I attended the Fund of the Future AI meeting in 2024 and had the chance to attend this year's event as well. It's a meeting hosted by West Monroe, specifically targeted to our PE/Fund clients who are navigating how to adopt GenAI, maintain a competitive advantage (or at least not fall behind), and achieve ROI on these investments.
I love this conference—every year, I learn something new, and it's fascinating to see how much the landscape evolves. In 2024, the discussion centered on "What are LLMs, and how will they impact your funds?" This year, the conversation shifted to "We are building and seeing others build unique LLM tools that are accelerating fund teams…if you aren't doing this, you're behind."
I thought I'd share a few key takeaways from the session below—these insights apply across industries, not just private equity. Although, the pace and impact will vary.
Details about WM's fund of the future POV have been posted on WM's website here. A big shoutout to EJ and Brad, who led the event—they did a fantastic job.
AI can absolutely make investment (and other) decisions faster and more accurately, but making bad decisions faster is still just bad decision making. AI works best when it augments human judgment—not when it replaces it. The best PE firms and companies know how to balance efficiency with accuracy and always keep humans in the loop to vet and validate.
Brad and EJ noted that AI differentiation is 70% people, 20% data, and only 10% GenAI tools. (I would argue it's 50% people, 40% data, and 10% AI tools…but that's neither here nor there.) Companies love to chase the latest AI software, but without the right people to guide, interpret, and adopt it, you're just wasting time. The real value happens when leadership focuses on upskilling teams, fostering AI literacy, and embedding AI into workflows to elevate employees.
Thankfully, I haven’t seen companies spending millions trying to build custom LLMs (unless they are Meta, OpenAI, xAI, etc.). There are many off-the-shelf solutions that, with a little tweaking, do the job just as well. The winning approach is to leverage existing LLMs and tools, then customize them with your proprietary data and specific needs. This way, you achieve differentiation without burning your budget on unnecessary development.
While data science is at the heart of the GenAI "engine" (and I recommend having a DS lead to help wrap your arms around these technologies, architectures, and best practices), the core teams customizing, developing, and delivering GenAI solutions are more product development focused. Prompt engineering doesn't require a data scientist, but the UI that enables the end user to derive value from the LLM does require a product developer.
Last year, I spent a lot of time explaining what data science was and how it fits within the data maturity curve. We had executives saying they wanted to implement data science solutions, but their data infrastructure consisted of Excel and Access databases. AI isn’t a magic bullet that fixes no data, bad data, or messy environments. If your data is flawed, AI will just help you make bad decisions more efficiently. Before diving into AI, firms need to:
Appoint a Data Lead – Someone responsible for decision making, driving adoption, and championing these efforts as a core part of their role.
Build a Modern Data Ecosystem – A data platform that integrates structured and unstructured data.
Prepare Data – To gain broader insights and enhance decision making get your data in one place, organize it, clean it, and label it.
During the session we got the question (we did in 2024 too), "what's the ROI here, why should I spend on this?" Frankly, while GenAI is driving value, quantifying that value remains challenging so I wanted to follow up on it.
Ahead of the sessions, WM's PE team interviewed many of our PE partners and reviewed work we had done to date. They found that GenAI is impacting four key areas:
Better Investment Decisions – AI helps analyze structured and unstructured data faster and more accurately.
Faster Deal Sourcing – AI can surface high potential deals before competitors even know they exist.
More Efficient Operations – AI can automate non-core tasks, freeing up employees for strategic work.
Smarter Investor Relations – AI powered tools are transforming how firms engage with investors and report on portfolio performance.
The challenge is that these efficiency gains are hard to measure. As luck would have it, a PE friend recently shared a research article on this very problem. In the paper, the authors find the following data points and key value drives for workers using GenAI:
Use Cases and Time Savings
Workers use GenAI primarily for writing, administrative tasks, data analysis, coding, and summarization.
On average, GenAI assists with 1% to 5% of total work hours.
Users report a 5.4% time savings in their weekly work, translating to significant productivity gains.
Productivity Impact
Higher GenAI use correlates with higher wages—frequent users earn up to 40% more than non-users.
Estimated aggregate productivity gains of 1.1% based on current adoption rates.
Managers and tech workers use GenAI more than administrative roles, despite predictions that office jobs would benefit most.
I thought figure 11 in particular was compelling for the "what's the ROI" question (citation below):
The key takeaway: There is undeniable value, but you need to vet and quantify it as part of your strategy. Personally, I believe that if you're not using these tools and ROI is your barrier to entry, you're likely overthinking it. These solutions cost between $5,000 and $12,000 per month typically for a small firm—if you're able to save just 1% of your employees' time, the ROI is there. The bigger risk not training your workforce on these tools, if you're competition is doing it you're falling behind.
AI adoption isn’t just an IT project—it’s a business transformation. During the working session the team outlined how to get started if you’re a leader thinking about AI:
Build an AI Leadership Team
Set a clear AI strategy and define what success looks like.
Keep AI adoption measurable and accountable (no "innovation theater").
Assign ownership—someone needs to drive this, not just talk about it.
Pick Your First AI Wins (And Don’t Overcomplicate It)
Find quick wins (AI powered research tools, document summarization, etc.).
Identify big bets (investment scoring, predictive analytics, automation).
Test, Learn, and Iterate
Don’t get caught up in perfection—AI is an evolving tool, not a one time install.
Pilot AI tools, measure impact, and adjust as needed.
GenAI tools are here and you need to be using them. There is a ton of value to be had but only if companies approach it the right way. Whether you’re in PE, utilities, healthcare, or any other industry—lead with strategy, invest in people, and let GenAI be the accelerator, not the driver.
Would love to hear how you're thinking about and addressing GenAI in your workplace!
citation:
Bick, A., Blandin, A., & Deming, D. J. (2024). The rapid adoption of generative AI (NBER Working Paper No. 32966). National Bureau of Economic Research. https://www.nber.org/papers/w32966
P.S
The picture at the top is from Lake Coeur d'Alene. I've been going there almost every summer since before I can remember—it's a beautiful place.