The first instinct of deal teams in Merger & Acquisition (M&As) transactions is to categorize technical issues into established domains such as intellectual property (IP), privacy and cybersecurity, and commercial contracts, assuming these will address most technology-related risks. By 2026, this approach might no longer suffice. For many startups and technology companies, AI is already a core enterprise value. However, as founders prioritize model performance, they frequently overlook the importance of digital due diligence (DD).
AI acts as a "risk amplifier." As a company moves toward an exit or a new funding round, the familiar due diligence playbook, which circles intellectual property and cybersecurity, is no longer adequate. The frameworks commonly used in 2025 leave significant gaps untouched, from black-box models and tainted training data to quiet reliance on third-party providers. Unlike traditional software threats, AI risks are fluid: models learn, shift with context, and can deteriorate over time even when code remains unchanged. Failing to prepare your AI’s legal and organizational groundwork in advance, your most valuable asset could be perceived by the buyer as a regulatory "time bomb" that will affect both the value of the transaction and its risk structure.
Time to admit: Legacy DD no longer works. So what are buyers actually looking for?
A common mistake among companies is assuming that legally owned code equals protection. In practice, modern DD delves deeper, focusing on three core areas that every entrepreneur should be familiar with:
1. Data Governance and Model Training: Buyers test both the model and what it "feeds" on. Where did the training data originate, and more importantly, was it obtained legally, particularly when it includes personal information or copyrighted content? Using data scraped from the web without proper permissions can expose a company to IP claims or trigger regulatory demands to "delete" the trained model altogether. Disorganized documentation of data sources and processes, such as absent data maps or model cards, signals a lack of maturity and poses a clear red flag for buyers.
2. Third-Party Dependence and the AI Supply Chain: Only a handful of companies build everything from the ground up. Most rely on cloud providers, open-source libraries, or established external models, particularly those offered by frontier LLM providers and hyperscalers.
3. These dependencies carry both commercial and legal risk. A sophisticated buyer will want to know whether the company has a viable contingency plan if an API provider unilaterally changes its terms, and whether supplier agreements quietly shift liability onto the company. It is also crucial to confirm that any customization layered on top of a third-party model remains the company’s property and can be transferred post-acquisition.
4. Transparency, Ethics, and Explainability: As regulatory pressure intensifies (under such frameworks as the EU AI Act), companies are increasingly required to explain how their systems reach decisions. When AI systems make consequential determinations about individuals, for example, in hiring or financial contexts, companies must be able to demonstrate that bias testing has been conducted and that Human-in-the-Loop (HITL) oversight mechanisms are in place. An organization that cannot offer a clear technical and legal account of how its system operates is viewed as a high-risk target, particularly in heavily regulated sectors such as finance and healthcare.
Corporate Governance as a Valuation Lever
The single most significant source of added value a company can bring into DD is the early adoption of a formal internal AI governance framework. This tool is no longer a "nice to have;" it is a prerequisite. Companies that can point to established risk-assessment protocols, in-model privacy safeguards, and a clear incident-response plan for AI deficiencies signal operational maturity and readiness for integration into a larger organization.
The difference between a successful exit and defeat at the negotiating table is preparedness. AI introduces a level of dynamism that demands ongoing expert oversight, professionals capable of bridging technical architecture and legal accountability, and day-to-day attention. Executives who embed a "digital DD" today will protect their companies and ensure the technology they have built commands the valuation it deserves. In 2026, companies without these updated risk-assessment frameworks will find themselves at a material disadvantage.
The authors are members of DLA Piper America’s AI and Data Analytics Practice.
Published by Globes, Israel business news - en.globes.co.il - on March 19, 2026.
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