Moving Beyond Efficiency: Value-Creating AI Use Cases
AI has been talked about in private equity for a few years now, usually in the context of speed.
Faster reviews. Faster reporting. Faster workflows. But as the technology matures, the conversation is shifting. The firms making real progress aren’t just looking at AI as a way to tidy processes. They’re using it to strengthen diligence, sharpen commercial thinking, and build companies that hold their value at exit.
In this live discussion, we invited three leaders who’ve seen this shift first-hand, to discuss what it really means in practice.
Hosted by Hugo Din, AIOP Lead at Montash
Speakers:
Manu Kumar – CEO and Chief Data & AI Officer | Former CDO, Bupa Group
An award-winning global data and AI leader with over 20 years’ experience driving transformation for Fortune 100 companies and startups.
Paul Whiteside - Strategic Advisor and Portfolio Chief Technology Officer, Crosslake Technologies
A private equity technology specialist with over two decades’ experience leading complex transformations, value creation, and board-level strategy.
Stephen Moffitt – AI Advisor to Private Equity, Plus or Minus Seven
Has a proven track record guiding private equity portfolios and global brands to unlock competitive advantage through data-driven transformation.
Watch the full discussion below:
Here are some of the questions they answered and the insights that stood out.
How is AI changing the conversation in private equity?
Across the panel, there was a clear consensus: AI has moved from “useful” to “strategic”.
It’s still speeding things up, but it’s now shaping how firms think about value creation, pricing, and risk.
“If the investment thesis includes value creation from AI, you really need to know that the company has the necessary foundations to deliver on that.”
Stephen Moffitt
That set the tone for the discussion: AI can accelerate what good investors already do but only when the groundwork is solid.
How is AI being used in due diligence?
In more ways than most people realise.
The panel talked through several areas where AI already supports diligence, including scanning data rooms, reviewing market signals, mapping competitor sets, and pressure-testing theses earlier in the process. But they were equally clear that AI isn’t replacing human judgement anytime soon.
“AI brings a lot to the diligence process… but you need to understand the investor concerns, and use the AI in an assistive way.”
Paul Whiteside
The message was consistent: AI helps teams move faster and with more confidence but only when the underlying data, governance, and culture are ready.
Where is AI delivering the most commercial value?
The biggest gains right now aren’t coming from experimental tools; they’re coming from mature, proven applications that have been delivering for years:
- customer analytics
- churn reduction
- onboarding
- workflow optimisation
- supply chain and inventory management
- predictive maintenance
These are the use cases with known ROI profiles and lower implementation risk.
“Where you’ve been able to drive value has been traditionally where the technologies are mature enough to deliver those things.”
Manu Kumar
It’s a grounded view, and one the industry often forgets when the hype cycle turns up the volume.
What should leaders prioritise before scaling AI?
The panellists agreed that foundations matter more than ambition: data quality, process integrity, governance, and realistic expectations.
“Be a bit pragmatic. Recognise that companies aren’t always ready for AI.”
Stephen Moffitt
Whether you're a fund, an operating partner, or leading transformation inside a portfolio company, the principle is the same: fix what’s underneath before you scale what sits on top.
Three Key Takeaways:
- Start with the data estate.
Audit quality, governance, access, and process. Most AI delays and failures start here. - Pilot with mature use cases.
Proven areas like onboarding, churn, and supply chain deliver faster, cleaner ROI than experimental generative AI. - Build capability in phases.
Many firms are following a model of tools → partners → in-house. It creates momentum without overextending.
Want the full insights?
Download the full key takeaways PDF below:
