John Tocado

The session will explore how organizations can move beyond one-off AI implementations to achieve readiness for service-wide adoption. Drawing on real-world experience, John will outline the critical steps and considerations for building a scalable AI roadmap. As Principal Analyst for Systems Development at JLG Industries since 2015, John has played a pivotal role in modernizing the North American Technical Support contact center. Over the past two years, he has led the development and deployment of an AI/ML solution. In this keynote, John will discuss the journey from isolated AI pilots to enterprise-wide adoption, focusing on practical strategies and lessons learned from JLG’s transformation.

Key Takeaways:

• Prioritizing use cases: Learn how to identify and rank AI opportunities that align with your organization’s service goals and deliver measurable impact.
• Creating a rollout plan: Discover how to design a phased AI deployment strategy that supports your business objectives and adapts to evolving needs.
• Assessing AI readiness: Gain insights into evaluating your current capabilities, data infrastructure, and change management requirements to ensure successful AI integration.

In an era where unplanned downtime can cost industries thousands per hour, predictive maintenance is revolutionizing field service operations by leveraging AI, IoT, and machine learning to anticipate equipment failures before they occur. By deploying IoT sensors to monitor real-time equipment health and feeding this data into advanced algorithms, organizations can transition from reactive, break-fix models to proactive, data-driven strategies that reduce downtime, lower maintenance costs, and enhance customer satisfaction. This session delves into the mechanics of predictive maintenance, showcasing how real-time analytics and machine learning enable precise failure forecasting, optimized resource allocation, and outcome-based service models. Through real-world case studies, attendees will explore successful implementations across industries, learn how to measure the impact of predictive strategies, and gain insights into overcoming common challenges such as data silos, cultural resistance, and integration complexities.

Key takeaways:

• Use AI and IoT to predict equipment failures early, reducing downtime by up to 50% and cutting maintenance costs by 30%.
• Shift to outcome-based service contracts focused on uptime guarantees to increase customer loyalty and enable premium pricing.
• Start predictive maintenance initiatives with high-value assets and foster cross-team collaboration for successful, scalable implementation.

This interactive working group session explores how field service organizations can design and implement customer self-service portals that deliver both efficiency and personalized support. Participants will discuss strategies for providing customers with convenient self-service options-such as appointment scheduling, real-time technician tracking, and access to FAQs-while ensuring the experience remains engaging and human-centered. The session will highlight the latest best practices for integrating automation, such as AI-driven chatbots and automated notifications, with opportunities for live agent support when customers need more tailored assistance.

Key Takeaways:

• Implement automation for routine tasks-like scheduling and status updates-to boost efficiency and free up staff for high-value, personalized interactions
• Integrate self-service portals with live support options, ensuring customers can easily transition from automated help to human assistance when needed
• Leverage customer data and feedback to personalize the portal experience, strengthen relationships, and continuously improve service delivery