Do we really need AI to improve business intelligence?

A recent conversation with Amazon’s S3 chief shows that perhaps we’ve mis-sold stuff. It should come as no surprise the IT industry always appears to want to seek out a “new solution”. One only has to look back at the business intelligence revolution of the 1990s and early noughties with the likes of SAS’ Jim Goodnight, Business Objects and Crystal Reports, which were both acquired by ERP giant SAP. They effectively provided a layer on top of relational database systems, which meant that you didn’t need to be an SQL whizz to run sophisticated queries in order to retrieve datasets to inform decision-making and drive downstream business processes. While a cynic would argue the IT sector is only driven by this need to sell customers more stuff, there is definitely an argument that when tech firms actually engage with their customners, they find exceptions to the problems their “solution” aims to fix. In its purest sense,this drives product development and hopefully everyone then benefits from the next iteration of the product that encompasses these customer pain-points. So what’s the problem with business intelligence? Mai-Lan Tomsen Bukovec, a vice president of technology at AWS responsible for data and analytics, believes that people simply don’t understand their data. She says: “One of the great ironies of BI software is it requires users to understand their data, or as she puts it “know your data”. “You have to know where your data is. You have to know where the schema is. You have to know if it’s the clean data set or just the data set that a bunch of teams are working with. You have to actually know your data to know your data.” In other words, a lack of understanding of datasets limits the usefulness of data to the population of people who can use BI tools effectively. And this is why Bukovec and other industry experts believe that artificial intelligence will become the interface for business intelligence. According to Bukovec, one of the reasons why AI inference works for business intelligence so well is because a lot of context can be gleaned from the way the question is asked. In the context of understanding business data, she says: “If your agentic AI infrastructure has some knowledge about your role based on the type of questions you’re asking, it can infer what to do with the answer in terms of how to frame the answer and how to frame options for your answer.” Bukovec may well have a point, but it does seem an awful waste of expensive AI tokens to address a problem BI was supposed to solve. Listen to the podcast >>