Private Equity Is Chasing AI. GFT’s Rishi Chohan Says the Industry Is Skipping a Step.

For an industry that prides itself on moving fast and thinking ahead, private equity has an old-fashioned relationship with its own data. Across firms of all sizes, critical information such as deal memos, due diligence files, board decks and limited partner (LP) correspondence sits fragmented across systems that were never designed to work together. The result is a significant inefficiency that compounds and incurs high costs.

Rishi Chohan has seen this problem firsthand as U.S. CEO of GFT, a global technology company with more than three decades of experience serving investment firms. Chohan is on the ground helping some of the largest PE firms untangle these challenges at the source with their underlying data and technology.

Right now, AI conversations are everywhere, but Chohan says that, under the AI topic, data problems are significantly plaguing the industry. “The problem we hear about above all others is data,” Chohan said. “Deal teams spend half a day tracking down information they know exists somewhere. That’s wasted time, and at the pace these firms operate, it compounds fast.”

These data issues also come at a time when pressure is intensifying on multiple fronts for firms. Companies are staying private longer, compressing the window for return, and LPs are demanding greater transparency and stronger performance. Firms are being squeezed from every direction, and many are trying to manage it all with PDF’s and Excel documents.

It is against this backdrop that AI has become the industry’s most discussed topic. But Chohan has a view of where the opportunity ends and the hype begins, and what firms need to get right before any of AI’s promise can be realized.

Large and middle-market firms are both chasing AI from different starting lines.

Not all firms are at the same point in their technology modernization journey. Chohan says that larger firms with significant assets under management have the resources to pursue large-scale digital transformation projects. They have dedicated technology teams and established vendor relationships to execute technology modernization across the firm. For them, AI adoption is largely a question of which use cases come first.

The middle market tells a different story. These firms are often running a patchwork of tools, such as a CRM that doesn’t integrate with their data room and quarterly reporting is still done in Excel. As a result,  these firms are earlier in their digital journeys. But Chohan pushes back against the assumption that this puts AI out of reach. “At a smaller firm where one person is doing three jobs, the impact of AI can be even more immediately apparent,” he said. The constraint, he argues, is less about firm size and more about data readiness – a problem that both small and large firms share.

Before AI adoption, the underlying data needs to be centralized and structured in a format that AI systems can use effectively. It is, as Chohan admits, unglamorous work. “This process isn’t as exciting as the AI rollout, but it is extremely critical to ensure AI investments drive long-term business results.”

Firms keep underestimating the same two things when they roll out AI.

When firms do push forward with AI adoption, they tend to hit the same obstacles. Chohan says the first is data siloing — AI is only as useful as what it can access, and right now, most firms’ information remains fragmented across systems that don’t talk to each other.

The second barrier is less technical and, in some ways, harder to solve: people. Firms consistently underestimate the gap between purchasing an AI tool and actually embedding it into how their teams work. “Buying a tool and training staff on it are two different things,” Chohan said. Firms need governance frameworks, realistic measurement, and feedback loops from the people using the technology.

Trust is the real barrier to AI adoption in PE, and domain knowledge is how you build it.

GFT ‘s work in financial services predates much of the current AI wave, which Chohan argues positions the company well to help firms navigate the shift. Generic technology solutions, in his view, simply do not translate to private equity — and with AI, the stakes of getting this wrong are higher than ever.

“If you build something on a flawed understanding of how the firm actually works, AI scales that mistake,” he said. A tool that misunderstands net asset value (NAV) methodology or can’t account for the nuances of LP reporting produces outputs that erode employee trust in AI tools.

Chohan says the importance of building this employee trust is easy to overlook. Deal teams are not inclined to hand over their workflows to technology they don’t fully understand. If the people building or implementing the tool don’t grasp how it accounts for compliance requirements, or can’t clearly explain where its outputs come from, adoption stalls. “Building AI solutions with this knowledge from the start and communicating that training to end users will be essential,” he said.

The practical results of GFT’s approach are visible in its client work. Working with a large U.S.-based asset manager whose legacy systems had become a bottleneck for LP reporting, GFT rebuilt the firm’s quarterly reporting infrastructure on the cloud over ten months — cutting 15 hours from the standard quarter-end workflow.

 In a separate engagement, GFT built a generative AI solution that reduced the credit memo process from days to minutes, generating a structured draft for human review rather than requiring analysts to construct the document from scratch.

AI changes when firms find out what’s going wrong

According to Chohan, the biggest shift for firms is moving from reactive to proactive actions. Most portfolio oversight today occurs through periodic board presentations and quarterly calls, meaning problems surface only after they have already started, but AI can change that.

With systems continuously analyzing operational data across portfolio companies in real time, firms can catch costs drifting out of line, revenue trends diverging and more, allowing them to act before any of it becomes a crisis. “Insight into these factors allows the firm to move proactively and minimize the chance of a larger escalation,” Chohan said.

The next generation of AI will build on what firms deploy today.

Chohan says the AI conversation likely isn’t going anywhere anytime soon. Right now, most firms are adopting generative AI, but the next wave, which he calls experiential AI, represents a more fundamental shift. These are systems that learn continuously through interaction with their environment, adapting over time in ways that more closely mirror human cognition.

“For private equity firms that want to prepare for this future, they need to focus on building a strong data foundation and deploying generative AI at scale,” he said, “because the next wave of innovation will build on the technology that’s adopted now.”