What AI due diligence now does to your exit multiple, and how to prepare in the 6 to 12 months before a sale.
If you lead a professional services firm that could change hands in the next few years, there is a new workstream in the buyer's due diligence pack. It sits alongside quality of earnings and commercial diligence: a structured assessment of your data, your technology and your AI capability. Every gap it finds becomes a negotiating chip the buyer uses to move your price down.
It is already happening. In Bain & Company's 2026 M&A Report, a survey of more than 300 M&A executives, one in five strategic dealmakers said they had walked away from a deal because of AI's anticipated impact on the target's business. More than three quarters of strategic acquirers said they had assessed AI's impact on a target, and use of AI inside the M&A process itself more than doubled in a year, to 45% of practitioners. The team that reviews your firm will arrive with a framework for testing your AI, and unprepared sellers get repriced.
It cuts both ways. Embedded, documented AI capability now commands a real premium. Superficial or ungoverned use does not, and increasingly attracts a discount.
The deal maths has changed
Bain's 2026 Global Private Equity Report coined the phrase "12 is the new 5" for what has happened to buyout economics. During the 2010s, a typical buyout needed only about 5% annual EBITDA growth to hit a 2.5x return over a five-year hold, because cheap debt and rising multiples did the rest. With borrowing costs at 8 to 9% and entry multiples flat, the same deal now needs 10 to 12% EBITDA growth to clear the same hurdle.
The pressure is compounded by a liquidity logjam: the industry is sitting on 32,000 unsold companies worth $3.8 trillion, and distributions to investors have run below 15% of net asset value for four consecutive years.
When financial engineering no longer produces the return, operational value creation has to. So buyers scrutinise whether your business can genuinely grow, and whether AI makes that growth credible or threatens it. AI is now both a value lever and a risk to be priced.
The valuation ladder: not all AI is worth the same
The useful way to think about AI capability is as a ladder. Each rung changes what kind of value AI creates, and buyers treat each rung differently. Only the top two move your multiple.
- Level one, opportunistic adoption. Ad hoc pilots and individual productivity tools. This is table stakes. The time staff save with everyday tools rarely reaches the EBITDA a buyer is pricing, so it barely moves valuation.
- Level two, operating-model enhancement. AI embedded in workflows, service delivery and the back office. The payoff is margin. Buyers pay for efficiency that shows up in the trailing numbers, but it mostly improves the earnings your multiple is applied to, not the multiple itself.
- Level three, product transformation. AI embedded in what you sell, changing what the client buys rather than what it costs you to deliver. This is where the re-rating starts: EisnerAmper's 2025 analysis of private company valuations found firms that embed AI into their offering achieving uplifts of 40 to 100% over comparable non-AI peers.
- Level four, business building. New AI-enabled revenue lines and data products a competitor cannot easily copy. BCG's research on AI maturity finds leaders growing revenue 1.7 times faster than laggards and delivering 3.6 times the shareholder returns over three years. Few mid-market firms are here, which is exactly why buyers pay for it.
Automating the back office earns margin, and margin is worth something. The step change comes from putting AI into what you sell: the buyer is then pricing a more valuable firm, not just a cheaper-to-run one.
See where your business sits on the AI value creation ladder with our AI Exit Readiness Score.
Embedded versus veneer: why the distinction decides the premium
Buyers have become good at telling genuine capability from a veneer, and the line they draw is not the one many owners expect. Nobody is discounted for licensing technology; every business runs on components it did not build. What attracts the discount is capability that is superficial or ungoverned: thin wrappers with no proprietary data underneath, pilots that never reached production, staff using public tools with no controls and no audit trail. What earns the premium is AI that is embedded in the offer, differentiated by data only you hold, and evidenced in the numbers.
EisnerAmper's case examples show both sides. A distribution business that deployed AI demand forecasting, and could evidence the improvement in inventory turnover, saw its multiple move from roughly 7x to roughly 9x EBITDA. A marketing agency heavily reliant on copywriting and design faced buyers pushing for a lower multiple, pointing to public AI tools that could replicate much of the core service. Same technology wave, opposite outcomes. The difference was whether the capability was defensible and evidenced.
BCG's widely cited 10-20-70 rule explains why a slick demo rarely survives diligence: AI success is roughly 10% algorithms, 20% technology and data, and 70% people and process. Without redesigned workflows, trained people and clean data underneath it, the capability is not real, and a buyer will not pay for it.
The sharpest risk on the veneer side is shadow AI. Mayer Brown, in its May 2026 note "AI: The Next Frontier of PE Deal Risk," calls it potentially one of the defining diligence issues of the next several years: employees quietly pasting contracts, client data or proprietary work into public platforms, creating data-leakage, confidentiality and IP exposure that never appears in a security log. Gartner predicts that by 2030 more than 40% of enterprises will have experienced a security or compliance incident linked to unauthorised shadow AI.
But simply banning it is an expensive mistake. Shadow AI is also the most honest map you will ever get of where AI creates value in your firm. The spreadsheet a fee-earner wired to a language model, the prototype a junior analyst built to triage documents: these exist because someone close to the work found a real efficiency. The right move before a sale is to triage, not prohibit. Retire the risky ones, then take the two or three that move a real number and rebuild them properly: secure, compliant, documented, owned. A liability and an asset are often the same discovery, handled differently.
What acquirers actually assess: the seven dimensions
Specialist technology diligence has professionalised. Crosslake, one of the leading tech diligence advisers, benchmarks targets against data from more than 6,000 prior technology M&A transactions. When a buyer commissions this work on your firm, expect scrutiny across seven dimensions:
- Data foundations. Is your data clean, owned, well governed and rich enough to sustain an advantage? A proprietary data moat is what separates level three from level two.
- Proprietary IP. Do you own your models, training data and code, or are you wholly dependent on a third-party API with no proprietary layer on top?
- Product and technology maturity. Is AI embedded in the service, or bolted on for the pitch deck?
- Governance. Are there policies covering acceptable use, human oversight, model monitoring and compliance? Ungoverned use is treated as a liability.
- Talent and key-person risk. Does the capability depend on one or two individuals who might leave? Concentration is a discount.
- Security. Where does your data flow, and through which third parties? Shadow AI and undisclosed data processors surface here.
- Documented EBITDA impact. Can you attach each AI initiative to a measured financial result? A story assembled in the final year reads as window dressing; proof built over time reads as value.
Every one of these dimensions is easier to defend when the evidence is already in the data room, and the quality of that evidence increasingly determines the quality of the multiple.
Take the AI Exit Readiness diagnostic to see how a buyer's seven-dimension review would score you.
How the price chip works in practice
The mechanism rarely arrives as one dramatic demand. A buyer signs a letter of intent at, say, 10x EBITDA. Confirmatory diligence opens. The tech findings memo lands: an open-source licensing exposure here, an undocumented dependency there, a security observation, a workflow reliant entirely on a third-party API. None of these individually kills the deal. Each becomes a "reasonable refinement": a slightly larger escrow, a new indemnity, an earnout that shifts risk back to you, a revised working-capital target. The cumulative effect can run to millions.
SRS Acquiom's deal terms research, drawn from more than 2,300 private-target deals, finds post-closing purchase-price adjustments on well over 90% of private deals, with adjustments owed to buyers averaging around 0.9% of transaction value before any AI-specific findings are added.
Buyers do not negotiate diligence findings so much as document them and reduce the price. The only reliable defence is to find the gaps before they do.
The 6 to 12 month pre-sale playbook
If you are 6 to 24 months from a sale or a private equity event, here is where to focus. The common mistake is to spend the whole window on housekeeping: audits, policy documents and governance frameworks, with value creation deferred to the end. That gets the economics backwards. Governance gaps cost you basis points in escrows and indemnities; capability gaps cost you turns of the multiple. So compress the hygiene into weeks, and spend the months building.
Weeks 1 to 6: baseline, triage and stop the bleeding
Establish honestly where you sit on the ladder; most owners overestimate by a level. Inventory every AI tool actually in use, including the unsanctioned ones. Confirm ownership of your models, code and data, and fix any ambiguous licensing before a buyer finds it. Stand up a lightweight governance framework: acceptable use, human oversight, model monitoring. Done with senior ownership, this is weeks of work, not months, and it clears the deck for the part that actually moves the price.
Months 2 to 9: build the value
The multiple is made here: three workstreams, run in parallel, each expected to ship something real every month.
Turn your data into an asset. Somewhere in your firm is data a competitor cannot buy: pricing history, client behaviour, matter records, claims outcomes, engagement benchmarks. Buyers pay for proprietary data; they discount data that is fragmented or of uncertain provenance. Consolidate it, document its lineage and ownership, and put it to work in at least one capability a buyer can inspect. Data in silos is a slide; data powering a product is an asset.
Evidence the real efficiencies. Pick the two or three processes where cost, error or cycle time constrains your margin most, and deploy AI against them in production, instrumented from day one so the impact lands in your management accounts. An efficiency visible in two consecutive quarters of trailing numbers is EBITDA; an efficiency described in a deck is a promise, and buyers do not pay for promises.
Productionise the best of your shadow AI. Take the prototypes your own people have already proven demand for and rebuild them to the standard a diligence team expects: access controls, audit trails, documentation. This is usually the fastest route to embedded capability, because the discovery work is already done.
Where possible, aim at least one workstream at the service itself: AI that changes what the client buys from you, not just what it costs you to serve them. That is the level-three move, and the single biggest lever on the multiple.
Months 9 to 12: prove, package and pressure-test
Let the improvements season in the trailing numbers. Attach each capability to a measured EBITDA or revenue result and a clean ownership claim. Reduce key-person risk by documenting how each system works and cross-training around it. Then run vendor due diligence on your own firm against the seven dimensions above, exactly as a buyer's team would, and close whatever surfaces while it is still cheap to fix.
One point cuts across all three phases: this fails without a senior owner. Diligence teams probe who is accountable for AI at leadership level, and "the IT manager, informally" is itself a finding. That owner does not have to be a permanent hire eighteen months before an exit; what matters is senior judgement applied on a real cadence, with something built, deployed or evidenced every month, laddering up to the equity story.
Where this leaves you
The buyers are already circling. A May 2025 survey by law firm Kingsley Napley of senior partners at the UK's top-60 accountancy firms found 86% of responding firms had been approached by external investors in 2024, and 27% had already taken private equity funding. EY's analysis of UK financial services M&A found wealth and asset management deals rising from 47 in the first half of 2025 to 61 in the first half of 2026, with disclosed value jumping from £0.2 billion to £22.7 billion, and a quarter of UK financial services CEOs ranking technology and AI capability as the single most important driver of their dealmaking.
The firms that win are not the ones with the most AI or the thickest governance binder. They are the ones that can prove, in evidence a diligence team will accept, that their AI is owned, embedded in what they sell and generating measured value, with someone senior accountable for all of it. That is a buildable position, and 6 to 12 months is enough, provided the window is spent in the right order: weeks on hygiene, months on value, a final quarter proving it. Knowing where you sit on the ladder, and which two or three builds would move your multiple, is the assessment worth doing early, while the answer can still change the price rather than merely explain it.
Get your AI Exit Readiness Score.
Sources and further reading
- Bain & Company, "Welcome to a New Era," Global Private Equity Report 2026 (23 February 2026). bain.com.
- Bain & Company, Global M&A Report 2026 (27 January 2026). bain.com.
- EisnerAmper, "How AI Is Shaping the Valuation of Private Companies" (2025). eisneramper.com.
- BCG, "AI Leaders Outpace Laggards" (30 September 2025). bcg.com.
- Mayer Brown, "AI: The Next Frontier of PE Deal Risk" (21 May 2026). mayerbrown.com.
- Gartner, "Gartner Identifies Critical GenAI Blind Spots That CIOs Must Urgently Address" (19 November 2025). gartner.com.
- SRS Acquiom, Deal Terms Study. srsacquiom.com.
- Kingsley Napley, "Nearly half of UK accounting firms are open to private equity investment" (survey of top-60 firms, May 2025). kingsleynapley.co.uk.
- EY, UK financial services M&A analysis, H1 2026 (6 July 2026). ey.com.