Inference-Centric: Trustworthy AI in Production

The "Production AI Failure Gap" is real. Are you ready to close it?

While the AI market is projected in the trillions, a critical metric is being ignored: 56% of AI models fail in production because of inaccuracies. The industry's obsession with building "perfect models" has led to pilot purgatory, where projects stall because they cannot guarantee accurate outcomes in a live environment. Leaders must shift their focus from building an accurate model to building an accurate system.

This white paper introduces the Inference-Centric approach—the necessary evolution beyond model-centric and data-centric strategies. By shifting focus to "inference oversight" and strategic human-in-the-loop validation, organizations can bridge the gap between pilot success and production reality, ensuring reliable results even when models fail.

Download to uncover:

• The Hidden Costs of Failure: Why 56% of models stall due to inaccuracies and the risks of the "Production AI Failure Gap".
• The Evolution of AI Strategy: Understanding the strategic shift from Model-Centric to Data-Centric, and finally to the Inference-Centric approach.
• Real-World Case Study: How an agentic flow solved a critical "Francisco" vs. "San Francisco" contextual error that a standard model missed.
• The Implementation Checklist: 6 actionable steps to operationalize "certainty metrics" and tier your human oversight for cost-effective reliability.

With strategic implementation, the right AI partnership can transform healthcare delivery—one reliable, accurate insight at a time.