The AI premium is real. Acquirers across the PE landscape are paying significant multiples above comparable non-AI companies when the target can demonstrate proprietary data assets, trained models, or AI-powered product capabilities. The thesis is straightforward: proprietary data creates defensible competitive advantage, and that advantage compounds as models improve.
The thesis is correct. The problem is that most AI-premium valuations skip a critical step: validating whether the data that justifies the premium is actually usable.
Data Value vs. Data Usability
There is a meaningful difference between data that exists and data that can be used. In the context of AI model training, "usable" has a very specific meaning: the data was collected with appropriate consent, is processed in compliance with applicable data protection law, and has no licensing restrictions that preclude its use for model training or AI development.
In our experience, a substantial proportion of the proprietary data that underpins AI valuations fails at least one of these tests. The failure modes are predictable:
- Consent scope mismatch: Data collected for one purpose (e.g., personalized marketing) used for a materially different purpose (AI model training) without an appropriate legal basis under GDPR Article 6 or a refreshed consent framework.
- Sensitive data categories: Health, behavioral, or inferred demographic data used in model training that triggers heightened requirements under GDPR Article 9, CCPA/CPRA sensitive personal information provisions, or EU AI Act high-risk system classification.
- Third-party data lineage: Training datasets assembled from third-party data brokers, scraped web data, or syndicated sources that carry their own licensing restrictions — restrictions that don't travel with the data when it's packaged into a training corpus.
- Cross-border transfer violations: Data transferred internationally for model training in violation of GDPR Chapter V requirements, SCCs, or BCRs — particularly relevant for US-based acquirers of European targets.
What Due Diligence Typically Looks For (and Misses)
Standard technology due diligence for AI-premium deals tends to focus on model performance, infrastructure scalability, and engineering team quality. These are legitimate questions. But they don't address usability risk.
The questions that surface data usability risk are different:
- What is the legal basis for processing each category of data used in model training?
- Has the company documented its data processing activities under GDPR Article 30?
- What is the consent framework for the original data collection, and does that consent extend to AI training use?
- Are there any third-party data licensing agreements that restrict downstream AI use?
- Has a DPIA (Data Protection Impact Assessment) been conducted for AI training activities?
- Under the EU AI Act, what risk classification applies to the AI systems being acquired?
In most cases, these questions are not being asked — or are being deflected to legal counsel who are not equipped to answer them in a technical context.
The Valuation Adjustment Mechanism
When data usability risk is surfaced in diligence, it creates a clear valuation adjustment mechanism. The question is not "is the AI capability valuable?" — it may well be. The question is "at what cost can it be made legally operational post-close?"
That cost has several components:
- Consent refresh cost: For datasets collected without sufficient consent for AI training use, the acquirer must either refresh consent (expensive, with significant drop-off rates) or retrain models on compliant data (costly and time-consuming).
- Data curation cost: Removing non-compliant data from training corpora and revalidating model performance on reduced datasets.
- Compliance infrastructure cost: Building the documentation, impact assessment, and monitoring infrastructure required for EU AI Act compliance if it doesn't already exist.
- Regulatory risk reserve: For high-risk AI systems, EU AI Act enforcement penalties can reach 3% of global annual turnover. That exposure should be reflected in representations and warranties — or in purchase price.
A well-structured AI data validation audit produces a concrete number for each of these components. That number either justifies the AI premium (if the compliance infrastructure is already in place and the data is genuinely usable) or becomes a negotiating lever for purchase price adjustment, seller-funded remediation, or expanded R&W coverage.
The Competitive Advantage of Getting This Right
The acquirers who run AI data validation audits pre-LOI have an advantage beyond risk mitigation: they can move faster post-close. When a target's data usability is validated before exclusivity, the 100-day plan doesn't start with a remediation workstream. It starts with deployment.
The AI premium is real. But so is the compliance gap. The acquirers who close that gap in diligence — rather than post-close — are the ones who realize the premium they paid for.