AI models choosing the right one
Your team tried ChatGPT for analyzing service data and concluded AI isn't that useful. Then someone used Claude for the same task and got dramatically better results. The model matters — but not the way most people think. Different models have different strengths: some are better at analysis, some at writing, some at following precise instructions. The practical approach is to avoid locking into one vendor. Build your data layer and agent infrastructure so the model is a swappable component. Today's best model won't be next quarter's best model, and your investment in data and architecture should outlast any of them.
Go deeper
Your behavioral health network just rolled out an AI tool to help with clinical documentation, but therapists are complaining it sounds robotic and misses nuance. Meanwhile, your billing team is using the same model to categorize claim denials and it works beautifully. That's the core issue: you matched a model strong at structured data to a task that needs empathy and clinical language. The fix isn't buying a better model — it's running a two-week trial where three different models each handle the same ten real tasks, and your actual users score the output.
The trap most companies fall into is letting IT or a vendor pick one model for the whole organization. That's like buying one size of wrench for every technician. Instead, categorize your AI use cases into buckets — customer-facing communication, internal data analysis, document processing — and test the top two models for each bucket. The cost difference between models is often 10x, so matching correctly saves real money.
Questions to ask
- Which three tasks consume the most staff hours in our operation, and have we tested more than one model on each?
- When our vendor says they use AI, do we know which model and whether we can swap it?
- What's our monthly AI spend per use case, and are we paying frontier-model prices for tasks a smaller model handles fine?