As artificial intelligence becomes an inevitable fact of life in every industry, the more visible its powers become to disrupt businesses. Nowhere is that more evident than in enterprise software. As a capability, the emerging technology now largely determines which players can compete in any given market.

It’s natural, then, that software-as-a-service (SaaS) companies turning to M&A to navigate the disruption would assume that the deal playbook they’ve used so successfully in the past is out of date in the AI era. But that’s not the case. The best practices honed over years of capability acquisitions still largely apply—albeit with some critical nuances that enable buyers to make the most of AI assets, including models, tooling, data sets, and talent.

In 2025, almost half of tech deals had some AI component, which was up from about one in four deals in 2024. The value of AI-related deals through the first three quarters of 2025 had more than doubled the total AI-related deal value for all of 2024, with companies such as Google and Palo Alto Networks turning to M&A to bolster product capabilities, access talent pools, and accelerate innovation (see Figure 1).

Figure 1

Creating the most value from such deals now requires knowing what remains the same and what has changed in the M&A process.

Tie deals to overall strategy

As in any industry, M&A strategy should follow business strategy. But when it comes to AI, companies need to take a more agile, scenario-based approach. Model capabilities, user adoption patterns, and inference economics are shifting rapidly, requiring regular refreshes of the M&A roadmap. The best corporate development teams forecast two to three plausible AI futures, with a view of “own vs. rent” for each that is refreshed quarterly. This keeps M&A synchronized with the overall AI roadmap without over-betting on a single path.

Look for AI risks and opportunities in due diligence

In due diligence, all the typical rules remain relevant. But now, there’s also the need to evaluate AI’s impact on a target. In Bain’s recent article “New Diligence Challenge: Uncovering AI Risks and Opportunities,” the authors highlight five questions that can convince an acquirer to proceed with a deal or walk away. A target (or the business unit or even a particular product) falls into one of three categories based on the level of impact that AI could have on the business: revolution, transformation, or augmentation.

There’s also now more motivation to look beyond the commercial opportunities to determine if the company has real, differentiated IP that will aid an acquirer’s AI ambitions—with the appropriate data and workflows, for example.

Clio is a player in legal practice management, with broad applications that help solo practitioners and small law firms manage their practices. The company recently acquired vLex, a legal research specialist with a proprietary data moat as well as an AI capability that enables lawyers to more efficiently research and analyze legal documents and then operationalize their findings into action within the core legal workflow. A key diligence priority was to validate the end-to-end workflows and the differentiated data assets that Clio and vLex brought to the table. Clio had all the case context, documents, next steps, and deadlines a lawyer needs to manage work. Meanwhile, vLex had the case law, statutes, secondary opinions, and legal news required to perform the work. It was a good fit.

Rethink revenue synergies

When it comes to delivering value, revenue synergies have become an increasingly important part of the equation for software companies. AI assets raise a hurdle because they typically don’t come with a lot of customers, so cross-selling opportunities are likely to be one-directional. Instead of both companies selling into each other’s customer base, the challenge becomes one of creating a go-to-market strategy for bringing the acquired company’s assets to the acquirer’s existing customers, focusing on a few sales plays and tackling issues such as pricing, training sales reps, and onboarding customers. For example, an offering may be sold and priced similarly to SaaS, but launching new AI features may require new motions to ensure that customers use the product effectively. And all of this needs to scale in real time.

With speed being such a priority, the best companies will make full use of the pre-close period to develop revenue synergy plays, designing offerings, pricing, and incentives.

Be realistic about product integration

As in all product integrations, plans and priorities should be based on the potential value they would deliver. With AI acquisitions, it’s important to realistically set the appropriate go-forward R&D envelope and one-time investments. Integration will typically require a significant and swift reprioritization of the roadmap as well as a reallocation of investments given that many AI acquisitions come with a small but rock-star technical team.

At Clio, the company developed a vision for the priority product workflows, naturally integrating vLex's AI capabilities into Clio's practice management platform. The company set a target to preview the integrated product at its customer conference about 100 days after signing the deal. To achieve this, Clio's and vLex’s product and engineering teams went heads down from the day of signing, dynamically reallocating investments and resources to accelerate the effort.

Get serious about the talent factor

A well-developed integration thesis should spell out the combined company’s operating model, specifying what teams will be integrated entirely, which ones will be partially integrated, and which ones will be kept separate and maintain autonomy from enterprise-level management. In some cases, reverse integration will be the right answer—namely, when the target’s team is better skilled than the parent’s team to lead a more scaled organization.

Acquiring AI talent is expensive, and the reality is that top performers, especially engineers, will have many opportunities to be poached by a competitor. Given their options, throwing money at these folks isn’t enough. Invest in a compelling vision for the combined business and their role in it, making it clear that they now will be poised to become industry shapers.

“Settle people and power issues early” is a mantra for all of M&A. In the AI era, that still holds. There are going to be sensitive decisions about who’s in charge and the fate of the acquired company’s core product roadmap, among many others. Such decisions (and the decision rights behind them) need to be worked out quickly. Or flushed out quickly. In the end (and despite the debate about AI replacing humans), AI acquisitions are all about people. The age-old challenges associated with people and power in integration still apply.