Writing a new playbook for AI M&A deals

AI M&A transactions represent a new kind of acquisition

For decades, mergers and acquisitions (M&A) followed a familiar script. You identified an asset—a factory, a brand, a software platform—drew a clean perimeter around it, ran diligence, signed, closed, and integrated. The thing you bought was well-defined before the ink dried. 

That script no longer works for artificial intelligence (AI), where the most consequential transactions of the past two years have redefined what it means to acquire. Instead of buying entire companies for full ownership and control, acquirers are racing to access capability—talent, intellectual property, and inference infrastructure—through hybrid structures that prioritize speed over exclusivity. The deal is no longer the finish line.

How AI M&A transactions are different from traditional acquisitions

Today’s most consequential AI transactions aren’t structured as traditional acquisitions at all. Instead of buying entire companies for full ownership and control, acquirers are racing to access capability—talent, intellectual property, and inference infrastructure—through hybrid structures that prioritize speed over exclusivity. 

Three models have emerged as the dominant AI deal archetypes:

• Acquihires bring in critical research and engineering teams without requiring a full entity transfer, allowing the original company to continue operating independently.
• Non-exclusive IP licensing gives buyers immediate access to differentiated technology—models, training data, tooling—without a change-of-control transaction.
• And strategic partnerships, often built around minority investments, compute commitments, and commercial distribution, align incentives between parties while preserving operational independence. 

What connects all three is a fundamental inversion: The deal is no longer the finish line. Value realization begins the moment the agreement is signed, and accomplishment depends on operational readiness—not just executed documents.

Why a traditional playbook might not work for AI M&A transactions

When the asset you are acquiring is a combination of people, code, and compute contracts, old assumptions break down in every dimension.

• Scope cannot be fixed up front. In access-driven deals, the transaction perimeter is often undefined at close. What exactly transferred? What stays behind? What is shared, licensed, or intertwined? These questions get answered after signing, not before—and the purchase agreement needs mechanisms to handle that ambiguity.
• Talent has agency. Unlike a plant or a patent portfolio, talent can walk out the door. Speed-to-retention is not a nice-to-have; it is existential. Deals that take months to close risk losing the very capability they were designed to secure.
• Diligence must go beyond point-in-time snapshots. Traditional diligence produces a static picture of the business at a point in time. AI transactions require a living, auditable baseline—across IP, data, infrastructure, and talent—because post-close evidence requests are inevitable, and significant consideration may be held back pending demonstrated completeness of transfer.
• Everything is interdependent. When the acquired asset spans people, proprietary code, and cloud compute contracts across multiple jurisdictions, siloed advisory models that hand off deliverables between functional workstreams cannot keep pace. Modern AI transactions need a single coordinated engine across M&A strategy, financial and tax structuring, accounting implications, operational diligence, data infrastructure execution, and post-close value delivery. 

The playbook for an AI-era deal: What a leading transaction looks like

The organizations getting this right share a common approach: They treat AI transactions as integrated execution challenges, not sequential phase-gate processes. 

Beyond any single transaction, a broader set of principles is emerging for how AI-era deals should be executed. The following six principles define the playbook. 

1. Stand up a deal engine that runs parallel tracks. Multiple interfacing workstreams must be mobilized within hours, not days or weeks, to support and execute strategic transaction decisions. Sequential handoffs between functional teams are a luxury these deals do not afford.
2. Use adaptive scope as priorities change. The transaction perimeter, talent plan, and integration approach may shift multiple times between signing and value realization. An embedded advisory model with adaptive scope is necessary to keep pace.
3. Expand diligence to operational readiness. AI diligence must go beyond financial and legal review to answer the question that actually matters: Can the buyer run the acquired capability on Day 1 and improve it by Day 30? Define what combination of people, IP, systems, data, and infrastructure capacity must be accessible within 30, 60, and 90 days to deliver business outcomes.
4. Create a defensibility package at speed. Build thorough, auditable records of IP at close—records designed for the post-close inquiries that inevitably follow access-driven transactions.
5. Launch value sprints immediately. Infrastructure strategy, vendor cost enhancement, and stand-alone readiness assessments need to be outlined at signing and performed right away. If the deal stalls between close and integration, the speed advantage that justified the structure is lost.
6. Define capabilities in operational terms. Rather than thinking in terms of assets to be transferred, frame everything around what is needed to run: people, IP, systems, data, and infrastructure capacity, mapped to specific timelines and business outcomes.

The takeaway for corporate leaders

If your M&A playbook was built for an era of defined perimeters and pre-close certainty, it needs an update. AI-era transactions demand adaptive scope, compressed timelines, and the ability to operationalize acquired capabilities at a pace that matches the technology itself. 

The companies that lead won’t just be the ones with the best deal terms. They’ll be the ones that can convert access into operational advantage faster than anyone else—supported by a team with the skill to drive decision-making and execution at speed.