(10 min each) 

Join this session to learn two tried and tested proof of concepts for AI that service leaders are executing with solution partners. You’ll have an opportunity to select which service leaders and their solutions providers that you would like to meet with for a hands-on one-on-one session to close out your learnings on day 3.

Case Study: Optimizing Service KPIs with Field Decision Intelligence 

Field service teams face mounting pressure to fix issues faster, reduce repeat visits, and control costs amid growing asset complexity and workforce constraints. This case study examines how service organizations are moving beyond static dashboards to enable real-time, AI-guided decisions across the service lifecycle. Using examples from industrial equipment, HVAC, automotive, and medical devices, attendees will learn how AI-supported decision frameworks help technicians, dispatchers, and service leaders consistently make the right decision in the field. The session highlights measurable outcomes, including improved first-time fix rates, reduced mean time to repair, and lower cost-to-serve, along with practical takeaways for service leaders focused on operational performance. 

What you’ll learn: 

• What Field Decision Intelligence is and how it differs from traditional FSM, analytics, and knowledge tools 
• How AI can guide in-the-moment service decisions across call center, field, and parts operations 
• Real-world case studies demonstrating ROI in 60–90 days 
• Practical steps to start your journey toward Autonomous Service Journeys with AI Advisors, Analysts, and Agents  Case Study: Achieve Service Operational Excellence with AI Agents

Josh Russell, Vice President, Products, Circuitry.ai

Case Study: Beyond Break-Fix: How Nokia Reduced Escalations by 95% with Physical AI

Most service organizations are still struggling to close the execution gap because their AI efforts stall on messy, unstructured data. In this session, Nokia and Ascendo AI show how a multi-agent AI architecture can deliver real-world service impact without months of data cleanup or traditional knowledge-base work.

Learn how Nokia used specialized agents to reason over technician notes, service logs, manuals, and historical cases — turning dark data into real-time guidance in the field. By orchestrating 16 purpose-built agents across triage, diagnostics, and logistics, Nokia connected failure patterns directly to service actions and spare parts planning, driving faster resolution and fewer repeat visits.

What you’ll learn:

• How to deploy AI using existing unstructured service data.
• Why multi-agent architectures outperform single copilots in complex field environments.
• How to operationalize expert knowledge to reduce escalations and improve first-time fix rates.
• How to connect service intelligence to parts forecasting and logistics.

Chris Dickerson, VP, Service Planning & Logistics, Nokia 

Kay Narayanan, Founder and CEO, Ascendo AI