Episode Details

Job titles tell you almost nothing about what someone can actually do. That might sound obvious, but it's a real operational problem when you're trying to figure out who can fill a gap, step into a new role, or get upskilled quickly. Large employers are solving this by moving from static org charts to something more powerful: skills graphs. Skills graphs map actual capabilities — what someone knows, how proficient they are, and what adjacent skills they could pick up next. Add AI into the mix, and you can normalize messy resume data, run gap analysis against future role requirements, and even stress-test hiring and mobility scenarios before you need them. It's workforce planning that actually reflects reality. But there's a catch. Garbage in, garbage out. If your historical data is biased — promotions that favored networks over merit, roles that were gatekept — AI will encode that bias at scale. The fix isn't more AI. It's investing in data quality, taxonomy, and DEI review first. In this episode, we break down how skills graphs work, where AI adds real value in the process, and why getting your foundation right matters more than any algorithm.