In the Age of AI, your tech debt is an even bigger issue than you think

Only 5% of enterprises say their data is ready for AI. I think the real number might be even lower. Most organizations are about to learn what I spent 15 years teaching companies in the Business Intelligence (BI) era: The problems you ignore don’t disappear. They wait. In business, perfection is impossible and there isn’t time to chase everything. Problems tend to get addressed only when they directly threaten the flow of money. I learned the hard way running a data and BI consultancy, because our work had a way of forcing organizations’ unaddressed sins to the surface. Miscategorized entries in a product catalog, for instance, don’t prevent customers from buying products off the shelf and so they can sit undetected for years. But when you start tracking sales performance by category over time, that formerly harmless data quality issue becomes critically important. Ditto for imprecisely tracked inventories, scattered duplicate customer records, and time zone mismatches across systems. These problems aren’t noticed until something new, like BI, or now AI, brings them into focus. That was the lesson of the BI era. It’s about to become a front-and-center crisis in the AI era. Scale makes debt worse, not better Here’s what leaders need to understand: Throwing more computers and engineers at artificial intelligence projects doesn’t solve underlying data friction. It amplifies it. I’m already seeing AI projects collide with the same below-the-radar data quality problems that derailed BI initiatives, except the stakes are higher and the pace is faster. And in the AI era, “data quality” itself has gotten bigger. AI demands more of your data than BI ever did. Historically, data quality problems meant inaccurate data sitting in a database-wrong product category, wrong inventory level. Incorrect data of that flavor results in incorrect decisions, whether it’s a human reading a misleading dashboard or an AI agent querying the data directly. This “original flavor” of data quality is more important than ever, and in my experience, there’s still much work to be done here.