This guided flow walks you through the only path that matters in AI: the right questions, in the right order, to ensure your investment is sound. If you’re assessing enterprise AI, let this decision tree guide your next move. It’s not about saying “yes” to AI—it’s about saying “yes” to the right AI.
1. Do you know exactly what business outcome the AI system is meant to achieve?
Yes → Go to Step 2.
No → Stop. Reframe. You can’t conduct meaningful AI systems due diligence if the outcome isn’t clearly defined—whether it’s fraud detection, operational efficiency, or customer retention. AI without intent leads to waste, drift, or failure to scale.
2. Has the model been tested in conditions similar to your operational environment?
Yes → Go to Step 3.
No → Request a test deployment or sandbox run. Models trained in clean labs often break in real-world chaos. Variations in user behaviour, data formats, or edge cases often lead to model fragility.
3. Is there full documentation of the model’s training data sources?
Yes → Go to Step 4.
No → Flag this. Data transparency is foundational to ethics and governance. Without knowing what’s inside the model, you’re opening your company to reputational, regulatory, and technical risks—especially in high-stakes use cases.
4. Has a third party validated the model’s fairness, bias controls, and governance?
Yes → Go to Step 5.
No → Involve your legal and ethics leads immediately. You need an outside lens. Internal assurances are insufficient. A formal fairness review is a growing requirement in sectors like finance, health, HR, and insurance.
5. Is there an explainability layer that non-technical users can interpret?
Yes → Go to Step 6.
No → Ask yourself how this system would be defended if it made a harmful decision. Explanations aren’t just for regulators—they’re how your teams and users build trust in the system. An AI that can’t be questioned shouldn’t be deployed.
6. Does the model include automated monitoring for data drift or performance decay?
Yes → Go to Step 7.
No → Consider this a serious technical gap. Post-deployment model failure is rarely immediate—it creeps in unnoticed. AI systems due diligence isn’t a point-in-time check; it’s a forward-looking resilience check.
7. Are internal stakeholders (e.g., Legal, Risk, HR) involved in evaluation?
Yes → Go to Step 8.
No → Pause. Re-engage them. AI evaluation is cross-functional. If governance functions haven’t weighed in, you’re likely ignoring ethical blind spots or regulatory gaps that won’t emerge until it’s too late.
8. Is there a formal override or appeal process for users impacted by AI decisions?
Yes → Go to Step 9.
No → This is a compliance and trust issue. Override mechanisms ensure your system doesn’t become untouchable or unjust. Without them, your AI risks alienating users—or ending up in legal hot water.
9. Will someone inside the company be responsible for the system post-launch?
Yes → Go to Step 10.
No → AI needs long-term owners. Many systems fail not because of technical flaws, but because no one is tasked with retraining, revalidating, or sunsetting them. Accountability isn’t optional—it’s survival.
10. Final checkpoint: Would you be willing to explain this system’s decisions to a regulator, a journalist, and your customers?
Yes → You’ve likely conducted effective AI systems due diligence. Proceed, but keep monitoring. Trust is not a one-time setup.
No → Trace back to the weakest area. Don’t ship until you’re proud to stand behind it—ethically, operationally, and reputationally.
When done right, AI systems due diligence protects not just the investment, but the integrity of your decisions. Don’t rush what can’t be undone.