Originally published in October 2025, this updated March 2026 edition reflects the market’s shift from AI experimentation to building the data infrastructure required to unlock value from Frontier AI models such as Claude.
It introduces the Deal Engine framework and expands practical guidance for creating the proprietary context, institutional memory and integrated data foundations that allow private equity firms to apply these models effectively.
Private equity is standing at the edge of its next competitive frontier, and our latest guide, Why every private equity firm needs a proprietary market data engine, explores how firms can get there.
Drawing on data from the AI Pathfinder Private Equity Benchmark Survey (September 2025), the guide reveals that 47.8% of firms are still only piloting AI tools, while fewer than 11% have achieved true scale. Most have already invested in the plumbing. They have CRMs, market data platforms, and analytics tools. What they have not done is connect any of it into a single, context-driven intelligent system that learns and improves over time.
That is the opportunity the guide unpacks. How leading firms are turning their datasets, CRMs, and proprietary knowledge into a unified data engine that shifts deal teams from reactive to proactive, spotting opportunities earlier, identifying relationship inroads, and building faster conviction.
This updated guide distills insights from work with leading firms to give deal, portfolio, and data teams a practical playbook for what is next.
Inside, you will find:
What a PE data engine is, and what it is not
How to buy, build, or "build with", and why "with" wins
Best practices for deployment, adoption, and measuring success
Real benchmarks from the AI Pathfinder Private Equity Benchmark Survey
Too many deal teams are still waiting for deals to come to them. They respond to inbound flow, work from static target lists, and chase opportunities that are already in market. The firms pulling ahead have flipped that model. They are systematically hunting against defined criteria, picking up live signals, and reaching founders before a process ever begins.
Making that shift requires more than better data subscriptions. It requires infrastructure. Signals around leadership changes, hiring spikes, new certifications, and facility expansions need to surface automatically and in context, rather than sitting buried across disconnected platforms. When those triggers are unified into a single operating layer, deal teams stop missing the moments that matter.
The industry's leading data providers have reshaped how dealmakers source and qualify opportunities. But with so many firms licensing the same high-quality datasets, access alone no longer creates differentiation.
The next edge will come from how firms engineer, orchestrate, and apply intelligence to that data. A market data engine makes this possible by connecting internal systems and third-party sources, enriching them, and surfacing the most relevant opportunities in real time.
As Phil Westcott, Founder and CEO of Deal Engine, puts it: "AI will not create advantage on its own. Proprietary, well-engineered context will. Firms that build a true data engine today are building the institutional memory and architecture that tomorrow's AI will depend on."
Most firms are closer to this than they think, but common gaps in their current setup are costing them deals.
Fragmented relationship data. Key intel sitting in individual inboxes or spreadsheets rather than a unified system means lost visibility into founder and advisor relationships.
Weak signal tracking. Without automated monitoring for hiring trends, leadership changes, or revenue inflections, firms miss companies that are quietly becoming ready to transact.
Disconnected data and CRM. When market intelligence and relationship history live in separate tools, deal teams spend time reconciling systems instead of building conviction.
No standardised workflow. Inconsistent processes across team members mean dropped follow-ups, duplicated outreach, and institutional knowledge that walks out the door.
A data engine closes these gaps. Not by adding more tools, but by connecting what already exists into a governed, compounding system.
The AI Pathfinder data shows clear momentum. Nearly half of all firms are actively piloting AI, yet most are stalling because of architecture rather than ambition. Over half rate their data as adequate for pilots but not scalable. The firms that pull ahead will not be those with access to the best models. They will be those with the best proprietary intelligence behind them.
The firms that act now will identify better deals, make faster decisions, and build lasting competitive advantage across the investment lifecycle.
Download the guide to see how.