Mihir Joshi

ServiceMax Asset 360 review showing how the Salesforce-based platform improves asset visibility, contract management, warranty tracking, and service revenue growth, along with its integration and customization challenges.

ServiceMax Asset 360 Review: A Strong Foundation for Asset-Centric Service, But Not Plug-and-Play

Case Study & Tool Review /

ServiceMax Asset 360, built on Salesforce, helps manufacturers gain visibility into assets, contracts, and warranties across the full lifecycle. This review explores how it enables revenue growth by standardizing asset tracking, defining serviceable markets, and running campaigns. We also highlight common challenges like integration with SAP/MDM, hierarchy limits, and customization needs. Learn where Asset 360 excels, where it falls short, and whether it’s the right fit for your service strategy.

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Lessons from global manufacturers on scaling field service operations, focusing on fixing process blind spots, avoiding tech-first traps, enabling technicians with AI, and building customer-outcome-driven service models.

What Global Manufacturers Can Teach Us About Scaling Field Service Operations

Field Service Strategy & Transformation /

Scaling field service isn’t just about adding headcount or tools -it’s about building a discipline around customer outcomes. This article shares five lessons global manufacturers have learned the hard way: from fixing process blind spots and avoiding tech-first traps, to empowering technicians with AI self-help, navigating complexity, and scaling around customer outcomes. A practical, experience-driven guide for service leaders ready to grow smarter, not just bigger.

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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 & Business Growth /

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 successful manufacturers do differently, and how to turn after-sales and service into real, scalable growth

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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 & Emerging Tech /

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 lies in augmentation: letting AI handle routine tasks while empowering people to focus on safety-critical decisions, relationship-building, and innovation.

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Expectation–reality gap faced by service leaders as customers demand Amazon-level convenience and zero downtime while organizations struggle with budget limits, talent shortages, and fragmented systems.

Solving the Right Pains: A Smarter Playbook for Service Leaders

Service Revenue & Business Growth /

Customers today expect Amazon-level convenience, Tesla-like innovation, and zero downtime from every product and service interaction. Yet, service leaders in manufacturing, automotive, and industrial organizations operate in a reality defined by budget limits, talent shortages, fragmented technology, and competing corporate priorities. The result is an inevitable expectation – reality gap: not every pain point can, or should, be solved.

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Next-generation service KPIs focused on outcomes, customer experience, and organizational capabilities beyond traditional metrics like FTF, MTTR, and SLA adherence.

The Next Frontier of Service KPIs: Outcome, Experience, Capability

KPI & Performance /

Service organizations have long relied on efficiency metrics like FTF, MTTR, and Uptime, but these no longer capture what customers truly value. Expectations have evolved: today’s customers look for outcomes that deliver business impact, experiences that build lasting trust, and capabilities that ensure consistent, future-ready service. The next frontier of service KPIs reframes success across three pillars: outcome, experience, and capability. By moving beyond efficiency alone, organizations can bridge the gap between internal performance and external expectations, strengthen customer relationships, unlock new revenue opportunities, and future-proof their service models in a rapidly changing landscape.

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Future service organization in 2030 powered by AI, smart products, and customer value, illustrating the shift from break-fix support to outcome-driven service models.

The Service Organization of 2030: Powered by AI, Led by Customer Value

AI & Emerging Tech, Field Service Strategy & Transformation /

By 2030, service will no longer be about fixing what’s broken – it will be about delivering continuous outcomes. For manufacturers and service leaders, this marks a profound shift. Customers won’t just buy machines; they’ll buy uptime, sustainability, and results. And in a decade defined by AI, intelligent products, and connected ecosystems, service will become the ultimate differentiator. This article explores how the service organization of 2030 will evolve, the macro forces shaping it, and what today’s leaders must do to get there.

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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

AI & Emerging Tech, Field Service Strategy & Transformation /

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, helping manufacturers unlock efficiency, revenue, and customer satisfaction at scale.

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