Workforce Capability as Infrastructure: Larry Durham in HRTech Cube

Leaders increasingly agree that organizations need AI-ready workforces and adaptable capabilities at scale. Yet alignment on the destination does not mean the organization is prepared to reach it. In an executive interview with HRTech Cube, Larry Durham examines the structural gap between transformation intent and the systems needed to turn that intent into measurable workforce capability.

Larry identifies disconnected data and measurement as a primary constraint. Skills data, learning activity and performance signals often sit in separate systems that cannot show whether capability is actually forming. Without that visibility, leaders may overinvest in programs that generate activity without improving performance or overlook areas where the organization is developing genuine structural exposure.

AI makes this challenge more consequential. When technology performs the transactional and analytical work that once gave employees repeated opportunities to learn, judgment deficits can accumulate unnoticed. The risk is especially pronounced in the supervisory layer, where professionals may be asked to review AI-generated work without ever developing an experiential baseline for evaluating the work themselves.

Building effective workforce capability infrastructure requires more than implementing another platform. Larry points to a common skills language across workforce planning, talent acquisition, learning and performance; measurement that captures capability formation closer to real time; and governance that treats learning investment as enterprise infrastructure. Together, these foundations help organizations connect talent decisions to strategy and build resilience that can withstand the pressure of ongoing change.