Internal AI Governance Structures

Establishing who makes decisions about AI - and how those decisions get made - is foundational to everything else in AI governance. Without clear structures, you get inconsistency: one team deploying AI with rigorous testing and oversight while another launches something similar with none. Effective governance structures define roles and responsibilities clearly. Who approves new AI use cases? Who is accountable when something goes wrong? Who monitors ongoing performance? Common models include centralised AI governance committees, federated models where business units have autonomy within defined guardrails, and centres of excellence that provide guidance and standards without gatekeeping every decision. The right model depends on your organisation's size, maturity, and risk appetite. Whatever structure you choose, it needs to be practical - governance that exists on paper but doesn't function in practice is worse than useless because it creates a false sense of security. Ensure that the people in governance roles have sufficient technical understanding to evaluate AI proposals meaningfully, and that the process is fast enough to keep pace with the speed at which AI projects move.