Do we have documented AI use policies that our employees actually follow?

Policies that exist but are not followed create liability without protection. Effective policies are short enough to read, specific enough to apply, and enforced enough to matter.

How do we test for bias in our AI systems — and how often?

Bias testing is ongoing, not one-time. Models develop bias as data shifts. Test on deployment, re-test quarterly, and re-test whenever the model or data pipeline changes.

What would the headline be if our AI use went wrong — and are we comfortable with our safeguards?

The headline test is practical risk assessment. If the worst-case failure would make the news, your safeguards should be proportional. If you are not comfortable, strengthen the guardrails.

Safety, Risk & Governance

Responsible AI & safety standards

By Mark Ziler · Last updated 2026-04-05

Responsible AI standards are the emerging rules of the road for deploying AI in business — covering transparency (can you explain how the AI made a decision?), fairness (does it treat all customers equally?), and accountability (who is responsible when it makes a mistake?). Even if your industry doesn't require these standards yet, adopting them now protects you from regulatory catch-up and builds trust with customers who increasingly ask how you use AI.

Go deeper

Your largest client just added a new question to their annual vendor review: 'Describe your AI governance framework, including bias testing, transparency practices, and incident response procedures.' You don't have one. You have six months before the review. Building a responsible AI framework retroactively — after you've already deployed AI across your operations — is five times harder than building it as you go. Every AI tool you deployed without documenting its decision logic, testing for bias, or establishing an incident response plan is now a gap you have to close.

The trap most companies fall into is treating responsible AI as a future compliance requirement rather than a current business risk. You don't need a regulation to mandate it. Your clients, your insurers, and your partners are already starting to ask. The companies that can answer these questions confidently are winning contracts over those that can't.

Questions to ask

If a client asked us today to explain how our AI makes decisions that affect them, could we answer?
Do we have a documented process for what happens when an AI system produces an incorrect or biased output?
Which of our AI deployments touch customer-facing decisions, and have any of them been tested for fairness across different customer segments?