Complex assets do not sustain themselves. Aircraft and defense platforms depend on people who know how to build, inspect, repair, and operate them over long lifecycles. That workforce is becoming harder to secure.
In aerospace and defense, the issue is visible from several angles at once, as rising demand and increasing defense spending place additional strain on industrial capacity, even while experienced workers retire and younger talent proves more difficult to attract. At the same time, skilled trades remain in short supply, and the growing importance of digital, cyber, data, and software capabilities adds further pressure, all within organizations where the knowledge required to keep assets working is still often poorly documented.
A 2025 AIA and McKinsey workforce study describes a U.S. aerospace and defense sector facing persistent talent shortages, supply chain disruption, production challenges, and attrition that remains high compared with other industries. The study notes that industry attrition was still nearly 15 percent in 2024, despite years of effort to improve retention and engagement. It also argues that aerospace and defense companies will need a major productivity unlock from the workforce they already have.
The UK defense sector shows a similar pattern. Skills England’s 2025 assessment of defense skills describes a sector that draws on advanced manufacturing, digital, technology, construction, professional services, facilities management, and research and development. That overlap means defense organizations are often competing for the same people as other high-demand sectors. The report identifies three major skills gap categories: craft skills such as electrical engineering and welding, specialist skills such as nuclear engineering and naval architecture, and newer skills such as digital, cyber, and green capabilities. It also notes that craft skills, including welders, aircraft maintenance technicians, and electricians, were reported as the hardest to fill.
The important point is that the workforce crisis is not limited to software developers, data scientists, or AI engineers. Those roles matter, especially as asset-heavy industries digitize maintenance, production, and sustainment. But the shortage also sits in the practical trades and technical roles that determine whether equipment can actually be manufactured, inspected, maintained, repaired, and returned to service.
For defense and other asset-heavy sectors, this creates a direct link between workforce continuity and operational performance. A missing software engineer may slow a modernization program. A missing aircraft mechanic, welder, machinist, electrician, or maintenance planner can delay readiness in a much more immediate way, which is explored in more detail in our article on why skilled trades are the bottleneck in defence modernization.
Hiring challenges are only the visible part of the crisis. The deeper risk is knowledge continuity.
Asset-heavy industries depend on knowledge that takes years to build. Some of it is formal and documented, such as maintenance manuals, engineering drawings, inspection criteria, work orders, configuration records, quality standards, and training material. But much of the most valuable knowledge is tacit. It lives in the judgment of experienced workers.
A technician knows which recurring fault looks minor but usually indicates a deeper problem. A planner knows which delay is likely to create larger issues later in the program. This kind of practical judgment is often difficult to document but critical to operational performance.
PwC’s article on the generational transition in aerospace, space, and defense makes this problem clear. It describes an aging workforce where highly skilled professionals are nearing retirement, creating a loss of vital and often unwritten knowledge. The article points to long-cycle examples: naval shipbuilding programs can span decades, aircraft development and certification can take 15 to 20 years, skilled aircraft mechanics or avionics technicians can take 5 to 7 years to fully train and certify, and defense electronics systems can remain in service for more than 30 years.
If a project lasts longer than the tenure of many people working on it, then organizational memory becomes part of the asset. When that memory is not captured, the organization loses more than headcount.
Military Review’s article “The Knowledge Paradox” offers a useful way to describe this problem. It focuses on “unknown knowns,” which are things an organization knows but cannot recognize, access, or use when needed. The article describes tacit knowledge, compartmentalized information, unexamined assumptions, and historical lessons that remain trapped in silos or informal networks.
This is highly relevant to asset-heavy industries. In many organizations, the knowledge needed to solve a problem already exists somewhere. It may be in a retired worker’s head, an old maintenance note, a spreadsheet, a SharePoint folder, an email thread, a supplier conversation, an after-action report, or the memory of someone who worked on a similar asset five years earlier.
The problem is retrieval and application. The organization cannot reliably bring the knowledge into the workflow at the moment it is needed. That is how avoidable problems repeat. Teams rediscover the same failure modes, recreate the same workarounds, ask the same questions, and repeat the same manual checks because the previous learning never became structured organizational knowledge.
The defense sector makes the stakes easier to see because workforce continuity is directly connected to readiness.
A 2025 U.S. Government Accountability Office (GAO) report on the U.S. defense workforce notes that the Department of Defense relies on federal blue-collar workers in trade, craft, and labor roles across Army depots, Air Force bases, and Navy shipyards. These workers maintain and repair electronics and missile control systems, air and space weapons systems, and nuclear aircraft carriers and submarines.
The report highlights several recruitment and retention challenges, including competition with the private sector, lengthy onboarding, wage constraints, and difficulty matching federal job descriptions and wage surveys to highly specialized work. At Tobyhanna Army Depot, for example, officials said complex electronics work performed by federal wage employees was not comparable to private industry jobs in the local wage area. At Norfolk Naval Shipyard, officials identified lengthy onboarding, security clearance requirements, drug tests, physicals, and private sector competition as challenges.
This is an important lesson for any asset-heavy organization. Workforce planning is not abstract. It affects the ability to execute work. When organizations cannot recruit, onboard, and retain critical skilled workers fast enough, maintenance capacity, asset availability, and operational performance all begin to suffer.
GAO also found that workforce targets matter. Norfolk Naval Shipyard and Tobyhanna Army Depot used measurable targets to determine workforce needs, while Edwards Air Force Base did not have measurable staffing targets for recruiting and retaining its Federal Wage System workforce. GAO recommended measurable targets because they help leaders assess whether recruitment and retention actions are working and whether the workforce can meet the mission.
The same principle applies beyond government. Asset-heavy organizations need to know which roles, skills, certifications, and knowledge areas are mission-critical. They also need to know where those capabilities are fragile. A workforce plan that counts roles without mapping operational knowledge will miss the real risk.
The workforce crisis is also becoming a digital capability crisis. As asset-heavy industries modernize, the work required to operate and sustain complex assets is becoming increasingly digital. Maintenance decisions are informed by data rather than intuition alone, operational processes rely on connected systems rather than isolated records, and organizations are expected to coordinate information across long asset lifecycles. This shift demands people who can bridge physical operations and digital technologies, translating data into action and ensuring that modern tools genuinely improve how assets are managed, maintained, and supported.
A report by The Center for Security and Emerging Technology (CSET) within Georgetown University, “The Race for U.S. Technical Talent” shows how difficult this is for the defense community. The report found that the defense community was not replacing or expanding its technical workforce at the same rate as other sectors. It also found that more than 75 percent of technical talent flows for the DOD were outgoing between 1998 and 2021, while Big Tech firms saw much stronger inflows.
The same report found that the defense community remains relatively isolated from other sectors in terms of talent cross-flow and geographic hubs. That isolation can slow technology adoption because talent mobility is one way ideas, methods, and practices move between organizations.
This creates a difficult combination. Asset-heavy organizations need people who understand the physical asset, the operational environment, the maintenance reality, and the digital systems that increasingly support lifecycle performance. Those hybrid capabilities are scarce. A software team without operational context may build tools that frontline teams avoid. A maintenance organization without digital capability may remain dependent on paper, spreadsheets, and manual updates even as asset complexity increases.
The technical talent issue should not be treated separately from the trades issue. In modern asset-heavy industries, the future workforce will need both. The organizations that perform best will be the ones that connect experienced operational knowledge with modern digital execution, rather than allowing those worlds to develop in parallel.
Recruitment, apprenticeships, graduate programs, and reskilling are essential. They are also insufficient on their own. New employees cannot absorb decades of operational context through onboarding alone. Apprenticeships can build capability, but they need experienced mentors. Training programs can teach procedures, but they cannot easily transmit judgment. Digital skills programs can raise literacy, but they do not automatically capture why a maintenance step is done a certain way, which errors to watch for, or how a frontline team works around poor system design.
Skills England notes that apprenticeships and training routes are important pathways into defense occupations. Within the population surveyed by the Joint Economic Data Hub (JEDHub), a collaborative initiative based in the UK Defence Solutions Centre that helps improve understanding of the defence sector’s contribution to the UK economy, 14 percent of total recruitment in 2022 were apprentices and 8 percent were graduates. The same assessment also highlights barriers: training standards can be too broad, too narrow, or too slow to change, and employers want more flexible models such as modular courses, bootcamp-style training, and shorter apprenticeships for upskilling the current workforce.
That points to a broader truth. Asset-heavy industries need talent pipelines, but they also need knowledge pipelines. A talent pipeline brings people into the organization. A knowledge pipeline helps them become productive, safe, and effective faster by giving them access to the operational knowledge that previous generations built.
The AIA and McKinsey workforce study argues that aerospace and defense companies need to drive 30 to 40 percent greater productivity within the existing workforce. That kind of productivity should not be understood as asking people to work harder under worse conditions. In asset-heavy environments, productivity is often lost through friction: unclear ownership, repeated manual entry, disconnected documents, missing context, rework, duplicated effort, and time spent searching for information.
In maintenance, that friction can be painfully ordinary. A technician searches for the latest instruction. A planner waits for confirmation from another team. A supervisor checks whether a task was actually completed. A quality team tries to understand who changed which data and when. A shift handover depends on notes, memory, and informal communication.
These are workflow problems, but they are also knowledge continuity problems. When work is fragmented, knowledge is fragmented. When execution is disconnected from documentation, learning does not accumulate in a reliable way.
In many asset-heavy organizations, the official process and the real workflow are not the same.
The official process may sit in an ERP, CMMS, PLM, MES, or quality system. The real workflow often moves through Excel files, email threads, SharePoint folders, PDFs, paper binders, local databases, personal checklists, and the experience of people who know how things actually get done.
Based on our experience working with prime contractors in the defense and aerospace sectors, this operational reality appears consistently. Organizations often rely on a mix of Excel, email, SharePoint, paper records, and fragmented databases. In one case, teams coordinated asset deployments and tracked future actions across Excel files, email threads, and SharePoint documents. In another, project and maintenance activities were dispersed across documents, printed spreadsheets, and systems that lacked reliable validation and a clear operational overview.
This fragmentation creates a specific kind of risk. It does not always look like failure. Work still gets done. Experienced people compensate. They remember what to check, who to call, which document matters, and which field in the spreadsheet cannot be trusted. The organization becomes dependent on human workarounds.
That dependency works until people leave, teams scale, demand rises, or the operational tempo changes. In practice, this gap between official processes and real execution is explored further in our breakdown of the reality of operational workflows in complex asset organizations.
Spreadsheets and emails persist because they feel controllable. People know them. They can open the file, edit the field, forward the message, add a comment, and keep a local copy. In security-sensitive environments, that familiarity can feel safer than adopting a new system.
However, familiarity is not the same as control. In our experience working with defense organizations, Excel sheets and emails often create an “air of false safety.” Users feel they have control, while the organization faces risks around incorrect data entry, file corruption, uncontrolled sharing, missing reminders, and poor accountability.
The same pattern appears in maintenance knowledge. A spreadsheet can record a task, but it cannot reliably govern the work. An email can communicate an update, but it cannot create a controlled handover. A paper binder can describe a procedure, but it cannot learn from every execution of that procedure across assets, teams, and sites.
Want to understand how knowledge loss actually happens in day-to-day operations and why it’s so hard to prevent? Explore the patterns behind disappearing expertise and what organizations can do about it. Why Maintenance Knowledge Walks Out the Door.
ERP systems are essential in many asset-heavy organizations. They manage finance, procurement, inventory, cost, and enterprise records. The problem begins when organizations expect ERP to carry the full burden of maintenance execution, asset lifecycle knowledge, and frontline workflow.
ERP systems are effective for their intended purpose, both in theory and in practice. The problem arises when organizations extend them into adjacent activities where they are not specialized, such as detailed maintenance execution. While they remain strong for financial management, compliance, and enterprise reporting, they can be difficult for end users who need practical, task-level support. As a result, frontline teams often rely on supplementary tools and workarounds to manage the day-to-day activities required to keep assets operational.
That does not make ERP the enemy. It means the system of record and the system of execution need to work together. Asset-heavy organizations need structured workflows that connect enterprise data to the actual work: the inspection, the repair, the validation, the handover, the exception, the configuration change, the approval, and the lesson learned.
Many organizations try to preserve knowledge after the fact. They run exit interviews, create lessons learned documents, update manuals, record training videos, or ask experienced employees to write down what they know before they retire.
Those efforts can help, but they are often too late and too separate from the work.
Knowledge continuity improves when capture happens inside the workflow itself. The best time to preserve operational knowledge is when a task is planned, executed, paused, handed over, validated, repeated, or improved. That is when context is fresh. That is when exceptions appear. That is when the gap between the written procedure and the real operation becomes visible.
Before experienced workers leave, organizations need practical ways to capture and transfer the knowledge that keeps operations running smoothly. Learn how to do this effectively in our guide: How to Capture Tacit Knowledge Before Experienced Workers Retire.
Static instructions tell people what should happen. Operational knowledge often appears when something different happens.
Situations like these often emerge during routine work, where a component does not fit as expected, a measurement appears inconsistent with previous readings, or a supplier document lacks critical information needed to proceed. Tasks may take longer than planned because of hidden dependencies or unclear instructions, and technicians may begin to notice patterns, such as the same issue appearing across multiple assets or a component failing more quickly under certain operating conditions. Supervisors may also observe recurring problems in handovers that consistently delay task completion. These kinds of deviations from the expected process are not isolated incidents but signals that valuable operational knowledge is being generated in real time.
These moments are valuable because they represent the points at which real operational knowledge is created, refined, and tested against actual conditions rather than theoretical expectations.
When they remain confined to emails, informal conversations, or individual memory, the organization gradually loses that learning and is forced to rediscover the same insights repeatedly; however, when they are captured within the workflow, linked directly to the asset, and made accessible to the next person performing similar work, they contribute to a growing body of usable knowledge that strengthens capability over time.
Knowledge continuity is not the same as storing more documents. Many asset-heavy organizations already have too much information and too little usable context.
Military Review’s knowledge paradox is useful here because it reframes the issue. Unknown knowns are failures of knowledge management rather than failures of knowledge acquisition. The organization may already have the information it needs, but it cannot find, connect, or apply it at the right moment.
For asset-heavy organizations, usable knowledge has to be tied to the asset, the task, the role, the configuration, and the operating context. It should be clear who did the work, what changed, which data was validated, which exception was approved, and what the next team needs to know. That is how knowledge becomes part of execution rather than a document stored somewhere else.
A generational transition is already underway. Younger workers are entering industries where many of the most experienced people are close to retirement. They often bring different expectations around digital tools, usability, collaboration, and access to information. That can be a strength, but only if organizations give them the context needed to work safely and effectively.
Explore how organizations can successfully navigate the grey-to-green transition in aerospace and defence.
Structured knowledge transfer, mentorship, and knowledge-capture sessions with experienced employees can help preserve critical expertise before it is lost. Organizations can also benefit from dynamic digital knowledge platforms that make information easier to access and apply. Continuous learning, job rotations, peer co-development, and digital upskilling further support the transfer and development of knowledge across the workforce.
The practical goal is not to turn every experienced worker into a document writer. It is to make knowledge transfer part of how work is done.
Experienced employees should be able to leave behind more than stories, local habits, and informal advice. Their judgment should be reflected in templates, workflows, decision records, inspection notes, recurring fault patterns, task structures, and validated procedures that help the next person perform with confidence.
Asset-heavy organizations already understand the lifecycle of physical assets. They track design, production, assembly, delivery, maintenance, overhaul, modernization, and disposal. But they often treat knowledge as something separate from that lifecycle.
Treating knowledge as separate from the asset lifecycle creates unnecessary risk because knowledge about how an asset is built, configured, used, repaired, inspected, modified, and sustained is itself part of the asset’s lifecycle value. It directly influences future maintenance quality, uptime, training, safety, warranty management, spare parts planning, customer support, modernization efforts, and overall readiness.
If that knowledge is scattered, the asset lifecycle becomes harder to manage. If it is captured and structured, every maintenance action and production step can improve the next one.
A practical knowledge continuity model does not need to be complex. It starts by identifying which roles and skills are critical, where knowledge is at risk of being lost, and which workflows rely on fragmented tools or informal processes. From there, organizations can embed continuity into daily work by standardizing tasks, capturing exceptions and decisions, structuring handovers, and linking knowledge directly to assets and operations. The goal is to make knowledge usable in context, so teams can work more effectively and new employees can reach proficiency faster.
The Knowledge Continuity Playbook for Asset-Heavy Organizations - Learn how to build a practical knowledge continuity approach in your organization.
The workforce crisis in asset-heavy industries is often described in terms of shortages: not enough welders, not enough aircraft mechanics, not enough software engineers, not enough technicians, not enough young people entering the sector. Those shortages are real, but the challenge of maintaining continuity is even greater.
Organizations are losing the people who understand how assets behave and how maintenance actually works in practice. Replacing those people one for one will be difficult. Replacing their knowledge after they leave will be much harder.
The path forward is not only to hire more people. It is to make the knowledge of experienced people easier to transfer, easier to apply, and harder to lose.
Asset-heavy organizations that treat knowledge as part of the workflow will be better prepared for retirements, demand surges, modernization, and long-term lifecycle support. They will onboard faster, repeat fewer mistakes, preserve more context, and build a stronger bridge between experienced workers and the next generation.
The goal is not to remove human expertise from operations. The goal is to stop losing it every time a senior worker retires, changes roles, or walks out the door.