Data Silos Are Now an Agent Problem
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AI agents are getting better at looking at different types of data in businesses to spot patterns and create value. This is making data silos increasingly painful. This is why I increasingly try to select software that lets me control my own data, so I can make it available to my AI agents. Because of AI’s growing capabilities, the value you can now create from “connecting the dots” between different pieces of data is higher than ever. For example, if an email click is logged in one vendor’s system and a subsequent online purchase is logged in a different one, then it is valuable to build agents that can access both of these data sources to see how they correlate to make better decisions. Unfortunately, many SaaS vendors try to create a data silo in their customer’s business. By making it hard for you to extract your data, they create high switching costs. This also allows them to steer you to buy their AI agent services — sometimes at high expense and/or of low quality — rather than build your own or buy from a different vendor. Unfortunately, some SaaS vendors are seeing AI agents coming for this data and working to make it harder for you (and your AI agents) to efficiently…
Context
An that needs information from several business systems can be blocked by the terms on which those systems expose data. Andrew Ng illustrates this in a post about data silos: connecting an email click with a later purchase requires access to records held by different services.
He reports that one vendor wanted more than $20,000 for API access to his team's customer data. That is an account of one vendor interaction, but it explains his purchasing criterion: assess whether a business can route its own records to the people and agents it chooses. Data access can determine whether a cross-system workflow is feasible.
- AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
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