come back with something that actually happened than keep writing about what I intend to do.

So, switching gears. One night last week, while my husband and I were sitting on the deck with the fire pit and Georgia, he was telling me about one of his co-workers who used to sell vacuums door to door. The company has been selling essentially the same machine since the 1970s (maybe longer). The reason it came up is that you can only buy it from a door-to-door salesperson, and allegedly it’s so good it doesn’t break.

That last part is the problem I want to talk about this week. A vacuum that never breaks is a vacuum nobody buys twice, which means no replacements, no upgrades, and no repeat customers. The company made something so good that every happy customer is also a former customer. Companies like Hinge, which is a dating app, boast this in their ads. Their goal is for you to meet someone and delete the app. It’s an interesting business model.

I told the story on our weekly sales call Monday morning, and I didn’t have to reach very far for the business version. We’ve had clients where we did such a good job advising them that they took what we taught them and ran.

The other side of that coin is that the person who led that work on the client side is now one of our biggest evangelists. Anyone who will listen, she’ll tell them about us. When I posted the idea in our team’s ideas channel, John called it a huge topic and named the trap underneath it: every business ends up choosing between doing something amazing and building a revenue stream.

The question isn’t how to keep people coming back. The question is what you want them to leave with, and what they do with it after they go.

Fewer requests isn’t the same as less value

You don’t have to run a consulting firm or sell vacuums for this to apply to you.

If you’re on an analytics team and you build a self-serve dashboard that actually works, the ad hoc requests slow down. If you lead AI enablement and you train your marketing team well, they stop opening tickets for every prompt that misbehaves. If you write the documentation nobody else would write, people stop needing to interrupt you to ask how the thing works. In every one of those cases you did exactly what you were supposed to do, and from the outside your workload looks like it’s shrinking.

That’s where good teams get into trouble, because “fewer requests” can read as “less valuable” to whoever is looking at the headcount. I used to work with a data team that outright refused any automation. They claimed it was because what they did was too complex, but in reality, they didn’t want to appear “less valuable” and chance having to cut their headcount down.

This is a Performance problem. In the 5P Framework by Trust Insights™ (Purpose, People, Process, Platform, Performance), Performance is how you decide whether the work succeeded. If you measure success by how often people come back to you, you’ve built a scorecard that punishes you for doing the job well, and you’ve quietly given yourself a reason to do it a little less well. Nobody sets out to make the vacuum break, but a measure like that nudges you toward it.

Good enablement has an end date

This matters more right now than it did a few years ago, because so much of the work happening around AI is education. Whether it comes from a consultant (like Trust Insights) or from someone inside your own company, good enablement should be built with an end date, and that’s a feature.

If you’re the person who helps everyone else get comfortable with AI, success looks like people not needing you for the basics anymore. That’s the goal, not a failure. It can be as plain as: “My team is getting fewer requests because people can now pull their own reports. That’s time we’re putting into the analysis nobody had room for before.”

What to do next

Count your graduates. Make a list of the clients, teams, or colleagues who no longer need you because of something you taught them or built for them. Put it somewhere you’ll see it, because that list is evidence of the work, not evidence that the work dried up.

Measure what they can do without you. Add one measure that captures capability you handed off. For example, count how many people on the marketing team pulled their own campaign report this month, compare it to the count from the quarter before you trained them, and put both numbers in your regular update next to the usual activity metrics.

Decide what “done” looks like on day one. At the start of a project or an engagement, write down what the other person should be able to do on their own when you walk away. Make it as specific as “the content team can build and run the monthly performance report without help.”

Stay in touch with the people who left. The evangelist only exists if the relationship does. I’ll be honest, staying in touch in a way that’s useful to them (and not just to us) is the part we’re still figuring out, too. Being so good that someone doesn’t need you anymore isn’t putting yourself out of business. It’s the reason the next person calls.

What have you built so well that people stopped needing you?

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This week, we dig into part 5 of 5 about what responsible AI is in practice. Responsible AI is one of those amorphous terms that can mean whatever the speaker wants it to mean, and it has perilously few practical tactical things we can do.

In this five-part series, we aim to correct that by providing practical tactical things you can do.

We have five core practices you can use to reduce your AI impact:

Use AI for what it’s good at.
Use cloud AI as little as possible.
Use AI for the tasks you hate.
Use local AI as much as you can.
Prohibit AI for what it’s bad at.

Part 5: Don’t Use AI For Things AI is Bad At

At its core, all generative AI is probabilistic in nature. It uses probability to generate results. Sometimes the probabilities are easy to guess:

I pledge allegiance to the ____
God save the ____

In both of those cases, you can guess what the most likely word is going to be in that sequence with near-certain probability. In both cases, chances are the blank will never be “rutabaga”.

This forms the basis for our fifth rule. First, don’t use AI for low probability tasks. You won’t enjoy the results. Second, don’t use AI for deterministic tasks, tasks where randomness isn’t allowed.

Low Probability Tasks

It’s easy to reach for AI to do creative tasks like writing, drawing, music, etc. And generative AI tools are competent at those tasks. On the bell curve, they’re firmly above average in terms of quality.

But we as humans don’t love above average. We want great. We want unique, novel, fresh, inspiring, unspoiled – and by definition, those are low probability tasks. It’s easy to write a story like woman meets woman, woman falls in love with woman, woman breaks up with woman, women get back together and live happily ever after. That’s bland and boring, done a million times, and AI as a result is really good at writing that.

A story like woman meets woman, woman falls in love with woman, woman turns out to be a time traveling cyborg from a shadow dimension that’s here to terminate the other woman before she accidentally becomes ruler of the M83 galaxy… that’s a lot harder to predict, and thus we’re probably going to enjoy it more.

If you’re facing a task that needs freshness or uniqueness, AI is the wrong tool for the job because it’s not going to generate something low probability. It can help you refine an idea, find blind spots, or determine if the idea’s been done before, but that ideation is something it shouldn’t do.

Non-Probability Tasks

A deterministic task is a task where randomness isn’t allowed. For example, in a base 10 system, 2 + 2 always equals 4. It’s never not 4. You don’t accidentally end up with 5 one day or 3 the next day, or 12 when the moon is full. It’s deterministic – you can determine the outcome predictably and reliably.

AI tools predict probability, not outcomes. In the early days of AI, they would make hilarious mistakes like miscounting the number of Rs in the word strawberry or say that 29 + 30 = 31, because 31 is the most probable next item in that sequence. That’s a prediction, and that’s not how math works.

Any task where there’s a clear, determined answer or outcome that has no randomness, no variability is a task you shouldn’t use generative AI for, at least not by itself. Almost all modern generative AI systems now will write code and run that code to do deterministic tasks like mathematical analysis, but by themselves, they’re still bad at it.

The same is true for the data itself. AI has no understanding of truth, only probability. Something can be probable (like people who believe the earth is flat) but factually wrong. If you’re dealing in facts, and those facts are immutable and have no randomness, then you must provide them to AI, never letting it guess for itself.

Wrapping Up

Over this 5 part series, we’ve looked at ways to responsibly use AI, to reduce its economic impact, its environmental impact, and how it can frustrate your efforts to get things done. More companies are putting AI in places where AI doesn’t belong, and these 5 core principles should help you better understand where AI should and shouldn’t be used.

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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