Workforce Strategy for the AI Era
Strategic approaches to building, retaining, and modernizing teams when AI changes what work looks like every quarter.
The Hiring Problem Nobody's Solved
Recruiting hasn't caught up. Organizations can fire people for not adopting AI — there's a metric for that. But how do you identify the four components of who someone is to make sure you're hiring the right people for a future that's still taking shape? The cover letters all read the same because everyone's using the same free tools. The signal-to-noise ratio in hiring has never been worse.
Cut Once or Cut Five Times
Do you make one strategic cut and risk losing people you need? Or do you trim gradually and watch your culture die as top performers leave for somewhere stable? Twitter's mass layoffs led to quiet rehires. Five rounds of layoffs leave you with the people who couldn't get another job. Neither approach is clean — the real work is building a workforce strategy before you need to cut.
Impact Over Productivity
You can put a lot of effort into holding up a wall that isn't going to fall. Productive? Sure. Impactful? Not at all. The organizations that survive the AI transition are the ones that learn to measure impact — actual outcomes changed — rather than activity. The people consuming the most tokens in meaningful ways are the ones worth keeping.
Questions We Explore
- ▸How do I hire for AI readiness when the tools change every month?
- ▸What signals indicate someone has the automation mindset?
- ▸How do I restructure without destroying team morale?
- ▸What metrics actually predict workforce effectiveness in the AI era?
Explore Other Topics
AI & Automation
How artificial intelligence and automation are reshaping roles, eliminating positions, and creating new opportunities across every industry.
Technology Leadership
What it takes to lead technology organizations through the most significant shift since the internet.
Operational Excellence
The intersection of proven operational discipline and emerging AI capabilities — and why treating people like machines fails when actual machines show up.