AI M&A pricing · UK · 2026

AI due diligence cost in the UK (2026)

AI technical due diligence in the UK typically costs £20,000 to £150,000 — roughly 0.5% to 1.5% of the deal — with an AI-specific layer on top for model provenance, training-data rights and EU AI Act exposure. Scope, not deal size alone, sets the number.

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How much does AI due diligence cost? For most UK deals, budget £20,000 to £150,000, or about 0.5%–1.5% of the transaction value. A £6m acquisition with a modest software footprint tends to land near £40,000; a small deal under £1m can be done for £10,000–£25,000, while enterprise-scale reviews run past £150,000. Those figures are for a proper technical review — code, architecture, security and the engineering team — not a light checklist.

When the target actually ships AI, there's a second layer that generic software due diligence doesn't touch, and it's the part that catches buyers out. Someone has to trace where each model came from, whether the training data was licensed, and what the EU AI Act will require once it's your asset. That AI-specific work usually adds £15,000–£60,000, depending on how many models are in production and how much of the value depends on them. The rest of this page breaks down what you're paying for, and how to keep the bill honest.

What AI due diligence costs by deal size (2026)

Engagement	Typical UK cost	Timeline
Deal under £1m	£10,000–£25,000	2–3 weeks
Deal £1m–£10m	£25,000–£60,000	3–5 weeks
Deal over £10m	£60,000–£150,000+	4–8 weeks
AI-specific layer (add-on)	£15,000–£60,000	+1–2 weeks
Senior reviewer (boutique)	£800–£2,000 / day	per day

Sources: SoftwareDevelopment.co.uk UK technical due diligence costs 2026; Papermark due diligence cost 2026; Peony due diligence cost breakdown; Valutico AI vulnerability in M&A 2026; fractional-CTO day-rate benchmarks (941 Consulting, Fractional.quest). £ Indicative ranges, updated September 2026.

Read that as scope, not a menu. The deal-size rows assume a standard technical review; you bolt the AI layer on when the target's value genuinely rests on models it has trained or fine-tuned. Boutique specialists tend to sit at the lower end of each band — the Big Four typically charge 30–50% more for comparable scope, which is worth knowing when a headline quote looks steep. A well-run process, with a proper data room and AI-assisted document review, can trim the total by 20–40%, mostly by cutting the hours spent chasing files.

What the AI-specific layer actually checks

Ordinary technical due diligence tells you the code is sound and the architecture will scale. It stops at the model. When AI is where the value sits, the review has to go further — and provenance comes first, because everything downstream inherits from it.

Model provenance and IP — is each production model trained from scratch, fine-tuned from an open-weight base, or a thin prompt-and-retrieval layer over someone else's foundation model? The answer changes what you're actually buying.
Training-data rights — every dataset needs a licence, a source and a consent basis. A scraped corpus is the single largest unquantified liability in most AI deals, because you can't un-train it out of a shipped model without a costly retrain.
EU AI Act exposure — the AI Office's full enforcement powers over general-purpose AI apply from 2 August 2026, and providers owe a copyright-compliance policy plus a public summary of their training content. If the target sells into the EU, that obligation becomes yours on completion.
Inference economics — reconstructing gross margin with AI costs fully loaded. A product that looks profitable can turn thin once per-query model costs and compute commitments are counted properly.
Evaluation evidence and key-person risk — is there real proof the models perform, and does that performance walk out of the door if one or two people leave?

None of this shows up in a financial or legal data room, which is why buyers who skip it inherit surprises. Our own view of AI data engineering starts exactly here — data lineage and rights aren't a footnote, they're the asset — and the same goes for how we assess model monitoring and evaluation in a live system.

What moves the number, and how to buy well

Four things decide whether you pay £25,000 or £120,000 for the same-sized deal.

Number of production models

One fine-tuned model is a contained review. Five, spread across features and vendors, multiplies the provenance and evaluation work — and that's where the hours go.

How much value rests on AI

If AI is a nice-to-have feature, a light review will do. If it's the whole investment thesis, you want the deep workstream, and paying for a shallow one is false economy.

State of the data room

A target with documented data lineage, model cards and evaluation results is quick to review. One that hands you a shrug is slow and expensive — the cost tracks the mess.

Who you hire

Boutique specialists cost less than the Big Four for the same scope and often go deeper on the technical detail. Match the reviewer to the risk, not to the logo.

Sharp Code is a founder-led, senior-only UK studio, and we build and operate our own AI products in regulated and consumer-facing sectors — so when we look at someone else's model, we read it the way we'd read our own before it ships. That perspective also shapes how we think about AI-driven development and what "production-ready" honestly means. For related pricing, browse more AI insights.

Red flags in an AI due diligence quote

No AI-specific line — a quote that prices generic software due diligence and calls it done has skipped the part that carries the real risk.
Silence on training data — if the scope doesn't mention provenance, licensing or consent, the reviewer isn't going to find the liability that matters most.
No EU AI Act view — with enforcement live from August 2026, a review that ignores it leaves a compliance cost off your model of the deal.
Fixed price, unseen data room — a firm number quoted before anyone has looked at what's available usually means padding, or a thin review; a good reviewer scopes first.
All juniors — provenance and architecture judgement is senior work. A team of analysts running a checklist will miss what an experienced engineer spots in an afternoon.

£20k–£150kTypical UK AI due diligence, per deal

~1%Of deal value, on average

£15k–£60kAI-specific layer, on top

2 Aug 2026EU AI Act GPAI enforcement live

AI due diligence cost: FAQs

Straight answers to what UK buyers and investors ask before commissioning a review.

For most UK deals, budget £20,000 to £150,000 — around 0.5% to 1.5% of the transaction value. A deal under £1m can be reviewed for £10,000–£25,000, a £1m–£10m deal for £25,000–£60,000, and larger deals for £60,000 upwards. When the target genuinely runs its own AI, add roughly £15,000–£60,000 for the model and data provenance work.

Technical due diligence checks the code, architecture, security and engineering team — is the software well built, and will it scale. AI due diligence adds the questions that only matter when the target ships models: where each model came from, whether the training data was licensed, what it actually costs per query, and what the EU AI Act will require. One flags that AI is present; the other tells you whether it's an asset or a liability.

A standard technical review runs two to eight weeks depending on deal size — two to three for a small target, four to eight for an enterprise system. The AI-specific layer typically adds one to two weeks, most of it spent tracing model and data provenance. A well-organised data room shortens all of it; a disorganised one is the main reason reviews overrun.

The core domains are model provenance and IP, training-data rights and consent, contract data rights, architecture, inference economics and gross margin, compute commitments, evaluation evidence, governance and key-person risk. Provenance comes first because everything downstream inherits from it — a model built on a scraped, unlicensed corpus is a liability you can't easily undo after completion.

It can, and buyers underweight this. If the target sells into the EU, its obligations become yours on completion. The AI Office's full enforcement powers over general-purpose AI apply from 2 August 2026, and providers must keep a copyright-compliance policy and publish a summary of their training data. A review that ignores the Act leaves a real compliance cost — and potential enforcement exposure — out of your deal model.

Boutique specialists typically cost 30–50% less than the Big Four for comparable scope, and often go deeper on the technical detail because the people doing the work are senior engineers rather than analysts. The Big Four make sense when you need brand cover for a board, or a very large multi-domain review. For the AI-specific technical layer, a focused specialist is usually the better value.

Scope the review to where the risk actually sits rather than paying for a blanket audit, insist on a proper data room so reviewers aren't billing hours chasing files, and use AI-assisted document review where it helps — together those can cut the total by 20–40%. Don't economise by dropping the provenance work, though; that's the line that protects you from the most expensive surprises.

Weighing an AI acquisition or investment?

Tell us what the target has built and where its models come from. We'll scope an honest technical review — and tell you plainly whether the AI is an asset or a liability.

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