Previously, data analysis required deep proficiency in tools like Excel. Now, AI platforms handle the technical manipulation, making the ability to ask insightful business questions—not technical skill—the most valuable asset for generating insights.
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As platforms like AlphaSense automate the grunt work of research, the advantage is no longer in finding information. The new "alpha" for investors comes from asking better, more creative questions, identifying cross-industry trends, and being more adept at prompting the AI to uncover non-obvious connections.
The most powerful use of AI for business owners isn't task automation, but leveraging it as an infinitely patient strategic advisor. The most advanced technique is asking AI what questions you should be asking about your business, turning it from a simple tool into a discovery engine for growth.
Instead of presenting static charts, teams can now upload raw data into AI tools to generate interactive visualizations on the fly. This transforms review meetings from passive presentations into active analysis sessions where leaders can ask new questions and explore data in real time without needing a data analyst.
With AI agents automating raw code generation, an engineer's role is evolving beyond pure implementation. To stay valuable, engineers must now cultivate a deep understanding of business context and product taste to know *what* to build and *why*, not just *how*.
AI is commoditizing knowledge by making vast amounts of data accessible. Therefore, the leaders who thrive will not be those with the most data, but those with the most judgment. The key differentiator will be the uniquely human ability to apply wisdom, context, and insight to AI-generated outputs to make effective decisions.
The entire workflow of transforming unstructured data into interactive visualizations, generating strategic insights, and creating executive-level presentations, which previously took days, can now be completed in minutes using AI.
Before diving into SQL, analysts can use enterprise AI search (like Notion AI) to query internal documents, PRDs, and Slack messages. This rapidly generates context and hypotheses about metric changes, replacing hours of manual digging and leading to better, faster analysis.
Beyond automating data collection, investment firms can use AI to generate novel analytical frameworks. By asking AI to find new ways to plot and interpret data inputs, the team moves from rote data entry to higher-level analysis, using the technology as a creative and strategic partner.
Technical implementation is becoming easier with AI. The critical, and now more valuable, skill is the ability to deeply understand customer needs, communicate effectively, and guide a product to market fit. The focus is shifting from "how to build it" to "what to build and why."
