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“No amount of money ever bought a second of time.”
That line from Avengers: Endgame lands unusually well in private equity. Capital matters. Strategy matters. Talent matters. But the hold period is finite. Every quarter spent exploring AI without a clear maturity baseline is a quarter not spent turning it into measurable value.
For PE firms, the question is no longer whether AI could improve portfolio company performance; it is which companies are ready, which are exposed, and where operating teams should intervene first.
That is why AI maturity assessments are becoming a practical value creation tool. They help private equity firms move beyond broad AI ambition and build a clearer view of where AI can support revenue growth, margin improvement, productivity, product capability, risk management, or exit value before the window to act starts to narrow.
A portfolio company may appear to have AI potential. But potential is not readiness. Before a PE firm can turn AI into commercial results, it needs to understand what sits underneath: the data, technology, talent, governance, ownership, and ROI logic that make AI value possible.
That is the role of an AI maturity assessment for portfolio companies.
It gives PE firms a structured baseline across strategy, data, technology, talent, governance, and ROI, so AI potential can be translated into investment decisions, operating priorities, and portfolio-wide value creation.
Why AI Maturity Is Becoming a Value Creation Lever in Private Equity
Private equity value creation has changed.
The traditional levers still matter: pricing, procurement, working capital, sales effectiveness, operational efficiency, leadership, and strategic focus. But digital capability and AI maturity are increasingly part of how those levers are delivered.
AI can help portfolio companies automate processes, improve sales productivity, accelerate product development, personalize customer experiences, strengthen pricing decisions, reduce service costs, and improve management visibility. It can also reveal risk: weak data foundations, fragmented tooling, shadow AI use, poor governance, or product strategies exposed by AI-native competitors.
This is why AI maturity matters to private equity. It is not only a technology question. It is a value creation question.
BCG found that PE-backed companies that systematically build cutting-edge AI capabilities across functions have nearly twice the return on invested capital as companies that do not.
That is a strong signal. AI maturity is not simply about experimentation. It is about the capability to turn AI into measurable business performance.
But there is a catch. AI value depends on the foundations around it. BCG also reports that digital initiatives alone can deliver 15% to 20% ROI, while total returns can reach 30% to 35% when AI is built on those digital foundations.
For private equity firms, this matters because the investment window is limited. A portfolio company cannot spend half the hold period discovering that its data is fragmented, its processes are not measurable, or its AI use cases are disconnected from the value creation plan.
AI maturity assessment helps answer the practical question earlier:
Is this company ready to create value from AI, or does it first need foundation work?
Three Ways PE Firms Are Using AI Maturity Assessments Today
Private equity firms can use AI maturity assessments at three critical points in the investment lifecycle: before acquisition, immediately after close, and across the portfolio.
1. Pre-Acquisition Due Diligence: Reduce AI Uncertainty Before the Deal
In diligence, AI maturity assessment helps a PE firm understand whether AI strengthens the investment thesis, exposes a hidden risk, or creates a post-close value creation opportunity.
Traditional commercial due diligence may examine market position, revenue quality, customer concentration, competitive dynamics, and growth prospects. Technology due diligence may assess architecture, cybersecurity, technical debt, and scalability.
But AI maturity assessment asks a different set of questions:
Does the target have AI use cases already in motion? Are those use cases connected to measurable value? Is the data foundation strong enough to support AI? Does the company have the technical and organizational capability to scale AI? Are there risks around governance, privacy, vendors, or model reliability? Could AI materially improve the value creation plan?
This does not mean every acquisition target needs to be AI-advanced. A company with low maturity may still be an attractive investment if the opportunity is clear and the gaps are fixable. But the PE firm needs to know what it is buying: AI capability, AI potential, AI risk, or AI debt.
2. 100-Day Plan Input: Turn AI Ambition Into Sequenced Action
After close, the question changes. The firm no longer needs to decide whether to invest. It needs to decide what to do first.
An AI maturity assessment can feed directly into the 100-day plan by identifying quick wins, critical blockers, and the sequence of AI-related interventions.
For example, the assessment may show that a portfolio company has strong sales data and a clear revenue opportunity, making AI-assisted sales prioritization a near-term candidate. Or it may show that customer service automation could reduce cost, but only after knowledge management and workflow data are improved. Or it may reveal that product engineering has AI potential, but the architecture and data layer are not ready.
This helps PE operating teams avoid two common mistakes: launching AI pilots that are not ready to scale, or delaying AI action because the opportunity feels too broad and undefined.
A good assessment turns the 100-day AI conversation from “Where could we use AI?” into “Which AI opportunities are value-relevant, feasible, and ready to move?”
3. Portfolio-Wide Benchmarking: Compare Readiness Before Allocating Capital
The highest-value use case may be portfolio-wide benchmarking.
A single AI maturity assessment gives insight into one company. A portfolio-wide baseline gives the PE firm an operating agenda.
When 5, 10, or 20 portfolio companies are assessed using the same framework, patterns become visible. The firm can see which companies are ready to scale AI, which need data foundation work, which have governance risk, which lack talent, and which have high-value use cases that justify immediate investment.
This creates several advantages.
It allows the PE firm to prioritize support where the value creation potential is greatest. It helps operating partners compare maturity consistently across companies. It identifies shared capability gaps that may be better solved centrally. And it helps the firm build a repeatable AI playbook rather than treating every company as a one-off case.
For private equity, comparability is powerful. It turns AI maturity from an opinion into a portfolio management tool.
What to Assess in a Portfolio Company’s AI Program
An AI maturity assessment should not only ask whether a portfolio company is “using AI.” That question is too shallow.
The better question is whether the company has the conditions to create value from AI in a repeatable, governed, and measurable way.
Exadel’s assessment framework looks across six dimensions.
1. Strategy & Integration
This dimension asks whether AI is connected to the value creation plan.
For a PE investor, this reveals whether AI is being used to improve the business in ways that matter: growth, margin, productivity, product capability, customer retention, pricing, risk, or exit value.
If AI activity is disconnected from the value creation thesis, the company may be busy without being strategic.
2. Data Foundation
AI depends on data that is accessible, trusted, integrated, governed, and relevant to the use case.
For a PE investor, this reveals whether the company can support scalable AI or whether it first needs data modernization, better ownership, clearer definitions, stronger quality controls, or improved integration.
BCG found that just 15% of portfolio companies claim “very mature” IT capabilities, while nearly 75% report only moderate maturity.
That is a useful warning. Many portfolio companies may look digitally capable at first glance, but still lack the foundation needed for AI at scale.
3. Tech Stack & MLOps
This dimension examines whether the company can deploy, monitor, improve, and maintain AI systems in production.
For PE investors, this reveals whether AI can become a working business capability or whether it will remain trapped in experiments, spreadsheets, vendor demos, and disconnected pilots.
This is also where AI-Enabled Product Engineering becomes relevant for software and technology portfolio companies. If AI can strengthen the product itself, maturity depends not only on data science but on architecture, engineering practices, product strategy, and delivery discipline.
4. Talent & Organization
AI maturity depends on people, ownership, and operating model.
For a PE investor, this reveals whether the company has the leadership, technical skills, business engagement, and accountability needed to execute. It also shows whether AI ownership is clear or scattered across functions.
Many companies do not fail because no one is interested in AI. They fail because no one owns the path from idea to value.
5. Governance & Ethics
This dimension asks whether AI is being used responsibly, securely, and with appropriate oversight.
For PE investors, this reveals risk. Are teams using AI tools informally? Is customer or proprietary data exposed? Are vendors being assessed properly? Are AI outputs reviewed? Are decisions explainable? Are there policies for responsible use?
Weak governance does not only create compliance risk. It can create reputational, operational, and commercial risk.
This is especially important for regulated portfolio companies. For example, an AI readiness assessment for financial services has to examine model risk, auditability, data governance, explainability, and third-party AI vendor exposure in much more detail.
6. Investment & ROI
Finally, the assessment should ask whether AI initiatives have a credible business case.
For PE investors, this is essential. AI maturity should connect to value creation. The assessment should identify which initiatives can reduce cost, improve revenue, increase productivity, protect margin, lower risk, or strengthen the exit story.
Without ROI logic, AI becomes activity. With ROI logic, it becomes part of the investment plan.
That is why this post should also connect naturally to the question of building an AI business case for investment.
The Due Diligence Use Case in Detail
AI maturity assessment can add a layer of insight that standard commercial due diligence may miss.
Commercial diligence may show that a company has strong market demand. Technology diligence may show that its systems are stable enough to support growth. But AI maturity assessment can show whether the company is positioned to use AI as part of the next stage of value creation.
For example, in a B2B software company, the assessment may reveal whether AI can enhance the product roadmap, improve customer onboarding, reduce support cost, or create new pricing potential.
In a services company, it may show whether AI can improve utilization, knowledge management, delivery efficiency, or sales productivity.
In a financial services or fintech asset, it may reveal whether AI readiness is constrained by model risk, data governance, compliance, or legacy architecture.
In an e-commerce or consumer business, it may show whether personalization, forecasting, pricing, and customer service AI are realistic or blocked by poor data quality.
The point is not to turn diligence into a full transformation project. It is to identify the AI-related assumptions that could affect value.
A good AI maturity assessment can help answer:
Where could AI accelerate the investment thesis? Which use cases are credible in the first 12 months? Which capabilities are missing? Which risks are being underestimated? What would need to be true for AI to affect EBITDA, growth, or exit multiple? Where should the 100-day plan focus first?
This makes AI diligence more than a technology review. It becomes a value creation lens.
And again, time matters. The earlier these questions are answered, the more of the hold period remains to act on them.
Building a Portfolio AI Maturity Baseline
Portfolio-wide AI maturity assessment is where the PE firm can move from one-off insight to repeatable advantage.
The first step is to assess each portfolio company using the same dimensions and scoring logic. That does not mean every company needs the same AI roadmap. It means the firm needs a consistent way to compare maturity, opportunity, and risk.
A portfolio-wide baseline can show which companies fall into different categories: companies:
- Ready to scale AI now.
- With high potential but weak foundations.
- Exposed by governance, vendor, or data risk.
- Where AI could strengthen the product or exit narrative.
- Where the opportunity is limited or not yet worth prioritizing.
This helps PE operating teams make better resource decisions. Rather than spreading AI support evenly across the portfolio, they can concentrate effort where the value creation case is strongest.
It also helps identify shared needs. If multiple companies lack data governance, the firm may create a portfolio-level data playbook. If several companies are experimenting with copilots without policies, the firm may develop common responsible AI guidance. If software portfolio companies all face similar product AI questions, the firm may build a reusable product engineering framework.
This is where assessment becomes leverage.
BCG found that when digital maturity lags or there is underinvestment, 40% of investors have experienced a valuation haircut of 5% or more.
For PE firms, that is the point. AI maturity is not just a technical score. It can become part of how value is protected, created, and explained at exit.
What Good AI Program Management Looks Like at Portfolio Level
Good AI program management at portfolio level does not mean the PE firm controls every AI decision made by every company.
It means the firm creates enough structure to help portfolio companies move faster, avoid repeated mistakes, and connect AI activity to value creation.
At portfolio level, good looks like this:
- A shared AI maturity framework across companies.
- Consistent reporting on maturity, use cases, risks, and ROI.
- Clear ownership between the PE firm, operating partners, and portfolio company leadership.
- A prioritized pipeline of AI opportunities linked to value creation plans.
- Common governance principles for responsible AI useю
- Reusable playbooks for high-frequency use cases.
- Access to AI engineering, product, data, and governance expertise.
- A regular review cadence tied to business outcomes, not AI activity alone.
The best PE firms will not treat AI as a side project inside each portfolio company. They will treat it as a value creation capability that needs structure, sequencing, and measurement.
That is the strategic pressure. AI is becoming part of the PE operating model.
But the firms that capture value will not be the ones that simply encourage every portfolio company to “do more with AI.” They will be the ones that build a repeatable system for identifying readiness, prioritizing investment, managing risk, and measuring outcomes.
From AI Potential to Portfolio Value
AI can support private equity value creation in many ways. It can improve productivity, reduce cost, accelerate product development, sharpen pricing, strengthen customer operations, and create new growth opportunities.
But AI potential is not the same as AI maturity.
McKinsey’s analysis of 471 PE-backed companies found that companies at the highest level of its AI value creation ladder traded at a median revenue multiple of 31x between 2023 and 2025. The same analysis found that companies at that level saw median revenue per employee increase by $180,000, a 52% jump from the level below.
That distinction matters. Productivity is valuable, but the bigger opportunity is often broader: rethinking products, workflows, customer experience, commercial decision-making, and operating models around AI.
That requires maturity.
A portfolio company does not need to be perfect before it starts. But it does need to know where it stands. It needs to understand which use cases are ready, which foundations are missing, which risks need attention, and which investments are most likely to create value during the hold period.
For private equity firms, an AI maturity assessment is therefore not just a diagnostic exercise. It is a way to make better investment decisions.
It helps operating partners decide where to focus. It helps portfolio company leadership understand what needs to change. It helps the firm compare maturity across assets. And it helps AI move from scattered experimentation to portfolio-level value creation.
In private equity, time is not just a constraint. It is part of the value creation equation.
AI maturity assessments help firms avoid spending the hold period guessing. They show which portfolio companies are ready to scale AI, which need foundation work, which risks need attention, and which opportunities can realistically support growth, margin improvement, productivity, product advantage, or exit value.
Capital can fund the work. But it cannot buy back the quarters lost to unfocused experimentation.
Whether you are assessing a new acquisition target or building a portfolio-wide AI improvement program, Exadel’s AI maturity assessment for portfolio companies gives you the structured baseline you need to make investment decisions with confidence — across strategy, data, technology, talent, governance, and ROI.
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