While content generation is impressive, the highest value for financial professionals lies in using AI as a verification layer. A tool that can audit a complex model and catch a single, costly mistake provides more immediate ROI than one that simply builds the model from scratch.
AI labs profit from token generation, creating a "big token" economy that conflicts with enterprise budgets. The solution is to use a portfolio of models—large ones for complex tasks and smaller, cheaper ones for simple edits—to optimize the cost-performance ratio.
Enterprises that track and reward high AI token usage risk incentivizing the wrong behavior. This is a modern "Cobra Effect," where employees generate unnecessary output to hit metrics, much like people who bred cobras to collect a bounty. The focus must be on utility, not volume.
The rapid improvement in AI technology has enabled a fundamental shift in strategic thinking. The world's largest consulting firms are now actively questioning the future of core business tools like Excel and PowerPoint, a conversation that was impossible until very recently.
Initial AI products for PE focused on high-level tasks like summarizing deal memos. However, analysts and associates need to perform this work manually to gain the molecular-level understanding required for investment committee discussions, rendering the automation counterproductive.
AI will automate routine but complex tasks like chasing portfolio companies for financials and updating models. The associate's role will shift to managing these automated workflows, setting quality checks, and handling exceptions, much like a conductor leading an orchestra.
The best vertical AI tools aren't built by simply using the latest LLM. They require shaping the model's behavior like training a new analyst, including "vibe checks" from industry experts to ensure outputs align with professional norms, rather than just passing technical benchmarks.
The competitive edge in AI tools is moving beyond access to powerful LLMs. The real value now lies in creating a specialized "harness" or framework—an "Ironman suit" for the model—that enables it to perform narrow, high-value tasks with precision and industry-specific nuance.
The diligence process will be partially automated, with an AI agent on the buyer's side generating follow-up questions that are then proactively answered by an agent on the seller's side. This will dramatically speed up information exchange and reduce manual work for deal teams.