Every large company now has responsible AI principles. Fairness, transparency, accountability. Nicely designed PDFs. Almost none of them change what happens on a Tuesday.
Principles don’t govern anything. Operating models do.
Responsible AI, done properly, looks unglamorous. It’s a data labeling standard everyone actually follows. It’s knowing which data can train what, and having the classification to prove it. It’s intake gates that ask about risk before the build starts, not after. It’s clear ownership: who approves, who monitors, who pulls the plug.
EY’s research found that 99% of companies reported financial losses from AI risks, averaging US$4.4M, while only 12% of C-suite leaders could identify the correct controls. (EY Responsible AI Pulse survey) That’s the gap between the principles deck and the operating model. One of them costs real money.
At Maple Leaf Foods we did the unglamorous work first. A ten-step data governance strategy. The CISA Traffic Light Protocol as the labeling standard across business and legal. When the AI wave arrived, the foundation was already there. Teams didn’t need to slow down for governance, because governance was already in the floorboards.
That’s the reframe. Responsible AI isn’t a document you publish. It’s plumbing. And like plumbing, you only notice it when it’s missing.
AI is the gas pedal. Governance is the steering wheel. You need both, and only one of them decides where you end up.