What Are the Best AI Due Diligence Practices for Private Deal Flow in 2026?

A Practical Definition of AI Due Diligence in 2026

AI due diligence is the repeatable process of evaluating an artificial-intelligence company, its underlying technology, governance, commercial claims, legal exposure, and operational readiness before an investor, founder, customer, or acquisition partner commits capital or time. For private deal flow, the work is broader than testing a product demo. It asks whether the company can produce reliable results, explain how those results were obtained, protect the data involved, and survive scrutiny from customers, employees, regulators, and counterparties.

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The best practices in 2026 are moving beyond a collection of procurement questions toward evidence-based testing. A promising model should be examined in the context of its intended use, because a legal document assistant, autonomous trading system, and customer-service bot face different risks. The central question is not simply whether an AI product works, but whether it works consistently, within stated limitations, under realistic conditions, and with accountable human oversight. UNICEF’s work on applying AI principles in public-interest settings, for example, reinforces the distinction between technical performance and institutional accountability. That distinction matters in private transactions, where an attractive growth rate can obscure weak controls or an untested dependence on third-party models.

A sound process therefore combines corporate, technical, legal, security, ethical, and commercial review. The depth should reflect the transaction’s size and the consequences of failure. A small seed investment may justify a two-week review, while a regulated enterprise deployment or a negotiated acquisition may require several months of testing, documentation, and remediation. No single framework eliminates uncertainty, but a disciplined process converts vague technology claims into evidence that decision-makers can compare and challenge.

Establishing the Scope, Claims, and Decision Criteria

The first step is to define exactly what is being purchased, invested in, licensed, or partnered with. Deal teams should identify the legal entity, beneficial owners, material subsidiaries, model providers, data processors, subcontractors, and relevant intellectual-property owners. They should also separate the product from the company that created it: a company may use a third-party foundation model, a proprietary retrieval system, external annotation providers, cloud infrastructure, and commercially licensed training data. Each dependency changes the allocation of technical and contractual risk.

The review should begin with the company’s own claims. Ask for a current architecture diagram, a list of training and retrieval sources, model cards or equivalent documentation, known failure modes, incident history, evaluation results, and the exact AI-related representations made in fundraising, sales, and regulatory materials. Quantify claims where possible. Instead of accepting “98% accuracy,” determine the task, test-set size, language, demographic scope, class balance, confidence interval, and treatment of abstentions. A reported 98% result on 100 curated examples is not equivalent to 98% reliability across millions of live cases.

Decision criteria should be agreed before reviewing impressive demonstrations. A typical committee might assign weights to commercial evidence, data rights, security, model robustness, regulatory exposure, team capability, infrastructure resilience, and exit feasibility. High-risk applications may use minimum thresholds: every critical finding should have an owner and remediation date, and no material undisclosed dependency should remain unresolved. This does not make subjective judgment disappear, but it prevents the most persuasive founder or largest customer from determining the entire outcome.

## Evaluating Models, Data, Accuracy, and Human Oversight

Technical due diligence should test the actual system under conditions that resemble production. A controlled demonstration can show that software functions, but it rarely establishes how the product behaves with poor input quality, changing language, adversarial instructions, document conflicts, inaccessible interfaces, or incomplete records. Teams should use a written test plan with baseline models, fixed datasets, reproducible prompts, and pre-agreed scoring criteria. Results should be repeated across several runs because systems using external tools or nondeterministic sampling may produce materially different outputs.

The evaluation must include failure cases, not just headline accuracy. A founder may disclose that a system works well on ordinary documents while remaining unstable on scanned files, handwritten notes, unusual jurisdictions, or low-resource languages. Reviewers should measure false positives, false negatives, hallucination frequency, citation accuracy, calibration, latency, uptime, and the rate at which users override or disregard outputs. For generative systems, independent domain experts should verify factual claims against source material. “Human in the loop” should not be accepted as a complete control unless the reviewer has enough time, expertise, authority, and information to notice and correct an error.

Prompt injection deserves particular attention in 2026 because an AI system may process text, files, emails, or web content that contains instructions aimed at manipulating its behavior. Morgan Lewis’s analysis of prompt injection as a distinct risk category is relevant to any system that treats external content as instructions. Technical testing should include direct and indirect injection attempts, malicious documents, hidden instructions, poisoned retrieval content, and attempts to extract confidential context. A company should document which components are exposed, what isolation and monitoring controls exist, and how quickly a compromised interaction can be detected and contained. Passing a benchmark is less persuasive than showing a repeatable defense that has been tested under realistic conditions.

Checking Data Rights, Security, Privacy, and Operational Resilience

Data due diligence should trace information from collection to deletion. Deal teams need to know whether the company owns, licenses, or merely has access to its training, fine-tuning, evaluation, telemetry, and customer data. Contracts should distinguish customer data from company-generated data and clarify whether prompts, outputs, embeddings, and derived artifacts can be reused. Reviewers should examine privacy notices, consent and retention practices, cross-border transfer mechanisms, data-processing agreements, deletion capabilities, and whether personal information appears in logs or evaluation datasets.

Security work should follow a recognized risk framework rather than rely on a generic questionnaire. Relevant controls include asset inventory, identity and access management, encryption, secure development, vulnerability management, secrets handling, logging, backups, incident response, and business continuity. Ask for penetration-test summaries, the dates of the tests, scope, severity distribution, remediation status, and whether an independent tester performed the work. A report stating “no critical vulnerabilities” is not enough without scope and time context; an old test on a small web application says little about a production system connected to customer records.

Operational resilience is increasingly part of technical due diligence. Companies should explain what happens if a foundation-model provider changes pricing, removes an API feature, restricts data retention, or experiences an extended outage. Verify whether critical workflows can switch providers, whether prompts and retrieval indexes are portable, and whether the business can operate during a cyber incident. For businesses dependent on scarce chips or cloud capacity, examine committed capacity, substitution options, and unit economics rather than treating infrastructure as unlimited. The relevant standard is not perfect redundancy; it is a documented and economically credible recovery plan.

Reviewing Governance, Legal Exposure, and Human Rights

Governance should show who can approve an AI release, who monitors performance, who receives incident reports, and who has authority to suspend a system. A board or executive policy is useful only if it assigns measurable duties. Records should include release reviews, model and vendor inventories, risk assessments, training requirements, complaints, audit trails, and decisions to accept residual risk. In 2026, governance is moving from voluntary best practice toward a baseline expectation in many enterprise and public-sector settings, although the exact legal duties still depend on jurisdiction, sector, and use case.

Legal diligence must identify obligations that arise from the product itself. These can include privacy, consumer protection, discrimination, product safety, professional licensing, intellectual property, confidentiality, export controls, automated decision rules, and sector-specific rules such as financial services, health care, employment, or education. The company’s classification of itself as a “software provider” does not determine whether a particular use triggers additional duties. Regulatory inquiries should be documented, including any deficiencies in general risk assessment or customer due-diligence procedures.

Human-rights review is part of this broader analysis, particularly when systems affect people who cannot easily challenge an automated decision. Reviewers should examine impact assessments, accessibility, language coverage, proxy discrimination, labor conditions in data creation and annotation, and the company’s grievance process. Palantir criticism concerning alleged failures in human-rights due diligence for public contracts illustrates why corporate customers and investors may examine more than technical performance: government clients may create reputational, procurement, and political risks even when a contract is legally signed. The response should be proportionate, but high-impact uses deserve explicit questions about affected groups, mitigation, monitoring, and remedies.

Comparing the Main Due Diligence Alternatives

There is no single AI due diligence product that replaces a disciplined review. The practical choice depends on whether the priority is speed, technical depth, legal coverage, or independent assurance. The table below compares common alternatives and explains what each can and cannot establish.

A hybrid approach is usually strongest. A questionnaire and demo may be enough to reject an obviously unsupported claim, but they should not be used as final evidence for a high-value or high-impact transaction. Conversely, commissioning a broad audit before defining the use case can waste money because testers may measure the wrong system. Start with scope, assign a decision owner, select the minimum independent work needed to test the material claims, and preserve evidence for later monitoring.

Common Mistakes That Produce Weak Decisions

One common mistake is treating a polished interface as proof of defensibility or safety. Modern prototypes can be built quickly, and a model may perform well because of an incumbent provider, an open-source model, or substantial customer data. Diligence should ask what cannot be reproduced by a competitor using the same building blocks. Patents, proprietary data rights, switching costs, distribution advantages, and operating performance may be more informative than a visually sophisticated demo.

Another mistake is accepting aggregate metrics without denominators. A 95% accuracy figure may refer to 20 routine test cases, while the live system handles a much broader distribution. Ask for sample sizes, date ranges, language and demographic breakdowns, production-versus-test distinctions, and confidence intervals. Reviewers should also test whether the company is measuring accuracy at all, or merely counting usage and revenue. A system can generate a large volume of output while producing little verified business value.

Teams also make the error of reducing due diligence to one questionnaire. A form may be necessary for documentation, but it cannot establish whether an API is actually being used as represented, whether training data is properly licensed, or whether an incident was disclosed. Keep questions versioned, request evidence rather than assurances, and record every unresolved assumption. Do not confuse silence with compliance. As the research context shows in unrelated corporate examples, complex structures and opaque counterparties can make diligence difficult; that is a reason to investigate ownership and dependencies, not a reason to lower the evidentiary standard.

When to Act and How Much It Should Cost

Act quickly when a company asks for exclusivity, a binding purchase order, an investment decision, or access to sensitive data before basic diligence is complete. Early red flags include refusal to identify beneficial owners, inconsistent answers across documents, pressure to sign before security review, claims that evaluation data cannot be shared for competitive reasons, or no incident process. Speed is acceptable when uncertainty is bounded and low, but not when the potential loss is concentrated and the company controls all evidence.

For early-stage screening, a lightweight review may cost approximately $5,000 to $25,000. A more detailed technical, legal, and security review commonly falls between $30,000 and $150,000, while a complex cross-border, regulated, or acquisition-grade process can exceed $250,000. These ranges are planning estimates, not quotes; they depend on system access, data volume, number of jurisdictions, test environments, and whether remediation is included. Internal diligence also has cost through staff time, engineering access, and delayed investment decisions.

For the Mercer Club community, the most useful first step is usually a private, non-promotional diligence room where founders and operators exchange structured information about real AI deployments, failures, procurement requirements, and pricing. Such a network should not present confidential data as verified merely because it was submitted. The operator should be able to state the source, date, test conditions, and whether a claim comes from a company, customer, investor, or independent reviewer. By September 28, 2026, the practical standard is not “AI diligence is complete”; it is that decision-makers know which facts are verified, which are claimed, which remain uncertain, and who owns each next step.