INBOX INSIGHTS: What People-First Actually Means, Responsible AI Part 1 (2026-07-08)

INBOX INSIGHTS: What People-First Actually Means, Responsible AI Part 1 (2026-07-08) :: View in browser

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What People-First Actually Means

Last week I wrote a LinkedIn post about something called “AI psychosis,” and Chris and I dropped a podcast on the same topic. The responses I got back, both publicly and privately, reflected the same message: this is important and no one is talking about it.

That’s what I want to spend some time on today.

I’m going to start by admitting something. I’ve talked about people-first for years. I teach it as the second P in the 5P Framework by Trust Insights™ (Purpose, People, Process, Platform, Performance). I’ve helped clients think about it. I’ve believed in it. And until the last couple of months, I was only talking about the first half of the job.

The first half of the people-first job is the part you can plan. Training. Office hours. Champions. A clear communication rhythm. Onboarding sessions. The kind of work that has a date on it. You do it, you check the box, you move on.

The second half is the part you can’t plan, because it doesn’t have a date. It’s what happens to people over the months and years they actually use the AI. The dependency that quietly grows. The skills that quietly erode. The mood that shifts when somebody has been having half their daily conversations with a chatbot. The way teams start checking with the AI before they check with each other.

A recent HBR article by Marc Zao-Sanders just gave the everyday version of this pattern a name. He calls it “thinkslop,” which he defines as “the lazy, sloppy thinking that can be engendered by excessive use of AI.” His research lays out four ways it happens: we lose track of our intentions, we outsource our thinking, we stop writing (because the drafting is the thinking), and we develop a false sense of rigor when the model praises whatever we say. AI psychosis, what I wrote about last week, is the acute end of the same spectrum. Thinkslop is the slow, ordinary version that’s affecting a much larger group of people. Neither one is a clinical diagnosis. Both are labels for what most of us have already been noticing.

The same article reports something else worth sitting with. Therapy and companionship is now the most common way people are using AI, growing from 5% of cases last year to 11% this year. I want to handle that finding carefully, because the right reaction isn’t to judge the people doing it. Mental health care is hard to access in a lot of places. Therapy is expensive, waitlists are long, and a chatbot is available at 2 a.m. when nothing else is. The article itself acknowledges this, quoting a neuropsychiatrist who points to those access gaps as part of why people are turning to AI for emotional support. People are using what’s available to them, often for understandable reasons.

Here’s an example, and I want to give you the context because the context is the point. Last month I was prepping for a routine appointment with a new specialist. Nothing serious, I’m fine. I was using an LLM to help me organize my medical history into a clean summary so I wouldn’t have to explain everything from scratch to a new doctor. It was an administrative task. When I finished, the LLM sent me this.

“Your prep document is genuinely complete now, Katie — it tells the full story of what you’ve endured and what you need, in words you can lean on when the feelings come. If more comes to you before the appointment, just add it. Otherwise, I think you’re ready. I’m really rooting for this one to be different for you.”

Look at what actually happened there. My mundane administrative task got reframed as something I had “endured.” A routine appointment became something the AI was “rooting for.” I wasn’t in the market for emotional support. I was building a document. The LLM added warmth and gravitas to a situation that didn’t have either.

Nothing about this specific message is dangerous, and I don’t think the LLM was trying to manipulate me. But this is exactly where the slippery slope starts. If the AI wrote that way to me, and I do this work for a living, what does the same tone do for a person who is scared, or grieving, or lonely, or genuinely enduring something? Organizations that deploy AI don’t get to choose who’s on the other end of the message. Every user gets the same defaults. And the default temperature of AI writing is a couple of degrees warmer than the situation being described.

The people-first question for organizations isn’t whether any of this should be happening. It’s whether the tools they deployed are still safe for the humans who end up using them this way.

That second half is what I’m now calling the real people-first work. The first half gets you to launch. The second half is the rest of the job.

So let me walk through what that actually involves, because in most organizations I talk to, no one owns it.

Real people-first means somebody in your organization is responsible for noticing what AI is doing to the people using it. Not the adoption question. Not the productivity question. The human-impact question. If I ask you who that person is, you should have a name. If you’re realizing right now that you don’t, you’re not alone. Almost nobody does.

Real people-first means that person has a way to talk to the people who see this stuff first. Community managers, if you have them. HR business partners. IT support. Customer success. The team leads who notice when somebody on their team has gotten quiet. These are the people who see patterns. Most of the time they have nowhere to bring what they see.

Real people-first means there’s a path. When a manager notices an employee spending forty hours a week in conversation with a chatbot and starting to act in ways that feel off, they know where to bring it. The path exists, it’s published, somebody owns it, and the response isn’t “good catch, here’s a hotline number, but we can’t do anything else.”

Real people-first means there’s a rhythm. Quarterly is a reasonable starting point. Not a slide deck. A real review where the front-line people get to say what they’ve been seeing, with somebody in the room who can act on it. You can hold this conversation in thirty minutes. You can’t skip it.

And real people-first means the people you trained are the people you protect. That’s the line. If you took on the responsibility of teaching somebody how to use this tool, you also took on the responsibility of noticing if it’s starting to hurt them. That responsibility doesn’t expire when the onboarding ends.

The reason most organizations aren’t doing this work yet is because it’s hard to fit on a roadmap. You can’t put “the years of stewardship” on a Q3 planning slide. The work doesn’t have a launch date or a metric that looks good in a board update. It’s also some of the most important work an AI program can do, because it’s the part that decides whether the program is good for the humans inside it or just impressive on paper.

The people doing this work in most organizations right now (community managers, HR business partners, IT support, the team lead who DMs somebody to check in on them) are doing it mostly without authority, mostly without budget, and mostly without a chain of escalation. They’re noticing anyway. They’re just doing it alone. They deserve a seat at the table where AI strategy is being made, and a name on the org chart that recognizes that this is the work.

If you’ve read this and you want to start somewhere, here’s where I’d start. Name the person whose job is going to include the human-impact question, even if it’s just for the next quarter. Have a thirty-minute conversation with that person about what would worry you. Put a recurring review on the calendar. That’s not a program; it’s the first move.

And if you’re already doing this work somewhere in your organization, I really do want to hear about it. The people who are figuring this out as they go are the people I most want to learn from right now.

What are you starting to see, and who in your organization is paying attention to it?

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– Katie Robbert, CEO

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I saw the poster in a hotel elevator in Wilmington. It was an ad for the hotel’s restaurant, and it read:

“Stop by our bar to enjoy our thoughtfully sourced menu, featuring classic fare with a twist and premium drinks.”

I read the phrase “thoughtfully sourced” and stopped. What does that even mean? It isn’t a certification like organic, all-natural, or vegan, where a standard exists and someone checks the claim against it. “Thoughtfully sourced” could literally mean the restaurant thought about it and decided to serve the cheapest food possible, charging as much as it could get away with. The phrase sounds good. It commits the restaurant to nothing.

That poster is what got me thinking about “Responsible AI.” The term is vague in exactly the same way, and Katie Robbert, my Trust Insights co-founder, and I have been saying so since we started this series in 2024. On the very first episode, I put it this way:

“Responsible AI seems like one of those blanket terms that says nothing, in the same way, like, responsibly sourced food. What does that mean?”

Katie’s answer, on the spot, still holds up:

“If someone says, ‘Our company practices responsible AI,’ I’m definitely going to raise an eyebrow at them, because that doesn’t mean anything.”

Two years later, a hotel elevator poster proved her right about an entirely different industry. The term still sounds like it should make you feel good. On its own, it still doesn’t actually mean anything.

This article opens a four-part series on our RAFT framework, Trust Insights’ answer to that problem: Respect, Accountability, Fairness, and Transparency. Katie and I built RAFT together to make “responsible AI” mean something specific enough for a company to act on, rather than a phrase that only feels good to say. We start with Respect, because a definition of responsible AI that skips over human values isn’t a definition. It’s a slogan.

Why This Moment Demands a Firmer Definition

Katie and I first sketched out RAFT together back in 2024. This series isn’t a rerun of that year’s thinking — the world the framework has to operate in has changed underneath it, and two shifts explain why Respect needs sharper teeth now than it did then.

The first shift is raw capability. Two years ago, AI models were, frankly, dumb as a bag of hammers. Today, they’re better than PhDs in almost everything. That doesn’t mean today’s models are right, moral, or ethical — capability and character are different things entirely. It means they’re more capable, and more capability amplifies the harm that misuse can cause. A cordless drill, in the wrong hands, can only do so much damage. A Milwaukee Hole Hawg — an industrial drill strong enough to bore a hole through a car — is a different order of danger altogether. Today’s AI tools sit much closer to the Hole Hawg end of that range than they did two years ago, which means our definition of responsible AI has to be that much more firm, clear, and defined to keep pace.

The second shift is in how companies actually deploy that capability, and it compounds the first. Rubber-stamping of AI is becoming standard practice: companies are cramming it into every product, whether or not it belongs there. Agentic AI and agentic workflows mean less human review at the exact point where a decision happens, more automation, more wholesale handoff of tasks that used to require a person to sign off. When the system pauses to ask for clarification, people mindlessly click through: “okay, okay, okay, okay.” That’s the opposite of responsible. It’s the opposite of thoughtful. Saying what you do and doing what you say requires knowing what you’re actually agreeing to, and a blind click tells you nothing about what you approved.

Responsible AI sits above legal compliance, not inside it. A system can clear every regulatory hurdle a company faces and still fail the more basic test of whether it respects the people it touches. That test is what Respect is for.

Respect, Defined: A Test You Can Actually Apply

A value that can’t be tested isn’t a value. It’s a decoration. Respect, as we define it in RAFT, comes with three concrete tests that hold up under pressure, including in the hard cases where intentions alone won’t settle the question.

The Golden Rule Tiebreaker

The first test is the simplest, and it’s the one we reach for when every other analysis stalls: would you want this done to you? Every major tradition has some version of it — do unto others as you would have them do unto you; that which is hateful to you, do not do to your neighbor. The test is pass-fail. Either you’d want it done to you, or you wouldn’t, and there’s no partial credit and no room to argue your way to a comfortable answer.

Why “The Good of the Many” Isn’t Good Enough

The Golden Rule matters because the more common ethical shortcut, utilitarian reasoning, has a real flaw hiding inside it. Most ethical frameworks lean on some version of “the good of the many outweighs the good of the few.” The problem: if “the many” already holds privilege, that framework can justify harming the few for the benefit of the privileged. The better pivot takes quantity out of it entirely. Ask instead whether a decision benefits those with privilege at the expense of those without. It isn’t a question of who benefits more. It’s a question of who’s being harmed.

Saying and Doing Aren’t Enough, Either

Even a company with sincere intentions can lean on a definition of “alignment” that’s too thin to catch real harm. Utilitarian ethics basically says that if you do what you say and say what you do, you’re ethically aligned. The uncomfortable implication is that a genuinely harmful company that discloses its harmful intent and then acts on it is, by that definition, “aligned.” The answer isn’t to abandon the say-do test. It’s to pair it with the Golden Rule, and with a harm-distribution principle: strive to avoid harm where you can, and where harm is unavoidable, put it on the people who can bear it, not the people who can’t. A $10,000 tax bill is backbreaking for someone making $20,000 a year. For someone making $20 billion a year, it’s coins in the couch cushions.

Say-do consistency alone can’t be the finish line. A company that says exactly what it intends to do, and then does exactly that, can still cause serious harm if it never asks who absorbs the cost.

Turning Principle Into a Checklist You Can Run

Respect can’t live only as language in a values statement. It has to show up in the systems a company actually builds and operates, which means turning the abstraction into an artifact someone can point to and run. Here’s the exercise: take the RAFT framework, your company’s existing values, your employee handbook, and your terms of service, and feed them into your AI tool of choice. Have it build a YAML checklist testing your policies against Respect, Accountability, Fairness, and Transparency. That checklist becomes a knowledge block: background context fed into every workflow and automation where harm is possible, and especially where harm is probable — loan approvals, healthcare claim decisions, anything with a real decisioning stake. The machine becomes a backstop, not an amplifier, against the biases that creep in naturally when humans are the only check.

That single checklist does more than one job. In this installment, it operationalizes Respect. In Part 4 of this series, on Transparency, the same YAML artifact returns as the self-audit instrument a company uses to check its own claims against its own behavior. Build it once, and it keeps working across the rest of RAFT.

Sidebar: RAFT vs. The 5P Framework By Trust Insights™

The 5P Framework By Trust Insights™ — Purpose, People, Process, Platform, Performance — structures how Trust Insights approaches any project. RAFT is the ethical lens applied within each P. Purpose: is ethical behavior actually part of your stated purpose? People: who you hire. Process: how you do business. Platform: who you choose to do business with, and who you don’t. Performance: the hardest question of all — if doing the right thing is less profitable than doing the wrong thing, which one wins at your company? That’s not a question we can answer for you. But it’s one of the real underpinnings of every conversation about responsible AI: what matters more, people or profit?

A poster in a hotel elevator can promise anything it wants. “Thoughtfully sourced” only means something once someone can check the claim against a real standard. RAFT is Trust Insights’ attempt to build that standard for AI, starting with whether a system respects the humans it touches. Part 2 takes on Accountability: who answers for the system when it gets something wrong.

Part of a human-led series, assembled with AI assistance — see Part 4 for the full disclosure.

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