Friday, June 5, 2026
FRIDAY – AI FOR THE C SUITE®
Read time: 9-10 min · Read online
Hi, it’s Chad. Every Friday, I serve as your AI guide to help you navigate a rapidly evolving landscape, discern signals from noise and transform cutting-edge insights into practical leadership wisdom. Here’s what you need to know:
1. Sound Waves: Podcast Highlights
This past Monday I was joined by Melissa Reeve, founder of HyperAdaptive Solutions and author of HyperAdaptive: Rewiring the Enterprise to Become AI Native. We dig into quite a bit including the fact that you’ve got a handful of AI power users with everybody else stuck using it to draft email. That gap won’t close on its own. PwC’s fix even has a name, the “prompting party”, and Melissa explains why cheap social-learning beats another one-and-done e-learning module every time. Listen in wherever you get your pod on.
Apple · Spotify · iHeart · Amazon · YouTube
Subscribe for free today on your listening platform of choice to ensure you never miss a beat.
2. Algorithmic Musings: The Tool That Knows the Room
This week I’m stepping off my usual perch. Most Fridays I’m the guy in the corner reading the room and reporting back, the one with nothing to sell you. Today I’m selling something. We’ve launched The Result Center, a national collective of seven senior practitioners (with more on the way) built for the mid-market companies the rest of the advisory world keeps getting wrong. If you want the tidy version, the press release is right over here, using words like operationalizes and meaning them.
So before I go any further, do me a favor: point the same skepticism at me that I’ve spent over a decade teaching you to point at everyone else. I’d rather earn your trust on the page than ask you to take it on faith.
A launch announcement is a fine thing to send out on a Thursday. What I’d rather spend your Friday morning on is the machinery humming underneath it, because the collective is only the visible half of a bet we placed quite a while back. To get there, I’ll start with the problem it was built to solve.
For most of the past four years, a mid-market executive shopping for AI help has faced two bad doors. Behind the first sits a commodity chatbot that treats the CEO of a $400 million manufacturer exactly the way it treats a college sophomore cramming for a midterm. Behind the second sits a brand-name consulting firm whose rate card assumes you have the budget of a Fortune 100 and the patience of a saint. Neither door leads anywhere good if you run a company that’s too big for the cheap stuff and too lean for the expensive stuff.
I’ve spent those four years sitting across the table from the people stuck in that hallway. Some understand the stakes and have invested deeply. Others have neither policy nor training, and I’ve watched a few of them run board decks through free consumer tools to “save a little time.” (True story.)
So about a year ago, we made a decision that, depending on your tolerance for risk, was either brave or mildly unhinged. Instead of renting somebody else’s intelligence and hoping for the best, we built our own. We call it CLAIR.
The trouble with renting. When you rent intelligence from a commodity platform, you also rent its terms. Last September, in this very newsletter, I pointed out that the protection gap between enterprise customers and individual users is “like the difference between flying first class and being checked baggage.” Then I watched a quiet parade of smart people climb into the cargo hold anyway, board decks and customer lists and acquisition math, all of it poured into tools whose business model runs on remembering everything.
We didn’t want to be the advisors who tell clients to demand better stewardship while quietly doing the opposite in the back room. That’s a special kind of hypocrisy, and you’d be right to smell it. So we built CLAIR on independently audited infrastructure, on AWS Bedrock, where your inputs are never shared with model providers and never used to train anyone’s models. That part the platform guarantees. The part we had to choose for ourselves came next: we declined to bolt on an application layer that hoards what it sees to get smarter for the next client. Your patterns stay yours. Not ours, not anyone’s.
Where we tell on ourselves. Our foundation is audited against the alphabet soup that earns its keep (SOC 2, ISO 27001, HIPAA-eligibility, PCI DSS). The application we built on top of it is not yet independently SOC 2 audited, and rather than bury that, we say so right on the page. The audit’s on the roadmap, and we’ll tell you when it’s done. Most privacy pages exist to dodge the hard questions. We wrote ours to answer them. Call it a stewardship posture, or call it the plain discipline of not writing a check your footnotes can’t cash.
What four years taught us. A year of building only worked because of four years of listening. Three lessons did most of the shaping.
The first is that executives don’t cluster by industry the way the consulting world insists they do. A manufacturing CEO and a healthcare CEO wrestling the same growth ceiling have far more in common with each other than either has with the competitor down the street. Once we started sorting what we were learning by leadership orientation instead of industry vertical, the patterns sharpened in a hurry. It changed how we think, how we match a client to the right practitioner, and how we built everything that came after.
The second is that the most valuable thing in any advisory relationship is the stuff nobody writes down. The tacit knowledge. The pattern a seasoned advisor only recognizes because they’ve seen its cousin forty times. We wanted tooling that makes a practitioner faster at surfacing and applying that judgment. What we did not want was a tool that vacuums up a client’s confidences to get smarter for the next client. Those are two very different machines, and the difference is the whole ballgame.
The third is the one I already confessed. For a mid-market leader, privacy is a strategic asset to be guarded and put to work, and treating it that way turned out to be a feature clients could feel.
Why a collective needed a backbone. Which brings me back to the seven of us. Each Result Center practitioner carries decades of experience in a distinct domain and, by design, no more than thirty percent overlap with anyone else in the room. A group that lean shouldn’t be able to deliver work that used to take a building full of associates. CLAIR is how we pull it off. It hands every practitioner the same enterprise-grade research and synthesis horsepower, so a coach in one city and a fractional CFO in another can bring big-firm muscle to your problem without the big-firm overhead. What we share across the collective is the infrastructure. Your engagement stays sealed inside its own walls, never pooled, never borrowed for the next client down the line.
A caveat, because I’ve learned to futurecast with my hands up. None of this is finished. It won’t be finished next year either. We’ll keep building, keep auditing, and keep telling you what we haven’t gotten to yet. And later this year we’ll have more to say about EPIE, the subscriber-facing side of this work built specifically for mid-market leaders. That’s a story for a future Friday.
My takeaway? The mid-market doesn’t need another tool that treats every leader like an interchangeable prompt. It needs intelligence that knows the room, keeps its mouth shut about what it learns, and is honest about what it can’t do yet. We spent a year building exactly that.
If you want to talk about what AI stewardship looks like inside your own shop, or you just want to tell me which of the two doors has had you stuck in that hallway, drop me a line.
3. Research Roundup: What the Data Tells Us
STORYSCOPE: WHY THE AI DETECTOR YOU JUST BOUGHT IS ALREADY OBSOLETE
Before you renew that AI-detection contract, know this: a major publisher pulled a horror novel this year over AI allegations, the first commercial title yanked that way. Most detectors read surface style, the word choices a newer model or a quick edit erases. Maryland and Google DeepMind researchers found a tougher signal underneath: machines and people build stories differently at the structural level, and that is far harder to fake.
The numbers that matter: The team studied over 60,000 stories, pairing 10,000 human pieces with versions from five leading AI models. Reading structure alone and ignoring style, the system told human from machine with accuracy in the low nineties. Just thirty structural features captured most of that separation. The kicker: when AI stories were professionally edited to strip the obvious tells, detection barely moved.
What this means for your Monday morning: Style-based detectors are a fading test, and they fail quietly. The five AI models all cluster into the same narrow habits, over-explaining themes and keeping plots tidy and linear. Human work stays varied and unpredictable. Originality is becoming a structural property, not a stylistic one.
The catch: Structural detection tooling isn’t commercialized yet, and the study covers fiction, not marketing copy or contracts.
Action item: Ask any detection vendor one question this week: does your score rely on writing style, or on structure and provenance? If it’s style alone, treat it as a hint, not proof.
Read our full analysis of this research at AI for the C Suite®.
4. Radar Hits: What’s Worth Your Attention
Trump’s scrapped AI rules leave companies in regulatory limbo. With the Biden-era AI executive order dead and no federal replacement in sight, California, New York, and Texas are writing their own rules. That’s the compliance patchwork federal policy was supposed to prevent. Don’t wait for Washington to sort itself out. Get your legal team building internal AI governance now, and map which state frameworks you’re already operating under.
Why AI startups are about to get “Sherlocked”. Nest co-founder Matt Rogers watched Apple’s OS updates erase Tile, Pebble, and f.lux overnight, and he says foundation models like OpenAI and Anthropic will do the same to thin AI tools. Translation for buyers: if the vendor you’re betting on is a model wrapper or generic copilot, it could get absorbed and disappear. Before you sign, ask what makes it hard to rip out.
America’s new AI map. Microsoft’s diffusion report puts Texas (35.4%) ahead of California, with college towns like Williamsburg, Virginia hitting 73.7% adoption. The bigger signal: a 16.8-point urban-rural gap that holds even after controlling for age and income. AI use is now baseline across most of the country, which means your team and your competitors are already using it. The question this week is whether you’re capturing that productivity or watching it walk past you.
5. Elevate Your Leadership with AI for the C Suite®
Privacy stops being abstract the second you ask one question: what does our AI remember, and who gets to see it? If your shop hasn’t answered that, it’s worth answering before your next board deck goes into a chatbot. (You know who you are.) That’s squarely the work I do. Want to pressure-test your own stewardship posture? Let’s talk. And if this would land for someone on your team, forward AI for the C Suite® their way.
Stay safe. Stay healthy. Be strong. Lead well.
Chad