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From Pilot to Production: Scaling Agentic AI in Modern Customer Support

04 February Online

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About this event

Add to the calendar - https://streamyard.com/watch/RpCBSyNVDerZ

Deploying AI agents involves more than just selecting a model; it requires a complete rethinking of the customer journey. This talk provides an overview or the impact on deploying agents for customer support, looking at user experience, ROI, and different authentic options

Speaker:
Leonardo De Marchi holds a Master degree in Artificial intelligence and has worked as a Data Scientist in the sports world, with clients such as the New York Knicks and Manchester United. He now works in Thomson Reuters as VP of Labs and also provides consultancy and training for small and large companies. His previous experience includes being Head of Data Science and Analytics in Bumble, the largest dating site with over 500 million users, heading the team through acquisition and an IPO.

- And later you will get a chance to listen to session #2.

Fireside Chat with US Bank SVP and Head of AI: Measuring ROI and Impact When AI Becomes Digital Labor

As agentic AI moves from analysis to execution, ROI shifts from model level metrics to enterprise productivity and realized business value. Impact increasingly comes from AI systems operating continuously across workflows, accelerating decisions and actions, and improving how work gets done at scale. This fireside chat reframes agentic AI ROI around measurable gains in operational efficiency, execution speed, quality of outcomes, and economic lift, rather than pilot success or technical benchmarks. The discussion focuses on how organizations identify, measure, and sustain value once AI functions as digital labor inside core business processes.

The second half explores what determines whether productivity gains compound or stall over time. Sustained impact depends on keeping agentic systems aligned with business intent, cost boundaries, and governance expectations while they operate. We will discuss how enterprises track value realization alongside control signals such as agent drift, inefficiency, and operational friction, and why Trust Posture Management becomes essential once AI runs continuously. The goal is to help teams treat AI initiatives as managed operating systems rather than static deployments.

Participants leave with a practical framework for evaluating agentic AI investments based on sustained productivity, defensible value creation, and readiness to scale.

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