ECI weighs AI risks & upside in private equity deals

ECI Partners has outlined how it assesses artificial intelligence risks and opportunities during private equity due diligence, with AI now at the top of the agenda in deal reviews.

Investors are looking beyond technical novelty to whether a business can defend its market position, retain customer trust and use AI in ways that strengthen pricing and growth. Management teams seeking backing also need to show a clear view of how AI is changing their market, operating model and product strategy.

Private equity groups have weighed software and data issues in transactions for years, but generative AI has broadened that scrutiny across commercial, technology and operational workstreams. AI is no longer a narrow technical diligence topic; it now shapes broader judgments on a company's resilience and value potential.

That extends to leadership. Investors want to know whether founders and executives understand both the threat of disruption and the potential for new revenue, and whether they can distinguish between experiments and initiatives that could materially affect the business.

"We've considered AI and machine learning in our investment and value creation plans for years, but in the wake of GenAI, these have moved to the top of the agenda as part of due diligence," said Duncan Ramsay, partner at ECI Partners.

One of the first tests is whether a company's subsector and business model are vulnerable to AI-driven change. The analysis varies by market, with different implications for a travel company than for an insurance platform. Investors expect management teams to show they have considered product diversification, monetisation, talent needs and regulatory constraints.

Moat test

A central issue is the depth of a company's moat: the structural barriers that make it harder for competitors or customers to bypass the business. ECI examines whether clients could switch to a do-it-yourself approach, whether AI-native challengers are gaining traction and whether adjacent companies could use AI to move into new parts of the value chain.

The firm uses internal data and AI expertise alongside external diligence providers to test those risks. That reflects a broader shift in buyout markets, as firms try to distinguish between companies that can absorb AI disruption and those whose economics could quickly come under pressure.

At the same time, due diligence also focuses on the upside. ECI pointed to portfolio companies including Paragin Group, which has incorporated AI into exam and assessment products, and Croud, where autonomous AI agents are automating repeatable, data-heavy internal workflows.

Moneypenny was cited as another example. The communications company has introduced an AI Receptionist and Voice Agent that combines automation with human escalation, and has built patent-pending guardrails intended to keep responses accurate, on-brand and compliant.

Trust and pricing

A broader theme in ECI's assessment is that customer relationships matter more as AI lowers barriers to building similar tools. The firm is placing greater weight on businesses with products or services embedded in customer workflows, strong retention and advocacy, and high-stakes or mission-critical functions.

That has implications for valuation. Businesses with trusted brands, proprietary features and strong customer reliance may be better placed to withstand low-cost competition or customer attempts to replicate a service internally, while still using AI to improve what they offer.

"We've always viewed technical complexity as a weak moat in isolation, as inevitably technology will catch up with any product, but this is especially true in the wake of AI. Ultimately, whether your competitors could build your tool or offer your service for less isn't the most relevant question. The key question is customer impact and trust in your product," Ramsay said.

Pricing is another area under review. Investors are asking whether a product or service should keep the same price if AI makes it cheaper to deliver, or if customers use fewer seats while still achieving the same outcome. Those questions are most acute where software or services replace human work rather than support it.

The strongest businesses are those that use AI to deliver more value, not simply the same output at lower cost. Pricing models are also starting to shift towards outcome-based or hybrid structures and, in some cases, towards seat-based models designed to protect revenue from future seat compression.

Avantia, the digital home insurance platform in ECI's portfolio, was presented as an example of AI supporting both service quality and economics. Its Holmes tool improved fraud detection accuracy by 3.4 times and completed payment calculations with 98% accuracy, while also helping agents assess claim coverage, payment amounts and next steps in complex cases.

ECI, which manages around £3 billion and invests in growth businesses valued at up to £350 million, said the message for management teams is that investors want evidence of both defence and execution. A convincing AI story is now less about novelty than about whether a company can protect its position, maintain customer trust and turn new tools into durable value.