AI depth charge

A shortage of skills in FM is creating huge pressures to prioritise tasks, meet compliance deadlines and ensure preventative maintenance targets are achieved. Esther Coleman, Senior Product Manager at Idox believes enhancing CAFM with AI can begin to bridge the gap

With 70 per cent of facilities managers still using spreadsheets according to Idox research, it may seem a leap to even consider Artificial Intelligence (AI). Yet it is already pervasive. Almost everyone, irrespective of their digital maturity, is likely using AI in some form even if only for automatically enhanced online searches.

Within a facilities management context, AI has a strong role to play in the future in exploring the depth and breadth of insight held within CAFM systems. Just consider the vast amount of historical and real-time data, such as work orders and maintenance records, that hold invaluable insight into trends in breakdowns and replacements. Or the burgeoning use of the Internet of Things (IoT) to provide continuous information about key building facilities including temperature, occupancy and lighting.

For FMs struggling to manage increasing workloads, escalating compliance demands and the push towards net zero, the ability to harness this knowledge will play a key role in maximising the productivity and efficiency of existing resources. From optimising maintenance schedules to gaining new understanding of building utilisation, with the right approach, AI will further enhance the operational and cost benefits that CAFM systems can deliver.

AI has an extraordinary power to sift through unimaginable data volumes to eradicate the irrelevant and enhance the desired information. The quality of that data is, however, essential if the AI is to deliver real value in a business context. Rather than the misinformation provided by Generative AIs, such as ChatGPT, trained on unchecked internet data, there is growing use of industry-specific Large Language Models (LLMs).

BENEIFITS OF LLMS

LLMs are already being used in practice. For example CAFM Explorer® room booking utilises GenAI using LLM to interpret natural language into structured data. This not only fast-tracks the booking process by allowing users to fine-tune requests but also provides additional insight into space utilisation. There is also growing interest in adding simple chatbots to CAFM to provide mobile engineers with a fast, simple way to find the information they need for each job. These features can also facilitate better collaboration with colleagues, helping to share skills and knowledge.

One of the most compelling areas of AI innovation for FMs, is the chance to build on the Predictive Maintenance improvements already provided by CAFM systems. Adding AI further hones functionality by analysing a raft of data from equipment sensors as well as historical maintenance data to predict when maintenance is needed, delivering even greater cost reductions and efficiency gains.

In addition to preventing unexpected breakdowns, minimising the number of repairs, extending asset lifecycle and ultimately saving costs, AI will provide FMs with powerful insight into future events and support the efficient use of resources that will deliver further boosts to productivity throughout the workforce.

EVOLUTIONARY JOURNEY

Accurate data resources are fundamental to successful AI usage and with just 41 per cent of FMs currently using the CAFM systems required to achieve accurate and immediate cross-operational information, clearly the industry has some way to go before the use of AI is ubiquitous. Furthermore, while many organisations are gaining significant efficiency improvements through the use of mobile CAFM solutions that can transform engineer productivity and efficiency, digital maturity and confidence varies throughout the workforce.

As the examples above indicate, CAFM vendors such as Idox are actively researching the role AI can play within existing solutions. We recognise the value of the data held within each CAFM system to deliver new insights that can further enhance the performance of the workforce and reduce the pressure on stretched resources. It will be vital both to ensure any new AI enabled features within CAFM are intuitive and use reporting to highlight data integrity to maximise the accuracy of this powerful data resource.

CAFM systems must balance the innovation that can improve efficiency and reduce the pressure on staff with simplicity in order to ease the transition. Engineers may embrace chatbots, but there will be no explicit need to use AI. Indeed, for FMs, the use of AI will simply enhance existing activities, further optimising schedules and boosting efficiency to reduce pressure on existing staff.

CONCLUSION

The concept of AI may appear daunting, especially to organisations yet to make the step to CAFM, but change can be gradual. By championing a more granular, precise understanding of AI in all its complexity CAFM systems can improve the day-to-day experience for FM staff, enhancing productivity, streamlining compliance and ensuring companies maximise the value of the existing skill set. Furthermore, for an industry struggling to attract the next generation, the addition of innovative tools will be a vital step in gaining the skilled digital natives that represent the future of FM.