84% of PE funds expect AI to have a significant transformative impact on their business (EY, 2026). Most are still at an early stage of addressing it.

Where AI is landing - and where it's not

Published earlier this week, FTI Consulting's 2026 Private Equity AI Radar surveyed 200 fund and operating leaders and found the following: the top deal-cycle AI use cases are data analysis and summarisation (27%), financial due diligence support (27%), and document scanning and processing (26%).

But adoption drops off sharply at later stages.

The reason: diligence and analytics use cases dominate because deal and portfolio teams are focusing AI on tasks that are data-heavy, streamline repetitive work, or generate analytical insights. AI adoption is currently stronger in front-end diligence and evaluation than in late-stage execution, because early-stage activities involve large volumes of structured data where AI can deliver immediate efficiency gains. Late-stage exit work is more judgement-driven and harder to automate.

Where the advantage grows

That pattern maps onto an article from Sequoia partner Julien Bek, published here earlier this month. His argument: the biggest AI opportunity isn't building better tools - it's becoming the service and the work itself. The playbook starts with outsourced, intelligence-heavy tasks: well-defined work, existing budget lines, clear ROI, and a buyer already purchasing an outcome. That's the wedge.

As AI compounds proprietary data over time, it expands into higher-judgement work that was previously too complex to automate. The firms that get there first build a data advantage that makes the service faster and harder to compete with, regardless of what the underlying models do next.

The implication for private capital is clear. Diligence, IC prep, and portfolio monitoring are intelligence-heavy, data-rich, and increasingly repeatable. Firms embedding AI from diligence through to IC and exit aren't just more efficient today. They're compounding an advantage over time. Firms still running isolated pilots and only utilising AI in the early stages of the cycle are the ones falling behind.

That's exactly what we're building for at Capsa. Intelligence through diligence and across the full deal lifecycle, where institutional memory and pattern recognition matter most. Deploying AI in the later stages is the key opportunity for firms looking to pull ahead.

Our recent product updates include:

• Portfolio Monitoring: A new quarterly update reporting workflow, pulling automatically from your financial models, transcripts, and presentations

• Workflow Views: Customise your Capsa interface to surface the workflows you and your team use most

• Bulk Processing: Our platform can now write and execute its own code across thousands of items at once, unlocking bulk workflows that weren't previously possible - like sourcing 500+ targets on fund-specific criteria

Read more about our updates → here.

We're also delighted to have made Notion Capital's Cloud Challengers 2026 list for the second consecutive year. Callum Downie, our Co-Founder and CTO, is speaking at today's launch event hosted by Notion Capital in partnership with Google Cloud at their London office.

Out of 200,000 European companies analysed, only 100 made the cut. Cloud Challengers has become the benchmark for Europe's breakout B2B software companies, with past cohorts including Synthesia and Lovable before they became household names.

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