2026.20: Same Rodeo, Different Bronco.
Happy Friday friends!
Quick note before we get into it. I wrote a piece this week for Osborne, the firm I spend most of my days with, called "Same Rodeo, Different Bronco." It was strong enough that I wanted it in front of S2S readers too, so I've rebuilt it here for this audience. If you already read it on the Osborne side, skim and skip. If you didn't, this one matters.
The short version: AI isn't failing in the field. It's failing at the point of purchase. Boards are buying the same vendor story they bought in 2003, 2010 and 2018, the cheque is clearing, and the front-line delivery never happens. The pattern is so loud you can set your watch by it.
Let's break it down.
Signal:
THE BOTTLENECK ISN'T AI. IT'S HOW BOARDS ARE BUYING IT.
The work order said: digitize the safety forms onto a tablet.
The reason was harder. A field worker had been killed on the job. Someone with a name. Someone with a family waiting at home. Someone whose crew came back to work the next week because the work doesn't stop, even when it should. A loss that nobody at that table was ever going to be able to put back.
The energy company was doing what regulated industries do after a death like that. Investigations. Audits. New procedures. A reckoning with the gap between the way the work was meant to be done and the way it actually got done on a hard day. The brief that landed on my desk came downstream of all of that, and downstream of a crew still trying to make sense of why.
On paper it was a software brief. Take the paper tailboard form. Put it on an iPad. Make sure crews complete it before they touch live equipment.
I was the lead from Apple on the account, partnered with a development studio I trust to push harder on the opportunity. We could have built what the brief asked for. A pixel-perfect copy of the binder, with a battery in it. That would have changed nothing.
The honest question wasn't "how do we digitize this." It was "if we sat the crew supervisor, the safety engineer, and the regulator at the same table and started from scratch, would any of us still build this form the way it exists today?"
The answer was no.
That's when the work got interesting. We rebuilt the process from the field crew up. What got asked. When it got asked. Who saw the data afterward. What happened the next time a hazard showed up at the same kind of site. The iPad was almost incidental. The thinking was the project.
The first version shipped to a small group in 2017. They used it. They pushed it. They told us what was broken. We fixed it. Then they trusted it. Within five years they had mitigated 847,000 hazards through the app. Tailboards complete in half the time. 185,000 sheets of paper a year, gone. More important than any of those numbers, the field crews have a tool that respects what they actually do for a living, and the data trail that comes with it makes the next fatality less likely.
That's not a moonshot. It's what works when you refuse the brief.
And refusing the brief is the opposite of what most boards are about to do with AI.
Same rodeo. Same dust. Different bronco.
Ian Shepherd wrote a short piece a couple of months back called "Running in Fog" that I haven't been able to shake. He laid out the pattern of how organizations bought their way into the early web era. Phase one, this isn't real retailing. Phase two, the startups doing it are cheating. Phase three, oh god we're behind, get a big consulting firm in here. Phase four, write the biggest cheque you can to leapfrog the field. Phase five, oops.
If you've sat in a boardroom in the last six months, you've seen at least three of those phases play out. Except now they're stamped with the letters A and I.
The pattern isn't new. We rode it with databases. We rode it with the web. We rode it with mobile. We rode it with RPA. Each cycle, the leadership team that has never built the thing buys a story from the only people in the room who claim to understand it. The cheque clears. The vendor disappears. The reorg starts. The narrative gets rewritten as a learning opportunity.
The economist Carlota Perez has a name for the stage we're in. She calls it Frenzy. Speculative capital floods in. The technology gets oversold. The bubble inflates. Eventually a Turning Point lands, and the deployment of the technology becomes boring, useful, and profitable, for the companies that didn't blow themselves up first.
We are squarely in AI Frenzy. The signal is loud.
MIT's NANDA project published research in August 2025 showing that 95% of enterprise generative AI pilots have delivered zero measurable P&L impact. Not weak impact. Zero. Klarna has quietly rehired customer service staff after publicly declaring its AI replacement complete. Air Canada was held legally liable by a Canadian tribunal for a refund its chatbot invented out of thin air. McDonald's killed a two-year IBM drive-thru voice AI test after the system ordered nine sweet teas instead of one. DPD's chatbot wrote a poem mocking its own company before being yanked offline the same afternoon.
These aren't fringe failures. They're enterprises with money, talent, and brand to protect, running off the cliff in the fog because nobody slowed them down.
This isn't an argument against AI. AI is going to reshape how mid-market companies operate. The argument is that the way most enterprises are buying AI today is the same way they bought their web platform in 2003, their iPad rollout in 2010, and their RPA program in 2018. I watched the RPA wave play out. The bots that looked great in pilot died in production inside two years, brittle to every upstream change. Most enterprise programs stalled before they scaled. The ones that survived rarely paid back what was promised. The investment cycle ended quietly, and the conversation moved on to AI.
We've already been thrown by this bronco twice. We know how the ride ends. The question isn't whether AI is real. The question is whether your organization is going to ride it, or perform the ride until it bucks you off again. Will you last the eight seconds, or perform for the board on the way down?
Scale:
FIVE MOVES TO DELIVER AI INSTEAD OF PERFORM IT.
The MIT Sloan piece "The Eight Core Principles of Strategic Innovation" by Gina O'Connor and Christopher Meyer is the cleanest read I've found on what separates companies that build new growth from companies that buy decks about it. The whole article is worth your time. Here's how five of the eight principles land on the AI delivery problem specifically.
Refuse the brief. The tailboard project worked because we didn't build what was asked for. The energy company's brief was a tablet version of their safety form. What they actually needed was a re-thought safety process where the tablet was almost incidental. Most AI strategies in 2026 are starting the wrong way around. Take the existing process. Put AI on top of it. Trust the vendor's productivity claim. That's the digitized-PDF version of AI. It's expensive, it's safe, and it changes nothing. The MIT principle here is what O'Connor and Meyer call setting domains of innovation intent. Pick the real domain where you have an unfair right to win. Don't accept the version of the problem the vendor wrote on the slide.
Treat the work as a portfolio, not a pipeline. A pipeline asks each project to justify itself on a go/kill timeline. A portfolio asks a cluster of small experiments to teach you something about a domain. Tailboard taught us things we couldn't have specified in a contract. The right unit of measure isn't whether one project ships. It's whether the portfolio is learning faster than the market is moving. If you're running AI today as five disconnected vendor pilots with five different success criteria, you don't have a portfolio. You have a procurement habit.
Build discovery, incubation, and rollout as three different jobs. Most companies hand a single team the work of finding the opportunity, proving it, and operating it at scale. That team usually fails at one of the three, because the skills are different. Discovery is field work. Incubation is experiment design. Rollout is operational integration. Confusing the three is why so many AI projects look great in the pilot and die in the operating unit. Naming the three jobs and staffing them honestly is the cheapest way to lift your delivery rate.
Make the function permanent, not a program. IBM's Emerging Business Opportunities program added more than $15 billion in new revenue to the company before it got shut down. Why was it shut down? Because it was a program, not a function. Programs end. Functions endure. If your AI work lives inside a hub that reports to nobody and disappears in the next budget cycle, the work will disappear with it. The working setup for a mid-market company is concrete. One senior leader who owns it. An operating budget that doesn't get cut at the first margin pinch. A mandate that survives quarter to quarter. The hub never delivers any of those.
Pace the portfolio, don't kill it. When margin compresses, AI is usually first on the chopping block. The MIT research is clear that this is the most expensive cut a company can make, because the expertise you lose takes years to rebuild. The smarter move is to pace the work down. Carry the strongest two domains forward. Park the weaker ones in a way you can pick up later. The companies that come out of a downturn ahead are the ones who didn't zero out their learning while everyone else did.
None of this is exotic. It's the same delivery discipline that worked on the tailboard project nine years ago. The reason it doesn't get done in AI today isn't that the principles are unclear. It's that the board wants a press release, the vendor wants a contract, and the CIO wants to look like they're moving. The friction those three create is the friction that produces a 95% pilot failure rate.
If you're in the middle of that decision right now, with the AI line item already on the budget and the board breathing down your neck, remember this. The path through the fog is small experiments, fast learning, and the willingness to stop performing AI for a board that has already been told the answer.
Deep Dive:
No deep dive this week. Just take the five moves into your next AI conversation, and watch which one your vendor flinches at first. That's the one you most need.
Thanks for reading!
The MIT Sloan article by O'Connor and Meyer is the source worth your time. Find it and read it before you sign the next AI contract.
Where have you seen this go right? Where have you seen it go sideways? Drop a comment or send me a note at jt@jasontate.ca. Push back on anything I got wrong.
The newsletter isn't the conversation. The conversation is the conversation.
See you next Friday.
Best, JT
PS - If someone forwarded this to you and you want it in your inbox directly, subscribe HERE.
