In the face of rising inflation, skyrocketing spare parts costs, and tariff uncertainties, there is opportunity to turn economic pressure into innovation. Instead of focusing solely on cutting costs, hear how Nokia adopted a circular economy approach, designing for reuse, building customer trust in refurbished components, and proactively shifting the perception from “used” to “smart.” Chris Dickerson, VP, Service Planning & Logistics for Nokia will discuss lessons learned that delivered cost savings, strengthened reliability, sustainability, and customer value, including:

• Repositioning refurbished as reliable, educating customers on the reliability of refurbished electronic components
• Training technicians to identify refurb opportunities in the field, support part returns, and build trust around reuse during customer conversations
• Determining optimal pricing that reflects the real value of service operations
• Reducing total parts cost by a significant margin, increasing customer satisfaction with refurbished parts adoption, and using circular economy initiatives as a key differentiator in service contracts
• Using refurbished offerings as an offensive sales strategy, positioning the company as innovative, environmentally responsible, and forward-thinking 

(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: Achieve Service Operational Excellence with AI agents 

When customer escalations drop by 95% and technical documentation time falls by 80%, something fundamental has changed. In this real-world case study, we explore how AI Agents reshaped field service operations from reactive firefighting to predictive excellence. Through a tech-enbled approach, field teams are able to instantly find solutions for complex issues while bringing out operational excellence synthesizing insights across enterprise data, experts and customer environment. Find out how to map failure patterns to spare parts to ensure a future-proof, proactive service organization. Walk away with key insights to help:

• Drastically Improve Service Efficiency: Achieve instant, expert-level AI agents by synthesizing data from vast multi-modal data stores. No cleanup or creating knowledge is necessary
• Accelerate Knowledge Creation: Reduce technical documentation creation time by over 80% while ensuring consistent, accurate quality
• Proactive & Predictive Service: Use log analysis to provide proactive failure alerts, shifting to a predictive field service model
• Embed AI Across the Lifecycle: Strategically deploy AI Agents from NPI through product maturity to guarantee field readiness and continuous service excellence

Karpagam "Kay" Narayanan, Founder and CEO, Ascendo AI

Chris Dickerson, VP, Service Planning & Logistics, Nokia