
AI Systems Due Diligence: 5 Critical Steps And The Pitfalls That Derail Them
There’s one uncomfortable truth many acquirers discover too late: AI systems often look smarter than they really are. But under pressure, what matters is whether the AI holds up—legally, technically, and operationally. That’s where a structured AI systems due diligence process comes in. Not just to confirm claims, but to catch what’s missing.
Step 1: Identify where AI exists in the stack—don’t rely on labels.
Many companies claim to be AI-powered. Few have mature systems operating beyond experimental stage. Before you dive into audits, ask: where exactly does the AI live? Is it part of the product? Is it used internally for automation? Is it core to the business or a side feature?
Pitfall: Teams often take the pitch at face value. One firm touted AI-driven customer scoring. In reality, it was a set of manually tuned heuristics. Without a technical walk-through, the acquirer nearly paid a valuation premium for rules dressed as machine learning.
A structured AI systems due diligence process doesn’t assume— it maps. You’re looking for real models, version control, training workflows, and evidence of deployment cycles.
Step 2: Analyse the data—origin, structure, quality, and ownership.
AI runs on data. But not all data is equal. You need to verify not only where the training data came from, but whether it's clean, representative, and legally usable post-acquisition.
Pitfall: In one review, the data powering the recommendation engine was scraped from public forums—without terms of service clearance. The acquiring team didn’t realise this until late-stage legal review, by which time it affected pricing, integration, and timeline.
Robust due diligence using AI systems includes legal, technical, and data governance lenses. It asks: who owns the data, what’s the usage license, and can this scale across jurisdictions?
Step 3: Examine model behaviour—under normal load and edge conditions.
It’s easy to present AI working well under a happy path. It’s harder to show what happens when inputs vary, context shifts, or edge cases arise. You need stress tests. You need examples of drift. You need audit logs and rollback capability.
Pitfall: A computer vision model demoed perfectly—until it was tested on data from a different camera format. Accuracy dropped 40%. The system had been trained on ideal inputs, not real-world deployment feeds.
Good Artificial Intelligence systems due diligence means looking beyond static results. Ask how the model responds to noise, scale, and novelty.
Step 4: Assess the team—who builds, trains, monitors, and improves the AI.
AI is never “done.” It requires ongoing retraining, maintenance, and monitoring. Who handles that? Is there a dedicated ML ops team? Are updates tested? Are there monitoring alerts for performance drops or bias creep?
Pitfall: A startup had a strong NLP model but only one ML engineer. When she left, the system degraded. No one knew how to retrain it. Within three months, customer complaints surged and accuracy plummeted.
Due diligence using AI systems must evaluate not just technical assets, but human ones. Sustainability isn’t about the model—it’s about the team behind it.
Step 5: Review compliance, explainability, and regulatory alignment.
Depending on the region and industry, AI systems may need to justify outputs, allow for auditability, and meet specific ethical standards. Can your models explain why they made a decision? Can a customer contest an outcome?
Pitfall: A fintech acquisition failed to close when it was discovered the credit model couldn’t justify its outputs—violating upcoming transparency regulations. The delay cost the acquirer six months in market entry and millions in sunk costs.
Smart AI systems due diligence looks ahead. It’s not just about compliance today—it’s about readiness for what’s coming.
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Read this five-step guide to AI systems due diligence—complete with hidden pitfalls, real-world risks, and insights buyers can’t afford to skip when buying.