Traditional DD is slow and expensive. For good reason.
When a Czech company is sold for a quarter of a billion crowns, legal due diligence usually looks like this: a team of three associates, coordinated by a senior partner, gets access to a data room. Inside: 800 documents — contracts with key clients and suppliers, employment agreements, leases, IP licences, ongoing litigation, regulatory rulings. Over three weeks the team divides them up, reads, and fills out a structured DD checklist. The senior reviews, aggregates, writes the report.
The result: a competent DD report, billed in the high six figures CZK. The client receives it just before the closing-condition deadline. Most red flags were correctly identified. A few — through pure human exhaustion on day twenty-five of reading — slipped through.
This was the standard for twenty years. In 2026 it's beginning to look outdated.
What AI actually handles today
It's important to separate two things here: what AI can do in a lab (a lot) and what's actually being used in client work (less, but growing fast). We're focused on the second. These four things AI does better and faster than an associate today:
Extracting key clauses across hundreds of contracts
Change-of-control, assignment, non-compete, confidentiality clauses — that's typically what DD looks for. AI walks through 200 contracts; for each one it identifies whether the relevant clause is present, where it is, and how it reads. Output is a table that an associate would build over three days — AI delivers it in an hour.
Identifying non-standard clauses
This is where associates often fail: "this looks normal, doesn't it?". AI has tens of thousands of contracts in its training context and knows what an average limitation-of-liability clause looks like in a Czech supplier agreement. When something stands out — limit unusually high, an unusual notice period, an exotic arbitration clause — AI flags it. An associate seeing the contract category for the first time often misses anomalies of that kind.
Summarising 200 contracts into one table
"Send the client a summary of key parameters across all supplier contracts: counterparty, subject, term, notice period, liability cap, arbitration clause." Traditionally three days of manual copying. With AI: two hours, including formatting for the Excel sheet the client opens and understands.
Multi-language DD in parallel
The target has contracts in Czech, English and German depending on its trading partners. For an associate team that means splitting work by language skills — and in mid-size firms with limited German capacity, an outright bottleneck. AI works across all three at once. Output is in whichever language you choose.
AI in due diligence isn't about whether you'll find a substitute for associates. It's about giving seniors a tool that doubles their effective capacity.
What AI cannot do
A fair view. AI in DD is not a substitute for legal expertise. Here are four things where AI structurally fails — and where your work is irreplaceable:
Legal judgement and contextual risk assessment
AI will tell you that the lease has a non-standard capital-expenditure clause. What to do about it — whether it affects the price, whether to negotiate it out, whether to handle it via additional indemnities from the seller — that's a judgement call AI doesn't have. You do.
Negotiation with the other side
When the seller's counsel sits across the table trying to dismiss your finding, AI doesn't solve that. Nor would it make sense — DD is an intellectual discipline; its endpoint is commercial negotiation.
The strategic go / no-go call
AI won't deliver a DD report stamped "we recommend against the acquisition". That's advice you sign your name under. AI is the tool that gets you to that recommendation faster, with more data on the table.
Liability
Who's accountable for the DD report? The lawyer who signed it. The insurer who underwrites it. AI cannot carry liability — and won't be able to in any foreseeable future. That's not philosophy; it's underwriting math.
A worked example: acquiring a Czech distributor
To make this concrete, a brief worked example. Target: a mid-size distributor of medical devices, CZK 300 m annual revenue, 60 employees, 200 supplier contracts, 80 customer contracts (hospitals, pharmacy chains), five major contracts with the German parent. Buyer: a private equity fund, requires a DD report within four weeks.
Traditional approach
- Team: 3 associates + 1 senior partner
- Duration: 3 weeks of intensive work
- Hours: ~ 320 associate hours + 60 senior hours
- Invoice: ~ CZK 500,000
- Output: 80-page DD report, 12 red flags identified, 3 missed
AI-assisted approach
- Team: 1 senior + 1 paralegal with AI tools
- Duration: 1 week
- Hours: ~ 60 senior hours + 40 paralegal hours + AI processing
- Invoice: ~ CZK 200,000 (and yet your margin is higher — your senior rate is higher)
- Output: 80-page DD report, 14 red flags identified, 0–1 missed (AI doesn't make fatigue errors)
For the client this is straightforward: cheaper, faster, sharper. For the firm it means higher margin, less burnout, more capacity. The only people who don't profit are associates being shifted to more demanding (and intellectually more interesting) work. That's not a bad thing.
What this means for your market
Corporate clients — exactly the ones writing the largest DD bills — are starting to notice. Big advisory firms have been offering AI-assisted DD since last year. Mid-size Prague firms that haven't made the shift yet will be at a 30–50 % pricing disadvantage within two years. That's a gap clients feel.
So the question isn't "whether to adopt AI in DD," but "when and how." Our recommendation: pick one DD type, build an AI workflow around it, refine it, and only then expand. It's not a two-month project. It's a discipline a team grows into.