Data & AI in Private Equity: H1 2025 trends from the field
Analyzing projects we've led at REKOLT, discussions with peers across the US and UK, and conversations with fund Partners and their portfolio teams, a clear pattern emerges: PE funds and their portfolio companies aren't deploying data science with the same objectives or urgency.
On one side, GPs are investing heavily in automating their own investment processes. On the other, they're pushing their portfolio companies toward thoughtful digital transformation, far from the technology sprinkling we observed just a few years ago.
Fund-side: The obsession with decision efficiency
Deal sourcing & analysis: Funds are deploying AI systems capable of processing teasers, extracting signals from unstructured documents, and automating initial analyses.
Example: A mid-cap PE fund developed an AI pipeline to automatically classify land use documents and extract indexation clauses, transforming weeks of manual work into hours of processing.
Deal support: Due diligence automation is gaining momentum with automated market sizing projects, large-scale customer interviews, and AI-augmented competitive landscaping.
Example: A fund recently deployed a system that automatically analyzes +1,000+ customer reviews to identify a target's strengths and weaknesses, completing in days what previously took weeks.
Portfolio analytics: Automated synthesis of data from portfolio companies is becoming standard (as an objective at least, when it comes to deployment it’s another story…).The goal: a consolidated vision to compare financial KPIs and benchmark operational KPIs across the portfolio
Example: A PE firm implemented the normalization and KPIs benchmarks across its 40 portfolio companies, identifying best practices and improvement areas.
Talent analytics: This is the emerging trend. Some funds are testing predictive algorithms on executive assessments, cross-referencing hundreds of historical evaluations with actual performance to improve their recruitment decisions. Why the surge? As funds dig deeper into data to find performance predictors, they're recognizing that HRremains the number one factor in value creation. The ability to predict executive success becomes a significant competitive advantage when every leadership decision can determine a portfolio company's trajectory.
Portfolio-side: Thoughtful transformation
BI & Reporting: The fundamentals remain a priority. Unifying disparate data sources, creating real-time management dashboards, and automating investor reporting.
Example: An automotive equipment manufacturer unified data from its 12 European plants into a single platform, reducing monthly reporting production time from 5 to 2 days.
Operational Excellence: Projects focus on performance optimization through data. Deploying predictive KPIs, real-time alert systems, and production chain optimization.
Example: A restaurant chain implemented a predictive system that anticipates stockouts and optimizes orders, reducing waste by 15%.
Finance Transformation: Professionalizing budget cycles, optimizing cash flows, implementing automated forecasting.
Example: A specialized distributor automated its provisioning process, moving from monthly manual calculations to daily automated updates, improving forecast accuracy by circa 20%.
Data maturity audits and AI use cases roadmap: This is the new trend we're observing. Before investing in expensive technologies, funds are commissioning comprehensive audits of their portfolio companies' data ecosystems. These audits are ultra-specific to functions and use cases, contrasting with what we see at large corporations or other mid-market companies that want to assess all the company and see how AI and GenAI can be useful to them. This approach reveals that technology investment decisions have become extremely reasoned, far from the AI rush we could observe just two years ago
Example : Evaluating digital maturity across 150 manufacturing facilities to identify quick wins and priority investments. This approach reveals that technology investment decisions have become extremely reasoned, far from the AI scattering we could observe just two years ago
Example : conducting targeted AI assessments for sales teams to implement tech-enabled solutions that drive productivity and reduce CAC
Two strategies, same data bet
This dichotomy reveals PE funds' strategy: maximize their own decision-making efficiency while transforming portfolio companies into data-driven cash machines. Fund-side, we automate to save time and reduce risks. Portfolio-side, we transform to create value and improve EBITDA, but with a methodical approach that starts by auditing what exists.
The timing isn't the same either. Fund-side projects target immediate gains on investment processes, while portfolio-side projects have a 3-5 year value creation horizon. But the bet remains identical: data science and AI have become critical levers to perform in an environment where multiples are tightening and every EBITDA point counts.