Real intelligence first: building our experts up instead of automating them out
Strip the marketing off most AI pitches and you’re left with one sentence: fewer people, same output. We’re not buying it, and not (just) because we’re sentimental about jobs. The math doesn’t hold up.
I sat down with our CEO, Bill Di Nardo, and our Chief Technology Officer, Joel Farquhar, to talk through the bet they’re making: Real Intelligence (RI) plus AI. The order is the whole point. Real intelligence first—the judgment and hard-won domain knowledge sitting in the heads of people who’ve actually shipped the work—and AI as the multiplier stacked on top. Bill’s framing is that the company spent five years assembling experts, and so the last thing he wants to do is push them out the door before they get to apply what they know through the tools that Joel’s team is building.
The case for augmentation starts with a basic fact about the tools. They don’t do exactly what you need on their own. They need someone smart prompting them, steering them, and catching them when they wander off. Where the tool hits a wall, the expert knows how to climb over it. That approach flips the scary question on its head. The thing to watch isn’t whether AI can replace your expert (it can’t). It’s how much further your expert can run once the busywork is off their desk.
Joel’s turning point came the first time he used a serious code assistant and watched a domain expert stand up something that used to take months in one afternoon. That’s the mindset he’s adopting. Not “how do I grind through this task, “but” how do I get past it so I can go do the hard, interesting thing.” For a lean company, that’s how you scale without hiring a hundred more bodies. You get a lot more out of the team you already have, and you get to keep them.
Customers feel the shift as a change in what they’re actually buying. Bill describes walking in to do systems integration—work we’re already known for—and offering it faster and cheaper. What the customer wants to know is how much more of their three-year roadmap they can finish on the same budget. The conversation moved off the billable hour and onto the outcome: show me you can hit it, at a price, with more certainty, and I’ll spend.
Then there’s the human piece, which is where Bill and Joel get sharp. The companies slashing headcount and crediting AI are often talking about labour they’d already turned into a commodity, and AI just makes that obvious. Pivotree never described its people that way.
Enable an expert and they get more valuable, here and anywhere else they might go. So the play is to teach relentlessly, be the place people want to stay, and keep both the company and its people in demand while the ground moves under everyone.
The most surprising result is that Bill expected the young folks to sprint ahead while the senior people dragged their feet. The opposite happened. His most senior architects and business leaders became the power users, solving big problems without waiting on anyone.
Joel watched the technical-skills wall start to crumble. He maps people on two axes: how much they know about the business and how much they can build. High on both, and AI hands them a tenfold multiplier; so he calls them multipliers. Strong on the business, light on the technical, and you’ve got what he calls visionaries, people who finally get to build the ideas they’ve carried around for years with nowhere to put them.
The RI + AI point of view underneath all of it is clear. Don’t automate your people out. Build them up, and let real intelligence hyperscale.
Listen to Episode 3 of Data vs. Commerce wherever you get your podcasts.
