2025 saw a record 1,568 M&A transactions in Central and Eastern Europe worth €36.64 billion — a 42.5% increase from the previous year (CMS/EMIS Emerging Europe M&A 2025/26). Private equity dry powder reached a historic record: €410 billion in Europe, $1.1 trillion globally (PipelineRoad, Feb. 2026). And 72.6% of M&A advisors expect further growth in deal flow in 2026 (Capstone Partners/IMAP, Jan. 2026).
Nevertheless: no existing AI platform in the world natively supports Slovak, Czech, Hungarian, and Polish with judicial accuracy and integration of the ORSR, RPVS, ARES, and KRS registries.
This is not a market gap. This is a white space the size of an entire region.
Chapter 1: The Productivity Gap — Why Europe Is Lagging Behind and What AI Has to Do With It
The numbers are ruthless. Hourly labor productivity in the EU is $72 compared to $116 in the US — a 38% gap (WEC Europe, Dec. 2025).
In the ICT sector, the situation is even worse: 27.2% productivity growth in the US compared to 6.5% in the EU for the period 2019–2024 (ECB Economic Bulletin, Sept. 2024). And in professional services—precisely where due diligence is performed—the ratio is 18.7% to 5.0%.
Mario Draghi summed this up in a single sentence in September 2025: “Europe is in a tougher spot today than it was a year ago.” Of his 383 detailed recommendations, only 11% had been implemented after a year (European Conservative, Sept. 2025).
But here comes a paradox that most analysts overlook.
For companies building AI-native solutions in Europe, the Draghi gap is a bullish signal. Regulatory burdens constrain the big players. Bureaucracy slows implementation. And the window for an agile challenger with compliance by design remains open.
A CEPR/VoxEU analysis from February 2026 (covering 12,000+ European firms) shows that AI adoption causally increases productivity by an average of 4% at the firm level. Every percentage point of investment in software increases the AI productivity effect by 2.4 pp; every point invested in employee training by 5.9 pp (CEPR/VoxEU, Feb. 2026).
Let’s break it down: due diligence is 100% knowledge work. It is precisely the area where AI delivers the most measurable ROI. And in CEE, where analytical teams are small, the language barrier is high, and AI adoption in DD is practically zero—the relative impact on the client is 10–15 times higher than in London or Frankfurt.
Chapter 2: AI Disruption in Due Diligence — Not Theory, Reality
Forget the speculation. Look at the numbers from production deployments:
BCG and Harvard (2023): 758 consultants, 18 analytical tasks — 25% faster completion, 40% higher quality, 12% more tasks completed (Forbes, Sept. 2023).
McKinsey Lilli: 30% time savings on information gathering, 20% increase in quality. 94% of 45,000 employees used the tool (Business Insider, April 2025).
Hebbia: 30–40 hours saved per deal for investment bankers. 75% reduction in time spent reviewing loan agreements. Clients: BlackRock, KKR, Carlyle, Centerview (FinanceFeeds, Jan. 2026).
Rogo: $2 billion valuation as of April 2026. 25,000+ daily users at Rothschild, Jefferies, and Lazard (Rogo, April 2026).
Harvey: $190 million ARR, $11 billion valuation. 50% of AmLaw 100 firms as clients (CNBC, May 2026).
Anthropic Economic Index: Current AI models can increase U.S. labor productivity by 1.8% annually over the next decade—with the highest efficiency gains occurring in professional and legal tasks ($119 per task on average) (Anthropic, Nov. 2025).
These are not experimental hypotheses. These are production deployments at top-tier global clients.
And yet — not a single one of these platforms natively covers the CEE language and jurisdictional space.
Chapter 3: The 14 Phases of Due Diligence — Where AI Is a Game-Changer and Where It Isn’t
Due diligence is not a single process. It consists of 14 interconnected phases, each with different potential for AI automation. Understanding this distinction is crucial for anyone looking to build or acquire in this space.
Fully automatable phases (with audit):
- Parsing CIM documents and extracting KPIs — from days to minutes
- Ingestion of thousands of VDR documents — from weeks to hours
- Target screening across registries — from 3–5 days to 4–6 hours
- Sanctions, PEP, and adverse media screening — dramatic reduction in false positives
- Detection of financial red flags — automatic anomaly detection
- Competitive mapping and market scan — 80% time savings
Human-in-the-loop phases (AI assistance + mandatory human validation):
- OSINT and source discovery — AI synthesis, human reliability validation
- Litigation and regulatory checks — hallucination rate still 17% even with the best tools (Stanford, 2025)
- ESG screening — requires expert materiality judgment
- IC memo synthesis — AI generates, partner reviews
Too sensitive for standalone AI (AI support, human decision):
- Red-team review — adversarial thinking requires senior investment professionals
- Geopolitical risk — human geopolitical expertise is essential
- Management assessment — human judgment is irreplaceable
The FCH principle of “Courage of Truth” applies here literally: anyone who claims that AI will replace the entire DD process is lying. Anyone who claims that AI is irrelevant in DD is also lying. The truth lies in precise distinction — and in the fact that an expert-verified AI model is the only defensible approach.
Chapter 4: The CEE White Space — Why No One Is Addressing It
Let’s look at the global map of AI-native DD platforms:
| Platform | Valuation | Focus | CEE coverage |
|---|---|---|---|
| Harvey | $11 billion | Legal AI | Weak |
| AlphaSense | $4 billion | Market intelligence | No CEE-specific |
| Rogo | $2 billion | Finance AI / IB | None |
| Hebbia | $700 million | Document AI / PE-DD | None |
| Sayari | $1 billion+ | Risk intel / BO mapping | Coverage exists, but without local enrichment |
| ComplyAdvantage | — | AML/KYC SaaS | 75 countries, but without CEE language depth |
Billions of dollars are flowing into these platforms. But none of them understand the Slovak corporate structure with a Cypriot holding company via RPVS. None can parse the Czech ARES register with judicial precision. None can distinguish the nuances of the Polish KRS in the context of an M&A transaction.
Why? Because the CEE mid-market (€10–100M enterprise value) isn’t large enough for a Silicon Valley focus. But for a European challenger with local know-how, it’s an ideal entry point.
Three reasons why the CEE mid-market is the right entry point:
1. Size and momentum. CEE PE deal volume reached an all-time high of 330 deals in 2025. Genesis Capital (GPEF V: €225m), Enterprise Investors (Fund IX: €340m), Innova Capital (€407m) — these are active funds with real demand for DD solutions (CMS/EMIS) .
2. Price sensitivity. The Big Four’s FDD fee of €30–100k per deal is high relative to the fund economics of a mid-market PE fund. An AI-assisted platform for €5–25k/deal addresses a real pain point. DD costs typically represent 0.5–2% of deal value (Peony, May 2026).
3. The language barrier is greatest here. Western PE firms or strategic buyers entering the CEE mid-market—where the target is a small Slovak company without English-language reporting—are the most qualified customers, because their pain is the most acute.
Chapter 5: Why “expert-verified AI” — and not “AI-only” or “people-only”
Hallucination risk is real. Global business losses from AI hallucinations reached $67.4 billion in 2024 (Four Dots, May 2026) . GPT-4 hallucinates in 58% of cases on general legal tasks. Even specialized legal AI tools—Lexis+ AI 17%, Westlaw AI 33% — have significant error rates (Stanford, 2025).
RAG (Retrieval-Augmented Generation) reduces hallucinations by 71%. But even that is not enough when it comes to investment decisions worth tens of millions of euros.
The only defensible model is a hybrid one:
- AI speed for data extraction, screening, and synthesis
- Human verification for key conclusions
- Audit trail for every claim
- Confidence scoring at the claim level
The result? An analyst who traditionally produces 3–5 DD reports per quarter will produce 10–18 with AI assistance. The cost per report drops from €6,400–€9,600 to €1,600–€2,800. The gross margin rises from 30–45% to 60–75%.
This is not low-margin consulting. This is a high-margin software + services hybrid — exactly where billion-dollar valuations are created.
Chapter 6: EU AI Act — Not a Hindrance, but a Moat
Starting August 2, 2026—that is, in three months—the full obligations of the EU AI Act for high-risk AI systems will take effect. Audit trail, human oversight, technical documentation, EU data residency.
An IMF analysis (WP/25/67, April 2025) shows that the combination of the EU AI Act and national sectoral regulation could reduce the EU’s productivity gains from AI by more than 30% compared to an unregulated scenario (IMF, April 2025).
For most companies, this is a threat. For an AI platform built with compliance by design from day one, this is the strongest defensible moat.
American AI platforms—Harvey, Rogo, Hebbia — will have to invest millions in EU compliance if they want to operate in the European regulated space. A European player that has EU data residency, human-in-the-loop architecture, and an audit trail as a core feature has a regulatory advantage you can’t buy—you have to have it built in from the start.
The FCH principle of “Trust First” takes on a new dimension in the context of AI regulation: a platform that transparently shows how its AI works, where it gets its data from, and what its limits are, will win out over a platform that says “trust us.”
Chapter 7: Window of Opportunity — 18 Months
Every strategic opportunity has a shelf life.
CEE M&A is at a historic high. PE dry powder is pushing for deployment. AI technology is demonstrably productive. Regulation creates a moat for compliance-ready platforms. And no global player has yet invested in CEE language localization.
This window of opportunity has an estimated timeframe of 18–24 months — after which Western challengers will inevitably arrive. Rogo, Hebbia, or Harvey will invest in CEE expansion. And at that point, it will be extremely expensive to build what can be built now for a fraction of the cost.
Eilla AI has just completed the first fully AI-driven acquisition in the EU (Raconteur, April 2026). Deloitte is forming a mega-merger in EMEA (€20 billion, 132,000 employees) — paradoxically, this consolidation creates governance overhead and delays, opening a window for an agile challenger (Consultancy.eu, May 2026). More than $4 billion flowed into legal tech startups in 2025, nearly double the amount from 2024 (Business Insider, May 2026).
The market is shifting. The question isn’t whether AI will change due diligence. The question is who will be in a position to capitalize when it happens in CEE.
Chapter 8: What This Means for Leaders — Three Decisions That Can’t Wait
If you’re a CEO, CFO, managing partner of a PE fund, or head of an M&A team in Central Europe, here are three things you need to do right now:
1. Map out your DD costs and time.
How many hours does your team spend on target screening? How much does a single adverse media check cost? How many days does it take to parse a VDR? If you don’t know these numbers—you don’t know how much you’re losing. And you are losing. The BCG/Harvard benchmark is clear: 25% faster, 40% higher quality. That’s not a marginal improvement—it’s a structural advantage.
2. Test AI-assisted DD on a real deal.
Not in a sandbox. Not on a training dataset. On a real counterparty due diligence. With real Slovak registries. With real Czech documents. With a real pressure test for linguistic accuracy. If the result isn’t reliable at the jurisdictional level—ditch it. If it is—you’ve secured the future of your analytics team.
3. Decide whether you want to be a first mover or a fast follower.
In CEE mid-market DD, there is currently exactly zero AI-native competition. First movers are building a data moat, an expert network, and client relationships that are then extremely costly to replicate. Fast followers have the advantage of learning from others’ mistakes—but in a market with an 18-month window, that may not be enough.
The FCH principle of “Solving Ahead of Time” applies here in its purest form: a leader who waits for a “proven” AI implementation in CEE due diligence is waiting for something that someone must first build. And that someone will gain a position from which it is difficult to dislodge them.
Due diligence in Central Europe is facing its biggest transformation in the last twenty years. AI technology is ready. The market is growing at a record pace. Regulation is creating a moat for responsible players. And the white space—the entire CEE region without a single AI-native DD platform—awaits whoever occupies it first.
This is not a question of technology. This is a question of courage.
Whoever decides first defines the rules.
