Every service operation faces the same hidden cost: the dispatch decision. A technician sent to the wrong job, a specialist pulled for an emergency, a first visit without the right part — each one compounds into missed SLAs, repeat visits, and eroded margins. For manufacturers running dealer networks and field teams across wide geographies, getting dispatch wrong is not an occasional problem. It is a daily business outcome failure.
In this episode, Petchi and Olav explore how AI Agents powered by Reinforcement Learning are turning dispatch into a competitive advantage. By learning from real operational patterns across work orders, IoT signals, and service histories, these agents reduce unnecessary truck rolls, improve first time fix rates, and protect SLA commitments at a scale no rules based system can match. The result is not just an operational improvement. It changes what manufacturers can promise their customers and how profitably they can deliver on it.
Reserve Your Spot
Speakers
Petchiraj Piramuthu
Dr. Olav Laudy
Key Takeaways
The Real Cost of Getting Dispatch Wrong
Every missed SLA, repeat visit, and idle technician has a price. We break down what reactive dispatch actually costs in penalties, wasted capacity, and lost customer trust, and why smarter decisions at dispatch deliver faster ROI than adding more resources.
Agents That Think Ahead, Not Just Right Now
Reinforcement Learning powered agents learn from real operational patterns to optimize across an entire week, not just the next job in queue. They anticipate demand, balance workloads, and protect high-value SLAs before a breach ever occurs.
Outcomes That Show Up on the Scoreboard
First time fix rate, SLA compliance, technician utilization — we connect the AI capability to the business metric and give you a framework for building a board-ready ROI case from your own operational data.
From Pilot to Production, Without Friction
Going live is where most AI initiatives stall. We cover what it actually takes, like data readiness, change management, and human-in-the-loop design, so AI Agents empower your service teams.
Smart Dispatch Optimization
AI improves routing, cost, and
SLA outcomes
Predictive Service Intelligence
Anticipate delays, demand, and
service risks
Scalable AI Deployment
Production-ready AI with
human-in-loop
Who Should Attend
Field Service & Operations Leaders
VPs of Field Service, Service Operations Directors, Dispatch Managers at OEMs and large dealer networks
Aftermarket & Service Revenue Heads
Leaders accountable for uptime SLAs, service contract profitability, and first-time fix performance
Digital Transformation & AI Executives
CIOs, CTOs, and Innovation Officers evaluating production-grade AI in manufacturing field operations
Agenda
| Agenda Flow | Duration | Key Highlights | Speaker |
| Welcome & Introduction | 5 mins | Overview of the 2026 Aftermarket Intelligence Unlocked Webcast Series Setting the stage: the hidden business cost of getting dispatch wrong Why field service in manufacturing — industrial, medical equipment, automotive, and large dealer networks — is at an inflection point | Petchi |
| Market Context: AI in Field Service | 5 mins | How reactive dispatch erodes service margins and customer trust at scale The business case with tangible numbers: missed SLAs, repeat visits, idle technicians The opportunity: leveraging work order data, IoT signals, and warranty information to get ahead of issues | Petchi |
| Deep Dive: AI Agents in Dispatch | 15 mins | Why Reinforcement Learning is the business-ready answer for large field organizations What the agent learns: demand patterns, parts-aware routing, SLA-priority balancing RL agent vs. rule-based vs. optimizer — three strategies, measurable outcomes Live scenario walk-through: The Cascade, Wrong Tech Wrong Part, Seasonal Spike ROI framework: truck rolls, first-time fix rate, SLA compliance, technician utilization From pilot to production: data readiness, change management, and human-in-the-loop design | Olav |
| Interactive Q&A + Open Discussion | 5 mins | Audience Q&A on deployment readiness, data needs, and business case Tavant perspective: the path to production for a 20–50 technician organization Closing remarks and next steps | Petchi & Olav |