When you “Hit the Target” but Miss the Mark in AI
I’ve got a childhood memory that’s weirdly perfect for how businesses do AI. My brother and I were wandering in the woods with our BB guns, looking for a target. He pointed to a dark spot on a fallen log. So naturally, I aimed and shot precisely where he was pointing… which turned out to be the tip of his finger. Oops. Clear goals apparently do not guarantee safe execution.
The AI Parallel
That little mishap maps exactly to AI projects. Leadership points to “improve customer service,” and the tech team builds a polished chatbot demo that answers FAQs beautifully. But months later, call wait times are still climbing, and satisfaction scores are tanking. Why? Because the real target – reducing wait times and boosting satisfaction – was never actually hit.
Production-First Thinking
If you aim your AI project without clarity, coordination, and production-grade discipline, you’re just firing a prompt into the woods, and hoping for the best. The real work starts before the design or coding:
- What are the true business KPIs?
- How does your system integrate with existing tools?
- How will you monitor performance, costs, and unintended behaviors?
- How do you prevent the slippery drift from prototype to legacy burden?
So before you launch that shiny chatbot or fancy model, ask yourself: are you aiming where the business pointed, or were you unknowingly shooting off-target?
Because if you don’t build with production in mind, even your best AI might leave you feeling the pinch… literally.
