AI Strategies for Private Equity M&A

AI has entered a phase of deeper interplay with the rest of the economy, with huge profit zones at stake; but AI is more likely to destroy an old competitive moat than provide a new one.

In a series of white papers, we explored how AI is simultaneously: (1) setting the stage for new architectures and systems that will reshape many industries¹ and (2) accelerating competitive forces, destroying the competitive moats throughout industry,² and forcing leaders and investors to seek durable alternatives. We proposed a solution: businesses can adopt closed-loop, agentic architectures and systems to unlock large performance gains and acquire foundational patents to wrap them with a durable moat.³

Below we turn to implications for private equity M&A strategies. Note that this is distinctly different from the benefits of private equity investment in the patent asset class, about which we have written elsewhere.⁴

The Challenges for PE

By compressing development cycles, AI upends long-valued pillars of differentiation and threatens to narrow the competitive strategy playbook. Ironically, as intelligence itself improves, it no longer differentiates. Sources of durable competitive advantage are becoming much more scarce.

“AI should unlock large performance gains in almost every industry, but performance improvements do not assure profitability if fast followers can quickly replicate them.”

AI should unlock large performance gains in almost every industry, but performance improvements do not assure profitability if fast followers can quickly replicate them. In fact, AI is far more likely to disrupt an existing moat than provide a new one. AI-enabled development has weakened technology, product, software, speed, team, operational excellence, and even intelligence itself as sources of differentiation.

This is the challenge for all PE-funded companies and the investors who fund them, in the coming era when we can expect much deeper interplay between AI and the rest of the economy. Company leaders across all industries should be wary of the rapid erosion of profit zones. Investors should prepare for the possibility of widespread commoditization.

Today’s PE AI Strategies

“High uncertainty as to the actual computational requirements of future models makes it impossible to determine whether these will turn out to be profitable.”

In some cases, a player with a breakthrough technology can create longer-term structural advantages, such as by establishing favorable contractual terms with suppliers and customers. Large private equity players have supported this strategy by funding large, long-term commitments to purchase GPUs, construct data centers, and secure water, energy and real estate needed for AI infrastructure. The hyperscalers have made similar commitments. High uncertainty as to the actual computational requirements of future models makes it impossible to determine whether these will turn out to be profitable. Intense competition for the resources has likely inflated prices, placing pressure on future margins.

Recognizing the need for complementary strategies, PE players have turned to applied AI, where AI models have the potential to play a much deeper role in enterprise operations. Major players have funded OpenAI’s The Deployment Company and Ode with Anthropic to capitalize on what some forecast as a $20T+ enterprise AI sector. To match the high-touch, human-capital-intensive deployment strategies of OpenAI and Anthropic, major cloud hyperscalers are aggressively shifting from selling raw infrastructure to launching dedicated, multibillion-dollar "Forward Deployment" organizations.⁵ As with the infrastructure race, well-capitalized players are investing heavily in very similar strategies. While new opportunities for differentiation will appear as AI interplays with other technologies in each industry, it remains unclear which, if any, players will be able to establish a durable moat in the face of the widespread availability of largely interchangeable AI models that allow competitors to rapidly replicate capabilities.

We believe the AI players are likely to need an alternative moat if they are to enjoy sustained profitability over the decade or more it will take to recoup large infrastructure investments.

The Durable Patent Moat as a Key Value Driver

Details vary by industry, but for any business that is generating revenue, using a patent moat to defend margins and market share has the potential to create a large impact on company value. Market leaders with strong patents consistently achieve superior operating margins.⁶ Many also add very high-margin licensing revenue. To illustrate, if two companies each generate $500 million in annual revenue, but one has a patent estate that allows it to sustain higher growth and better margins, along with some licensing revenue, the valuation impact is large.

	No patent moat	Patent moat
Revenue build
Core product revenue	$500M	$500M
Patent licensing revenue	—	$25M
Total revenue	$500M	$525M
Growth
Core revenue growth (share gain, pricing power)	3%	8%
Licensing revenue growth	—	50%
Blended revenue growth	3%	10%
Expected added growth from new products (Yrs 2–5)	~1%	~4%
Profitability
Core operating margin	10%	18%
Licensing operating margin	—	~85%
Blended operating margin	10%	~21%
Annual operating profit	$50M	$111M
Valuation
Valuation multiple	15×	23×
Illustrative company value	$750M	~$2.6B

Illustrative Valuation With and Without a Patent Moat

In the illustrated case, even with modest assumptions about impacts on growth, margins, and revenue, the patent moat more than triples company value. Over the 10-year protection period (half of a full patent life), the additional $60+ million in annual operating profit amounts to $610 million in cumulative incremental operating profit, before taxes, discounting, and the modest costs of maintaining or enforcing the patents.

Our experience is that the impact of patents on margins and market share varies by market stage. In the emerging/growth phase, while ecosystem business structures are still forming and share is up for grabs, true category leaders can establish near-monopoly positions around very high-margin offerings and negotiate large equity or revenue share positions in alliances, JVs, distribution partnerships and other transactions. Later, as the market matures, the base for licensing expands, setting the stage for the licensing part of the playbook.

Strategy 1: Transition a Target Company to Agentic Orchestration and Add a Patent Moat

“For forward deployment players, we believe there are certain keystone architectures and systems that will repeat, scale and remain durable for a decade or more across many industries.”

As the cognitive capabilities of AI become standardized, widespread and “abundant,” the greatest economic impacts (forecasted in tens of trillions) will come in the application layer, where AI is deeply integrated into enterprise operations.⁷ What is missing for most organizations is not better AI models, but the critical “connective tissue” that sits between AI models and other enterprise systems, and the architectures that unlock systemic economic benefits. This involves not only sophisticated technology integration, but also major organizational and human factors. Those factors are the gating issues to unlocking maximum value from AI. Organizations that do this well have the potential to enjoy large performance advantages over competitors. For forward deployment players, we believe there are certain keystone architectures and systems that will repeat, scale and remain durable for a decade or more across many industries. These are among the highest potential strategic control points for those players.

Example: Agentic Orchestration Architectures

One class of architectures that will be at the heart of many future Applied AI deployments is the agentic orchestration architecture, where ubiquitous sensors will feed real time digital data to context-capable AI models that make decisions and implement actions via software-defined assets and workflows. At the most fundamental level, the most powerful Applied AI solutions will leverage a closed loop that includes inputs from various systems and outputs that adjust an operating environment.

New Agentic Orchestration Architectures

This core type of architecture, which became feasible only in 2026 with the combination of generative coding and agentic orchestration, is a durable structure around which domain-specific variations will form. Particular variants will fuse various combinations of data sources to understand current context and orchestrate very different software-defined assets (e.g., robots, infrastructure, smart products and more) and workflows (factory floor, R&D, finance, sales and marketing and more) in each industry. Extensions, such as governance systems, security, digital twins, transaction rails, and more will serve as additional durable components in variations adapted for specific use cases.

For a PE player, we believe acquiring a company that has a steady revenue flow, transitioning it to agentic orchestration to unlock performance gains, and wrapping the company with a durable patent moat can rapidly unlock compounding advantages and a large increase in multiple.

Strategy 2: Execute an AI-Enabled Rollup, Then Apply Strategy 1

The convergence of AI, software-defined operations, and increasingly connected physical assets creates an opportunity to rethink the traditional private equity roll-up. Historically, consolidation has been difficult because acquired companies operate different systems, follow different processes, and depend on local knowledge and management. AI can reduce these barriers while enabling a combined enterprise to coordinate activities in ways that individual businesses could not achieve independently. Of course, the combined enterprise can then execute Strategy 1, described above, to unlock compounding benefits.

“AI can transform the private equity roll-up from a strategy for aggregating businesses into a platform for rearchitecting how an industry operates.”

AI can transform the private equity roll-up from a strategy for aggregating businesses into a platform for rearchitecting how an industry operates. Five benefits are particularly important:

Traditional roll-ups create value primarily by aggregating revenue, eliminating redundant costs, and improving purchasing power. AI-enabled roll-ups can pursue those benefits while creating an additional source of value: the ability to coordinate an entire operating system across the acquired businesses. Lower integration costs can expand the universe of viable acquisitions; automation can improve margins; and enterprise-wide orchestration can unlock value that remains inaccessible when businesses and workflows operate in silos. The ultimate opportunity is to build not just a larger company, but a fundamentally more capable one.

Where traditional roll-ups focused on elements like:

• Centralized finance, HR, and administration.
• Lower purchasing costs.
• More efficient sales and marketing.
• Better utilization of labor and equipment.
• Improved access to capital and larger customers.

Next-generation roll-ups can use AI and integrated software architectures to change how the consolidated industry operates. As examples:

• Industrial automation: Acquire several automation integrators and build a common architecture for monitoring and coordinating factories across an enterprise.
• Construction: Consolidate specialty contractors and connect estimating, procurement, project scheduling, equipment, and field operations into a coordinated operating platform.
• Healthcare, wellness and hospitality: Combine specialty practices and venues and use integrated systems to coordinate scheduling, billing, staffing, and customer/patient workflows.
• Testing and inspection: Consolidate laboratories and field inspectors, then coordinate equipment, personnel, compliance records, and results through a shared platform.
• Specialty distribution: Combine regional distributors and integrate inventory, procurement, logistics, and demand forecasting across the network.

The table below elaborates on some of these examples and provides others:

Industrial automation & systems integration
AI-enabled integrationConnect different industrial software platforms, equipment, and control systems.	Real-time coordinationRespond to production changes, equipment conditions, and shifting demand.	Control-tower opportunityCoordinate production, maintenance, energy, and capacity across multiple factories.
Testing, inspection & certification
AI-enabled integrationIntegrate laboratory systems, field-service applications, and compliance records.	Real-time coordinationMatch inspectors, equipment, laboratories, and deadlines to changing priorities.	Control-tower opportunityOptimize capacity, technician utilization, turnaround times, and compliance across a network.
Specialty building & infrastructure services
AI-enabled integrationConnect estimating, project management, procurement, and field-service systems.	Real-time coordinationCoordinate crews, subcontractors, materials, schedules, and project changes.	Control-tower opportunityOptimize labor, equipment, procurement, and project execution across a portfolio of jobs.
Vertical-market software
AI-enabled integrationConnect complementary applications and legacy systems without immediate wholesale replacement.	Real-time coordinationCoordinate workflows across applications as customer needs and operating conditions change.	Control-tower opportunityCreate industry-specific platforms that manage end-to-end business processes rather than isolated software functions.
Industrial maintenance & field services
AI-enabled integrationConnect equipment records, service-management systems, and customer asset data.	Real-time coordinationAdjust service schedules based on equipment condition, technician availability, and operational priorities.	Control-tower opportunityOptimize maintenance, spare parts, technician deployment, and equipment uptime across distributed assets.

The strongest opportunities likely lie in fragmented industries where the combination of many small operators, shared data, connected assets, and interdependent workflows creates the potential for an enterprise-wide control tower. In these markets, the roll-up itself is only the starting point. The larger prize is a coordinated platform that can continually improve the performance of the entire system.

Once a roll-up is complete, Strategy 1 can be applied to the combined enterprise. Performance advantages enabled by new architectures and a durable patent moat can combine to create a true “category king.”