Private equity has always been an industry defined by speed and precision. The world’s most successful private equity shops have made it their business to identify the right deals quickly, complete due diligence with confidence but also speed, and then begin the process of driving value in their portfolio companies more effectively than their competitors.
The rise of Gen AI has the potential to make private equity processes even faster. With success so dependent on speed, AI promises to shake up the market. Incumbents and challengers alike know that their success depends on their ability to adopt AI and so create new levels of efficiency. The more time they save by automating repetitive tasks, the more time they can spend on relationships and strategy, giving them an edge over the competition.
So, a new metric is rising to prominence. Return on investment remains key, but firms are also now concerned about Return on Time (ROT) too. ROT is a way of measuring value that focuses not on money directly, but on time saved and how that time is reinvested.
Where AI saves time across the private equity lifecycle
First and foremost, a private equity professional earns their keep with deal sourcing, and this process can be streamlined using AI-powered tools which scan market data, company filings, and news at scale to surface relevant opportunities far faster than traditional research. What used to take weeks of manual sifting is now compressed into hours, allowing deal teams to dedicate more energy to analysis and relationship-building.
When a deal has been agreed, due diligence processes ensure that private equity firms are making good investments and company fundamentals are solid. In this field, AI excels at structuring unstructured data. From contracts to financial statements, algorithms can extract, standardise, and highlight anomalies across thousands of pages in minutes. Instead of spending hours combing through PDFs, analysts can focus on interpreting findings and stress-testing assumptions.
Once a deal has gone through, private equity involves ensuring that portfolio companies are performing well. To monitor this, AI-driven dashboards pull live data from multiple sources to track performance in real time. Early warning signals of underperformance can be flagged automatically, while automated reporting saves countless hours of manual compilation. The time saved gives partners greater bandwidth to engage with management teams on value creation.
Fundraising and investor relations is also vital. From drafting due diligence questionnaires to standardising pitch materials, AI accelerates processes that are traditionally repetitive and time-consuming. This allows investor relations teams to devote more energy to relationship management and strategic storytelling.
Finally, back-office operations. The back-office may not be the most exciting part of a private equity business, but it is essential. Here, AI reduces the friction in compliance, audit preparation, and fund administration by automating reconciliations and error-checking. These time savings compound across the business, improving responsiveness and efficiency.
At each stage, the common denominator is the same: AI reduces the burden of low-value, manual tasks and creates time for higher-value activities. This is why “return on time” (ROT) is arguably the most important metric for measuring AI’s impact in private equity.
How to Unlock ROT
So, the potential of AI to achieve ROT is clear. However, many firms struggle to move beyond pilots and quick wins to scaled adoption that saves time for employees. The difference between success and stagnation often comes down to implementation strategy. For firms who want to achieve success, there are a few things to keep in mind.
Firstly, start small, but think big. Overly ambitious AI adoption roadmaps often fail because they try to do too much at once. Firms should start small by identifying high-friction pain points such as data extraction in diligence and solve them first.
Second, humans must remain in the loop. Systems should be designed to augment analysts, not replace them. This maintains confidence in outputs, preserves the relationship-driven culture of private equity, and ensures the firm’s collective knowledge is embedded in decision-making.
To ensure trust, data quality, security, and compliance must also sit at the core of any AI program. Firms should define governance policies that set clear boundaries for tool use, address investor and regulatory concerns, and prevent sensitive data from leaking into unsecured systems.
With such a fast-moving technology, change management is also important. Cultural alignment matters, so training and workshops should be used to staff see AI as a partner rather than a threat. Involving deal teams in identifying use cases ensures the tools solve real problems. When adoption is bottom-up as well as top-down, buy-in is stronger.
To measure impact, traditional ROI measures like cost savings are important, but time is also a powerful metric. Tracking hours saved and how they are redeployed — whether into sourcing, portfolio optimization, or investor engagement — ensures that AI’s contribution feels tangible.
Looking Ahead
Over the next few years, AI will reshape private equity operating models. By automating data-heavy tasks, firms will run leaner and smarter, while preserving the human expertise that drives competitive advantage. For large firms, AI will unlock even greater scale; for smaller firms, it will level the playing field by making insights and efficiencies more accessible.
In a market where speed is everything, time is the ultimate currency. AI, applied strategically, is the tool that allows private equity firms to earn a stronger return on their people’s time.
