Maintenance Mindset: AI and the industrial future: What maintenance work will get automated and what will endures

Artificial intelligence is not coming for jobs in the abstract. It is coming for tasks. 

In every trade, discipline, and department, work can be divided into two categories. The tasks that follow a repeatable pattern, and the tasks that require judgment, experience, and the ability to diagnose real problems in real systems. The first category is easily automated. The second is not … yet.

The maintenance and reliability world sits directly at this crossroads. On one hand, AI can process years of work orders, interpret patterns in vibration and oil analysis, map failure precursors, and generate predictive recommendations faster than any human analyst. On the other hand, no algorithm can yet replace the skilled craftsperson who understands how a machine feels, sounds, and behaves under load. Nor can it replace someone who has lived through enough unexpected failures to know what questions to ask when a data trend violates theoretical expectations.

The percentage of work at risk from AI

My great-grandfather came over from Ireland as a blacksmith. He had a good business with the majority of product made was Horseshoes. This was great until the automobile was mass produced and the horse and carriage were replaced. He began making suspension springs for cars – a shift in product, not ability. 

Major studies estimating the percentage of job tasks that can be automated tend to converge on a clear pattern. The more routine the activity, the higher the technical potential for automation:

• Accommodation and food services: roughly 70-75% of tasks technically automatable
• Manufacturing: roughly 55-60%
• Transportation and warehousing: roughly 55-60%
• Retail and administrative support: roughly 40-55%, depending on the level of clerical work
• Professional, scientific, engineering and technical work: roughly 25-35%
• Healthcare, education, and human services: roughly 20-35%.

At the occupational level the pattern becomes even sharper. Nearly half of all administrative, clerical, and customer service tasks are exposed to automation by existing large language models. Legal support work sits around 40%. Engineering tasks average 30-40%. Maintenance and repair, construction, and trades sit at the lowest levels, between 10-20% for current AI technologies.

The logic is straightforward; AI can automate what it can predict. If the task is standardized, defined by rules, dependent on documentation, or conducted entirely within a digital environment, AI can do it better. If the task involves contextual judgment, embodied skill, improvisation, or accountability for safety, AI supports rather than replaces the worker … yet.

Where wage compression will occur

Any industry that relies heavily on routine clerical or scripted work will feel wage pressure first. This includes office administration, customer support centers, basic accounting and payroll, routine legal support, mass-content production, and junior-level software development. These positions depend on a high volume of structured tasks. When the task set can be automated, the labor market compresses.

Where the work is interchangeable, wages flatten, and the early signals are already visible. Companies are freezing clerical hiring. Call centers are shifting to AI triage. Content mills are replacing teams of writers with a single editor supported by generative models. Junior developers are being outpaced by code assistants that write, and debug faster than they can. 

There is a realignment. The blacksmith was making horseshoes, now are making automobile springs. In industrial environments, the equivalent category is the data-heavy but judgment-light work. Report compilation. Dashboard generation. Preliminary analysis. Routine documentation. Predictive models trained on these tasks do not get tired, do not lose focus, and do not produce inconsistent summaries. The value of these tasks will decline.

Where wage expansion will occur

Here is the counterweight – the more AI reduces the grunt work inside a profession, the more valuable true expertise becomes. AI magnifies the impact of anyone who can frame a problem correctly, design an experiment, and interpret real-world deviations. 

Those on the line will continue to understand machinery well enough to challenge the model. There will be a continued need to translate diagnostic findings into reliable action. These people will not only remain essential, but they will also increase in economic value.

Certain professions already show signs of expansion.

• Skilled trades: electricians, millwrights, machinists, heavy equipment technicians
• Reliability engineers: especially those who integrate condition monitoring, lubrication forensics, and root cause analysis
• Senior engineers and system architects
• Cybersecurity and industrial control system defenders
• Healthcare practitioners and care-focused roles
• High-concept creative and strategic thinkers
• Educators and trainers who design human-AI hybrid learning

In each case, AI automates the procedural work and amplifies the decision-maker.
This dynamic will be especially strong in maintenance and reliability. AI can tell you that a bearing shows a spectral signature associated with early-stage lubrication failure. Someone still needs to determine whether that means a contamination pathway, an improper grease selection, an installation error, an incorrect load, or a system resonance issue. 

AI can detect anomalies. Humans still own responsibility…for now.

The path forward: A practical framework for leaders in maintenance and reliability

Expect your best people to become even more valuable. AI will expose the difference between someone who follows procedures and someone who understands the machinery.

Consider the following:

• Automate the repetitive half of the job.
• Reports, scheduling, parts list generation, document review, and the first-pass interpretation of condition monitoring data are ideal candidates.
• Invest in developing judgment, not just procedural skill.
• The workforce of the next decade needs people who understand systems. Tribology, materials behavior, vibration fundamentals, designed experiments, and failure physics matter more than ever.
• Redesign roles around expertise, not around paperwork.
• The value of a technician or engineer lies in how they diagnose, correct, and prevent. Let AI handle the filing cabinet.
• Treat AI as a reliability tool, not a silver bullet.
• AI can magnify a competent program or accelerate a weak one into failure. The fundamentals still matter: contamination control, proper lubrication, correct installation, and disciplined failure analysis.

AI does not diminish the importance of human expertise in maintenance and reliability. It clarifies it. When the routine work disappears, what remains is the work that truly defines a professional. The ability to discern root cause from noise. The ability to design experiments that reveal what the data cannot. The ability to see the whole system and not just the trend line. 

In a world where AI can replicate repetitive thought, the highest-paid professionals will be those who think in ways the model cannot. That is good news for the craftspeople, engineers, and reliability leaders who already understand that machines fail in ways no algorithm can anticipate … yet.

Our job is not to compete with AI. Our job is to become part of the system AI cannot replace … yet.