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The FM industry has been talking about digital transformation for years. Where does the conversation actually stand today?

The discussion has progressed, but implementation often lags behind. Today, most FM organizations recognize that technology should provide visibility, enable problem prediction, and streamline operations. The challenge lies in translating this knowledge into an effective real-world application. Additionally, client expectations have increased. They’re no longer simply impressed by a compelling demo. They seek detailed insights into how an FM provider will leverage the technology, understand the back-office team’s knowledge of digital deliverables, and identify the expected results.

Likewise, teams frequently face challenges with data that lacks context. For example, a platform may indicate high energy use, but without additional metrics such as weather, occupancy, or regression data, that information isn’t very helpful. It must be relevant to the decision-maker. The key to successful implementation is connecting system insights directly to operators’ needs.

Most FM organizations today recognize that technology should provide visibility, enable problem prediction, and streamline operations

How do you tell the difference between technology that helps and technology that just creates more work?

I use three tests before committing to any platform.

First, does it truly accelerate decision-making? If the team still needs to manually interpret the results or run additional processes to understand them, the issue remains unresolved. Second, does the data make sense within its context? Relying on a single metric can be misleading. For example, energy consumption only becomes meaningful when combined with factors like weather, IT load, and asset condition. Therefore, a comprehensive view is essential. Third, does the system guide the operator on what actions to take, or does it merely send alerts? Alerts without resolution suggestions or clear priorities place the burden back on the operator and field staff.

Technology that passes all three tests — faster decisions, data in context, and clear next steps — is worth having. Anything that doesn’t is likely making the job harder, not easier.

Digitizing maintenance workflows remains a key step in the FM industry’s technology journey.

What typically goes wrong when FM teams are pushed to adopt new technology too quickly?

The most common problem is anchoring new technology into old processes instead of changing the process first. Technology often becomes an additional layer on top of a legacy manual process, and results in little to no meaningful improvements. Often, technology is introduced to support outdated workflows, simply digitizing inefficient processes rather than transforming or improving them.

Another problem is attempting too much simultaneously, like pushing IoT, automation, and AI onto a team before they’ve mastered the fundamentals. When users feel overwhelmed, they often avoid using the new platform and instead rely on workarounds or stick to their usual routines. As a result, the client sees little benefit from the system, which reduces trust in the technology.

Ultimately, effective implementation involves phased adoption. First, familiarize the team with basic visibility to build their trust in what they observe, then expand from there. The selection of participants in the design process is equally important. At ENGIE Solutions, successful rollouts have been possible by involving operations managers, contract managers, and engineers in the design phase, not only developers. While a data team might focus on moving averages and anomaly detection, a contract manager needs clear guidance on what actions to take, where, and when. These are distinct discussions, and it is crucial to address them early on.

How should AI fit into FM? As a support tool, or something that takes the lead?

As a support tool. Accountability always sits with the person, and that shouldn’t change. What AI does well is close the gap between a signal and a response. In predictive maintenance, for example, it can tell you that an asset or part of an asset is likely to fail within a specific timeframe with a defined level of confidence and accuracy. This is very useful, but the engineer who has looked after that building for years must decide, as they know what that particular sound usually means or what the asset history looks like. The system provides the input, while the decision remains human.

AI can identify potential issues, but accountability and decision-making remain with the engineer

Understandably, framing AI as a people replacement creates resistance and slows adoption down. When it’s set up properly, AI makes experienced engineers more effective by surfacing the right information at the right time, eliminating routine interpretation and freeing up attention for decisions that truly require judgment.

Where is AI delivering real, measurable value in FM right now, and where is the industry getting ahead of itself?

The clearest returns are in predictive maintenance, resource allocation, and energy optimization, but only when the data foundation is solid and the system is properly set up. The misconception is that you can connect an AI layer to an IoT-enabled facility and start seeing savings and increased productivity straight away.

The AI requires time to analyze data, study patterns, plot moving averages, and calculate the consistency index over a typical learning period of 8–10 months, using diverse weather pattern data. Its accuracy depends heavily on the quality of input data; mishandling commissioning or providing inconsistent data can lead to incorrect predictions. Once the team loses confidence in the system, restoring trust becomes very difficult.

The industry often overestimates how fast results can be seen, expecting outcomes within weeks of adopting a platform. In practice, it usually takes eight to twelve months for data to stabilize enough to make meaningful insights and confirm if behavior has genuinely changed. If teams remain reactive and stick to their usual routines, it suggests the technology hasn’t been fully integrated, no matter how advanced the platform is.

What does the next generation of FM professionals need to look like?

The core engineering knowledge, digital/computer literacy and good decision-making, but I’d put decision-making at the centre. Engineering and technology are inputs; the decision is the output.

Experienced individuals may understand a building more thoroughly than any system can, for example, by identifying issues from a single sound. However, without the ability to work with the necessary tools, that knowledge remains unused. Basic computer literacy is now essential and is the foundation for effective work.

As systems advance and teams gain confidence in the data, the quality of decisions built on that data differentiates good FM from great FM. If AI functions as the nervous system of a facility, the decision-makers represent the brain. In FM, the capacity to adapt swiftly and take responsibility for results won’t be automated. These qualities will set apart the FM professionals who will shape the industry’s future.

Source: Facilities Management Middle East

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Author: Mohammed Suhail, Innovations & Solutions Manager at ENGIE Solutions