In reality, many employees are actually poorly equipped, poorly led, or stuck in broken processes. AI that surveils, second-guesses, or replaces them without addressing systemic issues will only breed resentment and inefficiency.
Before investing in AI, organizations must ask these questions:
1. What specific problem are we trying to solve? (Not just, “We need AI because everyone else has it.”)
2. Is this a problem of data, process, or culture? (AI can’t fix bad leadership or siloed workflows.)
3. Have we exhausted non-AI solutions? (Sometimes, better training or process redesign works better.)
4. Are we prepared to govern AI properly? (Without oversight, AI can amplify biases or errors.)
AI holds tremendous potential—but only when applied to well-defined problems. Right now, much of the AI frenzy resembles past tech bubbles, where hype outpaced practical utility. The organizations that will succeed with AI do the following:
♦ Acknowledge their real inefficiencies (not just the ones vendors claim AI fixes).
♦ Listen to employees about pain points before imposing AI solutions.
♦ Treat AI as an enabler, not a magic wand
Otherwise, we’ll see more expensive experiments ending like the oil company’s KM project: two broken systems instead of one and the same old problems lingering underneath.
AI isn’t the problem. Not knowing why you’re using it is.