What Problem Is AI Actually Trying to Solve?
The hype around AI has reached a fever pitch. Billions of dollars pour into AI startups; enterprises scramble to integrate AI into their workflows; and tech leaders evangelize its transformative potential. Yet, amid all the noise, a fundamental question remains unanswered: What problem is AI actually designed to solve?
Surprisingly, many investors and executives struggle to articulate a clear answer. A quick scan of LinkedIn reveals a common refrain: “We’re still exploring the best use cases for AI.” If that’s correct, why were billions invested before identifying concrete problems? This suggests that, in many instances, AI is the proverbial solution in search of a problem—a technological hammer looking for nails to smash.
I don’t mean that AI lacks value. It can solve real challenges, but only if organizations first define what those challenges are. Too often, AI is deployed reactively—thrown at symptoms rather than root causes—leading to wasted resources, disillusionment, and even deeper inefficiencies.
Data Overload and Poor Metadata Management
One of AI’s most compelling use cases is managing the overwhelming volume of unstructured, poorly tagged data that organizations accumulate. Many companies sit on tens of billions of files—documents, emails, images, logs—with no effective way to categorize or retrieve them. AI can:
♦ Automate metadata tagging at a scale impossible for humans.
♦ Improve searchability, making it easier to locate critical information.
♦ Identify redundant or obsolete data, helping organizations clean up digital clutter.
But here’s the catch: The existence of so much unmanaged data points to a deeper issue—poor data governance. Companies have spent decades hoarding information without lifecycle policies, retention rules, or systematic organization. AI can help clean up the mess, but it doesn’t address the cultural and procedural failures that created the mess in the first place.
Decision Making Under Complexity
Another legitimate problem AI tackles is decision paralysis caused by information overload. Modern enterprises generate staggering amounts of data yet struggle to extract actionable insights. AI-powered analytics can:
♦ Surface patterns in large datasets that humans might miss.
♦ Predict trends (e.g., supply chain disruptions, customer churn).
♦ Automate routine decisions, freeing up human bandwidth for strategic thinking.
However, AI’s effectiveness here depends on the quality of input data. Garbage in, garbage out. If decision-making processes are flawed because of bad data practices, AI won’t magically fix them— it might just automate the dysfunction.
The Illusion of ‘Better Search’ as Innovation
Many AI applications are, at their core, enhanced search tools. Whether it’s ChatGPT retrieving information or enterprise AI indexing internal documents, the underlying function is often just finding things faster.
But why do we need better search? Because things are lost in the first place. The fact that employees waste hours hunting for files or re-creating existing work are symptoms of bad information management, not a lack of AI. Throwing AI-powered search at the problem without fixing the underlying disorganization is like using a flamethrower to light a candle—overkill—and potentially dangerous.