By Candi Robison, Ultimo
The maintenance engineering sector faces twin challenges that threaten operational reliability: an accelerating skills shortage as experienced technicians retire, and the persistent underutilisation of available talent. While 63 percent of organisations identify workforce aging as their most critical trend and 50 percent report major disruption from recruitment challenges, women represent only 7.6 percent of manufacturing maintenance technicians.
This isn't merely an equality issue; it's a strategic vulnerability. In an industry where unplanned downtime costs UK manufacturing alone billions annually, leaving qualified talent untapped is an operational risk that organisations cannot afford.
The Business Case for Workforce Diversity
Data from Ultimo's Maintenance Trend Report reveals the urgency. Across manufacturing, utilities, and facilities management, maintenance departments struggle to fill critical positions. Yet the sector continues to overlook a substantial talent pool. Research from organisations like Women in Reliability and Asset Management (WIRAM) demonstrates that technical capability has never been the constraint - access to training, visibility of career pathways, and workplace culture have been.
WIRAM brings together professionals who've entered maintenance through diverse routes: technicians who began on the shop floor, engineers transitioning from related disciplines, and specialists in reliability engineering and energy systems. Their presence proves that maintenance expertise can be developed through multiple pathways, and that technical excellence isn't correlated with traditional demographic patterns.
AI as a Knowledge Multiplier
The introduction of agentic AI systems into maintenance operations creates unprecedented opportunities, and risks. These systems function as digital coworkers, learning from daily interactions, capturing decision-making processes, and building institutional memory that persists beyond individual tenure.
For maintenance departments, AI can preserve tacit knowledge: the vibration patterns that signal imminent bearing failure, the thermal signatures that precede motor burnout, and the troubleshooting sequences that come only through years of hands-on experience. This capability addresses one of maintenance's most pressing challenges - the loss of expertise as senior technicians retire.
Properly implemented, AI systems can accelerate junior technician development, reduce administrative burden through automated documentation, and make expertise accessible across shifts and sites. Condition monitoring data becomes more actionable when paired with AI that recognises patterns trained from experienced practitioners.
The Training Data Challenge
Here lies the critical juncture: AI systems learn from the workforce they observe. If maintenance teams remain overwhelmingly homogeneous whilst these systems are being trained, the resulting AI will encode a limited perspective on problem-solving approaches, communication patterns, and definitions of expertise.
This isn't theoretical. Bias in AI systems has been well-documented across industries when training inputs lack diversity. Maintenance engineering can avoid this outcome, but only through intentional action during the current implementation window.
Practical Steps for Maintenance Organisations
First, recruitment and retention of women in maintenance roles must become a measurable business objective. This requires modernising job specifications, expanding sourcing beyond traditional channels, ensuring equitable access to shift patterns and advancement opportunities, and establishing team environments where professional respect is non-negotiable.
Second, organisations should strengthen technical education pathways. Partnerships with colleges, apprenticeship programmes, and STEM initiatives can make maintenance engineering visible as a career option earlier -particularly to young women who may never have been encouraged to consider the field.
Third, AI implementation must include deliberate knowledge capture design. Whose work orders become training examples? Which technicians are interviewed for system development? Who validates AI recommendations before deployment? Diverse technical teams should inform every stage, as system quality depends directly on the breadth of expertise it learns from.
Finally, women currently working in maintenance roles should be recognised as subject matter experts whose knowledge will shape both the next generation of technicians and the digital tools supporting them. Their expertise should inform CMMS configurations, preventive maintenance schedules, and reliability improvement programmes.
The Implementation Window
AI systems are being trained now, learning from today's maintenance practices and today's workforce. Organisations that broaden participation immediately will develop more robust systems - systems that reflect the full spectrum of technical expertise and make maintenance careers more accessible for all qualified candidates.
The maintenance professionals needed to meet future reliability challenges already exist. Many are women prepared to contribute technical skill, operational insight, and innovation. Pairing this talent with intentional AI deployment doesn't merely address a staffing problem, it strengthens the technical foundation of maintenance engineering itself.
For maintenance organisations facing both skills shortages and digital transformation, the path forward is clear: invest in diverse technical teams whilst AI systems are learning. The result will be more capable systems, more resilient operations, and a stronger pipeline of maintenance expertise for decades to come.
