TECHNOLOGY, CAFM & IT
BUSTING THE MYTHS
Artificial intelligence is often misunderstood, surrounded by spurious ideas instead of facts, believes Greg Hill, Director of Product, Joblogic. But once those myths are challenged, the benefits become clear.
If you ask any facilities manager what they think about integrating AI into their CAFM system, you'd likely get a mix of enthusiasm, cautious optimism, and down- right skepticism.
Why? Because in facilities management, technology change has often promised more than it’s delivered. From the introduction of digital job sheets to mobile CAFM apps, every new wave has come with the same pitch: it’ll make life easier.
AI is just the latest buzzword to face
that test But beneath the hype, there’s substance.
AI isn’t about replacing systems or people; it’s about enhancing what you’ve got. The assumptions held about AI are usually based on myths, not fact. Once challenged, it quickly becomes clear how AI supports practical improvements in CAFM that save time, cut costs, and raise service quality.
Myth 1: “AI is too complicated for
our teams” There’s a common perception that AI means complex systems, steep learning curves, and endless training. In truth, the best applications sit behind the scenes, making everyday tasks faster and easier.
Take scheduling, for example. AI can analyse skills, location, and job urgency, to suggest the most efficient engineer for each repair. Teams don’t need to understand the algorithm, just how to apply it to their unique operation.
The result: engineers spend less time driving and more time fixing.
Myth 2: “We don’t have the right data” Many FM leaders worry their asset registers are incomplete or job histories are inconsistent. While clean data is important, AI doesn’t need flawless information from day one.
Predictive maintenance is a simple starting point. By consistently tracking failures on even one asset type, useful patterns emerge quickly. Within months, systems can flag which assets are likely to fail and when to intervene.
In short: don’t wait for that pristine dataset (it’s unlikely to arrive). AI’s strength is delivering value from the outset.
Myth 3: “Compliance still needs manual oversight”
Many believe compliance processes are too critical to automate. After all, auditors require accuracy and detail. But that’s exactly why AI helps.
Digital checklists completed on-site can be verified automatically. Missing signatures, outdated certificates, or incomplete fields are then flagged immediately. Managers can access live compliance data for all assets without trawling through files.
Instead of weakening assurance, AI strengthens it, keeping teams permanently audit-ready.
Myth 4: “AI is about replacing people” This is perhaps the biggest myth of all. Engineers often fear AI will take decisions out of their hands, or worse, make them redundant. In practice, the opposite happens.
AI takes away repetitive admin so people can focus on what they do best: solving problems. Engineers spend less time chasing paperwork or repeating work, and managers spend less time firefighting, and more time planning.
It’s all about reducing “busy work” and surfacing actionable insights, not replacing human judgement or expertise. We still hold onto the decision-making.
Moving past the myths If there’s one take away, it’s this: AI isn’t a magic wand. It won’t fix every operational headache overnight, and it won’t deliver industry-heralded promises without a clear purpose.
The FM businesses I see reaping real gains are using AI to enhance their CAFM systems and do the basics brilliantly. They’re responding faster, proving compliance instantly, and making smarter decisions in real time.
Put simply, if your technology isn’t helping you prioritise, predict, or proactively manage your sites, it’s just more noise.
That’s what AI in FM should be about. The rest is expertise only an engineer, a well-oiled site team, and a bit of real- world know-how can bring.
www.joblogic.com
30 | TOMORROW’S FM
twitter.com/TomorrowsFM
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60
