Readiness Over Frameworks

Welcome to "The Future of Work: Insights and Strategies from a CHRO." In this course, you'll unpack insights from Inna Landman, Chief People Officer at Procore Technologies, drawn from her Transform 2026 fireside chat with Kyle Forrest, Future of HR Leader at Deloitte. Landman shared what it actually looks like to lead AI transformation — without a perfect playbook. Across three units, you'll explore how to choose high-readiness starting points instead of over-engineering frameworks, communicate with clarity and vulnerability during workforce uncertainty, and redesign HR workflows around real problems rather than departmental silos.

Let's start with the lesson Landman learned the hard way: stop building the perfect structure and go where the readiness already exists.

In the fireside chat, Landman candidly described her own over-engineering instinct. When Procore first approached AI strategy, she went straight to centralized control:

Landman: "I was like, oh, we need an AI czar and we need someone that looks after it and we need every executive team member to do X and Y and here's all the frameworks."

The result? It stalled. The turning point came when she asked a simpler question:

Landman: "Who's ready and where is the tech that's ready?"

The answer was specific, not abstract. Procore's product and technology organization was already seeing AI-powered coding tools fundamentally change how engineers work. Meanwhile, the chief customer officer had independently built a workflow that cut customer meeting prep from two hours to ten minutes. Rather than forcing every function into a uniform AI adoption timeline, Landman redirected Procore's transformation energy toward these two functions, because both the technology capability and the leadership appetite were already there. For you, this means the most strategic move may not be the broadest one. It's finding the leaders who are already running and clearing the path ahead of them. To evaluate whether a function is genuinely ready and not just interested, ask yourself three diagnostic questions:

1. Has the function already piloted AI tools on its own? Organic experimentation signals both technical feasibility and cultural openness.
2. Is the function leader actively championing AI adoption? Executive appetite matters as much as tool availability. Without a sponsor, even proven technology stalls.
3. Are measurable results already emerging? Early wins like the CCO's two-hour-to-ten-minute prep reduction demonstrate that the use case is real, not theoretical.

If you can answer yes to two or more, you're likely looking at a high-readiness starting point. If the answers are mostly no, investing there first will feel like pushing, not clearing the path.