Workforce Shortages Are Here to Stay — AI Is the Only Scalable Solution

Workforce Shortages Are Here to Stay — AI Is the Only Scalable Solution

Facilities management teams are short-staffed. This isn’t a temporary condition — it’s structural. And unfortunately, the problem is getting worse, not better.

The retirement wave is real. Asset complexity is growing rapidly. Meanwhile, the pipeline of skilled workers entering the trades simply cannot keep pace with the experienced professionals leaving it.

Organizations waiting for the labor market to self-correct are going to be waiting a long time. For FM leaders facing this reality today, the only scalable answer is AI.

The Retirement Wave Is Accelerating

Electricians, HVAC technicians, building engineers, and maintenance specialists skew significantly older than the average American worker. In fact, in many FM departments, 30 to 40 percent of the team is within ten years of retirement age.

When those workers leave, they don’t just take their labor with them. They also take decades of institutional knowledge — which assets run hot in summer, which systems have quirks that aren’t in any manual, and which deferred work orders are quietly becoming emergencies.

Historically, organizations have never systematically captured this knowledge. It lives in people’s heads, and when those people retire, it’s gone permanently.

This is not a temporary staffing problem. Rather, it is a structural shift that will define facilities management for the next decade, and every FM leader needs a strategy to address it.

The Replacement Pipeline Isn’t Keeping Up

Enrollment in skilled trades programs has improved in recent years. However, it remains well below what’s needed to replace the volume of retirements projected through the mid-2030s. The Associated General Contractors of America consistently reports that over 90 percent of contractors struggle to fill skilled craft positions — a number that has barely moved despite increased attention to trades education.

As a result, a widening gap has formed between experienced FM professionals leaving the workforce and qualified workers entering it.

Simply put, organizations cannot hire their way out of this problem. The workers aren’t there in the numbers needed. Furthermore, even when recruiting succeeds, institutional knowledge takes years to rebuild — and that’s time most FM teams don’t have.

Asset Complexity Is Making the Problem Worse

At the same time the workforce is shrinking, the assets facilities teams manage are becoming dramatically more complex. Building automation systems, IoT-connected equipment, smart HVAC controls, integrated energy management platforms, and advanced life safety infrastructure all require specialized knowledge to maintain and troubleshoot.

In contrast to the FM professional of a generation ago, today’s technician is expected to manage a sophisticated layer of connected digital infrastructure that requires an entirely different skill set.

Consequently, the knowledge gap is widening in two directions simultaneously — fewer experienced people coming in, and more complexity waiting for them when they arrive. For most FM organizations, this combination is already creating real operational strain.

AI Is a Force Multiplier — Not a Replacement

Before going further, it’s important to clarify what AI actually does in a facilities management context. AI doesn’t replace skilled facilities professionals. The physical work of inspecting, maintaining, and operating buildings requires human hands and human judgment — and no AI changes that.

Instead, AI makes the people organizations already have dramatically more effective. Specifically, it captures institutional knowledge before it walks out the door, eliminates administrative overhead that consumes skilled time, and makes smarter decisions about how work gets assigned and prioritized — decisions that today rely on experience that is increasingly in short supply.

Smarter Craftsperson Assignment Changes Everything

Most organizations currently assign work orders through a combination of dispatcher judgment and technician availability. When dispatchers have deep experience, this approach works reasonably well. However, when they don’t — or when volume is high — work gets assigned suboptimally. The wrong technician arrives at the wrong job, and callbacks and rework follow.

IMS.ai analyzes historical work order data — including technician assignments, resolution outcomes, time to complete, and callback rates — to recommend the optimal craftsperson for each job. Rather than simply identifying who is available, the system identifies who is most likely to resolve this specific issue, in this specific location, correctly the first time.

The downstream impact compounds quickly. Better assignment leads to fewer callbacks. Fewer callbacks, in turn, free up technician hours. More available hours mean more work gets done — without adding headcount.

When an FM team is running lean, getting assignment right isn’t a nice-to-have. On the contrary, it’s often the difference between staying operational and falling behind on critical maintenance.

Freeing Skilled People for Higher-Value Work

In many FM organizations, skilled technicians and supervisors spend hours each day on tasks that don’t actually require their expertise. Writing up work orders, searching for asset history before heading to a job, manually compiling reports, answering routine status requests, and navigating complex IWMS workflows just to close a ticket — all of these activities consume time that should be spent in the field.

This administrative overhead functions as a silent tax on an organization’s most valuable and scarcest resource.

IMS.ai eliminates much of this burden through natural language interaction with the IWMS. Rather than navigating multiple screens, a technician simply asks a question and gets an immediate answer. Instead of manually generating a maintenance summary, a supervisor describes what’s needed and receives a formatted report. When a craftsperson arrives at a job, AI has already surfaced the relevant asset history, likely root causes, and resolutions that worked in similar past situations.

In organizations where technicians currently spend two or more hours per day on administrative tasks, recapturing that time represents one of the highest-value investments available. Furthermore, when teams are constrained, every recovered hour goes directly toward the work that actually requires skill, judgment, and experience.

The Case for Acting Now

There is a timing dimension to AI adoption that most organizations haven’t fully considered. Because AI learns from data, organizations that implement now — while experienced staff are still on board — gain a critical advantage. They can use AI to capture and encode institutional knowledge before their most seasoned people retire, creating a foundation that will support the team long after those individuals are gone.

By contrast, organizations that wait will implement AI against a thinner data set, without the benefit of experienced staff who can validate recommendations and fill in gaps. The result is a slower, less effective ramp-up at precisely the moment when the workforce challenge is most acute.

In short, the organizations that act now don’t just solve today’s staffing problem. They also build an operational and knowledge foundation that compounds in value as the workforce challenge deepens over the next decade.

A Workforce Strategy — Not a Technology Decision

It’s worth reframing how AI in facilities management gets discussed. Too often, the conversation focuses on the technology itself — the algorithms, the capabilities, the demos. However, that framing misses the more important point.

AI in facilities management is fundamentally a workforce strategy. It is the answer to a question that every FM leader is already wrestling with: how do I maintain or improve service levels with a team that is getting smaller, less experienced, and more stretched?

Hiring more people isn’t the answer — the market won’t support it. Deferring the problem isn’t viable — it gets worse every year. Instead, the path forward is to fundamentally change how the team works: capture knowledge before it leaves, assign work with precision, eliminate overhead that consumes skilled time, and build a system that gets smarter as it accumulates more data.

That is exactly what AI makes possible. And the window to build that foundation — while the most experienced people are still in the building — is open right now.

IMS.ai: Built for This Problem

IMS.ai runs natively inside Archibus, with no public cloud exposure and no external data sharing. As a result, it gives facilities teams AI-driven knowledge capture, craftsperson assignment optimization, and natural language IWMS interaction — all in one secure platform.

Version 2 is live. The capabilities are proven. Moreover, the organizations implementing now are building the operational advantage that will matter most over the next decade.

About IMS Consulting:
For over a decade, IMS Consulting has been at the forefront of delivering comprehensive services across multiple platforms, including Archibus, ServiceNow, and ESRI, to our diverse clientele in both public and private sectors. As a dedicated small business, we offer personalized attention from experienced and certified consultants. Our experts collaborate closely with clients to gain a deep understanding of their operational processes, identify unique requirements, and uncover opportunities for enhanced management of their infrastructure. We are committed to helping you make informed capital budgeting decisions that yield benefits today and sustainably into the future.