AI Consultancy in the UK: What You Get, What It Costs, How to Choose

An AI consultancy helps a business decide where AI will pay off, then designs, builds or oversees the systems that deliver it. For most UK mid-size firms, the right first purchase is a short, fixed-price discovery that ends in one working pilot, not a strategy deck.

This guide is for managing directors, COOs and finance directors of UK companies without an in-house AI team. It covers what an AI consultancy does and where UK businesses are with AI. It then sets out what published rate cards say about cost, the data protection points to settle first, and how to judge a firm before you sign.

What does an AI consultancy actually do?

An AI consultancy does three kinds of work, and most firms are stronger at one than the others:

• Strategy: finding the processes where AI saves time or money, ranking them by value and effort, and checking whether your data is ready.
• Implementation: building the pilot, connecting it to your systems, testing it with real users and running it in production.
• Governance: setting the rules for which tools staff may use, what data may go into them, and who checks the output.

A software agency builds what you specify. A strategy consultancy tells you what to build. A good AI consultancy for a mid-size firm does both: it recommends, then ships the first working version and hands it over.

Where UK businesses are with AI

AI use is still the exception in the UK, not the rule. In government research, around 1 in 6 businesses (16%) were using at least one AI technology. Adoption rises with size: 36% of large and 23% of mid-sized businesses were using AI (DSIT and DBT, 2026). The survey covered 3,500 businesses, interviewed between 12 February and 2 May 2025.

The barriers are about direction and skills more than technology. Asked what prevents or prevented them, 71% of businesses named a lack of identified need for AI, and 60% named limited AI skills, expertise and knowledge. Of the businesses that cited them, 76% rated high costs and 72% rated unclear or uncertain regulation as a significant barrier (DSIT and DBT, 2026).

Most adopters bought ready-made tools rather than building their own. Only a small minority fully outsourced development, peaking at 18% for hardware related to AI (DSIT and DBT, 2026).

For a mid-size firm, that points to what a consultancy should provide: a clear use case, the skills you lack, and a straight answer on cost and regulation.

Which type of AI consultancy do you need?

Most UK mid-size firms do best with an implementation partner, plus a senior owner on their side. If nobody inside the business can hold a supplier to account, start with a Fractional CTO.

What does AI consultancy cost in the UK?

Several UK consultancies now publish their prices. They are useful benchmarks, as long as you compare like with like. All figures below exclude VAT and come from each firm's own website, checked on 14 October 2026.

Sources: The AI Consultancy, 2026, Boardman, 2026, Intelance, 2026.

The AI Consultancy also lists a day rate of £950 to £1,500, used only where scope is too open-ended for a fixed fee (The AI Consultancy, 2026). That is a sensible rule. A fixed fee for a defined outcome puts the risk of overrun on the supplier.

What moves the price is rarely the model. It is the state of your data, the number of systems the pilot must connect to, and how much security review the use case needs.

Data protection and security: what to settle first

Settle these before any company or customer data reaches an AI tool:

• UK GDPR applies to personal data in AI systems. The ICO's guidance on AI and data protection sets out how the principles apply, including accountability and what to consider in a data protection impact assessment (DPIA). The ICO says the guidance is under review after the Data (Use and Access) Act (ICO, 2023). Ask the consultancy who writes the DPIA, and get the answer in the contract.
• Know where data is processed. Ask which models and hosting regions the consultancy uses, whether your data is used to train them, and how long prompts and outputs are kept.
• Build securely from the start. The NCSC's guidelines cover four areas: secure design, secure development, secure deployment, and secure operation and maintenance (NCSC, 2023). A credible consultancy can show how its work maps to each.
• Use the free government self-assessment. AI Management Essentials is a DSIT self-assessment tool that helps organisations assess and implement responsible AI management (DSIT, 2026). It makes a practical starting point for an AI governance framework.
• Check the EU AI Act if you sell into Europe. It applies to providers and deployers located outside the EU where the output of the AI system is used in the EU (EU AI Act, Article 2).

How to judge a consultancy: 8 questions for the first call

• 1. Who will build it? Ask to meet the engineers, not only the partner who sells the work.
• 2. What will be live after 6 weeks? A good answer names a user, a process and a measure.
• 3. Who owns the code, the prompts and the data pipelines? You should, from the first invoice.
• 4. Where will our data be processed and stored? Ask for the model provider and hosting region in writing.
• 5. How will you measure success? Agree the baseline before the pilot starts.
• 6. What happens at hand-over? Ask for documentation, training and a defined support period.
• 7. Which models will you use, and why? A firm tied to one vendor may not choose the best fit for you.
• 8. Can we speak to a client with a similar problem? Ask what went wrong and how it was handled.

A real example: consolidate first, then automate

Finance in a Box, a US financial services firm, ran each client's general ledger across separate spreadsheets, with no single view of status. Levich worked with the firm as Fractional CTO, product development and AI implementation partner (Levich case study).

Close management shipped first, giving the team one live place to see every client. The AI agent layer and a contract analysis tool came after. The firm reports an 85% reduction in operational workload (Levich case study).

The lesson for any buyer: the useful AI work came after the data was in one place. A consultancy that wants to start with the model, before the data, is starting in the wrong place.

Your first 90 days with an AI consultancy

• 1. Weeks 1 to 4, discovery: map two or three candidate processes, check the data, agree one use case and the measure of success.
• 2. Weeks 5 to 12, pilot: build the first version, put it in front of real users, and measure against the baseline.
• 3. End of the quarter, decision: scale it, change it or stop it. Stopping a pilot that did not work is a good result if it was cheap and fast.

For what to automate first, our guide to workflow automation covers the selection test. For the jump from pilot to production, see prototype versus production.

Written by

Ajo John

Co-Founder, CPO & Head of Engineering

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