VIEWPOINT Why agentic AI won’t run on legacy BI Autonomous agents are only as trustworthy as the data beneath them. Migrating legacy business intelligence onto a modern Microsoft foundation is where agentic readiness really begins Neelabh Saxena (top) is a principal and Satish Jha is an associate principal at global management consulting and technology firm ZS Leaders deciding how quickly to deploy AI agents need to know whether their business intelligence (BI) foundation is ready. Microsoft’s own research points to the rise of the frontier firm, an organisational model built around human-agent teams. AI agents are only as reliable as the data they reason over. As organisations look to Microsoft tools including Fabric, Power BI and Copilot to support new ways of working, many are discovering their existing BI estate was never designed for autonomous decision-making. Walk into a typical large enterprise and you’ll find reporting scattered across Tableau, Qlik or MicroStrategy, with business logic hard coded into hundreds of individual dashboards. A human analyst can navigate that fragmentation. An autonomous agent cannot. Ask an agent which accounts are at risk and it needs one governed definition of what ‘at risk’ actually means, not five conflicting versions living in different teams. Static dashboards were built to be read, not to be reasoned over. That is why agentic readiness is, before anything else, a data architecture problem. Legacy BI is also expensive to maintain. Enterprises routinely pay three to five times more to license and run legacy BI than a modern platform would cost, before the hidden overhead of separate servers, gateways and specialist skills is even counted. Every dollar spent keeping legacy reporting alive is a dollar not invested in AI. Modernising typically frees 30 to 40 per cent of that spend, which can be redirected into AI and data engineering. Many organisations already own the platform they would migrate to. Photo: istock/SOMKID THONGDEE 76
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