Featured in Filament Syfter’s latest guide ‘Why every private equity firm needs a data engine – and how to build it’, a new benchmark survey just released by AI Pathfinder shows an industry eager to harness artificial intelligence, but still struggling to convert curiosity into capability.
For Filament Syfter, whose mission is to help firms deliver on their AI roadmaps through data-engine technology, the findings confirm both the momentum and the execution gap defining today’s market. By getting the pulse of 47 middle market private equity professionals, the industry has its first AI implementation ranking. Let the race begin!
Most firms are still early in the AI journey
Nearly half of survey respondents (4%) describe their firms as still exploring or piloting AI use cases, while only one in ten say AI is scaling or strategic across the lifecycle. The appetite is real, but the infrastructure isn’t yet there. Most funds operate mid-market portfolios, often with fragmented systems and data locked inside silos. These conditions make it hard to advance beyond proofs of concept.
Data quality remains the core constraint for the majority
For an industry that has historically prolifically instituted data cleanliness practices at portfolio companies, it comes as a surprise to some that the firms themselves aren’t skillful data stewards. More than half of survey respondents (54%) rated their data as merely adequate for pilots, and just one in five reported good or excellent data readiness. Without integrated, governed, and searchable data, AI models won’t scale or deliver consistent, reliable results.
Where AI is gaining traction
Early adoption centers on deal sourcing and diligence, with over 60% of respondents experimenting or running pilots in these areas of the firm. Portfolio-management applications are close behind. Roughly half of the survey respondents cite operational efficiency and faster decision-making as their top realized outcomes so far, which means there’s a high potential for true technology maturation ahead.
Preparing for 2026
The survey paints a picture of an industry at an inflection point. Looking ahead 12 months, firms plan to focus AI investment on operational efficiency (57%), due diligence (71%), and portfolio value creation (43%). This, to us, means that there is clear conviction in AI’s potential, but uneven maturity and fragmented data foundations.
Our new guide distils insights from this survey, plus work with leading firms to give deal, portfolio, and data teams a practical playbook for what’s next.
Inside, you’ll find:
What a PE data engine is (and isn’t)
How to buy, build, or “build with” , and why “with” wins
A 90-day adoption roadmap to show measurable impact
Real benchmarks from the AI Pathfinder Private Equity Benchmark Survey
The firms that act now will identify better deals, make faster decisions, and build lasting conviction across the investment lifecycle. Access the guide, today.
How Filament Syfter can help
Filament Syfter’s engine and proprietary data-matching capabilities help firms realize measurable value by scoring opportunities, accelerating initial screenings, facilitating IC preparation, and surfacing signals human analysts might miss.
For Filament Syfter, this reinforces the need for a “build-with” model, which helps firms accelerate their AI journey through purpose-built data infrastructure, private equity-specific taxonomies, and pre-mapped integrations. To learn more or book a demo, click here.