For the past two years, I have watched organizations rush toward artificial intelligence with the same urgency that companies once reserved for cloud computing and digital transformation. Boardrooms are demanding AI strategies. Investors are asking about AI roadmaps. Press releases announcing AI initiatives appear almost daily.

Yet from where I sit, many companies do not have an AI problem. They have a data quality problem.

That distinction matters because organizations are spending enormous amounts of money trying to unlock value from AI while overlooking the very thing that determines whether those investments will succeed. AI is only as good as the information it receives. If the data is incomplete, inaccurate, inconsistent, or misunderstood, the output will reflect those flaws at scale.

The technology is not the bottleneck. The data is. The enthusiasm surrounding AI is understandable. A survey shows that 88% of executives plan to increase AI-related budgets, while nearly 79% organizations report some level of AI agent adoption.

What concerns me is that many organizations are investing in AI before they fully understand the condition of the information that powers it. In many boardrooms, the sequence has become backwards. Leaders decide they need AI. Budgets are approved. Public announcements are made. Expectations are established. Only later does the organization begin to discover that the underlying data is fragmented, outdated, inconsistent, or simply wrong.

That is not an AI failure. It is a foundation failure.

The reputational risk may be even greater than the financial risk. When a CEO tells investors, directors, employees, and customers that AI will transform operations, those promises create expectations. If the technology fails to deliver because the underlying information was never reliable, the........

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