AI & Emerging Technology

Applying AI where service operations actually break

This hub explores how AI and emerging technologies can improve service execution, decision-making, and customer outcomes. It focuses on practical use cases, readiness considerations, and cross-functional impact rather than isolated pilots or tool-specific features. The perspective is shaped by working with service organizations testing, scaling, and sometimes intentionally stopping AI initiatives based on data quality, process maturity, and change readiness.

Core AI Insights

Fully Autonomous After sales Service Ecosystem

The Fully Autonomous Service Organization: What AI-Led Aftersales Really Looks Like

Aftersales is entering a new phase, one where service decisions are made before customers notice problems. This article explores what a fully autonomous service organization could look like, how AI-led orchestration changes warranty, parts, and dispatch, and why the shift is as much about operating models as technology.

Agentic AI in manufacturing and automotive after-sales service, showing how AI agents coordinate CRM, ERP, FSM, IoT, warranty, and CPQ systems to close execution gaps that automation cannot solve.

Agentic AI in After-Sales: The Missing Execution Layer That Automation Couldn’t Fix

Discover how agentic AI transforms after-sales and field service by closing the execution gaps that automation alone couldn’t. Learn how it orchestrates cross-system workflows, improves technician productivity, elevates customer touchpoints, and drives closed-loop feedback into product design. Packed with real business use cases, KPI impacts and insights, this article shows why forward-looking manufacturers must adopt…

Generative AI hype cycle declining as enterprises confront data gaps, poor ROI, integration challenges, and shift toward realistic, measurable AI impact.

GenAI Hype Is Over: Welcome to Reality

Generative AI hype is fading as organizations confront the reality of data gaps, weak ROI, and integration fatigue. Gartner’s 2025 Hype Cycle shows GenAI peaking while traditional AI quietly delivers results. Learn why mature enterprises are hitting pause, what’s driving this shift, and how to navigate the post-hype era with clarity, readiness, and measurable impact.

Platform-of-platforms architecture for service organizations, showing how CRM, ERP, field service software, IoT platforms, and CPQ systems connect to unify data, workflows, and quote-to-cash processes.

Platform of Platforms: Why Individual Tools Are No Longer Enough

Discover how a “Platform of Platforms” approach transforms field service management. Learn why integrating planning, execution, quoting, and analytics under one connected architecture is key to service-led growth. Explore enablers, business benefits, and real-world use cases that show how unified platforms drive efficiency, revenue, and customer experience.

Quantum computing applied to industrial and automotive after-sales service, enabling smarter maintenance, routing, inventory decisions, and materials innovation when combined with AI and IoT systems.

Quantum Computing in After-Sales Service

Quantum computing is beginning to reshape after-sales service in the industrial and automotive sectors- enabling smarter maintenance, routing, inventory, and materials innovation when integrated with AI, IoT, and enterprise systems. This article explores how early pilots are delivering value, what kinds of organizations should adopt, and how to determine when the quantum leap is truly…

AI augmenting customer service in industrial and automotive after-sales, showing how technology enhances knowledge access and workflows without replacing human judgment and accountability.

AI Won’t Replace Customer Service: It Will Redefine It

AI, GenAI, and agenticAI are often hyped as replacements for customer service, but in reality – especially in after-sales and field service for manufacturing and automotive industries – they are enablers, not substitutes. These technologies cannot replace the human expertise, accountability, and customer trust required in complex, high-stakes service environments. The future of service transformation…

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Featured Articles

Handpicked insights on the biggest shifts shaping after-sales and field service

AI use cases improving after-sales operations through faster diagnostics, accurate service workflows, optimized parts management, and better customer support.

How AI Can (Actually) Help After-Sales Service – Beyond the Hype

Discover how AI is revolutionizing after-sales in industrial manufacturing – from complaint triage and technician scheduling to predictive maintenance and customer companion bots. This in-depth guide explores business challenges, real-world use cases, and measurable KPIs,…

Why service revenue remains untapped in manufacturing and after-sales, highlighting execution gaps such as organizational misalignment, fragmented systems, weak data foundations, and AI pilots that fail to scale.

Why Service Revenue Remains Untapped (Even When Everyone Has a Strategy)

Service revenue remains one of manufacturing’s biggest untapped opportunities. Many leaders have the strategy, but execution falters due to data gaps, weak change management, and unclear ownership. This article explores why most initiatives stall, what…

Diagram of a field service KPI framework showing technician performance metrics, dispatcher and planner KPIs, service manager dashboards, and AI-driven predictive KPIs across customer, operational, and strategic dimensions.

The Field Service KPI Dashboard: What Executives, Managers & Technicians Should Really Track

A complete guide to field service KPIs by role, from executives to technicians. Learn which metrics drive profitability, efficiency, and customer experience. Based on the SmartServiceOps Field Service KPI Framework, this article helps service leaders…