How Much Does an AI Audit Cost in the UK?

Published UK prices for an AI audit start below £1,000 and reach five figures. Most firms that publish a fee sit between £1,500 and £5,000 for a standard scope, governance-heavy readiness work runs to around £16,700, and Big Four programmes cost far more. The range is wide because "AI audit" covers at least three different jobs. Before you compare quotes, work out which one you are buying.

This guide is for founders, CTOs and operations leads who are about to spend real money on AI and want an independent view first. It sets out the published UK market ranges, what drives the price, what each band should deliver, and how to tell a useful audit from an expensive questionnaire.

What the UK market charges

We only quote figures that firms or guides publish themselves, with links, and we checked each source on 15 October 2026. Prices are usually ex VAT, and they are other firms' published prices, not ours.

Engagement	Published UK range	Source
Focused audit for a small business	from about £750 to £2,500	Sharp Code; Niddy guide
Typical published starting prices (nine UK firms surveyed)	£999 to £10,000, most between £1,500 and £5,000	M22 survey, Sept 2026
Full readiness assessment for an SME (data, infrastructure, governance, skills)	£3,500 to £8,000, over two to four weeks	Sharp Code
Senior-led assessment ending in a costed roadmap and business case	£8,000 to £20,000	Sharp Code
Governance readiness against ISO 42001, NIST AI RMF and the EU AI Act	£8,500 to £16,700	AI Governance UK, published fees
Big Four readiness audit	from around £50,000	Niddy guide; Sharp Code puts enterprise programmes at £40,000 to £150,000+

Two other public reference points help. Consultant day rates for AI governance and assurance suppliers on the government's Digital Marketplace sit between roughly £300 and £1,450 a day, and UK LLM penetration tests commonly run £3,000 to £12,000 per system, both as reported by AI Governance UK. Sharp Code also notes that London rates typically sit 20–40% above the rest of the UK.

Three different things called an "AI audit"

Most confusion about price comes from comparing different products.

1. Opportunity audit: "where would AI pay?"

This looks at how the business actually runs and returns a ranked list of places where AI would save time or money, and places where it would not. It suits a company that has not yet built anything. It sits at the cheaper end of the market because the scope is processes and data, not live systems.

2. Readiness and governance audit: "are we safe to proceed?"

This scores your data, infrastructure, policies, skills and risk controls, usually against one or more recognised frameworks:

• ISO/IEC 42001:2023, the certifiable standard for an AI management system.
• NIST AI Risk Management Framework (AI RMF 1.0), with its Generative AI Profile (NIST AI 600-1) for LLM use cases.
• The EU AI Act, if you sell into the EU. Most of its rules have applied since 2 August 2026, but the AI Omnibus, in force since 27 July 2026, moved the high-risk obligations to 2 December 2027 for Annex III systems and 2 August 2028 for AI built into regulated products.
• The ICO's guidance on AI and data protection, which is the UK baseline whenever personal data is involved, including when a data protection impact assessment is needed.

It suits regulated businesses and anyone selling AI into enterprises that will ask hard procurement questions. The price rises with the number of use cases in scope, because each one brings its own owner, data and risks.

3. Technical audit of an existing AI system: "does what we built actually work?"

This is the one most often skipped, and it is where we spend most of our time. It reviews a model, pipeline or LLM application that already exists: accuracy against real data, evaluation coverage, failure modes, security, cloud cost and whether it can survive production load. For an example, see our independent NLP audit case study, a review of an ingredient-classification NLP system. For LLM products, it should also test against the OWASP Top 10 for LLM Applications and check the serving, evaluation and cost controls we describe in our guide to LLM deployment services.

A technical audit is priced on the size of the system, not the size of the company. A ten-person start-up with a complex LLM product can need more work than a large firm with one simple chatbot.

What drives the price

Whichever type you buy, the same factors move the quote:

• Number of systems or use cases in scope. This is the biggest driver. Name them up front.
• Access to real data and code. An audit that works from interviews and slides is cheaper and much less useful than one that runs your data through your system.
• Seniority of the people doing it. A junior analyst with a template costs less than an engineer who has shipped production AI. Ask who will do the work.
• Regulation. Healthcare, financial services and anything touching personal data add governance and evidence work, such as a data protection impact assessment or EU AI Act risk classification.
• Depth of output. A scorecard is cheap. A costed, sequenced plan you can hand to a delivery team costs more and is worth more.
• Cloud and infrastructure review. If GPU or inference spend is part of the question, the audit has to look at bills and architecture, not only models.

What a good audit should leave you with

At any price point, insist on:

• A clear verdict for each use case: build now, fix first, defer or drop. A good audit is willing to say "don't build this".
• Evidence, not opinion: test results, data samples and cost figures behind every finding.
• A prioritised remediation list with effort, owner and order.
• Cost and risk estimates for the recommended next step, so finance can decide.
• Everything in writing, yours to keep, with no obligation to buy the build from the auditor.

If a proposal offers mostly workshops and a maturity score, you are buying the cheaper product. That is fine, as long as you know it.

Is it worth paying for?

The alternative is usually more expensive. Gartner reported in January 2026 that at least 50% of generative AI projects were abandoned after proof of concept, citing poor data quality, inadequate risk controls, escalating costs and unclear business value. Every one of those can be spotted in a short audit before the build budget is committed. We cover the wider pattern in why most enterprise AI projects fail, and the cost of the build itself in enterprise AI cost in 2026.

An audit is not worth it if you already know what you want to build, have the data and need delivery capacity rather than advice. In that case, spend the money on a short, well-scoped first phase instead.

How to scope an audit so it pays off

1. Write down the decision the audit must support. For example: "Do we fund the support-automation build this quarter?" If no decision depends on it, don't commission it.
2. List the systems and use cases in scope. Fewer, deeper findings beat a broad survey.
3. Give the auditor real access to data, code and cloud accounts, under NDA, from day one.
4. Agree a fixed fee and a fixed output before work starts.
5. Ask for a named senior lead and meet them before you sign.
6. Plan the next step now. Know who will act on the findings: your team, the auditor or someone else.

How we approach it

Our audits are run by experienced engineers who build and operate production AI systems, backed by 25+ years of engineering experience and 500+ projects for clients including Walmart, Porsche, Mozilla, Coca-Cola and Fortune 500 companies. We look at the real system and data, tell you plainly what works and what doesn't, and hand you a written plan you can use with or without us. When the answer is "this isn't ready", we say so. Our AI and machine learning consultancy covers the follow-on work if you want it (see all services), and our guide to building an AI roadmap shows how the findings turn into a plan.

Get an honest view first

If you are about to commit budget to AI, or you have a system that is not doing what it should, bring the details to a free 30-minute call. We will tell you what an audit should cover in your case, and whether you need one at all.