AI is no longer a back-office experiment. It is changing how firms source deals, run diligence, build value, and manage risk. At the recent Private Equity Insights London conference, Jerome Pottier sat down with Imran Akram, Skander Kamoun, and Feliphe Lavor to discuss the impact of data analytics and AI on value creation in private equity. As practitioners who are already weaving AI into their workflows, their message was clear: firms that learn to use AI thoughtfully can create real, measurable value, but the path forward comes with its own challenges.

Reshaping sourcing and value creation

AI is reconfiguring the full investment lifecycle. The biggest shift is occurring in sourcing, where junior analysts once spent hours combing through company lists, conferences, and fragmented data. AI-driven platforms now aggregate signals across multiple sources and align them to a fund’s mandate, eliminating much of the manual screening work.

The impact extends through diligence and into value creation. AI can review large volumes of structured and unstructured data, flag risks in contracts, surface patterns, and compress the time it takes to get to insight. Deal teams can redirect time from rote analysis toward commercial judgment and thesis refinement.

EQT’s Motherbrain illustrates how quickly AI has moved from discrete tool to firm-wide mindset. Initially a sourcing product, it grew into a broadly adopted platform embedded across investment teams, underscoring the democratization of data science skills.

However, two structural challenges remain. First, many firms lack the clean, structured datasets required for high-quality analytics. Second, cultural inertia can slow adoption. Without a willingness to change workflows and upskill, firms risk falling behind. AI’s trajectory suggests its role will expand, and the firms that evolve with it will be best positioned to capture the advantages.

Moving the needle

AI’s value often begins with small, incremental improvements before scaling into transformational change.

At the foundational level, automation of routine tasks creates immediate efficiency. One example: using GPT-based tools to generate board minutes. Savings appear modest on an individual task basis but compound across teams and time.

At the operational level, AI is already driving measurable revenue impact. A battery portfolio company used data science to identify optimal locations for EV charging stations, increasing utilization by 50% and materially improving revenue per asset.

At the strategic level, AI is influencing business-model reinvention. A Spanish design-asset company, initially threatened by generative AI, built a simple front-end interface that made the technology accessible to its customers. Usage surged, video creation rose 50%, and a competitive threat became a growth engine.

Because many portfolio companies lack technical capabilities, firms are building central data science teams to partner with management. This accelerates adoption, creates repeatable playbooks, and helps bring innovation into businesses that would otherwise move slowly.

Evolving expectations

Expectations are rising in two waves. First, firms want professionals to actively use AI tools to augment their work. Leadership teams are setting the tone by encouraging use of platforms such as ChatGPT and Notebook LM to improve efficiency, accessibility of data, and quality of insights.

The second wave is about hybrid skills. Investment professionals are increasingly expected to pair traditional financial and commercial expertise with an ability to work with data science concepts and lightweight software tools. It is becoming common to see senior leaders building simple applications or automation flows that streamline their own processes.

At the portfolio level, early AI projects such as the previously mentioned EV charger placement optimization are strengthening conviction in the value of analytics. Managers who see the operational impact firsthand become more open to embedding data science in their workflows.

Measurement remains a challenge. Private equity cycles are long, and full benefits of recommendation engines or data-driven decisioning will take time to appear. Firms must maintain commitment through this transition, recognizing that technology adoption is cumulative and path-dependent.

Speed, accuracy, and human judgment

AI is improving diligence in both accuracy and efficiency. Tools can now scan large data rooms, highlight anomalies, summarize contracts, and direct human review toward the most material issues. This materially reduces the manual burden traditionally placed on junior staff and reallocates their time toward analysis and deal structuring.

But AI does not eliminate the need for human oversight. Examples of fraud that appeared consistent in the data highlight the limits of algorithmic review. Human pattern recognition, skepticism, and the ability to interrogate inconsistencies remain essential.

An emerging dynamic is cross-generational collaboration. Juniors bring technical fluency in prompt design and tool usage; seniors bring contextual and commercial judgment. Pairing them elevates both groups and accelerates firmwide adoption.

As AI evolves, some tasks will become fully automated, but core diligence responsibilities that require critical thinking will remain human-led. The objective is not replacement but augmentation: better data, better focus, better decisions.

Managing risk and ensuring returns

The most effective risk mitigation is incremental progress, rather than large, monolithic AI deployments. Foundational work like improving data consistency, standardizing reporting, and building API connections that allow information to flow are critical for downstream AI success.

Next, piloting AI tools in contained environments where impact can be measured is important. For example, testing scheduling optimization in a subset of clinics or experimenting with AI-generated customer communications. Early wins create internal advocates and build the case for scaling.

A “fail fast” philosophy is also essential. Many AI initiatives fail not because the models perform poorly, but because they are poorly integrated into workflows. Firms that iterate quickly are better positioned to redeploy resources and refine their approach.

The consensus? Methodical experimentation with fast feedback loops is the safest path to value. The greatest risk is not failed pilots, but avoiding AI altogether.

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