AI systems are easy to demo but hard to trust. As organisations move from experimentation to live, customer-facing AI, the real challenge is no longer capability - it’s confidence.
How do you know when a probabilistic system is ready to go live, how it’s performing over time, and how to explain that clearly to non-technical, risk-averse stakeholders?
Drawing on real experience shipping AI into public-sector environments, Paul Stanton (Head of UX, AI and R&D at Jadu), will explore why traditional software delivery and testing models often fail for AI, and how a single wrong response can damage trust in an entire system. I will demonstrate how we transitioned from "black box" models and subjective sign-offs to straightforward, repeatable confidence signals that both teams and customers could reliably depend on. By bringing evaluation and reporting to the front of the delivery process, and distilling complex behaviour into clear red, amber, and green signals, we reduced delivery risk, shortened UAT cycles, and enabled faster, safer go-live decisions. The result: customers who could self-serve trust at scale, and organisations that could ship AI with confidence, not fear.
This webinar is part of Public Sector AI Week by Digital Leaders, and is free for all to attend.
