According to the latest KPMG AI Pulse Survey, the share of organizations in the driving-adoption phase rose significantly in Q2, increasing from 13% to 22% and representing the largest movement observed anywhere along the AI maturity curve. But while AI governance is evolving from principle to practice, only about a third say roles, responsibilities, and processes are clear and well managed. Many organizations still work to translate governance principles into operational discipline, with only 29% pointing to a named C-suite executive as a single point of accountability for AI-informed decisions. The issue with AI governance is broader than operational discipline. Just as cybersecurity and testing need to be incorporated early in the software development lifecycle, the seeds of governance need to be incorporated directly into AI strategy and innovation, and governance needs to always be traceable back to the business objectives of the applications being governed.

Full opinion : As organizations drive adoption and scale AI implementations to get beyond pilots and shiny distractions, it’s important to retain focus on strategy and innovation so governance doesn’t diverge from business objectives.