“The problem with most AI audits is that they focus on output accuracy, not process integrity.”
—Chief Technology Officer, Fintech Platform
Accuracy grabs headlines. A model that predicts customer churn at 92% accuracy looks great in a demo. But what the CTO above is warning about is deeper. AI systems are built on training data, model assumptions, and optimisation logic that may not hold over time. If your AI due diligence only checks output snapshots, you’re missing the far more volatile layer: how the system makes decisions, and how that logic evolves.
Smart acquirers don’t just ask what the AI outputs—they ask why. They look at auditability, reproducibility, and bias handling. Because if something breaks after acquisition, the damage isn’t just technical—it’s legal, ethical, and reputational.
“Explainability isn’t a feature—it’s a requirement. If a model can’t justify itself, it can’t scale.”
—Director of Data Governance, Healthcare AI Firm
In many sectors, particularly regulated ones, explainability isn’t optional. When investors or acquirers conduct AI due diligence, they’re looking for more than performance—they’re looking for traceability. What variables influence decisions? Can outputs be linked to specific patterns in the data? Can those patterns be audited later?
In practice, explainability is often deprioritised in favour of speed or accuracy during early development. But if the company being evaluated can’t explain their AI’s decision-making in human terms, it’s a ticking risk. This is especially true in high-stakes sectors like finance, insurance, and health. If a machine denies a loan or flags a medical risk, and you can’t explain why—it’s not just poor governance. It’s potential liability.
“Founders say their models are proprietary. What I want to know is—are they defensible?”
—Partner, Investment Firm
A common issue during AI due diligence is overstatement of “proprietary algorithms.” Many times, the architecture is based on open-source models with minimal tuning. What actually matters is not novelty—it’s defensibility. Is there unique value in the training data? Is the deployment pipeline robust? Has the company developed internal capabilities, or are they dependent on one person or a set of undocumented scripts?
A well-conducted due diligence process in AI evaluates both claims and infrastructure. You’re not buying just code—you’re buying the people, the culture, the documentation, and the ability to evolve. Proprietary doesn’t matter if it can’t be maintained without heroics.
“The true risk isn’t bad AI. It’s unmanaged AI.”
—Chief Risk Officer, Enterprise SaaS Company
This quote cuts through the hype. AI doesn’t fail dramatically—it fails quietly. A model left unchecked, drifting on outdated assumptions, can do more harm than one that never existed. During Artificial Intelligence due diligence, one of the most overlooked checks is lifecycle governance: how often are models retrained? Who signs off on updates? Is there version control? Are failure cases documented?
Too many teams treat AI like a product that gets launched once and left alone. The best teams treat it like a living system. They don’t just build—they monitor, review, and recalibrate.
“We don’t just look at model performance—we look at organisational maturity. How decisions get made around AI tells us everything.”
—AI Risk Analyst, Global Consultancy
This quote gets to the heart of sustainable adoption. An AI model may be high-performing, but if the organisation lacks version control, testing protocols, or documentation standards, that performance won’t last. AI isn’t just tech—it’s team, process, and mindset.
During due diligence in AI, this means evaluating workflows, data governance, update policies, and ethical review processes. Is the company treating AI like a product—or like an experiment that never really graduated? Acquirers who look only at the demo miss the bigger picture.
