# Algorithmic Due Diligence: The Case for Code-Level Audits in AI-Enabled PE Acquisitions
## White Paper Article | Touch Stone Publishers | WP Category 600
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## The Problem That Speed Created
Private equity due diligence moved faster in 2025 and 2026 than at any prior point in the industry’s history. Generative AI tools compressed VDR review from weeks to days. Pattern recognition across thousands of financial documents became routine. The volume of deals that firms could evaluate in parallel expanded significantly. For a competitive dealmaking environment, these are genuine operational advantages.
They also created a specific problem that the industry has not yet fully reckoned with: the acceleration of due diligence accelerated the acquisition of liabilities that the due diligence process was not designed to detect.
The data makes the scope of the problem concrete. Twenty-eight percent of global M&A in 2025 was AI-related, with AI-enabled targets commanding a 3.2x market premium over comparable non-AI businesses (PitchBook, March 2026). Eighty-six percent of PE firms had incorporated generative AI into their M&A workflows by early 2026 (Deloitte, March 2026). At the same time, 62% of tech acquisitions failed to meet their financial targets (Bain and Company, March 2026), and Reps and Warranties insurers moved to explicitly exclude AI-specific risks from coverage (Mayer Brown, May 2026). The insurance floor disappeared precisely as the premium was rising.
The convergence of these data points describes a market in which PE firms are paying historically high premiums for AI assets, using AI tools to validate those premiums, and receiving no insurance coverage for the specific risks that the AI assets carry. The firms that understand this convergence correctly are in the best position to generate returns in the current environment. The firms that do not are systematically acquiring unpriced, uninsured liabilities.
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## What AI Due Diligence Tools Can and Cannot Do
Understanding the limitation of AI-powered VDR review is not a criticism of the technology. It is a prerequisite for using it correctly.
Generative AI due diligence tools are pattern-recognition systems applied to documents. They identify what is present in the documents in the VDR — financial patterns, contractual anomalies, disclosure gaps, language that correlates with specific risk categories. They do this with speed and consistency that human reviewers cannot match. For these purposes, they are genuinely valuable.
They cannot, however, perform four due diligence functions that are essential for AI-enabled acquisitions specifically.
First, they cannot validate whether the AI system described in the documents actually performs as described. An AI due diligence tool reviewing claims about another AI system’s accuracy has no method for testing those claims against the system’s actual output. It can identify that the claims are present and that their language is consistent. It cannot determine whether the language reflects reality.
Second, they cannot assess the provenance or legal defensibility of the AI system’s training data. EU AI Act Article 10 requires specific data governance practices for training data used in high-risk AI systems. Whether those practices were followed is a function of the data acquisition process, not the documentation of it. Document review finds the documentation. Only technical audit finds the underlying process.
Third, they cannot detect the gap between claimed and actual AI architecture. The Nate Inc. case, in which the DOJ established that $42 million was raised based on claims of autonomous neural network processing that did not exist in the production system, illustrates this gap precisely. The documentation described an AI company. The code described a different business model. Document review found the documentation. A code-level audit would have found the code.
Fourth, they cannot assess whether the AI system’s architecture is designed to maintain its performance in the post-acquisition operational environment. An AI system optimized for a specific data volume, customer segment, and error tolerance profile may perform precisely as claimed in its current context and fail to meet performance benchmarks in the acquiring entity’s operational context. This is an architectural assessment, not a document review.
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## The Regulatory Environment That Makes These Gaps Material
Before 2025, these due diligence gaps were significant but their consequences were primarily operational: post-close performance disappointments, integration complications, technology rebuild costs. In 2026, the regulatory environment has converted these gaps into specific legal liabilities with defined enforcement mechanisms.
The EU AI Act’s parental liability provision assigns responsibility for the compliance posture of acquired AI systems to the acquiring entity from the date of acquisition. For AI systems classified as high-risk under the Act’s Annex III — a category that covers AI used in employment decisions, credit scoring, access to essential services, and several other domains common in PE portfolio companies — the compliance obligations include data governance requirements under Article 10, transparency and documentation requirements under Article 13, accuracy and robustness testing requirements under Article 15, and quality management system requirements under Article 17. Fines for serious violations reach 3% of the acquiring entity’s global turnover, calculated on the fund’s consolidated revenues rather than the portfolio company’s.
The SEC’s enforcement framework, established through actions against Delphia and Global Predictions in 2026, treats AI capability claims that are material to investment decisions as securities fraud when they are inaccurate. The enforcement standard applies to the institutional investors who relied on those claims as well as to the entities that made them. The protection for sophisticated institutional buyers is not ignorance of the claims’ accuracy — it is evidence of independent validation.
The Delaware Chancery Court’s April 2026 ruling that AI-generated logs and chatbot records are admissible as evidence of intentional dishonesty changes the evidentiary landscape for post-close disputes. Earnout structures tied to AI performance milestones are now subject to challenge through the AI system’s own performance records. A buyer who validated the AI’s performance pre-close with an independent algorithmic audit has a factual baseline established before the deal closed. A buyer who did not has a factual baseline established by the AI’s internal records — records that may contradict the claims on which the earnout was structured.
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## The Algorithmic Audit: What It Is and What It Produces
The algorithmic audit is a structured technical review of the AI system’s actual capabilities, architecture, data, and compliance posture. It is distinct from and complementary to standard IT due diligence. Where IT due diligence assesses infrastructure, security, and systems integration, the algorithmic audit specifically addresses the AI system’s performance claims and regulatory exposure.
A complete algorithmic audit produces five categories of information.
A capability verification report documents the results of independent testing of the AI system against the performance claims in the information memorandum. The test protocol is designed by the audit team, not derived from the target’s internal testing framework. Results are presented as a comparison between claimed performance and observed performance, with specific variance by decision category, use case, and data type.
A training data provenance assessment documents the sources of the AI system’s training data, the acquisition process for each major data category, and the assessment of compliance with applicable data governance requirements under EU AI Act Article 10 and relevant data protection law. It identifies specific compliance gaps and their remediation cost.
An architecture scalability assessment evaluates whether the AI system’s design can accommodate the post-acquisition operational environment: higher transaction volumes, different customer segments, new regulatory constraints, and integration with the acquiring entity’s existing data infrastructure.
A regulatory compliance gap analysis assesses the AI system’s compliance status against the specific EU AI Act obligations applicable to its Annex III classification, documenting each gap and providing a quantified remediation cost estimate. This feeds directly into the CFO’s liability-adjusted acquisition price.
A dependency map identifies technical dependencies that could be affected by change of control: third-party data sharing agreements, licensed datasets, cloud infrastructure configurations, and API dependencies that require renegotiation or may not survive the transaction structure.
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## The Business Case: Why the Audit More Than Pays for Itself
The counterargument to requiring an algorithmic audit is deal velocity. The audit adds two to three weeks to the due diligence timeline. In a competitive deal process, that time has a cost.
The counterargument fails when the cost of the audit is compared against the risks it mitigates.
On regulatory exposure: a 3% global turnover fine for an EU AI Act violation on a fund with $500 million in portfolio revenues is a $15 million exposure per violation. An algorithmic audit that identifies and prevents one enforcement action pays for itself at multiples. The audit cost is a fraction of a percent of any deal at 3.2x premium pricing.
On acquisition pricing: the capability verification component of the audit provides the evidence base for a liability discount when the AI system’s actual performance falls below the IM’s claimed performance. For every deal where a performance gap exists — and the SSRN research from October 2025 on LLM-based M&A analysis suggests material accuracy gaps are common — the audit provides a quantified basis for repricing. The repricing value typically exceeds the audit cost by a significant margin.
On earnout structure: the performance baseline established by the audit creates earnout milestones that are defensible under the Delaware Chancery evidentiary standard. The risk of an earnout dispute in which the AI system’s own logs contradict the performance claims is significantly reduced when the baseline was independently established before close.
On litigation defense: the audit record demonstrates that the acquiring fund applied appropriate skepticism to AI capability claims before relying on them as the basis for investment. This is the specific evidentiary standard that the SEC’s enforcement framework creates for sophisticated institutional buyers. The record exists or it does not. It cannot be constructed after the fact.
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## Practical Implementation: Building the Audit Into the Standard Deal Process
The algorithmic audit should be positioned as a condition precedent to investment committee approval, not as an optional enhancement to the standard due diligence process. This positioning requires three organizational changes.
First, the investment committee approval template for AI-enabled acquisitions must include a mandatory section confirming that an independent algorithmic audit was conducted, identifying who conducted it, summarizing its findings relative to the IM’s capability claims, and documenting the liability discount applied to the acquisition price based on identified compliance gaps.
Second, the deal team must include or retain the technical expertise required to commission and evaluate an algorithmic audit. This expertise is not available from standard financial advisors or legal counsel. It requires AI systems specialists with specific experience in model evaluation, training data assessment, and EU AI Act compliance review.
Third, the deal timeline must be structured to accommodate the audit in parallel with standard financial and legal due diligence. The audit does not add sequentially to the total timeline if commissioned at the same stage as other specialist due diligence workstreams. It adds time only when it is treated as a final step rather than a concurrent one.
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## The Fiduciary Imperative
The 2026 AI acquisition market has created a specific fiduciary obligation that did not exist in the same form two years ago. The combination of premium pricing, insurance exclusion, EU AI Act parental liability, SEC enforcement risk, and Delaware Chancery evidentiary expansion means that approving an AI-enabled acquisition without independent algorithmic validation is not a conservative investment decision made with incomplete information. It is a failure to apply the standard of care that the current environment defines as appropriate for this asset class.
The firms that understand this will build the algorithmic audit into their standard process and will treat it as the due diligence investment that it is: a cost that is paid once, before the deal closes, to establish the factual baseline that protects every downstream decision. The firms that do not will discover the cost of the gap in the form that unpriced liabilities always appear: later, larger, and harder to manage than they would have been if found first.
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*WP Category: White Paper Article (600) | TSP_2026-020 | May 2026*
*Sources: PitchBook (March 2026); Deloitte M&A GenAI Study (March 2026); Bain and Company (March 2026); Mayer Brown (May 2026); DOJ, Nate Inc. indictment (April 2026); SEC enforcement actions, Delphia and Global Predictions (2026); Delaware Chancery Court (April 2026); EU AI Act, Articles 10, 13, 15, 17, Annex III; SSRN, From Disclosure to Prediction (October 2025)*