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Event24 Jun 20262 min read

Inside Prescient's first PE AI Value Creation event

Investors, operators and AI leaders joined Prescient to discuss where AI is already creating value across private equity and what it takes to move from experimentation to measurable results.

On 23 June, Prescient Labs brought together investors, operating partners, portfolio leaders and AI practitioners for our first PE AI Value Creation event.

The discussion centred on a question facing many firms: with potential AI use cases everywhere, how do you decide where to start?

Our panel - Sam de Silva, Aleksander Ficek, Hilary Roberts and Sarah Aoudia - brought experience spanning NVIDIA, UBS, Strand Logic, Baton Corporation and Prescient Labs. They explored where AI is already improving operations, where firms should remain cautious and what the next phase of AI-enabled value creation may look like.

One theme ran through the evening. The challenge is not generating ideas; it is identifying opportunities connected to a real workflow, a material business problem and a measurable outcome.

That requires evidence before implementation. Leadership teams need to know where work slows down, where handoffs or rework create cost and which intervention can move an operational or financial metric. Prescient provides that context by reconstructing workflows from operational data, surfacing bottlenecks and establishing an ROI baseline before a solution is selected.

Thank you to everyone who joined us, including attendees from Blackstone, TDR Capital, Astorg, Oakley Capital, ICG, Synova, GTO Partners, HSBC, Committed Capital, Brookstreet Equity Partners and Sheffield Haworth.

The quality of the discussion made one thing clear: private equity is moving beyond broad interest in AI towards a more practical question - where can it create measurable value now?

We are already looking forward to the next one.

GET A PERSONALISED DEMO

Find the AI opportunities worth pursuing.

Prescient uses operational data to show where work slows down, what can be automated and how impact should be measured.

What happens next
  • • We define the process and decision in scope
  • • We review the evidence sources already available
  • • We identify gaps, variants and priority opportunities
  • • We prepare recommendations and expected-impact assumptions