Building Competitive Advantage Through Agentic Firm AI Workflows

Private equity firms are moving from using AI as a basic research tool to building Firm AI. Most firms currently use AI to speed up content creation, like drafting presentations or summarizing transcripts. However, the real advantage comes from using agentic workflows that carry out a firm's specific internal procedures. This shift from passive research to autonomous execution builds a systemic moat, turning institutional memory into an active part of the decision process. Investors and operators who ignore this change risk falling behind firms that integrate AI into their core deal lifecycle, effectively automating the way they do business rather than just the output. This analysis is for professionals who want to understand how operational efficiency leads to long-term dominance.

The hidden cost of fast AI solutions

Most firms are focused on the immediate gains of frontier models, using them to generate models or pitch decks in hours instead of days. While this helps junior productivity, it is only a first-order optimization. The deeper issue is that these models are generic.

Ben Harrison, President of Industries at Intapp, argues that the real leverage is not in research, but in executing the firm's specific, hard-won procedures. When AI agents are trained on a firm's proprietary data--the history of interactions, the way an Investment Committee evaluates risk, and the nuances of past deals--the AI becomes a reflection of the firm itself.

> "If you can have AI draw on the entire history and knowledge of the firm, everything you've ever done surrounding that company and that management team, you have a specific way that private equity firm acts. Takes your plan, your strategy, your operating model, your pitch against that criteria. You're matching those things together."
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> -- Ben Harrison

The competitive advantage here is delayed. Firms that invest in mapping their internal processes into Firm AI now will have a system that learns and compounds over years, while competitors relying on generic tools will remain stuck in a cycle of manual oversight.

Where immediate pain creates lasting moats

The biggest hurdle in deploying agentic AI is governance, not the technology. Firms hold sensitive information, including confidentiality obligations and complex ethical walls. Harrison notes that AI does not naturally understand these constraints; it requires a structural layer of governance that knows the firm's specific policies.

This creates a difficulty moat. Most teams will avoid the heavy lifting of mapping their ethical walls and regulatory constraints into an AI-governed system because it is tedious and requires internal alignment. However, this discomfort creates the advantage. By building these constraints into the core of their AI agents, firms can operate at a speed others cannot safely match. They are not just faster; they are faster within the bounds of their legal and ethical obligations.

> "The number one questions that are unanswered are around governance and security... All of our technology and systems has to understand that procedure of confidentiality obligations to the market to the clients, those policies and procedures, those information barriers, those ethical walls. Those things have to be executed perfectly."
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> -- Ben Harrison

How the system routes around your solution

A common mistake is assuming technology will replace human capital entirely. Harrison observes that while the debate over replacing humans is complex, the system is already responding. Large firms are keeping their associate classes the same size while increasing output, which signals that they are not replacing the human, but supercharging them.

The system is avoiding the replacement narrative by focusing on margin pickup in the junior ranks. By automating data processing, the firm does not necessarily need fewer people, but it does need a different type of professional--one who can audit and iterate on the AI output. The winning firms will be those that treat the AI as a coworker in the Investment Committee, using it to challenge assumptions rather than just process inputs.

Key action items

• Audit your institutional memory: Over the next quarter, identify where your firm's unique process knowledge resides, such as email, CRM, or internal docs. If it is not digitized and accessible, it cannot be automated.
• Map your procedure vs. your output: Distinguish between the work you produce, like decks or models, and the procedure you follow, such as IC approval or the deal lifecycle. Prioritize automating the latter to build a structural advantage.
• Implement governance-first AI: Before deploying agents, ensure your information barriers and ethical walls are baked into the system architecture. This pays off in 12 to 18 months by allowing you to scale without regulatory or reputational risk.
• Shift from research to execution: Stop measuring AI success by time saved on research. Start measuring it by the number of agentic workflows executed without manual intervention.
• Focus on the mid-market advantage: If you are a mid-market firm, use this AI cycle to skip the line. You can now build infrastructure that previously required the massive R&D budgets of top-tier firms. Start this investment immediately to avoid being left behind.