Why We Need To Go Back To Basics When It Comes To AI

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• Why We Need To Go Back To Basics When It Comes To AI

09 March 2026

As with most industries, Artificial Intelligence continues to dominate the conversation in facilities management. Predictive maintenance, automated scheduling and AI-driven asset insights are all being presented as the next big leap forward for the sector.

According to Ivan Morley, Growth Director at Thermatic, the industry is at risk of skipping an unglamorous, yet critical, step. Before FM organisations talk about AI, they need to get their asset data right.

From an engineering perspective, we look at AI the same way we look at any system: if the inputs are wrong, the outputs will be wrong. It is a simple principle, but one that is frequently ignored. In FM, AI promises to automate decision-making, identify patterns and reduce risk. However, every one of those outcomes depends on complete, accurate and structured asset information. Without that foundation, AI does not create intelligence, it simply automates bad decisions.

A Solid Foundation

Despite years of digital transformation initiatives, many organisations are still struggling with the basics of asset management. In practice, this means asset registers that are incomplete, out of date or riddled with duplication. Systems often fail to integrate seamlessly, and data quality is undermined by inconsistent input from engineers. When this is your starting point, layering AI on top doesn’t fix anything, it just scales the problem.

In those circumstances, AI can create a dangerous illusion of control. Dashboards look impressive and reports feel sophisticated, but the intelligence beneath them is fundamentally flawed. In facilities management, where capital investment, compliance and operational risk are tightly linked, that false confidence can quickly become expensive.

Fixing The Foundations

Our approach has been deliberately pragmatic. Rather than treating asset data as an administrative by-product of maintenance, it is treated as a live operational tool. Every asset is QR-coded and managed through our CAFM system. On site individual codes are scanned, ensuring that work is logged against the correct plant every time. Every planned maintenance visit, reactive repair or intervention updates the asset history in real time. This means that our asset lists are not static spreadsheets they are live working tools.

The discipline behind consistent asset tagging, structured engineer notes and real-time updates is often undervalued, yet it is precisely what enables meaningful insight. Over time, we can compile a detailed maintenance history that supports lifecycle analysis, spares strategy, risk profiling and performance benchmarking.

It also improves day-to-day operations. Engineers attend sites with accurate information, improving first-time fix rates and reducing unnecessary follow-up visits. Data quality is not treated as an abstract benefit; it directly affects service delivery.

Where AI Genuinely Adds Value

Once we have asset data that is structured and reliable, AI truly starts to earn its place. With clean lifecycle data and failure history, trends and patterns begin to emerge that would be difficult for humans to spot at scale. That could include assets degrading faster than expected, sites drifting into higher reactive spend or equipment that is technically compliant but operationally unreliable. These are the things AI is very good at highlighting.

Used properly, AI can interrogate large volumes of trustworthy data and flag risks earlier. It supports more informed lifecycle planning, tighter budgeting and better-timed investment decisions. It can help teams move from reactive responses to proactive intervention. What it doesn’t offer is a short cut, it relies entirely on the groundwork that underpins it.

The Risks Of Skipping The Basics

Trying to deploy AI on top of poor data isn’t just ineffective, it’s risky. Incomplete or inaccurate asset registers lead AI models to draw the wrong conclusions at speed and with what appears to be real confidence. That’s when organisations start making the wrong capital decisions with potentially huge consequences.

The real power comes from AI supporting, rather than replacing, the judgement of skilled engineers. Only then does it enable the industry to move forward. In a sector increasingly drawn to the promise of intelligent systems we need to stay grounded. Sound engineering practice remains the foundation. However, get the basics right and AI can deliver. Skip them, and it is mostly noise.

Picture: An image of a glowing microchip with the letters "AI" in the center of a digital circuit board.

Article written by Dave Mapps | Published 09 March 2026

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