Most AI transformation frameworks focus on models, infrastructure, and tools. But the biggest bottleneck to real-world adoption often hides in plain sight: your data.

In this talk, I’ll introduce CC/CD: Continuous Calibration / Continuous Development, a framework we’ve applied across industries to make AI systems enterprise-ready. The twist is simple but overlooked: instead of starting with the model, start with your data. Every hallucination, pipeline breakdown, or poor ROI traces back to how well your system ingests, calibrates, and evolves with data.

We’ll explore three practical questions every enterprise AI practitioner should ask:

1. What signal is my data really giving me?
2. How often am I calibrating my AI outputs against it?
3. What’s my loop for continuous development as my data shifts?

You don’t need to chase every new model release to keep up. If you just look at your data, you’ll see the real path to durable, high-ROI AI systems. This session will share field-tested lessons from 50+ enterprise deployments where CC/CD turned noisy prototypes into production-grade value.

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