To increase the value of expert calls, investors should use them to validate or invalidate a pre-existing thesis. This structured approach yields more satisfying and insightful conversations than open-ended fact-finding.
Transcript libraries allow investors to quickly learn industry basics. This elevates the purpose of live expert calls from foundational learning to asking much deeper, nuanced questions that challenge a specific investment thesis.
Instead of manually conducting research, the modern investor's core skill is becoming the ability to architect systems. This involves designing AI prompts, workflows, and automated reports that create leverage for portfolio monitoring and idea generation.
Nearly 40% of AlphaSense's business comes from corporate clients. Corporate development, strategy, and investor relations teams are now major consumers of expert call libraries to understand how investors view their industry and competitors.
At the end of an expert call, ask the expert to consider a scenario where your agreed-upon conclusions are incorrect. This prompts them to reveal second and third-order risks and blind spots that may not have surfaced during the main discussion.
To test an expert's overall sentiment, ask an unrelated "burner question," such as about company culture. A sudden shift in tone can reveal underlying biases or problems not apparent when discussing business models or market structure.
All data inputs for AI are inherently biased (e.g., bullish management, bearish former employees). The most effective approach is not to de-bias the inputs but to use AI to compare and contrast these biased perspectives to form an independent conclusion.
AI tools are automating traditional analytical tasks, diminishing the edge from pure technical skill. The most valuable investors will be those who can apply superior judgment, market structure understanding, and pattern recognition to challenge and interpret AI-generated insights.
AI can be a powerful fraud detection tool by comparing a company's public statements against alternative data. For example, it can analyze satellite imagery of shipping traffic or factory activity and flag discrepancies with management's guidance.
While summarization is useful, AI's unique power is creating a massive grid comparing perspectives from management, sell-side analysts, and expert calls on key business drivers. This helps investors quickly identify the most critical debates for deeper research.
Previously, conducting large-scale surveys via expert calls was cost-prohibitive. AI-led interviewers remove human time constraints and dramatically lower costs, enabling investors to gather real-time market sentiment from hundreds of sources simultaneously.
During the time crunch of earnings season, AI excels at synthesizing disparate information. It can instantly compare a CEO's positive guidance against the recently reported cash flow statements of multiple competitors, flagging potential overconfidence or a genuine outlier.
AI isn't necessarily leading PE funds to do more deals. Instead, it compresses the initial, time-consuming phase of diligence from weeks to a single day, allowing teams to reallocate their energy toward deeper debate on core value creation drivers.