IA FORUM MEMBER INSIGHTS: ARTICLE
By Gautami Nadkarni, Senior Artificial Intelligence & Machine Learning Customer Engineer, GOOGLE
Every enterprise has access to the same AI. So why do some companies win, and most don't?
That pattern should sound familiar to anyone who has led transformation work. AI does not fail in isolation. It fails when the organization treats it as a technology deployment instead of a business capability that requires sponsorship, adoption, measurement, and operational discipline.
I've sat in enough boardrooms to know how this goes.
A company announces their AI initiative. A team is assembled, a use case is picked, a model is selected. Six months later, sometimes twelve, they're back to where they started. The pilot worked. The ROI didn't materialize. Leadership is frustrated. The team is demoralized. And somewhere in a slide deck, the words "Phase 2" are quietly collecting dust.
I've seen this play out across industries, across company sizes, across budgets. And almost every time, the failure had nothing to do with the model they chose.
The model was never the problem. It was everything around it.
Start With the Use Case, Not Technology
Here's the first mistake I see, and it's the most expensive one: companies lead with the technology instead of the problem.
"We're deploying an LLM." "We're building an AI agent." "We're integrating a foundation model." Fine. But deployed where? For whom? Solving what, exactly?
The organizations that actually see ROI from AI start somewhere different. They identify their highest-impact use case first, one that's specific enough to measure, painful enough that people care, and realistic enough to actually ship. Not everything at once. Not the most ambitious vision on the roadmap.
The one thing that, if it works, will prove the value of everything that follows.
This sounds obvious.
It isn't. Most AI initiatives start with a technology mandate from leadership and work backwards to a problem. When you work backwards, you end up solving for the technology, not for the business. And that's when pilots work beautifully in a demo room and quietly fall apart in production.
Pick the use case first. Make it count. Then build everything else around proving that one thing works. Read More...
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