The AI Future for Portfolio Operations:
“It’s not hard to imagine an AI sitting on the investment committee…”

Q: What are the biggest AI-driven value-creation opportunities you’ve seen across the portfolio already, either proven or emerging?

Regarding GenAI and Agents, I think about opportunities across three buckets. The first is “AI-as-employee” where the workforce is augmented by chatbots and autonomous Agents. Deploying a horizontal AI chat platform or turning on AI features in the existing tech stack are good places to look for early wins. Re-designing entire workflows using Agents is how you get real value here. The second is “AI-as-product” where AI becomes part of the value proposition to customers. For example, our Fire & Security portfolio company has been rolling out an intelligent video monitoring solution on top of their traditional offering. If you’re a SaaS business you should naturally be thinking about this as well. The third is “AI-as-customer,” where businesses need to become discoverable by chatbots (Generative Engine Optimization), and where Agents will increasingly make purchase decisions on behalf of consumers and procurement departments (Agentic Commerce). The first bucket (“AI-as-employee”) is broadly applicable across any business, but it’s a mistake to ignore the disruptive potential of the other two.

Regarding traditional ML, we’ve seen a lot of value from deploying advanced analytics and machine learning on commercial use cases like pricing, churn prediction, and demand forecasting. In particular, anything you can do to optimize price can usually drive near-term, bottom-line impact.

Q: How do you ensure AI solutions drive measurable value? Who owns execution, PortCo leadership, or your team, or a blend?

I’ll focus on GenAI. The easiest place to start is usually rolling out a horizontal AI platform like ChatGPT or Gemini Enterprise. This can be done fairly quickly, and it can enhance productivity and AI-literacy across the organization if deployed the right way, but in most cases, this bottom-up approach isn’t transformative on its own.

The more transformative approach is using AI Agents to automate workflows, including those that require some degree of judgement. To get transformative value here, you need to understand the nature of the work at a task-level and determine how it should be redesigned when you introduce intelligent, autonomous capabilities. Getting value here is first and foremost an exercise in operating model transformation, not tech implementation. These initiatives need to be owned by the business, with deep support from IT on technology and HR given the org implications.

In my value creation role, I spend a lot of time trying to understand where the technology is and how leaders are using it to create value so I can help our PortCos cut through the noise and set the right aspiration. I also see my role as discerning the right use cases to prioritize, driving rigor on business cases & value tracking, and helping make the right build/buy/partner decisions. Technical expertise is helpful, but what matters more is facility with strategic execution and process optimization, plus a lot of curiosity and a willingness to keep updating your perspective as the tooling and best practices change.

Q: Are there any “no-brainer” AI opportunities you think most PE firms still overlook?

There’s a whole class of advanced analytics and machine learning technologies that have been creating value for decades that are underutilized in the middle market. In addition to the commercial use cases I already mentioned, this includes things like scheduling optimization, route optimization, predictive maintenance, and any kind of forecasting or sorting based on historical data. 

Even before you get to AI, a lot of value is still sitting in non-AI digital work. Tech stack modernization (e.g., getting an enterprise-grade ERP, CRM, and HRIS in place), building a data platform and KPI dashboards, and implementing basic automation like RPA are high-return moves that also create the foundation for more advanced AI capabilities.  GenAI is absolutely transformative, but there is a whole set of mature technologies with proven impact that are still widely underutilized today, especially in the middle market.

Q: How do you think AI will influence asset selection and underwriting over the next decade?

We’ve already seen AI’s impact in our investing process. We have Agents supporting industry research, opportunity scoring, data room analysis, and portfolio monitoring. One example is a tool we built where deal teams can queue up any industry segment, and over the course of a few hours a Research Agent will scan public and proprietary data sources and expert interview transcripts to produce a ~100-page market report (including a podcast-style audio summary), transaction comps, and a starting database of potential targets, intermediaries, and industry insiders to engage with.

Going forward, I think there’s an incredible opportunity to enhance and automate the end-to-end investing process with Agents. In many cases, that transformation can happen faster than in our PortCos, since sponsors are smaller organizations with tighter spans of control and fewer integration constraints.  One application I’m particularly excited to explore is AI for IC decision support. It’s not hard to imagine an AI sitting on the investment committee that’s trained on historic decisions, outcomes, and the underlying deal data, and can pressure-test underwriting logic, surface risk factors, and provide a structured view on whether we should advance on a specific industry, target, or deal.

Q: What skills will the next generation of Operating Partners need to succeed in an AI-driven environment?

If you think about the GenAI / Agentic opportunity being primarily about operating model transformation rather than tech implementation, it’s clear the core value creation toolkit still matters. Aspiration setting, process optimization, org design, business case development, and change management become even more relevant as the pace of change accelerates.

One thing that does change I think is the importance of being able to drive and manage innovation. Automating entire workflows and launching new AI-enabled products are bold moves that require comfort operating in a different zone of the risk-reward curve than many middle-market operating partners may be used to. Finally, given how fast the environment is changing, curiosity becomes a real edge, as well as having a healthy dose of humility to know when you’re wrong and course correct along the way.