The biggest failure of BI tools is analysis paralysis. The most effective AI data platforms solve this by distilling all company KPIs into a single daily email or Slack message that contains one clear, unambiguous action item for the team to execute.

Successful B2B AI companies create "dashboard" products that become the daily home screen for a worker's core task, like Graphite for code review. This "cockpit" approach captures user workflow and attention, proving more valuable than "pipes" infrastructure that runs invisibly in the background.

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.

True innovation requires building features customers don't yet know to ask for. Bloomberg's success came from providing functionality users hadn't imagined was possible with computers, rather than just reacting to their explicit requests.

For data-heavy queries like financial projections, AI responses should transcend static text. The ideal output is an interactive visualization, such as a chart or graph, that the user can directly manipulate. This empowers them to explore scenarios and gain a deeper understanding of the data.

A common mistake is building a visually impressive data product (like Google Earth) that is interesting but doesn't solve a core, recurring business problem. The most valuable products (like Google Maps) are less about novelty and more about solving a frequent, practical need.

Many leaders focus on data for backward-looking reporting, treating it like infrastructure. The real value comes from using data strategically for prediction and prescription. This requires foundational investment in technology, architecture, and machine learning capabilities to forecast what will happen and what actions to take.

Bloomberg initially built its own computers because PCs didn't exist. Once commercial PCs became available, they immediately abandoned their hardware to focus on their unique value: data and software. This shows a ruthless focus on core competencies and an ability to pivot away from sunk costs.

The most durable moat for enterprise software is established user workflows. The current AI platform shift is powerful because it actively drives new behaviors, creating a rare opportunity to displace incumbents. The core disruption isn't just the tech, but its ability to change how people work.