The latest SFG20 report on the state of UK Facilities Management shows an industry under pressure and in need of the confidence to adapt. In the following Q&A, John Norris, Commercial Director at Samsic UK, addresses today’s Facilities Management challenges and explains the opportunities that new technology will provide.
Running an industrial facility effectively can be a daunting juggling act for facilities managers. On the surface, each juggling ball appears to be a disparate discipline – asset maintenance, security, hygiene, utilities management – and yet they are interdependent, with the overall productivity of a facility reliant on the smooth, consistent upkeep of all. Many of the commercial and technological pressures faced by UK industry are also common to FM, adding new challenges to this juggling act.
The State of Facilities Management 2026 Report by SFG20[i], a survey of 188 FM professionals, offers an insight into the sector’s most pressing challenges. Among those noted is a lack of confidence in the industry’s readiness for AI, and the concerns of FM teams around compliance changes, particularly as The Building Safety Act enforces stricter accountability.
To better understand these pressures and what can be done to alleviate them, MEPCA sought out the expertise of John Norris, Commercial Director at Samsic UK, a leading national provider of FM services.
FM is falling behind other industries in digital transformation and the embracing of smart technologies. What common factors are contributing to this technology lag?
The reason why facilities management (FM) is lagging behind other industries in the adoption of AI and smart technologies stems from cultural, structural and commercial barriers.
On the culture side, there is anxiety, fear and scepticism. Concerns include questions like: will it take my job? Am I allowed to use AI? What’s the policy? Will it really work? What cybersecurity risks are we opening ourselves up to? These all lead to a push-back in adoption.
Then there’s also a lack of workforce training that likely fuels this mindset. Companies might well have an AI policy, but they don’t always have a training policy on how to use AI tools effectively. This means people don’t know how to prompt the tools or chatbots, leading them to stick to traditional ways and means of facilities management.
AI tools only work as well as the foundations on which they are deployed. This means, if the AI is pulling from inconsistent data sources, it will likely give incorrect outcomes. Fragmented spreadsheets, systems and portals mean there is not one single source of truth, leading to confusion for AI tools. And then, from a commercial perspective, there are high costs to adoption, which makes organisations more cautious about investing.
What are the main facets of predictive facilities management, and how is it transforming the maintenance and cleaning regimes of industrial facilities?
Predictive facilities management is based on data-driven decision making. It relies on accurate data that anticipates needs, rather than reacting to them.
To achieve this, from a cleaning or maintenance perspective, you need occupancy sensors, digital dosing sensors, and cobots in areas you want to have sight of; for example, washrooms, offices, high-hygiene zones. These sensors then provide ongoing sets of data – be that over a week, month, or year – and then this goes on to help with dynamic, demand-based cleaning. Clients who adopt this cleaning model can adjust their cleaning schedules accordingly, for example, by having one cleaner on Monday, two on Tuesday, etc.
Going further, AI-driven contract cleaning can trigger interventions automatically and enable better automation. For instance, sensors can trigger cobots to clean areas needing attention, without human intervention. Ultimately, predictive FM means cleaning becomes value-based, rather than volume-based. It’s not based on number of hours or people employed, but rather whether the building is clean.
Why do companies struggle to achieve consistency in governing FM across multiple sites and how can AI help?
Facilities leaders responsible for multi-site office estates face a variety of pressures. These often come from fragmented visibility, delayed reporting and the persistent risk that a minor issue in one building escalates into a board-level conversation.
At a single-site level, cleaning performance is relatively straightforward to oversee. A facilities manager maintains oversight directly, and issues can be addressed quickly and informally. As portfolios expand, reporting habits and supervision models often diverge, weakening central oversight. This is where the ‘visibility gap’ begins and information silos start occurring. Each location sees its own performance, and central teams see a filtered version of reporting.
When inconsistencies surface, the instinctive response may be to increase supervision, such as increase audits, add more supervisors and tighten governance. In practice, this can be an expensive and inefficient fix.
A data-led approach changes this mindset. Sensor-driven monitoring, structured digital reporting and AI-supported analytics do not replace onsite supervisors. They provide continuous, portfolio-wide visibility across all sites, allowing central teams to compare performance consistently rather than relying on isolated reports.
When implemented correctly, data-led oversight provides predictability and stability. It allows central FM teams to see comparable data across sites and regions, and identifies emerging trends before complaints formalise and surface absenteeism risks (i.e., the biological contamination of shared surfaces) or usage spikes before standards visibly decline.
The State of Facilities Management 2026 Report by SFG20 revealed that nearly half (46%) of FM teams are not confident in meeting compliance. What advice can you offer FM teams struggling with compliance?
Firstly, I would say strengthen data integrity before deploying AI tools for facilities management, because if you give rubbish data to the AI, it will churn out rubbish reports and suggestions. Attached to this, invest in your staff’s digital literacy so they can interpret data correctly and ensure that whatever they are using AI tools to interrogate; they are then equipped to connect that back to compliance.
Secondly, prioritise governance over shiny features offered by an AI tool. The objective is not to digitalise cleaning, but to maintain portfolio-level consistency. Tools should be evaluated based on their ability to improve oversight and therefore compliance.
And thirdly, provide clear escalation pathways and data-aligned decision rights. If central teams can see issues, but lack the contractual or operational leverage to act, consistency can’t improve. Clarity around who has the authority to intervene when performance declines can, therefore, be key to achieving compliance.
MEPCAwould like to thank John Norris for offering invaluable insights for keeping those critical components of effective FM moving in tandem. For more information, please visit the Samsic UK’s website.
