Your team spends $3M on a new system. Six months later, it flags more problems than it solves. What went wrong?
The use of artificial intelligence is increasing quickly across facilities management. From predictive maintenance and capital planning, to condition assessments and repair prioritization, AI offers the potential to completely overhaul the industry.
But, not all AI is equal.
As adoption accelerates, asset managers need to focus on not just finding AI solutions to modernize portfolios, but finding the right kind of solutions to solve real challenges. Not every AI is built to solve the infrastructure problems facing a portfolio, with many tools relying on attractive marketing over actual intelligence.
So, what’s the difference?
- “Real” AI in facilities management is trained on civil infrastructure data, reflects the input of domain experts, and produces measurable and transparent results that professionals can act on. These models are trusted to improve over time, not just become a static relic that is out of date within a year.
- Performative AI, on the other hand, may use the language of innovation (machine learning, automation, digital twins), but it lacks the engineering background, transparency, and reliability to back it up. These systems are often built using a limited dataset, skip expert validation, and fail to explain how decisions are made or how costs are calculated.
The stakes could not be higher in an industry responsible for billions of dollars in capital assets. Choosing an AI system that’s more smoke than substance, can lead to poor maintenance decisions, missed budget opportunities, and most importantly, a loss of stakeholders’ trust.
But it’s a trap that can be avoided. When evaluating the quality of an AI solution here are some fundamental questions to ask.
Was the AI trained on civil infrastructure data?
To distinguish real AI tools, from performative vendors, start by examining the foundation: was the AI trained on civil infrastructure data? Many platforms use generalized datasets or synthetic inputs, which limit their ability to detect actual issues like surface fatigue, cracking, or water damage.
Credible and useful AI models in FM should be trained on varied examples of roads, roofs, lots and structures – all of which are labeled by civil engineers or subject matter experts. If a vendor cannot clearly articulate how their model was trained, or whether the data reflects infrastructure specific conditions, that’s a clear red flag.
Are condition scores validated by professionals?
Another mark of authentic AI in facility management is professional validation. If outputs, such as condition scores, are generated entirely by machines with no human oversight, then the output is just a prediction. Without oversight from engineers or technicians, there is a high risk of inaccurate results, leading to wrong calls.
Systems that truly integrate advanced AI involve a validation loop, where experts confirm or adjust outputs. This makes sure that assessments reflect not just the statistical patterns, but expertise from real humans. This hybrid approach means there is consistency across large portfolios, while preserving the critical role of professional judgement.
Are the outputs useful for decision making?
Fundamentally, real AI needs to support decision making. It is not enough to generate scores or heatmaps. Outputs must align with asset management workflows. Look for systems that translate technical data into formats that are useful for planning, budgeting, or acquisition.
The most effective tools allow teams to sort assets by risk, urgency or cost, and to export reports for stakeholders at any level, from field teams to executives. If the AI delivers data but no direction is accompanied from experts, it’s not solving real problems.
Does the AI improve with more data?
Continuous learning is essential as well.
Infrastructure is complicated. Materials age, climates shift, usage changes, and when AI doesn’t improve with new data it will quickly become out of date. Is the system retrained regularly? Can it incorporate feedback? A true AI system will get better the more it’s used, not fall into static patterns.
With the integration of truly advanced artificial intelligence systems across the real estate industry, it’s difficult to discern advanced systems, from simple automation.
The right AI can change everything.
Get it wrong, and you’re left with sunk costs, eroded trust, and data you can’t use. But when you choose a solution built for your infrastructure, validated by experts, and transparent in its logic, the benefits ripple through your entire operation.
Smart AI doesn’t just generate scores—it empowers smarter decisions, stronger budgets, and more confident teams.
The hype is loud. But the real work? It’s in asking the right questions.
To learn more contact info@sitetechnologies.io.