Artificial intelligence is moving from experimentation to execution in private equity, but the transition is proving uneven across the market. While many firms have embraced AI at the surface level, far fewer have embedded it into the workflows that drive sourcing, diligence, and portfolio value creation. Evan Berta, an associate at Hunt Scanlon Ventures, examines how agentic AI is reshaping operating models, widening performance gaps, and placing new demands on leadership and talent.
Multimodal’s latest report, The State of Agentic AI in Private Equity, highlights a critical inflection point. While AI adoption is widespread across the industry, very few firms have translated that activity into sustained operational impact.
Nearly two-thirds of private equity firms are actively piloting AI, and roughly 40 percent report having formal strategies in place. Yet only 10 to 15 percent have achieved systematic deployment across their organizations, creating a clear divide between experimentation and execution.
As the report makes clear, the advantage is no longer access to AI. It is the ability to operationalize it.
From Tools to Workflows
The shift toward agentic AI marks a fundamental change in how technology is applied in private equity. Unlike earlier tools that supported isolated tasks, agentic systems operate across multi-step workflows, integrating sourcing, diligence, and portfolio monitoring into continuous processes. This distinction is critical in an industry that has long relied on fragmented, manual workflows, with insights dispersed across spreadsheets, emails, and siloed systems.
“AI is not just changing how work gets done. It is changing how workflows are structured across the investment lifecycle,” said Evan Berta, an associate at Hunt Scanlon Ventures.
Firms that successfully integrate AI are not layering tools onto existing processes. They are redesigning operating models to embed automation directly into how decisions are made, creating more consistent, repeatable execution across deals and portfolios.
The Execution Gap Widens
Despite strong adoption at the surface level, most firms are struggling to scale AI beyond isolated use cases. Pilots succeed because they operate in controlled environments with curated data, but scaling fails when those tools encounter fragmented systems, inconsistent data, and real-world governance requirements. As a result, nearly 95 percent of AI pilots fail to translate into production environments.
“Scaling AI in private equity is fundamentally an organizational challenge, not a technical one, because it requires aligning data, workflows, and decision-making across the entire platform.”
For private equity, this challenge is amplified by its operating model. Firms must deploy AI across dozens of portfolio companies, each with different systems, workflows, and data structures, making standardization inherently difficult.
“Scaling AI in private equity is fundamentally an organizational challenge, not a technical one, because it requires aligning data, workflows, and decision-making across the entire platform,” said Mr. Berta.
The implication is clear. Firms that cannot move beyond pilots will struggle to generate meaningful returns from their AI investments.
Value Creation Moves to the Operating Layer
This execution gap is emerging at the same time private equity is shifting more decisively toward operational value creation. With elevated dry powder and constrained exit environments, firms are under increasing pressure to generate returns through performance improvement rather than financial engineering.
Agentic AI is accelerating that shift. Early applications are already expanding sourcing coverage, compressing diligence timelines, and enabling more continuous portfolio monitoring. What once required days of manual work can now be completed in hours, allowing teams to focus more on analysis and decision-making.
“Firms that embed AI into core workflows are effectively building a repeatable system for how value is identified and executed across their portfolios,” said Mr. Berta.
Over time, these systems create compounding advantages through better data, faster decision cycles, and more consistent execution across investments.
Talent Becomes the Scaling Constraint
While the technology is advancing rapidly, the report makes clear that talent and organizational readiness remain the primary bottlenecks. AI is already reshaping how teams operate, shifting junior professionals away from manual data extraction toward higher-value tasks such as analysis, judgment, and oversight of AI-driven workflows.
“The firms that scale AI effectively will be those that invest as heavily in talent and operating models as they do in the technology itself.”
This evolution requires new skill sets. Professionals must be able to interpret outputs, validate recommendations, and understand when to override automated decisions. At the same time, firms must build internal capabilities across data engineering, product development, and workflow design to support scaled deployment.
“The firms that scale AI effectively will be those that invest as heavily in talent and operating models as they do in the technology itself,” said Mr. Berta.
As a result, organizational design is becoming a key differentiator. Leading firms are building dedicated AI functions, integrating operating partners into deployment efforts, and creating cross-functional teams to drive adoption. These changes reflect a broader reality: AI implementation is not a software upgrade, it is a transformation of how the firm operates.
A Bifurcating Market
The result is a market that is beginning to split. A small group of execution-mature firms is embedding AI into core workflows, capturing measurable gains, and building compounding advantages through data and learning effects. Meanwhile, the majority remain in pilot mode, experimenting without achieving scale.
This divergence is expected to widen over time. Leading firms are already seeing improvements in sourcing efficiency, underwriting consistency, and portfolio performance, while lagging firms risk falling behind in deal execution, talent attraction, and LP perception.
“AI is becoming a multiplier on execution, which means the gap between firms that can operationalize it and those that cannot will continue to expand,” noted Mr. Berta.
The Big Shift Ahead
The report reinforces a broader shift already underway across private equity. Technology is no longer the differentiator. Execution is. Agentic AI will become increasingly embedded across the investment lifecycle, but its impact will depend on how effectively firms integrate it into their operating models.
For executive search firms and human capital advisors, this creates a clear opportunity, as demand rises for leaders who can operate at the intersection of technology, operations, and investment strategy. For investors, the implication is more direct. Performance will increasingly be determined by organizational capability, not just deal-making.
Returns are no longer just built through transactions. They are built through systems.
Article By
Evan Berta
Evan Berta is Editor-in-Chief of ExitUp, the investment blog from Hunt Scanlon Ventures designed for professionals across the human capital M&A sector. Evan serves as an Associate for Hunt Scanlon Ventures, specializing in data analysis, market mapping, and target list preparation.
