John Snow Labs 57d ago

Shipping AI without validation is still the default in most organisations. Especially in regulated industries, that approach does not scale. Anil Pantangi (Sr Product & Tech Leader, AI & Analytics at Capgemini) introduces H-SCALE — a framework for turning AI experiments into measurable outcomes and scalable systems. Timestamps: 00:00 Why most AI pilots never reach production 02:10 From “spray and pray” to hypothesis-driven AI 03:40 H-SCALE: defining the right hypothesis 05:30 Signals and success criteria that actually matter 07:10 Running experiments and deciding what to scale 09:10 Measuring real impact: from usage to business ROI 13:10 3F framework: flow, friction, feedback 16:20 Governance, drift detection, and production readiness The core idea is simple but rarely applied: experimentation is not about testing models, it is about validating business impact under real constraints such as compliance, data availability, and operational friction. The framework breaks down how to define success before deployment, how to capture both quantitative and qualitative signals, and how to avoid the common trap where pilots never reach production. A second layer adds behavioural measurement through flow, friction, and feedback, turning user behaviour into actionable signals rather than vanity metrics like adoption. This is a practical blueprint for teams working in healthcare, insurance, or other regulated environments where AI must be defensible, measurable, and production-ready. 📌 Applied Healthcare AI Summit 2026 — real-world AI in healthcare, from pilots to production systems. #ResponsibleAI #AIInProduction #MLOps #AIEvaluation #HealthcareAI #AIStrategy