The most expensive cost in private equity is often invisible. It rarely appears as a single line item or a sudden crisis. It shows up as friction.

This is the cost of resetting the capability clock on every deal. A portfolio company may spend four to six months building a data foundation that other companies in the same fund have already built in different, incompatible ways. A security issue may emerge in one company even though another has already learned how to prevent it. A cloud bill may stay too high because cost discipline exists somewhere in the portfolio but has not been made repeatable. A digital opportunity may be identified during diligence, but the value creation team cannot act on it until months later because the capability must be assembled from scratch.

Individually, these issues can look manageable. Across a portfolio, they compound.

That drag matters because operational value creation is carrying more weight in private equity returns. In a more demanding market, firms need value creation that is faster, more repeatable and easier to explain to LPs, management teams and buyers.

Digital transformation and AI are now central to that challenge. BCG’s recent survey of senior private equity investors found that nearly 30% integrate digital levers during diligence, while an additional 57% say digital levers are core to value creation planning (VCP). The same analysis found that PE-backed companies that systematically build AI capabilities across functions have nearly twice the return on invested capital as those that do not.

The message is clear: digital value creation cannot remain a per-deal project. It needs to become firm-level infrastructure.

The invisible tax

The reason this cost stays hidden is that it arrives in fragments.

It appears when one company spends months building a capability that another portfolio company has already created. It appears when cybersecurity, cloud economics, data foundations or AI delivery models are solved locally, but never converted into reusable firm-level capabilities. It appears when a value creation opportunity is identified early, but delivery starts late because the team, tools and operating model must be built from the ground up.

None of this feels like failure. But it slows the portfolio.

AI makes the traditional model harder to defend. When the digital lever was cloud migration or better reporting, rebuilding each time was inefficient but tolerable. When the lever is applied AI across commercial, operational and back-office functions, the gap between a firm that can deploy a proven pattern quickly and a firm that starts from zero becomes material.

The question is not whether each company needs its own strategy. It does. The question is whether every company should have to build the same horizontal capability alone.

The firm is the platform

Platform thinking in private equity should mean shared capabilities that are built once at the firm level and made available across the portfolio as services.

The platform is the firm’s repeatable value creation engine. That engine can include common data foundations, AI delivery models, cybersecurity frameworks, cloud cost discipline, procurement leverage, finance automation, digital product accelerators and enterprise platform plays across systems such as Workday, Salesforce, Microsoft Dynamics, SAP and Oracle.

In this model, the firm does not centralize everything. It centralizes governance, industrializes what is common and leaves room to customize where necessary.

Industrialize the common, customize the rare

Most portfolio-sharing initiatives fail because they try to transfer the wrong things.

A healthcare software business and an industrial manufacturer may share very little at the domain layer. Their customers, regulations, products and economics are different. A portfolio CEO is right to reject a playbook that does not fit the business.

But those same companies often share a lot at the horizontal layer. They both need secure infrastructure, reliable data, enterprise applications that work, AI capabilities that can be deployed safely, better reporting, stronger vendor terms, lower technical debt and more efficient back-office operations.

That is where repeatability works.

The practical platform play is to standardize the horizontal layer: data, cloud, cybersecurity, HCM, CRM, ERP, procurement, AI governance and shared delivery patterns. The customization then happens closer to the business model, where agents, applications, workflows and analytics can be tailored to the specific value creation plan.

The goal is to stop making every company rebuild the same foundations.

Repeatable advantage compounds

The payoff from platform thinking comes from four mechanisms.

1. The learning curve belongs to the firm, not the deal

In the traditional model, experience resets when the company is sold. In the platform model, each deployment improves the next one. The tenth data platform, AI use case or ERP modernization should be faster, cheaper and lower risk than the first.

2. Shared infrastructure changes the economics

A single mid-market company may struggle to justify a mature data platform, AI center of excellence or security operating model alone. Across a portfolio, those fixed costs can be spread more efficiently.

3. Vendor leverage increases with scale

Kearney has described cross-portfolio spend optimization as a baseline private equity capability and reported that one portfolio-wide spend analysis identified approximately 6% savings potential through supplier consolidation and renegotiation.

4. The diligence-to-value gap narrows

If capability already exists as a standing asset, the firm can identify a digital opportunity during diligence and begin acting on it from day one. The hold-period clock starts at close, not months later when the team finally assembles the capability.

This is already visible in the market. EQT partnered with Google Cloud to accelerate AI transformations among EQT’s 300-plus global portfolio companies.

The Anthropic partnership with Blackstone, Hellman & Friedman and Goldman Sachs points in the same direction. The group announced a new AI-native enterprise services firm designed to help companies bring Claude into core business operations, with Anthropic engineering and partnership resources embedded directly within the team. The firm is also backed by a wider consortium of alternative asset managers, giving it access to a broad network of companies.

These are not identical models and most firms will take a different route. But they show the same direction of travel: portfolio-scale AI capability is becoming a competitive lever.

The operating hurdle: Centralized governance, earned adoption

The advantages of platform thinking do not materialize automatically. To make shared capabilities work across a portfolio, private equity firms need the right operating model.

Too much central control can backfire. If a portfolio operations team imposes tools, standards and processes without enough context, portfolio CEOs may see the platform as something being done to them rather than something that helps them create value.

But leaving every company to decide everything for itself creates the opposite problem. The same capabilities keep being rebuilt in different ways and the firm loses the benefit of scale.

The better model combines centralized governance with flexible adoption. The firm defines the common standards: data principles, cybersecurity controls, AI governance, vendor strategy, reference architectures and repeatable delivery patterns. Portfolio companies then use those capabilities in ways that fit their own business priorities.

In practice, this means treating portfolio companies as customers of a shared platform. The firm can offer services they want to use, such as a reusable data foundation, cybersecurity support, AI delivery capability, procurement support or repeatable ERP, CRM and HCM modernization plays.

The principle is simple: governance should be centralized, but value must be clear enough for portfolio companies to adopt it.

This is also where partnerships are important. Few private equity firms want to build every shared capability themselves. The stronger model combines firm-level governance, portfolio-level adoption and partner-enabled delivery.

For HCLTech, this is where our private equity services connect directly to the platform model: GenAI-led innovation, M&A IT execution, shared services, technical debt reduction, AI-led transformation, cybersecurity, digital engineering, supply chain and enterprise modernization can become repeatable capabilities that are applied across the portfolio, rather than rebuilt deal by deal.

HCLTech’s AI Force also supports this shift by helping accelerate software and data engineering, IT operations and enterprise business process workflows through Agentic AI and GenAI capabilities.

But we sell the company

The sharpest objection to platform thinking is also the most important: if the firm builds capability inside a portfolio company and then sells it, does the value walk out the door?

The answer depends on what the firm is building.

If the firm funds a one-off implementation with no reusable method, pattern or service behind it, much of the learning leaves with the asset. But platform thinking is not about giving away the means of production. It is about making the output valuable to the buyer while keeping the capability reusable at the firm level.

The buyer gets a stronger company: better data, more scalable systems, cleaner technology, lower risk, faster processes and improved AI readiness. That should support the exit story.

The firm keeps the repeatable capability: the diagnostic model, delivery playbooks, governance templates, partner ecosystem, vendor leverage, AI patterns, migration methods and operating knowledge that can be applied to the next deal.

That is the difference between building value once and building the ability to create value repeatedly.

What this asks of private equity leaders

Platform thinking requires a shift in category, not only a shift in effort.

It asks firms to stop treating digital value creation as a series of projects funded per deal, owned by the company and reset at exit. It asks them to treat digital, AI and enterprise platform capability as infrastructure: built at the firm level, consumed by portfolio companies and improved with every deployment.

That does not mean centralizing every decision and instead deciding what should be common, what should remain bespoke and which services the firm should be able to deploy repeatedly.

The real shift is in how private equity firms define advantage. In the next phase of the industry, the best firms will not only be better at buying companies or improving companies. They will be better at building the repeatable conditions that help every company improve faster.

The company is still the asset. But the platform is the advantage.