You Don't Need an AI Platform. You Need a Use Case.
Why enterprises buying AI platforms before identifying use cases waste money and delay value. A use-case-first framework for AI adoption.
TL;DR: Stop buying AI infrastructure before you know what problem you're solving. Start with one workflow, prove value, then scale the platform.
I've seen this pattern too many times. Enterprise buys a shiny AI platform. Six figures. Maybe seven. Then spends six months figuring out what to do with it.
This is backwards.
The Platform Trap
According to Gartner, through 2025, 30% of generative AI projects will be abandoned after the proof-of-concept stage. A major reason? Organizations over-invest in infrastructure before validating use cases.
The pitch sounds compelling. "You need a unified AI platform to scale." "You need MLOps before you can do ML." "You need a data lakehouse before you can do anything."
What you actually need is a problem worth solving.
What's the real cost of platform-first thinking?
It's not just the license fees. It's the six months your team spends learning a platform instead of delivering value. It's the organizational patience that erodes while executives wait for ROI that never comes.
The Use Case First Principle
I call this Use Case First: identify a specific workflow, prove AI can improve it, then scale the infrastructure to support more use cases.
Here's what this looks like in practice:
- Pick one workflow. Something manual, repetitive, and annoying.
- Run a quick experiment. Use the simplest tool that might work. Vertex AI, Azure OpenAI, even an API wrapper.
- Measure impact. Did it actually help?
- Then invest in infrastructure. Scale what's proven.
"The best AI platform is the one you grow into, not the one you grow around."
Why does this work better?
Because you learn what your organization actually needs. Not what a vendor's slide deck says you need. Real use cases reveal real requirements.
Start small. Prove value. Then scale the platform to support success. Not the other way around.
What use case would you tackle first?
Morgan Atkins is a Cloud Engineering Evangelist specializing in enterprise AI adoption on Google Cloud.