翻訳待ち：Improving Service Performance in Complex Industrial Environments

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。

This interview analysis is sponsored by Aquant and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page. Industrial service teams face rising equipment complexity, a retiring expert workforce, and scattered service data, driving up cost-to-serve, suppressing first-time fix rates, and forcing AI and remote diagnostics from optional to necessary faster than most organizations can deploy them well. According to the U.S. Census Bureau, the share of manufacturing and wholesale trade employment concentrated at firms where at least a quarter of the workforce is over 55 nearly tripled between 2000 and 2022 — from 14% to over 40%. That aging base sits behind a projected talent shortfall that NAM, citing Deloitte and The Manufacturing Institute, puts at 2.1 million unfilled manufacturing jobs by 2030, with a potential economic cost of $1 trillion in that year alone. Specifically in technical and engineering-heavy trades, the U.S. Department of Energy has reported that 52% of skilled technicians and engineers may need to be replaced within the next 10 years. Research published through NIST estimates that maintenance-related expenditures and preventable losses across U.S. discrete manufacturing total roughly $222 billion annually, and the same study found that manufacturers relying more heavily on predictive and preventive maintenance experienced 52.7% less unplanned downtime than those still dependent on reactive approaches. Stanford HAI’s 2026 AI Index reports organizational AI adoption reached 88%. But the Institute’s own 2025 Index cautions that this reveals a widening gap between what AI can do and how prepared we are to manage it — the technology is outrunning the operating discipline needed to deploy it against real frontline problems. These pressures make knowledge continuity, diagnostic readiness, and frontline-focused execution central to sustaining service performance. Emerj recently hosted Mike Hughes, Group Service Director at Peak International Group, and Scot Burdette, Global Division CIO at ABB, on the AI in Business Podcast to examine how industrial service leaders are modernizing technician enablement, knowledge continuity, and diagnostic decision‑making as equipment complexity rises and expert talent retires. This article examines four frontline insights from industrial service leaders on how manufacturers can modernize service operations: Knowledge capture workflows for retiring expertise: Capture long-tenured technicians’ know-how as structured guidance before they retire, preserving troubleshooting accuracy as the experienced workforce leaves faster than it can be replaced. Unified diagnostic data for faster fault resolution: Consolidate case history, bulletins, and parts data into one environment, eliminating the multi-system hopping that slows triage and drives avoidable truck rolls. Remote diagnostic hubs for scalable expertise: Build and staff for remote diagnostic work as its own capability, reading signals and knowing when to intervene from data alone. Frontline-first sequencing for modernization ROI: Prove impact on one or two high-friction frontline use cases before scaling, avoiding the paralysis of platform-first modernization. Listen to the full episodes below: Episode 1: How Industrial Service Leaders Are Closing the Knowledge Gap Before It’s Too Late with Mike Hughes of Peak International Group Guest: Mike Hughes, Group Service Director at Peak International Group Expertise: Service Operations Strategy, Field Service Transformation, Data-Driven Service Management, Operational Excellence Brief Recognition: Mike Hughes is Group Service Director at PEAK International Group and previously served as Global Head of Service and Global Service Delivery Manager at Peak Scientific. His earlier experience includes leadership roles at Flint Group and Aviva, spanning technical operations, manufacturing, process engineering, and commercial functions. He also serves on the Advisory Board of Service Council, a global community for service and customer management leaders. Episode 2: The Real‑World Conditions That Shape Industrial AI – with Scot Burdette of ABB ​ Guest: Scot Burdette, Global Division CIO at ABB Expertise: Enterprise IT Strategy; IT Transformation; Enterprise Integration; M&A Technology Leadership Brief Recognition: Scot Burdette is Global Division CIO, Measurement and Analytics at ABB, where he leads technology strategy and modernization across a business operating in more than 60 countries and generating over $1.5 billion in annual revenue. He previously held senior technology leadership roles at ABB, Information Control Corporation, and EDS, leading enterprise transformations, M&A due diligence and integrations, and global technology programs. He began his career through EDS’s Systems Engineering Development program, supporting General Motors manufacturing operations, and holds a BA combining Business Administration and Computer Science from Glenville State College. Knowledge Capture Workflows for Retiring Expertise Mike Hughes launches the series by naming what he sees as the most urgent pressure facing industrial service organizations today: the accelerating loss of tacit, experience‑based knowledge as long‑tenured technicians retire. In his view, this is not a theoretical concern; it is a practical, day‑to‑day operational risk that shows up immediately in troubleshooting accuracy when expertise exits faster than it can be replaced. Mike describes the urgency of preserving frontline knowledge before it disappears: “The one that stands out for me in conversations is the silver tsunami — the aging workforce. In field service in particular, it’s very common to have engineers who’ve been doing the job for twenty or thirty years, and there is more information in those people’s heads than there is in all of your manuals. The next generation is not going to spend twenty or thirty years with one company, so the question becomes: how do you preserve that tacit knowledge while you still have it, and how do you speed up onboarding so an engineer with two years of experience can get close to the results you’d get from someone with twenty?” — Mike Hughes, Group Service Director at Peak International Group Scot Burdette explains that the complexity of modern industrial equipment means knowledge transfer cannot always be compressed; it requires long, intentional pairing between senior experts and incoming engineers, because the depth of expertise cannot be replicated quickly. Across both conversations, several leadership priorities for maintaining troubleshooting accuracy as experts retire emerge: Identify retirement timelines early so knowledge gaps are visible before they become operational failures. Pair junior engineers with senior experts for extended periods, because the complexity of modern equipment cannot be absorbed through short onboarding cycles. Shift from tenure‑based knowledge accumulation to structured capture, given that newer generations will not stay in roles for decades. Treat knowledge continuity as an operational capability, not a workforce‑planning exercise — troubleshooting quality depends on it. Build workflows that preserve expertise before it leaves, ensuring frontline teams maintain accuracy even as the experienced workforce contracts. Hughes and Burdette converge on the single point that knowledge capture is now foundational to service performance. Without structured workflows, organizations cannot maintain uptime, first‑time fix rates, or customer experience as the workforce turns over. Unified Diagnostic Data for Faster Fault Resolution Scott explains that remote diagnostics depends on reaching all relevant information in one place. When case history, sensor readings, and diagnostic signals sit in separate systems, experts cannot form a complete picture of equipment behavior. Fragmentation slows interpretation, delays intervention, and turns solvable issues into field visits. He also notes that rare or intermittent problems require fast access to historical patterns, which is impossible when data is scattered. Unified access also supports faster dispatch decisions because experts can determine whether to intervene remotely or escalate without delay. The conversations with Mike and Scot surface the operational problems unified diagnostic access must solve: Technicians moving between multiple systems — case history in one system, bulletins in another, parts information in a third, slowing triage and driving avoidable truck rolls. Technical information spread across platforms — increasing diagnostic error and reducing efficiency for both tech support and field engineers. First‑time fix failures caused by fragmented data — technicians arrive without the right part or the right knowledge. Technical debt across legacy and new products — expanding documentation across many systems and worsening fragmentation. Remote experts lacking a single source of operational data — limiting their ability to quickly interpret performance trends and anomalies. Early intervention blocked by missing or scattered data — stalling investigation and allowing issues to escalate. Rare or intermittent issues requiring historical patterns — which experts cannot access when data is split across systems. Slow or inaccurate dispatch decisions — because remote teams cannot see the full diagnostic picture in one place. Mike describes a concrete example of this fragmentation: “Before, when a technician needed to troubleshoot a unit, they’d review the case history in System A, refer to technical bulletins in System B, then move to System C to identify and locate the part they needed. On top of the technical debt of supporting a wide range of legacy and current products, they also have to jump across different platforms just to do their job. That fragmented experience hasn’t been great for them — we want to get to one place where everything a field engineer needs is at their fingertips.” — Mike Hughes, Group Service Director at Peak International Group Mike and Scot share a throughline: when diagnostic data is unified, fault resolution speeds up. Remote Diagnostic Hubs for Scalable Expertise Scot outlines why centralized diagnostic teams are becoming essential as equipment grows more complex: “It is a different kind of expert. If you’re standing in front of the equipment, you can look at it and assess it hands-on — that’s very different from remote. You have to understand the right questions to ask, you have to understand what the data is telling you, and you have to be more prepared to provide that support on an ongoing basis. It’s also about having a team that can see the trending, step in when something isn’t going the way you expect, and make decisions very quickly before an issue becomes something bigger.” — Scot Burdette, Global Division CIO at ABB Remote diagnostics is not a lighter version of field service; it is a different discipline. As equipment complexity increases and expertise becomes more distributed, organizations must build diagnostic hubs that can interpret signals, anticipate failures, and intervene before issues escalate. From Scot and Mike’s conversations, several requirements stand out: A shift in where expertise sits — complexity moves from the field to centralized diagnostic teams that specialize in interpreting signals rather than inspecting equipment hands‑on. Continuous monitoring rather than episodic troubleshooting — teams must track trends, deviations, and anomalies in real time to prevent escalation. A different diagnostic skill set — experts must know how to interrogate data, ask the right questions, and diagnose without physical access to the asset. Faster, higher‑stakes decision cycles — hubs must make [truncated for AI cost control]