The New Economics of AI Inference
Every time your AI processes a request, you pay. What most health plan leaders don't realize is that how much you pay has less to do with the model and more to do with your data. When provider and member data is fragmented across credentialing systems, claims platforms, and directories, AI models work harder to resolve ambiguity — consuming more tokens, more compute, more cost.
This paper, written by Gaine's CEO, makes the case that data architecture is your most important AI cost lever — and that the decisions you make now will determine whether you retrofit later at a far higher price.
What's inside
- The real economics of AI inference — what drives token consumption and why it compounds
- How data ambiguity in provider and member records forces AI to work harder on every query
- Why health plans face unique data quality challenges that generic AI deployments can't absorb
- The data architecture principles that reduce inference burden and improve model accuracy
- Why acting on data quality now is more cost-effective than retrofitting after AI is in production
- How Gaine HDMP serves as an operational control plane for clean, consistent data that AI can trust
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