Enhancing Service Performance in Complex Industrial Settings

Meta description: Peak International Group and ABB outline how industrial service AI tackles retiring expertise, fragmented diagnostics, and remote troubleshooting.

Tags: Industrial service AI, Field service transformation, Predictive maintenance, Skilled workforce, Remote diagnostics

Featured image: Abstract industrial service AI dashboard with equipment signals, knowledge graphs, and remote diagnostic pathways; no real people, company logos, or text.

Industrial service AI has become a continuity problem for manufacturers and field-service organizations, not just a technology upgrade. In an Emerj AI in Business Podcast analysis, Mike Hughes, Group Service Director at Peak International Group, and Scot Burdette, Global Division CIO at ABB, described how rising equipment complexity, retiring experts, and scattered service data are pushing manufacturers to modernize technician enablement, knowledge continuity, and diagnostic decision-making. The analysis was produced through Emerj Media Services and sponsored by Aquant under Emerj sponsored content guidelines.

2000-2022: Aging Workforce Data Becomes Structural

The pressures have a measurable history. U.S. Census Bureau data show 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%. NAM, citing Deloitte and The Manufacturing Institute, projects 2.1 million unfilled manufacturing jobs by 2030, with a potential economic cost of $1 trillion that year. 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.

2025-2026: AI Adoption Widens the Operating Gap

Maintenance economics sharpen the urgency. Research published through NIST estimates that maintenance-related expenditures and preventable losses across U.S. discrete manufacturing total roughly $222 billion annually. The same study found 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%. The Institute's own 2025 Index cautions that this reveals a widening gap between what AI can do and how prepared organizations are to manage it. For service teams, the technology is outrunning the operating discipline needed to deploy it against real frontline problems.

Now: Frontline Service Modernization Takes Shape

The interview analysis organizes the response into four frontline insights. First, knowledge capture workflows must preserve long-tenured technicians' know-how as structured guidance before retirement, because troubleshooting accuracy depends on tacit experience that manuals do not fully capture. Hughes notes that field-service engineers may carry twenty or thirty years of knowledge, that more information can sit in their heads than in manuals, and that newer generations are unlikely to remain with one company for that long. He frames the goal as speeding onboarding so an engineer with two years of experience can approach results from someone with twenty. Burdette adds that equipment complexity makes knowledge transfer difficult to compress; it requires long, intentional pairing between senior experts and incoming engineers.

Second, unified diagnostic data speeds fault resolution. Hughes describes a fragmented workflow in which technicians previously checked case history in System A, technical bulletins in System B, and parts information in System C. Burdette explains that when case history, sensor readings, and diagnostic signals sit in separate systems, experts cannot form a complete picture, rare or intermittent problems lose historical context, and dispatch decisions slow down. First-time fix failures can occur when technicians arrive without the right part or the right knowledge. Technical debt across legacy and current products further expands documentation and worsens fragmentation.

Third, remote diagnostic hubs create scalable expertise. Burdette describes remote diagnostics as a different discipline: experts must ask the right questions, interpret data, monitor trends, and decide when to intervene without physical access to the asset. This requires centralized teams that can read signals, anticipate failures, and act before issues escalate.

Fourth, modernization ROI depends on frontline-first sequencing. Leaders must prove impact on one or two high-friction frontline use cases before scaling, avoiding platform-first modernization that can paralyze service teams.

Burdette leads technology strategy and modernization at ABB Measurement and Analytics, a business operating in more than 60 countries and generating over $1.5 billion in annual revenue. His prior roles include ABB, Information Control Corporation, and EDS; he began 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. Hughes previously served as Global Head of Service and Global Service Delivery Manager at Peak Scientific, held leadership roles at Flint Group and Aviva, and serves on the Advisory Board of Service Council.

At this stage, industrial service modernization is moving from awareness to execution discipline. The visible signal is not simply more AI adoption, but the ability to capture retiring expertise, unify diagnostic data, staff remote hubs, and prove frontline impact before scaling.