What is AI due diligence and why is it becoming a standard?
AI due diligence is a systematic assessment of a company's readiness to implement and scale artificial intelligence, conducted by a PE or VC fund before or immediately after closing a transaction.
Traditional technology due diligence assesses IT infrastructure and data security. AI due diligence assesses something different—value potential: whether the company has the data, processes, people, and culture to leverage AI as a growth driver within the investment horizon.
The scale of change in Western markets is already measurable. According to a 2024 Deloitte study, 65% of PE fund managers are implementing or piloting AI in their investment decision-making processes. Bain & Company's Global Private Equity Report confirms that leading funds are already building structured AI assessment protocols—treating them as a standard on par with legal and commercial due diligence.
In Poland, this standard is just beginning to take shape. Funds that introduce AI readiness assessment of companies as part of their investment process now will gain an informational advantage when evaluating the same targets—before it becomes a market requirement.
The 5D Model – Five areas for assessing a company's AI readiness
Assessing a portfolio company's AI readiness requires analyzing five interdependent areas. Omitting any of them provides an incomplete picture—a company might have excellent data but zero cultural readiness, which in practice blocks any AI implementation. We always start the assessment with strategy, not technology.
Diagnosis (Problem Definition)
The starting point of any AI readiness assessment. Can the company articulate a specific business problem that AI is meant to solve? Not "we want to implement AI"—but "we are losing X amount of money monthly on manual data verification in process Y and we want to reduce it by 60%." A company that cannot formulate a problem in business terms is not ready for AI—regardless of data quality and infrastructure. This is an elimination criterion.
Data (Data Readiness)
Data is the foundation of any AI implementation. Data scattered across systems is not an insurmountable obstacle—AI can aggregate and structure data from multiple sources. The real problem lies elsewhere: whether data exists at all in sufficient volume, whether it is complete, and whether it is reliable. Incomplete or systematically distorted data—for example, selectively collected, depending on who and when it was entered—generates AI models that make incorrect decisions with high confidence. This is a risk that a fund must assess before investment, not after.
People and Culture (Adoption)
Technology implemented in an organization with cultural resistance does not work—it only works on paper. The assessment covers two levels: whether the management understands AI as a strategic tool, and whether senior management is ready to change their way of working. This is the most difficult area to assess in standard due diligence and most often determines the success or failure of an implementation.
Governance (Risk & Project Management)
AI without governance is a risk, not an advantage. The assessment includes: whether the company has defined AI risk management policies—including regulatory, operational, and reputational risk—and whether there is a project management structure for implementation. A company without clearly assigned responsibility for AI at the board level is structurally unprepared for scaling, even if a pilot was successful.
Technology (Systems Readiness)
Assessment of the company's technological infrastructure: whether operating systems allow integration with AI solutions, what tools are already in use, and whether there is internal technical competence or a proven implementation partner. A company declaring AI use should be able to demonstrate a working implementation—not a slide.
AI Maturity Score — how to assess a company on a scale of 1–4
The AI Maturity Score is a synthetic assessment of a company's readiness, resulting from the analysis of the five areas of the 5D model. It allows the fund to quickly compare companies, identify gaps requiring intervention, and embed AI in the value creation plan with a realistic timeline.
| Level | Name | Characteristics | What it means for the fund |
|---|---|---|---|
| 1 | Ad Hoc | AI initiatives are informal, experimental, and disconnected from strategy and processes | High risk. AI implementation first requires building foundations—data, processes, governance |
| 2 | Foundational | First AI actions in selected areas, partial data readiness, nascent governance | Good starting point. Value creation possible within 18–24 months with proper support |
| 3 | Operational | AI actively used in defined processes, measurable results, assigned responsibility | Rapid value creation possible. Priority: scaling what works |
| 4 | Strategic | AI embedded in strategic processes, mature data, technology, and organization-wide governance | Advantage already built into the business model. AI as an argument increasing valuation at exit |
For a PE fund, Level 2 is the minimum acceptable for an investment with an active value creation plan. Level 1 does not disqualify a company—but it requires a separate budget line and an extended AI ROI horizon.
How to conduct AI due diligence in practice — a checklist for funds
The following questions are a starting point for discussion with the company's management. The answers allow for assigning a preliminary AI Maturity Score and identifying areas requiring in-depth analysis.
Strategy
- What specific business problem do you want to solve with AI—and how much does this problem cost the company today?
- Who on the board is the sponsor of the AI initiative and what is their mandate?
- How does AI fit into the company's development plan for the next three years?
Data
- What data do you collect systematically and for how long?
- Are there situations where data is incomplete or inconsistently entered by different people or departments?
- Which process in the company currently has the best data quality—and why that one?
People
- Have you already carried out any AI project—what worked, what didn't?
- How does senior management react to the prospect of changing their way of working through AI?
- Who in the organization is currently a natural AI ambassador?
Governance
- Who is responsible for managing AI-related risks—including regulatory risk?
- How do you manage the AI implementation project: methodology, budget, milestones?
- Has the company analyzed the impact of AI Act regulations on its planned implementations?
Technology
- What AI tools are currently used in the company—can you show a working implementation?
- Do the company's operating systems allow integration with external AI solutions?
- Do you have internal technical capabilities to implement AI, or do you rely on external partners?
FAQ
Does a company need to be already using AI to pass AI due diligence positively? No. Level 2 in the AI Maturity Score—meaning initial actions in selected areas, partial data readiness, and nascent governance—is sufficient as a starting point for a fund with an active value creation plan. More important than the number of implemented tools is the quality of data and the management's readiness to change their way of working.
How does AI due diligence differ from traditional technology due diligence? Traditional technology due diligence assesses IT infrastructure and data security—i.e., the current state. AI due diligence assesses value potential—whether the company is capable of generating competitive advantage through AI within the investment horizon and whether this advantage will translate into valuation at exit.
How long does it take to conduct AI due diligence? With external support—from 5 to 10 business days. The result is a report with an AI Maturity Score based on the 5D model, a map of red and green flags, and recommendations for the value creation plan.
Need support in assessing AI readiness? Contact us.
