Bo FreseMost people use AI backwards - asking it for answers instead of bringing it their ideas. The real unlock is using it as a thinking partner that challenges you.
ReadEvery organisation is building with AI now. The ones doing it well got the foundation right first.
AI is a tool that simulates intelligence. Understanding that distinction is where everything starts.
The productivity gains are real — not the 10x figures the vendors are selling, but significant enough to matter. Teams that use it with genuine discipline ship faster, tackle harder problems, and maintain the quality that makes velocity sustainable.
There are two ways to miss this.
The first is to take the hype at face value and let the AI run. Velocity climbs fast. The demos look right. But AI-generated code passes review in ways it wouldn't if a developer had written it — because nobody recognises the patterns the way they do in code they own. Problems compound quietly. At a certain point neither the humans nor the AI can navigate what was built. That's not a problem you sprint out of.
The second is to step back entirely. Wait for the right policy, the right evaluation framework, the right moment. In the meantime, competitors using AI with discipline pull ahead — and the gap keeps growing.
The organisations that win take a disciplined approach grounded in how AI actually works — including where it falls short. Experienced developers aren't reviewers at the end of a pipeline. They're the central piece throughout it — using AI to explore architectural options faster, stress-test decisions earlier, troubleshoot more deeply, and keep the whole system legible as it grows. The AI extends their reach. It doesn't replace their judgment. With humans in control and taking full ownership of what gets built.
AI doesn't replace good engineering judgment. It depends on it.
— Bo Frese
I spent the past year building real products with AI, full time — two iOS apps, a full-featured AI-friendly CMS, four websites, an open-source development tool. I scrapped two of those projects. Both were built with AI, both looked right on the surface. What I do now is shaped by what went wrong.
There are three ways I work with teams on this, depending on where you are.
One hour. The real risks of AI adoption, not the marketing version. What the tools actually do, where quality breaks down, and what discipline looks like in practice. Works for mixed audiences: decision makers, architects, and developers in the same room.
See the talkA full day of hands-on work. The mental model shift from AI user to AI delegator. Context engineering, structured process, code review discipline, and the tools to build a repeatable framework — not a list of prompts to copy, but a way of thinking to take back.
See the courseHelping your team actually put it into practice. Structured process, context architecture, and the conversations the whole organisation needs to have — not just the developers. Get in touch to discuss what makes sense for where you are.
Advisory work often connects to the broader structural questions underneath AI adoption. Here's how I work in that context.
What AI adoption actually demands — technically, organisationally, and structurally.
Most people use AI backwards - asking it for answers instead of bringing it their ideas. The real unlock is using it as a thinking partner that challenges you.
ReadMost organisations already carry more technical debt than they can handle. AI is about to accelerate the problem significantly — unless you address the underlying quality culture first.
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