An owner preparing for sale can easily tell an attractive AI story. The business is more productive. Decisions are faster. Data is becoming an asset. New services are possible.

A buyer will test a harder proposition: is that value proven, repeatable and transferable?

This changes the preparation task. The aim is not to make the company sound more “AI-enabled”. It is to make the commercial effect and the associated dependencies easier to verify.

AI affects familiar value drivers

Business valuation depends on the transaction, sector, market, financial performance and deal terms. Owners need qualified corporate finance, tax and legal advice for their circumstances. AI does not replace those fundamentals.

It can influence them in practical ways:

• Growth quality: AI may support a stronger offer, faster response, better retention or access to a valuable market.
• Maintainable margin: redesigned workflows may let the business deliver more without costs rising at the same rate.
• Resilience: better knowledge access, monitoring and forecasting may reduce reliance on informal workarounds.
• Transferability: documented workflows, data rights and internal capability can make performance less dependent on the owner or one specialist.
• Buyer options: well-governed capability may give an acquirer credible routes to integrate, improve or scale the business.

These are possible mechanisms, not guaranteed outcomes. A buyer will still ask whether the claimed improvement appears in reliable operating and financial evidence.

The Office for National Statistics found that AI use had spread across UK businesses by 2026, but adoption was often shallow. That makes the label less useful as a differentiator. Depth, evidence and control matter more than the presence of a licence.

Buyers underwrite evidence, not potential

ICAEW describes due diligence as a way to establish the true value or cost of a transaction and understand the business, not a compliance rubber stamp. Financial, legal and commercial enquiries test different parts of the seller’s story.

For an AI initiative, the evidence chain should connect:

1.  the business problem and baseline;

2.  the use of AI and the wider workflow change;

3.  the full operating cost, including review, correction and suppliers;

4.  the measured operational result;

5.  the financial or strategic effect the seller is claiming;

6.  the controls and capability required to sustain it.

If the narrative jumps from “we deployed AI” to “the business is worth more”, the buyer has to fill the gap. Uncertainty usually weakens a case rather than strengthening it.

Prepare five evidence packs

The material should sit inside the wider sale process, not in a separate innovation presentation.

1. Value and performance

Show the original baseline, implementation dates, adoption, accepted-output measures, recurring costs and changes in relevant business performance. Reconcile operational measures with financial reporting where a financial benefit is claimed.

Separate observed results from estimates and forecasts. Explain other factors that may have affected performance. A modest, well-supported claim is more useful than an impressive number that cannot survive challenge.

2. Transferable operations

Document how the AI-enabled workflow runs, who owns it, how exceptions are handled and what skills are required. Include process maps, operating procedures, training material, change records and continuity arrangements.

The central test is simple: can a competent team continue the capability after the owner or current champion leaves? If essential knowledge exists only in prompts, personal accounts or one person’s memory, the capability is not yet transferable.

3. Data rights and provenance

Create an inventory of the important data, its source, permitted use, quality controls, access rights and retention. Clarify which information the business owns, licences or receives under customer and supplier contracts.

The Information Commissioner’s Office guidance on data sharing during mergers and acquisitions says data transfer should be considered during due diligence, including purpose, lawful basis, documentation, security and information for data subjects. The guidance is under review following legislative change, so sellers should obtain current specialist advice rather than treat a checklist as legal clearance.

4. Suppliers, technology and exit routes

List the models, platforms, integrations and subcontractors the capability depends upon. Retain contracts, service levels, security reviews, pricing terms, change notices and incident records. Explain how data and process logic can be retrieved or moved if a provider changes price, capability or terms.

The National Cyber Security Centre’s secure AI development guidance recommends supplier security assessment, documentation, logging, monitoring and incident planning across the AI lifecycle. Those are useful diligence questions even when the business buys rather than builds the technology.

5. Governance and people

Show the buyer who is accountable for each material use, what the system is allowed to do, where people review it and how performance or incidents are monitored. Include the use-case register, relevant policies, approval records, risk assessments, review minutes and evidence of role-based capability.

Governance is part of transferability. A buyer needs to know that safe operation does not depend on the seller’s personal judgement or an informal promise from a supplier.

Weak AI foundations can reduce confidence

AI can also expose issues that matter during a transaction:

• benefits were calculated from gross time saved, with no evidence that capacity improved earnings or service;
• customer or employee information has been used without a clear record of purpose and control;
• important workflows depend on consumer accounts, undocumented prompts or one vendor;
• generated material has uncertain intellectual property provenance;
• the owner makes every exception decision;
• automation has reduced visible cost while increasing correction, security or service risk;
• the company describes a capability as proprietary when it is largely an off-the-shelf feature.

These findings do not automatically prevent a sale. They can affect confidence, warranties, deal structure, integration cost and the buyer’s view of risk. Early preparation gives the owner time to fix the weakness rather than explain it under deadline.

ICAEW’s commercial due diligence guidance emphasises evidence about the market, competitors, customers, business model and commercial performance. An AI claim should fit that whole picture. It should not sit apart from it.

Build for transfer before writing the sale narrative

Do not begin with the information memorandum. Begin with the operating evidence.

Choose the few AI-enabled capabilities that materially affect the buyer’s case. Give each a business owner, evidence baseline, dependency map, control record and continuity plan. Close gaps in data rights, supplier terms and documentation. Test whether a different manager can run the process.

Only then write the narrative. It should explain what the capability changes, what proves the change, what it costs to sustain and what the buyer could reasonably do with it next.

A practical next step

Run a mock buyer review on one material AI-enabled workflow. Ask for its value evidence, operating documentation, data rights, supplier dependencies, governance record and handover plan. If the pack cannot be assembled without the owner explaining it, the value is not yet fully transferable.

The AI Centre helps owners make AI capability accountable, documented and commercially coherent before it reaches a transaction process. Work alongside your corporate finance and legal advisers, and talk to us about the AI readiness of the business.

Sources

• Artificial intelligence in UK businesses: 2023 to 2026, Office for National Statistics, 20 July 2026.
• Support for due diligence, Institute of Chartered Accountants in England and Wales, accessed 12 October 2026.
• Commercial due diligence, Institute of Chartered Accountants in England and Wales, accessed 12 October 2026.
• Due diligence when sharing data following mergers and acquisitions, Information Commissioner’s Office, accessed 12 October 2026.
• Guidelines for secure AI system development, National Cyber Security Centre, 27 November 2023.