Your AI model isn’t the problem. Your data was never ready for it
I’d built and defended executive dashboards for years before that meeting, and dashboards had trained me to believe imperfect data was a manageable, even routine problem. Experienced leaders read a dashboard with context. They know which numbers to trust, which ones need a caveat and which gaps to mentally fill in based on what they already know about the business. Predictive AI doesn’t have that judgment. Machine learning assumes the historical data it’s trained on represents reality as it actually is. If two departments define “active customer” differently, or if a critical field has been silently incomplete for two fiscal years, the model doesn’t notice or compensate. It learns the inconsistency as ground truth, and it repeats that mistake at scale, with confidence, every single time it runs.
That moment fundamentally changed how I approach every AI initiative. I stopped starting with technology and started with data integrity instead.
5 questions I ask before any AI platform conversation
Today, I rarely begin AI discussions by talking about technology. Before any conversation about platforms or vendors, I ask five questions of the leadership team. Can we explain, in plain language, where this data actually comes from? Do the business leaders in the room agree on what our core definitions mean, or does “revenue” or “active account” shift depending on who’s presenting? Would we rely on this data to make a multimillion-dollar decision without a human manually double-checking it first? Is there a specific, named person accountable for every critical dataset, or does ownership dissolve the moment something goes wrong? And underneath all of it, are we actually solving a business problem, or are we chasing a technology because it’s the thing everyone else is talking about this quarter?
