There’s an old idea in education that you teach the whole person: head, heart, and hand. It’s credited to Johann Heinrich Pestalozzi, a Swiss educator who was writing in those terms by 1805. He was talking about children in a classroom, not companies, so I’m borrowing it with some care.
I’ve heard that phrase for a long time. I finally looked up where it came from, and found something else along the way. In 1881, a Swiss school historian named Otto Hunziker described Pestalozzi’s ideal teacher as someone with “a clear vision, a warm heart and a firm hand.” Those are close to three things I’d been thinking about under other names.
In August I listened to a five-hour Lex Fridman podcast with David Heinemeier Hansson, the creator of Ruby on Rails and a co-founder of the company behind Basecamp. Most people call him DHH. That’s one person’s perspective, but when you listen to someone describe where things went sideways, you pick up patterns. I’d been turning this over for a while, and that conversation crystallized it. An organization needs three things for an AI transformation: vision, ability, and resolve. Head, hand, and heart.
These aren’t new ideas. Every big change I’ve been part of needed them. Ability is the most obvious of the three. You need people who can do the work, so you hire, or you train, or you bring in consultants. Vision and resolve sound simple too. What good looks like for those two is a lot less obvious, and the clearest way I know to see each one is to look at what happens when it’s missing.
I would start with the vision. DHH told a story from February of this year. The designers at his company picked up the AI tools and started building the next version of Basecamp themselves, describing what they wanted and letting the machine write the code. Every change made sense when you looked at it alone. Together they wrecked the architecture, and the team cleaned it up by hand.
They had the ability and plenty of drive. What was missing was someone holding the shape of the whole thing. Hilary Gridley said it well, in a line Marty Cagan quoted this summer: “it’s never been easier to run 10 times faster in the wrong direction.” Whatever direction you’re running, these tools get you there sooner. A loose vision gets expensive quickly.
Vision has a second problem. In my experience, the people who set it don’t always know what the technology can do, and it moves too fast for anyone to learn at a comfortable pace. The mistake I see is deciding you’re going to “use AI to do stuff” instead of figuring out what you actually do and pointing the new tools at those problems. I’ve told my teams the same thing for a long time, and it’s why we treat the developers we serve as customers. That was true long before AI. I want to solve their actual problems, not the ones we’ve assumed for them. Those sound similar. They’re not the same.
Ability next, and here Pestalozzi himself is the example. Around 1800 his “method” was the magic word in European education, the way AI is the magic word now. Governments wanted it. It mostly failed at scale, because there was no way to train enough teachers well enough or fast enough. The vision was there and so was the backing. The hands never arrived.
It’s tempting to call that a hiring problem. I think it goes deeper. You may not have anyone in the building who knows who to hire. And ability lives in how an organization is shaped as much as in the people on the payroll. DHH talked about organizations built for a time that no longer exists, and good people inside one of those will still struggle.
Then resolve. Google invented the Transformer in 2017, the design underneath ChatGPT and the tools like it. It had a working chatbot of its own, called LaMDA. After ChatGPT launched, Sundar Pichai and Jeff Dean told employees at an all-hands that Google had similar capability and had moved conservatively because of its size and the risk to its reputation. All eight authors of the Transformer paper eventually left the company.
That caution was arguably responsible. Resolve isn’t recklessness. It’s accepting a risk you can name for something you can see. And Google got it back, first with Bard and then with Gemini, so this is a leg you can regrow.
One company that looks like it has all three is Shopify. In April 2025 its CEO, Tobi Lütke, sent a memo saying “reflexive AI usage is now a baseline expectation.” It would count in performance reviews, and teams would need to show why AI couldn’t do a job before asking for more people. That’s a mandate, and a mandate with nothing behind it is hollow. This one stood on a CEO who was visibly using the tools himself and a company that had already hired and bought the capability.
The mandate is the part I’ve gotten wrong myself. Many times I’ve told people what we were going to do before I told them why. It’s one of the biggest mistakes I’ve made in my career.
When you hand people a what, they need a reason, so they fill one in. You can tell it’s happened because the questions change. They stop asking how and start challenging the position, arguing with a motive you never gave them. Now you have to talk them out of that story before you can offer the real one, and that takes far longer than leading with it. Simon Sinek has a great talk on starting with why. Restarting with it is much harder.
I’ve worked at a lot of companies and I’ve watched this play out many times. Maybe it’s a security change that adds a little friction. Maybe it’s a new procedure we’re asking people to adopt. Perhaps it’s a new tool we think will make their lives easier. There’s usually a specific, good reason behind it. What people hear is something generic, like “to protect our data,” which lands about like “because I said so.” It feels punitive. The worst case is they decide there’s an ulterior motive and stop trusting you.
With AI, there has been so much talk about it taking jobs that this is the fear people will fill in. It needs to be addressed head-on. If you don’t, people will assume you’re hiding something. So the why has to be the true why, including what all of this means for their work. One vendor survey this spring found 29 percent of employees admitting they had sabotaged their company’s AI strategy, and three quarters of executives saying that strategy was more for show. I read those two numbers together. The why brings trust, and trust is the foundation of resolve.
Resolve also means staying steady. This is going to move fast, it’s going to be imperfect, and it will change in ways none of us can see yet. Not getting worn out is part of the work. You can’t yada yada your way through the details, and you can’t take people faster than they’re able to go. You take them on the journey.
My own true why about this AI transformation is that it will change our jobs. I also believe it’s going to give us more time together as humans. People tend to avoid meetings because they feel like wasted hours. One of my teams cut its weekly recurring meetings down to one, and that one gets far more done. We didn’t eliminate the time we spend together. We made it more efficient. Time is the most valuable resource we have. Attention is how we spend it.
None of this is specific to AI. I’ve seen the same pattern with other changes in other places. The speed and reach of AI raise the cost of getting it wrong. When factories swapped steam engines for electric motors, the payoff took decades, and it came when people redesigned the floor around what the motors made possible.
If you’re leading an AI transformation, it might be worth asking how you’re holding up on all three. Ability is usually the easy one to find. For the other two, consider whether the people around you could say why you’re doing this, in their own words, and whether what they’d say is the true reason. We’re still people, solving the same problems with new tools.
Everything has changed. Nothing is different.
Uncomplicated systems. Uncommon results.