Humans
AI changes what people do, but the organizations that win will be the ones that understand what it doesn't change: the need for clear thinking, strong judgment, and teams that can adapt faster than their tools.
Team Building
Most teams are structured around tasks that AI is about to absorb. I build teams structured around the judgment calls that remain.
I've built and scaled three operations teams from scratch in AI-adjacent companies. The pattern I've learned: hire for systems thinking, not task proficiency. The person who's excellent at the current workflow is often the worst fit for a team that needs to redesign that workflow every six months.
My teams are deliberately small and cross-functional. I staff for range over depth, because the half-life of specialized operational knowledge is now measured in quarters, not years. Every team member is expected to be comfortable with ambiguity—not as a nice-to-have, but as the core job requirement.
The results speak for themselves: two of the three teams I built were rated top-performing within their organizations within 12 months, and team retention across all three has been above 90% annually.
Hiring & Development
In an AI-native company, the interview question isn't "can you do this job?" It's "can you figure out what this job becomes?"
I've developed a hiring framework built around three signals: how candidates reason about systems they don't fully understand, how they react when their assumptions are proven wrong, and whether they can articulate the difference between a task and the outcome the task is supposed to produce.
For development, I invest heavily in teaching people to work alongside AI tools—not as users following prompts, but as operators who understand the failure modes, confidence boundaries, and organizational implications of every system they interact with. This isn't training. It's a different kind of literacy.
The development programs I've built have consistently produced internal promotions at 2x the organizational average, with promoted employees performing in the top quartile of their new roles within the first six months.
On Evolving Roles
Essay
There's a conversation happening in every boardroom right now about how many people AI will replace. It's the wrong conversation. The right question is: what does the remaining work look like, and are we building organizations that can actually do it?
The roles that survive AI are not the ones that are "too complex to automate." Complexity is a solvable engineering problem. The roles that survive are the ones that require genuine judgment under uncertainty—where the cost of being wrong is high enough that you need a human who can be held accountable, who can explain their reasoning, and who can adapt when the context shifts in ways the training data didn't anticipate.
This means the future of work isn't about humans versus machines. It's about organizations that know how to design systems where humans and machines each do what they're actually good at. Most companies are failing at this, not because the technology isn't ready, but because their org charts, incentive structures, and management practices were designed for a world where humans did everything.
The operators who thrive in the next decade will be the ones who can redesign those structures—not once, but continuously, as the capabilities of their AI systems improve. That's the job. Not managing people. Not managing technology. Managing the interface between them.