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Why human judgment must stay at the centre of AI governance

Toyota’s management philosophy is built on four pillars: Philosophy, Process, People, and Problem-Solving. In earlier posts, I explored the first two:

  • Pillar 1: Philosophy — AI must serve a purpose rooted in trust, fairness, community benefit, and long-term value.
  • Pillar 2: Process — Ethical AI requires ongoing testing, learning, refinement, and continuous improvement.

Both pillars provide the foundation for responsible AI, but neither can succeed without the third: people. In Toyota’s system, people are not passive operators of machinery. They are active thinkers encouraged and expected to question, improve, and challenge processes in real time. The same must be true for AI.

Despite advances in automation, prediction, and generative capabilities, AI is not a substitute for human judgment. Instead, it shifts the responsibilities of staff:

  • From performing tasks to evaluating outputs;
  • From following instructions to raising red flags;
  • From relying on systems to challenging assumptions;
  • From accepting automation to improving it over time.

Organizations that treat AI as a machine to trust unconditionally place themselves at risk of drift, inequity, and even harm. Those that empower people to engage critically with AI strengthen accuracy, accountability, and public trust as AI governance depends on people who can ask hard questions

And so, a people-centred approach to AI governance requires:

1. Training staff to understand AI’s limitations (not just its features).  Teams need the skills to identify bias, anomalies, and system failure points (not just use the tool).

2. Giving people permission to pause or challenge outputs.  Toyota employees are trusted to stop the production line when something seems off (AI governance needs the same psychological safety).

3. Building cross-disciplinary decision making.  Technologists, policy experts, legal teams, and community representatives all play a role as no single discipline is capable of seeing the whole picture.

4. Treating human feedback as a governance input, not a technical glitch.  When staff repeatedly override or question a system, that is data that must inform improvement.

To connect back to the first two pillars, a clear philosophy (Pillar 1) gives people the moral and strategic grounding to evaluate AI. They can ask “Does this output align with our values, our community commitments, and our purpose?” A robust process (Pillar 2) gives people the mechanism to act. They can ask “How do we document concerns?”, “How do we check impacts?”, “How do we improve the system?”. People (Pillar 3) bring both pillars to life because without empowered, informed, and engaged staff, responsible AI remains theoretical.

In a time of accelerating AI adoption, organizations that keep people at the centre will outperform those that default to automation. The future of AI is human-led, not machine-led. When leaders equip their staff to question AI, flag risks, and contribute ideas, their organizations develop systems that are not only efficient but safe, fair, culturally aware, and aligned with their long term mission.

In the next post, I’ll explore the final pillar, Problem Solving, and why AI governance demands scientific thinking, curiosity, and structured methods for addressing root causes.

CT