The AI Trap: Are You Solving a Problem or Just Buying Tech?
The board meeting or the executive offsite ends and the memo that comes down from on high: “We need to use AI wherever we can.”
The intention is good. No one wants to be left behind in the biggest technological shift of our lifetime. But this "technology-first" thinking is a trap. It’s the business equivalent of buying a state-of-the-art hammer and then walking around your house looking for something to hit.
Before you evaluate a single vendor, before you sit through a single demo, the most critical question you must ask is not "What product should we use?" but:
"What is the most painful, inefficient, and frustrating problem we are trying to solve?"
If you don't have a clear, immediate, and painful answer to that question, you are not ready to be shopping for an AI-powered solution.
The Allure of the Shiny New Toy
The pressure to adopt AI is immense. The hype cycle creates powerful FOMO, where the perceived risk of not acting feels greater than the risk of acting unwisely. This leads to a scramble for solutions without a deep understanding of the problem.
Companies buy complex platforms to solve vague issues like "improving workflows" or "increasing efficiency." The result is almost always the same: a powerful, expensive tool that sits on the shelf, resented by the very people it was meant to help because it doesn’t solve a real-world, day-to-day problem.
How to Identify a Problem Worth Solving
Instead of starting with technology, start with your team. Specifically, start with their complaints. What are the tasks that make your most valuable people roll their eyes?
Ask these questions to uncover the "right" problems:
Where is the Friction? What process is manual, tedious, and universally disliked? (e.g., manually reviewing and coding legal invoices).
What Requires "Brute Force"? What task requires hours of human effort that doesn't actually require strategic thought? (e.g., cross-referencing documents or extracting specific clauses).
Where Do Mistakes Happen? What part of your workflow is most prone to human error, causing rework and frustration? (e.g., incorrect cost-center allocation).
What Can't You Answer? What critical business question are you unable to answer because the data is too messy or time-consuming to analyze? (e.g., "Which law firm gives us the best value on M&A work?").
The first problem you solve with AI shouldn't be a moonshot; it should be a migraine. Find the most persistent, annoying headache in your department and focus all your energy on solving that.
By starting with a real, human-centric problem, you shift the entire dynamic. The conversation is no longer about the abstract promise of "AI," but about the tangible relief of "finally fixing that awful process."
When you do that, adoption isn't a challenge; it's a celebration. And the success of that first small, sensible step will give you the momentum you need for the journey ahead.
