Like the early hype around data science, today’s AI buzz oversells general intelligence. The real value is in specialized use cases that automate repetitive tasks and boost efficiency.
By now, you’ve probably heard the big claims around AI: agents will handle complex decision-making, automate workflows end-to-end, and replace knowledge workers.
Those in the data industry might recall similar hype around machine learning a decade ago. Companies believed deep learning models would predict customer behavior with pinpoint accuracy, uncover hidden insights, and transform decision-making.
We’ve seen this play out before. Machine learning didn’t revolutionize decision-making. Instead, it found its biggest impact in narrow, well-defined use cases.
Instead of replacing analysts, it became a tool for automation and optimization, excelling at tasks like fraud detection, recommendation systems, and anomaly detection.
10 years ago, companies expected machine learning to:
The Reality: While ML proved valuable, its biggest wins came from specific use cases:
And now “AI Agents” are following the same trajectory as machine learning did a decade ago.
Just as companies once believed data science models could predict everything, today’s AI hype suggests that agents will be general-purpose assistants capable of handling any task.
In reality the most useful AI won’t be generalists. It will be specialized tools built for well-defined tasks.
In practice, this looks like…
Much like data science found its niche in churn prediction, fraud detection, and recommendation engines, AI agents will deliver real value when used to streamline specific, repetitive tasks, not as all-knowing business operators.
If you’re investing in AI, consider these questions to help cut through the noise:
