Field Service & Aftersales Insights
Perspectives on Service Growth, AI in Aftersales, Operation and Technology
This page brings together perspectives on field service, aftersales operations, and emerging technology in manufacturing and automotive service, grounded in real implementation experience.
The articles here explore what is changing, what is working, and where execution often breaks down. Topics range from KPIs and AI adoption to dealer operations, warranty management, and the day-to-day decisions that determine whether a transformation program succeeds or stalls.
Pick a topic below to explore further or browse the latest insights.
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Latest Insights
Case Study: Improving Field Service Uptime Through Cognitive Technician Self-Service
Dealer technicians often lose valuable time searching for information instead of fixing equipment. This case study shows how a material handling equipment manufacturer improved uptime by 15% by reducing cognitive load at the point of service, using a search-driven technician self-service approach rather than overengineering AI.
Why Most Service Transformation Programs Stall After Year 2
Most service transformation programs don’t fail, they quietly stall. Early momentum, strong leadership attention, and rapid implementation create the illusion of success. But as priorities shift, budgets tighten, and ownership blurs, outcomes plateau. This article explores why service transformations lose momentum after year two, and what leaders must do differently.
Service Leadership in the AI Age: From Cost Containment to Force Multiplication
In the AI age, service leadership is being redefined by a paradoxical mandate: lower costs, shrinking workforces, and rising customer expectations. Move beyond the hype of “predictive” service to master Data Orchestration and Force Multiplication. Discover the strategic framework to transform your service department from a cost center into a high-performance value engine.
The 2026 Service Blueprint: From Predictive AI to Agentic Operations
2026 marks the end of AI experimentation and the rise of Agentic Execution. As assets become software-defined and margins shift to uptime, service leaders must master Industrial FinOps and ‘teleporting expertise’ to protect Customer Lifetime Value. Discover the five pillars of the 2026 service blueprint.
2025 Aftersales & Field Service Review: From Ambition to Operational Reality
2025 was a reality-check year for aftersales and field service. Demand stayed resilient, but workforce shortages, asset complexity, AI scaling limits, and regulatory pressure reshaped execution. Leading organizations shifted from experimentation to fundamentals, anchoring AI to KPIs, prioritizing productivity over headcount, and treating service as an end-to-end system under real economic constraints across manufacturing and…
KPI of the Month #2: Mean Time to Repair
Mean Time to Repair (MTTR) measures how quickly service organizations restore failed assets to operation. In manufacturing and automotive after-sales, MTTR reflects diagnostic readiness, parts availability, process discipline, and system integration. Used correctly, it highlights structural bottlenecks; used in isolation, it can drive superficial optimization and missed root causes.
Agentic AI in After-Sales: The Missing Execution Layer That Automation Couldn’t Fix
Discover how agentic AI in after-sales and field service is closing execution gaps that traditional automation cannot fix. Learn how agentic systems orchestrate workflows across CRM, FSM, ERP, and service teams, improve technician productivity, enhance customer experience, and enable closed-loop feedback into product design.
Predictive vs Preventive Maintenance: Key Differences Explained
Understand the real difference between predictive and preventive maintenance, and why it’s not just about technology. Learn how manufacturers can master preventive maintenance first, assess readiness, and layer predictive intelligence to drive service-led growth. Explore common pitfalls, readiness checklists, and practical guidance for bridging the gap from reactive repairs to proactive, data-driven reliability.