Valuing technology-led deals: managing uncertainty and legal risk

Technology has long been central to securing post-deal value. Getting an acquired business onto common platforms (or, for a vendor, tidying up the systems and processes in a spin-out to make the process easier for an acquirer) can make all the difference to an integration process. For some private equity acquirers, it’s at the heart of deal rationale.

But technology is no longer simply pushing value (and valuations) up. It is widening the spread between businesses seen as AI-enabled or AI-defensible and those that may be threatened by AI. Buyers have moved on from asking only about technical debt (being worried about the need to invest in systems that have been neglected) to asking about ‘AI debt’: how much has a target invested in equipping itself to take advantage of this new technology? Have those investments been sound?

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How AI and technology are changing valuation models

This changes the basis on which value is assessed. Traditional valuation is anchored heavily on EBITDA and quality of earnings. That still matters, of course, but now buyers are putting more weight on intangible and forward-looking questions. How defensible is the customer proposition against general-purpose AI tools? How proprietary and usable is the data within the business? How robust is ownership of code and models? How sticky are the workflows that the software serves?

Software firms are being re-rated as buyers distinguish between AI-native, AI-resilient and AI-exposed businesses, according to PwC’s 2026 TMT M&A report.

That is one reason why broader software M&A has become harder to price, while more obviously strategic AI-related assets had continued to attract high levels of attention through the first half of 2026.

Subjectivity in valuation increases the importance of legal rigor on key terms. The wider the gap between buyer and seller views on future upside, the more pressure there is on deferred consideration, earn-outs, contingent value rights and detailed definitions and protections of performance metrics.

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Where technology value and legal risk converge

The legal consequence is that drafting becomes both more important and more difficult. Lawyers need to help clients define how revenue is counted, how synergies are measured, what operational freedom the buyer has post-closing, how AI-related cost savings are treated, and what protections apply if the business model shifts faster than expected.

This is also where legal risk and valuation risk begin to merge. The more value depends on data, code, AI models or future defensibility, the more buyers will want legal comfort around those assets.

“Data and, increasingly, AI capabilities are now key for a number of companies,” says Jean-Robert Bousquet, Partner, M&A and Private Capital, in Paris, at Eversheds Sutherland “We pitched for a deal recently and they requested that I, as the partner in M&A, would come both with an employment and a data IP/IT partner.”

Valuations are also increasingly impacted by issues such as cybersecurity, data protection, IT rights, source code ownership and customer churn. All now receive closer legal attention than they did a few years ago.

A buyer that previously accepted relatively standard warranties may now want specific warranties or indemnities around data handling, information-security incidents, prior regulator contact, training-data rights, IP infringement exposure or the target’s use of third-party models and tools.

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Downside risk: a brake on valuation inflation?

That approach is increasingly justified by the scale of downside risks that tech introduces to deals. We are aware of at least one serial acquirer that now takes an intensively forensic approach to information security during due diligence, having previously been fined for failure to prevent breaches.

That’s translated into negotiations for relatively large liability caps – even on smaller deals – because those fines can be tied to global turnover. The Information Commissioner's Office own guidance reinforces the point: in any merger or acquisition involving transfer of personal data, the buyer must consider data sharing during due diligence, including the purposes for which the data was originally obtained, the lawful basis for sharing it and whether those bases remain valid after the transaction.

It’s also apparent that pricing mechanisms are getting more complex. Purchase-price adjustments, for example, have become almost universal in US private M&A. In SRS Acquiom’s 2026 study16, based on more than 2,200 private-target acquisitions, working-capital purchaseprice adjustments now feature in more than 90% of private-target M&A deals; SRS says the provisions themselves are becoming increasingly customized.

A growth in earn-outs it recorded for its sample might also be a reflection on uncertainties in future earnings created by multi-sector technology transitions (most notably AI).

Technology-driven valuations, then, require technologydriven diligence. If the buyer is paying for proprietary data, the legal team must establish whether that data can lawfully be transferred and used in the intended way. If the buyer is paying for resilience against AI disruption, the legal team should test customer contracts, IP rights, third-party dependencies and model-risk exposure with that concern explicitly in mind.

“We always aspire to be in step with how the client views the deal, which is why it’s so valuable for the client to be more explicit around what the value drivers are,” says Jon Gill, Partner, M&A and Private Capital, in the UK, at Eversheds Sutherland. “For example, if they are concerned about customer churn because of AI replacement, that should be called out in the legal diligence, and in the warranties that underpin the share purchase agreement.”

The law firm’s role is no longer to summarize legal issues in isolation, but to help convert contested future upside into a contract structure and risk allocation that the client can live with.

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