AI and the evolution of knowledge work

With fund operations reliant on documents, workflows, and judgment-driven processes, GPs face both an opportunity and an imperative: reimagine how the business runs in a world where AI is rapidly reshaping knowledge work.

In his most recent appearance on The Distribution, Juniper Square CEO and Co-founder Alex Robinson outlined three key trends shaping the next era of the private markets—and what GPs should do now.

AI is evolving far beyond simple productivity tools. With the emergence of transformer-based models, it can now interpret complex language, summarize vast amounts of information, and perform tasks traditionally handled by analysts, associates, and operations teams. 

Robinson forecasted that “ninety percent of the work that's being done by humans today in and around the operations and management of a fund will be done by machines.” 

This includes tasks like data extraction, CRM updates, report preparation, reconciliation, memo drafting, pipeline analysis, and investor updates. These functions will increasingly be completed by AI, which is not only faster but will ultimately be more accurate and consistent. 

However, the rise of automation does not diminish the role of humans—it elevates it. Relationship building, decision-making, trust, and strategy become even more important as repetitive work is automated. 

As Robinson put it: “That is the thing that is rare. That’s the thing that’s special about GPs. They facilitate investing through relationships—long-standing, deep, hard-to-build relationships across industries. That’s what really differentiates a GP. If it’s about investing, it’s about the relationships. And if it’s not those things, then it’s a distraction.”

General-purpose AI is powerful, but not built for the nuances of the private markets. Complex documents like LPAs, subscription agreements, asset reports, and credit agreements require deep contextual understanding. “It's hard for the foundation models to go pick every little corner of fund administration and train and become an expert on them,” Robinson explained. “The value will be created by those companies that have deep domain knowledge.” 

This reflects a broader truth: the next generation of private markets technology will not simply be about embedding AI into existing systems, but about applying domain expertise to ensure accuracy, compliance, and trust. Industry-specific, validated, and secure AI will become essential infrastructure.

With AI capabilities accelerating exponentially, there is a widening gap between what’s possible and what firms are operationally prepared to adopt. Robinson warned that “The challenge is not going to be the presence of those tools. The challenge is going to be the reengineering, the rearchitecting of the business process that's required to take to take advantage of them.” 

GPs that wait too long will face steeper change curves, cultural resistance, and competitive disadvantage. Firms that begin experimenting now—building AI literacy, modernizing data infrastructure, and encouraging teams to rethink workflows—will compound their advantage over time.

The firms that will thrive in the next five years aren’t the ones waiting for AI to stabilize—they’re the ones actively preparing for it. As Robinson makes clear, the technology is advancing faster than organizational behavior, and the real challenge for GPs will be rethinking how their businesses operate, not choosing which tools to adopt. 

To stay ahead, GPs should focus on: 

1. Building AI literacy across every team. Teams don’t need to become technologists, but they do need to understand what AI can (and can’t) do. A shared baseline of knowledge accelerates adoption and reduces resistance—a critical edge in a period of rapid change.
2. Identifying knowledge-work processes that can be automated today. If 90% of operational work will ultimately be handled by machines, the most competitive firms will be the ones that begin shifting repetitive, document-heavy workflows to AI now. Start with reporting, reconciliation, onboarding, CRM hygiene, and investor communications.
3. Strengthening and standardizing data. Domain-specific AI can only perform as well as the data it is trained on. GPs should centralize, structure, and validate core data so future AI agents can operate with accuracy, transparency, and control—especially across fund administration and investor operations.
4. Begin experimenting—and learning—now. Rearchitecting workflows takes time, iteration, and cultural change. Small pilots create compounding learning and help firms understand how AI actually integrates into their day-to-day processes. Our AI survey found that VC firms are leading this charge, with 86% of respondents already piloting or using AI to scale and streamline work output and gain a competitive advantage. 

Listen to the full interview here.