Private Equity (PE) has always been about speed. PE firms buy companies they believe are undervalued, inject operational improvements, and aim to sell them within a few years at a higher multiple. But in today’s market, defined by higher interest rates, fiercer competition, and shrinking margins, financial engineering and cost-cutting alone no longer suffice.
A powerful shift is underway: data, analytics, and AI have become central levers of value creation. This isn’t just hype. It’s a structural change in how the most successful PE firms are outperforming their peers, and why Limited Partners (LPs) are increasingly asking how General Partners (GPs) plan to embed data-enabled transformation into their investment theses.
Beyond Buzzwords: Why PE Is Doubling Down on Data & AI
Over the last few years, the number of PE firms with dedicated data and digital teams has exploded. Compensation for these roles has also surged, because the stakes are high. As highlighted in the INSEAD working paper co-authored by Theodoros Evgeniou (« Leveraging Digital, Data & AI Technologies to Increase Enterprise Value: How Private Equity Firms Are Getting It Right »), these teams aren’t there to build technology for its own sake. Their mission is clear: to tie every data or AI initiative directly to enterprise value, whether through EBITDA growth, higher valuation multiples, or ideally, both.
The most advanced PE firms now follow a consistent playbook:
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Value First: Every project starts with defining a clear financial objective.
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Vertical, Not Horizontal: Instead of building sprawling data lakes, firms invest in focused capabilities linked to a specific growth lever such as pricing optimization or demand forecasting.
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Evidence-Based Growth Narratives: Growth modelling is becoming a cornerstone of exit readiness, offering a granular, real-time picture of what drives performance.
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Culture and Credibility: Digital initiatives succeed only when management teams buy in. The most effective PE data leaders are not just technologists but seasoned business operators fluent in the language of growth and finance.
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Repeatable Playbooks: Leading firms create and refine structured, proven approaches for deploying data and AI across their portfolios to build credibility and accelerate adoption.
In other words, PE firms don’t do “data transformation.” They do value transformation enabled by data.
The AI Opportunity and Its Challenges
AI, and more recently generative AI, promises transformational upside, but achieving rapid ROI isn’t guaranteed. As the June 2025 HBR article by Davenport and Mahidhar underscores, surveys show that only a small fraction of companies are seeing significant returns on generative AI investments today.
Yet the ambition is unmistakable. As Jeff McMillan put it on the Age of Intelligence podcast:
“There is certainly a lot of awareness on the part of the investor community. A lot of these PE firms are not in tech startups anymore—they’re investing in hospital systems that have 1% margins, and they are investing in them because they think that in putting GenAI in these legacy infrastructures, they are going to be able to generate 20% margin. These car companies, insurance companies—there are all of these organizations in the world that, if you reimagine these businesses using GenAI, could be fundamentally different and significantly better.”
Many PE firms are approaching this opportunity with a disciplined, phased process:
- AI Exposure Assessments to understand sector-specific risks and opportunities before investing.
- Diligence-Embedded Analysis that incorporates AI readiness and potential into pre-acquisition assessments.
- Use Case Flywheels that implement repeatable, high-impact applications demonstrating value quickly.
- Proprietary Data as Differentiator in a world where foundational models are becoming commoditized, unique datasets will increasingly determine competitive advantage.
This combination of optimism and operational rigor reflects the reality that while generative AI’s transformative promise is clear, proving value in the compressed timeframes of private equity remains the ultimate challenge.
What’s Actually Working Right Now
While GenAI is dominating headlines, many PE firms are still seeing faster returns from more established data and analytics initiatives:
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Commercial Enablement: Using data to identify the highest-value customers, optimize sales efforts, and drive conversion.
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Pricing Optimization: Applying machine learning to adjust prices dynamically and standardize them across products or regions.
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Demand Forecasting: Leveraging predictive models to match inventory and staffing more precisely to real-world patterns.
These initiatives may not always feel revolutionary, but they deliver EBITDA improvements that translate into higher valuations, especially when firms document progress with robust growth models and repeatable playbooks. Leading firms don’t reinvent the wheel each time. They draw on proven frameworks and tested methods to accelerate adoption and ensure consistency across investments.
A Look Ahead: The Long-Term GenAI Bet
If the last few years were about building foundational capabilities, the coming years will likely be about integrating GenAI into products and business models in ways that impact valuation multiples. As the INSEAD working paper highlights, embedding AI features that create new revenue streams or recurring revenue models could fundamentally reshape how companies are valued.
However, this requires more than proofs of concept. It demands:
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Clear alignment between AI initiatives and growth strategy.
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Readiness to rethink products, workflows, and customer experiences.
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Operational maturity and cultural buy-in across leadership teams.
For many PE-backed companies, the path will be iterative. But for those that succeed, the payoff could be exactly as McMillan envisions: legacy industries reimagined and dramatically more profitable.
Conclusion: What Other Companies Can Learn
For all companies, not just those owned by PE, there are valuable lessons here:
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Be clear about what “value” means in your context.
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Tie data and AI initiatives to growth levers, not technology agendas.
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Invest in data foundations incrementally, in service of specific objectives.
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Combine technical expertise with business fluency and credibility.
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Expect that GenAI will play a growing role, but be realistic about near-term returns.
Data and AI alone don’t transform a business. But when deployed with discipline and focus, they have become essential tools in the modern value creation toolkit. Nowhere is that clearer than in private equity.