SERVICE & MAINTENANCE


unnecessary interventions, and ensure optimal system performance. Maintenance practices can be further enhanced by


workfl ow and workforce management software. Using an all- encompassing digital platform that lets users track assets and manage teams, including sub-contractors, ensures that the right maintenance is carried out by the right people at the right time, every time. This approach can be particularly useful in streamlining and standardising maintenance across multiple sites: at NHS trusts or multi-school academies, for example. Predictive maintenance off ers a range of quantifi able benefi ts that improve M&E plant and system effi ciency and longevity. According to a Deloitte study on predictive maintenance, organisations implementing this approach can achieve: ■ 70% reduction in breakdowns through proactive fault identifi cation.


■ 25% increase in productivity by minimising unplanned downtime.


■ 20% increase in equipment uptime, ensuring smoother operations.


■ 5-10% reduction in overall maintenance costs due to better resource allocation.


These improvements translate into longer asset life, reduced carbon footprints, and better compliance with sustainability targets. Using real-time performance tracking and advanced fault notifi cations, FMs can ensure maintenance teams focus on preventing issues before they arise.


AI’s growing role AI – both admired and feared in equal measure – is transforming


predictive maintenance by refi ning data analysis, recognising anomalies, and improving prediction models. Machine learning algorithms continuously adapt based on real-time data, learning the optimal maintenance methods for specifi c assets and improving their accuracy over time.


AI also enhances maintenance team management by identifying high-priority tasks and suggesting the most effi cient resource allocation. With real-time insights, FMs and building maintenance teams can better allocate engineering staff , ensuring that attention is given to systems that need it most. Furthermore, AI-powered fault notifi cations and model


improvements reduce the guesswork involved in maintenance planning because they can quickly analyse large data sets. Predictive models evolve by learning from historical and current data, becoming increasingly eff ective in predicting potential failures.


Despite the potential, AI adoption in M&E maintenance faces


several challenges: ■ Legacy systems: Many older building and workfl ow management systems aren’t designed to integrate with AI tools, requiring costly upgrades.


■ Budget constraints: Proactive investments are needed, but limited budgets often lead to reactive approaches.


■ Data quality issues: AI relies on accurate and comprehensive data. Fragmented records can compromise predictive accuracy.


■ Skills gap: Staff need training to leverage AI eff ectively, requiring time and resources.


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As AI evolves, self-healing systems—where machines autonomously diagnose and resolve minor faults—are no longer a distant dream. However, AI should not replace human expertise but rather serve as a powerful tool to enhance decision-making and effi ciency. A balanced approach that combines AI-driven predictive maintenance with human oversight will empower building services and FM professionals to optimise asset performance, extend equipment lifespans, and future-proof maintenance. To ensure sensible AI adoption, FM and building services teams should: ■ Defi ne the problems: To avoid tech for tech’s sake, it’s critical to establish a clear sight of the problems you want to solve, supported by strong leadership and wider team engagement.


■ Assess current available systems: Evaluate the compatibility of available technology systems and AI tools to solve your problems and plan the necessary budget, programme, and resources.


■ Think big, act small, fail fast: Develop pilot programmes to test your solutions on a small scale, before full implementa- tion. This helps you refi ne, fi x or stop changes.


■ Invest in staff training: Equip every team with the knowledge and skills to work alongside AI systems.


■ Ensure continuous improvement: Analyse AI performance and refi ne predictive models based on feedback and results.


■ Recognise that data is key: AI requires a lot of quality data. Without it, you’ll fail.


■ Remember the basics: Developing and implementing trans- formational tech-driven solutions requires a deep understand- ing of your business and processes. There isn’t a quick fi x.


By embracing data-driven strategies and adopting AI thoughtfully, the built environment can rise to the challenge of improving maintenance standards while delivering greater value and reliability. Predictive maintenance, powered by real-time data and


increasingly, embedded AI, is not just a passing trend—it’s the key to unlocking long-term operational effi ciency and resilience in the M&E maintenance sector. As AI technology evolves, the potential for self-healing systems and smarter resource allocation will revolutionise maintenance practices. However, human expertise will remain essential in guiding and guarding AI to ensure its recommendations align with real-world conditions.


www.acr-news.com • May 2025 17


The National Audit Offi ce (NAO) has warned that ineffi cient and ineff ective maintenance practices, chronic


underfunding, and ageing stock in critical


public sector buildings jeopardise essential services and value for money.


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