Enterprises have deployed copilots, launched agentic workflows, and invested real budget in AI programs. The results keep disappointing. Agents that worked in staging stumble in production. Copilots that promised 40% time savings deliver 8%. The post-mortems blame integration, change management, adoption friction. But the real culprit is simpler: the AI was trained on the wrong data. Not bad data. The wrong kind entirely: documentation, system logs, theoretical workflows that describe how work is supposed to happen, not how it actually does.
This eBook makes the case that real human behavioral context is the single most important and most underinvested variable in enterprise AI. The enterprises that solve this first won't just build better AI. They'll build AI that compounds, creating a structural advantage competitors can't easily replicate.