Below are key highlights from our PE CFO Roundtable in New York City, October 2025
We recently convened a group of PE and VC CFOs to discuss AI adoption in their firms. The conversation revealed a pragmatic, measured approach offering valuable insights into what’s actually working.
The Basics
Most firms have standardized ChatGPT or Claude as foundational tools, deploying enterprise licenses with appropriate data controls. While compliance teams are understandably cautious about personally identifiable information (PII), these platforms are proving to deliver significant productivity improvements.
We found that using AI to significantly shorten task timelines is quickly becoming standard practice among us. This not only enhances efficiency but also sets a new benchmark for productivity.
High-Impact Use Cases
Everyone was excited to learn about practical applications that are working right now and that are generating immediate gains.
Expense management is a clear area for a quick win. Many firms use Concur or Ramp’s AI-enabled platform, which has effectively eliminated traditional expense reporting. The time previously spent by administrative staff on receipt management and reconciliation has been redirected to higher-value work.
Document analysis is proving valuable across functions. Teams are using AI to synthesize contracts, investment memos, and technical reports.
Transcribing calls and video conferences has emerged as a “killer app” for many users. The ability to capture details from conversations and quickly synthesize and analyze the information has become invaluable. However, there’s a consensus that not every conversation should be recorded. Currently, most firms rely on their judgment, but there’s an opportunity to establish clear controls to ensure that private conversations remain confidential.
Research and analysis applications are expanding, particularly around portfolio company monitoring and market analysis, though firms remain selective about data inputs.
Human review and refinement are still necessary, but AI’s efficiency and effectiveness in organizing and presenting information is undeniable.
The Future of Fund Administration
The most robust discussion centered on fund administrators. Overall, the consensus is that a significant disruption is coming within 3-5 years. Legacy providers offer reliability but poor data accessibility, while tech-first platforms provide superior data access but less operational maturity.
Interestingly, the economics create a slower adoption curve than you might expect. The switching threshold is high; most didn’t think a 10% or even 20% cost reduction would justify the transition risk.
The Consulting Landscape
Private equity firms are assessing partnerships with various consultants, including both larger and boutique ones. The feedback received has been mixed. Although these consulting firms provide valuable expertise, there is a reluctance to pay premium fees for untested solutions.
EY got specific praise for a hands-on AI session they ran for one firm’s entire back-office team. The focus was on technology, along with teaching people to prompt effectively and identify “quick wins” achievable with off-the-shelf tools.
Talent and Organizational Structure
Here’s where it gets tricky. Everyone wants AI expertise, but no one can compete with the compensation packages tech firms and bulge brackets are offering AI talent. Everybody agreed that instead of solely recruiting outside AI specialists, the primary goal should be to “turbocharge” and enhance the AI capabilities of existing teams.
The emerging model: leverage external expertise for discrete projects rather than building internal AI departments. Several firms have established cross-functional working groups to share learnings and evaluate tools, with representation from IT, operations, legal, compliance, and investment teams. In many other cases, it’s the CFO who leads a broader investigation into where AI can be applied most effectively and which technologies and consultants are worth considering.
The question is, how are firms upskilling existing talent at all levels to effectively leverage these tools? Will senior leadership risk falling behind if they do not build these capabilities? Are there opportunities for junior talent to advance more quickly when they do?
The Real Objective: Scale Without Headcount
Almost everyone agreed on this point: the goal isn’t huge headcount cuts. Most firms feel they already operate with a lean structure, and most finance departments are small to begin with (many firms aren’t big enough for staff reductions to move the needle anyway). The real prize is growing your AUM and portfolio without growing your team.
The near-term impact will likely concentrate on junior and mid-level roles that handle repetitive analytical work, though this raises questions about developing the next generation of leadership talent.
Infrastructure Realities
Data infrastructure was discussed as the biggest constraint. Many firms need significant investment in data cleaning, access, and standardization before they can capture AI’s full value. Legacy file structures, inconsistent data schemas, and unclear permissioning all require remediation. This access often conflicts with the resilience needed for unrestricted access across the firm. There are clear data privacy concerns, but a balance in access is also necessary.
Looking Forward with a Measured Approach
The most exciting conversation was about what will happen 12-18 months from now, when AI can handle an entire workflow end-to-end. Not just enhancing or accelerating discrete tasks, but actually replacing a complete process.
The most sophisticated PE firms are taking a measured approach. The ones that seem furthest along are those:
- Deploying enterprise tools with transparent governance
- Creating forums for cross-functional learning
- Focusing on “turbocharging” everyone in the firm with prompting skills and a framework for identifying practical applications
- Avoiding expensive vertical solutions that merely wrap base models
The real progress in AI adoption stems from cultural change that empowers teams to identify and solve problems independently. Curiosity and initiative matter more than top-down mandates, making it essential to nurture a culture where your team feels empowered to find and implement solutions on their own. The strategic question is no longer whether to engage with AI, but how quickly you can get started.
The Bottom Line: AI’s Sustainable Competitive Advantage
AI adoption in private equity is neither the dramatic revolution promised by vendors nor the insurmountable risk feared by compliance teams. Instead, it’s a pragmatic, infrastructure-heavy evolution demanding cultural adaptation and patience for incremental progress.
The most competitive PE firms aren’t chasing every new tool. They are building environments where teams are empowered to experiment and implement solutions that tackle real operational challenges. That culture is the sustainable competitive advantage in the AI era.