As the European Union (EU) moves forward with its Omnibus reform package and considers pausing its EU AI Act to reduce regulatory complexity, organisations may be tempted to think that fewer regulations signal permission to relax compliance efforts. But simplification should not be confused with deregulation, nor should it justify organisations neglecting essential safeguards or skill development. 

In fact, as regulatory frameworks evolve, the importance of robust internal governance, ethics and continuous upskilling becomes even more critical. Organisations that proactively strengthen their compliance posture now will be best positioned to navigate future developments in EU regulation, regardless of whether the rules become more or less strict.

Regulatory simplification must be paired with upskilling and internal engagement

Despite regulatory rules being simplified, every organisation still needs a team that can both understand and apply them. The simplification of the EU AI Act, for example, is intended to streamline external compliance and reporting. But that doesn’t define or diminish the internal governance required to use AI responsibly. Businesses will welcome the reduced administrative burdens resulting from clearer rules, but they must not lose their commitment to understanding, interpreting, and applying those rules effectively. Those developing and using AI need to understand how the law applies to them, meaning compliance remains an internal responsibility.

To ensure compliance is a priority, organisations must invest in upskilling their workforce and encourage internal employee engagement. This means going beyond a one-size-fits-all training model and instead implementing a risk-based approach tailored by generation, geography and role. Embedding AI literacy as a foundational skill across the organisation will be critical. 

Once regulations are clarified, employees that are using and deploying AI must know what actions are required of them. Training should go beyond theory – incorporating knowledge checks, simulations and scenario-based practice to help employees build confidence in applying regulations. Educating employees, testing their proficiency, and allowing them to practice applying that insight in a controlled environment will help them understand regardless of whether the law is simplified. This approach creates a culture where compliance is shared, understood and actionable. 

Compliance drives ethical innovation and business value

Compliance isn’t just about avoiding risk – it’s about building trust, ensuring responsible AI use and driving long-term business value. In emerging areas like AI, it builds fundamental transparency and accountability.

While simplified regulations may reduce complexity, it must not come at the cost of ethical rigor. Organisations must proactively build frameworks that are transparent, adaptable, and sustainable. It’s a ‘belt and suspenders’ approach that combines formal oversight with self regulation. This includes embedding compliance into the organisation’s mindset and operations, not just processes. 

Leadership plays a crucial role in shaping this culture. Business leaders must not only endorse compliance initiatives but actively model responsible behaviour and encourage ethical innovation across their teams. 

A framework for local compliance and AI transparency

As regulatory landscapes evolve and the future of the EU AI Act remains uncertain, organisations need strong established frameworks to ensure they remain compliant with local laws while aligning with global standards. This is especially true for AI, where transparency, explainability, and data governance are non-negotiable.

A strong compliance framework should include:

• An AI policy that defines ethical usage and transparency standards. Clear, detailed, and understandable policies are essential to ensure consistent compliance across every department.
• Regular audits to assess compliance and identify areas for improvement. These will provide important opportunities for continuous learning, so organisations can pinpoint areas for improvement and adapt to evolving ethical regulations. With AI, audits help employees strengthen their skills in ethical practice, compliance oversight and risk management.
• Cross-functional collaboration to ensure diverse perspectives are considered in decision-making. A collaboration of expertise from different departments – such as IT, HR, legal and policymaking – enables organisations to better comprehend the capabilities and challenges that AI introduces.
• Leadership accountability, with executives leading by example and championing responsible AI adoption. Clear internal communication from leadership will ensure that teams understand simplification as a shift in approach, not a lowering of standards. Reinforcing the continued importance of ethical AI practices and internal accountability will prevent complacency as regulations evolve. 

These components help organisations stay ahead of regulatory changes and foster a culture of continuous improvement. As a result, teams can respond faster and with more confidence to new requirements, reducing the risk of non-compliance and enhancing organisational resilience.

Simplification shouldn’t be a shortcut

Regulatory simplification offers the promise of reduced complexity and clearer expectations. But it should not be mistaken for a relaxation of standards. Compliance remains essential, especially as organisations face the ethical and operational challenges of rapidly evolving technologies like AI.

By investing in upskilling, building ethical frameworks, and fostering a culture of compliance, organisations can transform regulatory simplification into a strategic advantage, driving smarter, more sustainable and more responsible innovation.

Today, the IoT is everywhere – it connects machinery in manufacturing, smart grids in critical energy infrastructure and remote patient monitoring devices in healthcare. Its rapid growth is undeniable, with as many as 40 billion devices forecast worldwide by 2030, but as organisations scale their massive IoT deployments they must be wise to the cyberthreats they face. 

The IoT must be resilient as it scales and that means building security in at every stage to avoid damaging and costly outages caused by cyberattacks. 

The IoT needs scalable resilience and security to avoid downtime 

Unfortunately, the risk that companies and customers will suffer downtime from a security breach is high. Beaming’s cyberthreat report into UK businesses reveals that IoT devices were the most frequently attacked in 2024. What’s more, the daily attack average on those devices rose still further in the first quarter of 2025 to 178 times a day.   

If companies expand their IoT operations and grow their installed base of devices without baking in resilience and security, they run a serious business risk. Cybercriminals increasingly target sprawling, under-monitored device networks, forcing organisations to rethink how they secure growth at scale. 

Companies, and the solutions providers supplying them, must strive to stay one step ahead. Too often, resources are ploughed into cybersecurity only after a breach. By then financial, and most likely reputational, damage has already occurred. Instead, companies must maximise IoT uptime by planning proactively for security and scalability. 

IoT outages risk regulatory penalties

The UK’s National Cyber Security Strategy 2016-21 stated, “poor security practice remains commonplace across parts of the (IoT) sector.” Following that, a World Economic Forum State of the Connected World report examined governance gaps in IoT and related technologies and labelled cybersecurity the “second-largest perceived governance gap”. 

It was a situation that couldn’t continue. The IoT was becoming more deep-rooted in transport, energy, retail and healthcare infrastructure. Governments and authorities had to take note and began introducing more security regulations and standards to protect customer data and help prevent IoT outages. Now, scaling without protection is a major compliance, as well as operational, risk. 

Compliance can sometimes seem like an inconvenient overhead but in fact regulations and standards help businesses. They provide a framework – a best practice guide if you will – to securing IoT deployments so they will be resilient. That’s what everyone wants – businesses, whose revenues and reputations depend on reliability, and customers who want products and services that work without anyone stealing their data. 

Having said that, for most companies, the IoT merely supports and facilitates their core business. It isn’t their main focus. The ever-changing regulatory landscape can be a daunting place to know. Companies must work with experts in the field to understand and abide by the many rules that apply.  

The regulatory environment

They include the Digital Operations Resilience Act (DORA), and other resilience mandates that cover risk management, supply chains and application and device security. There is also the EU’s Cyber Resilience Act, China’s Cyber Security Law and the Telecom Security Acts in the USA and UK. 

A recent addition was EN 18031, which is of particular importance to businesses who sell or supply IoT devices in the EU. It is relevant to all connected radio devices from 1 August 2025 and is a cybersecurity add-on to the EU Radio Equipment Directive (RED), required to receive a CE mark. Non-compliant devices without the CE mark will be deemed unsafe and cannot be legally sold in the European Economic Area (EEA). 

To meet IoT regulations and standards, companies must set service level targets that can only be met by high availability and rapid, automated recovery from outages. Anything less isn’t good enough because regulators and customers expect more, and companies should demand more of themselves for their reputations and bottom-lines.   

Resilient and secure IoT requires real-time visibility and threat detection  

Companies can scale IoT securely despite growing and ever-evolving cybersecurity threats, but only through a range of measures that all start with design. Security must thread through the end-to-end solution spanning people, process and product. The weakest link in the chain might not be the IoT device, it could be neglected security training or a user access control policy that is not fit for purpose. 

A fully rounded approach to IoT security defends against, detects and reacts to incidents through the lifetime of the product or service.

It defends through technology – identity and access management, multi-factor authentication, encrypted data, endpoint protection, patch management, cloud authentication, software updates, encrypted communications and secure APNs – but also through processes – change control procedures, version control for configurations and audits carried out against regulatory standards.

It detects through real-time visibility and threat detection that monitors devices and networks to spot anything unusual, such as a change in target URLs or data usage. Detection engines can be AI-assisted to analyse data feeds and score potential threats with automated or manual action, according to business rules, to isolate threats or send them for review. 

It reacts with automated threat responses, self-healing systems, fallback connectivity and the execution of detailed – and rehearsed – disaster recovery plans. 

Growing the IoT without risk to infrastructure or data

An IoT solution may have one connected device, or many thousands, but it must be resilient against security threats and designed in such a way that it can grow and evolve without risk to infrastructure or data. Cyberattacks will find and exploit any security weaknesses in technology, processes or the actions of employees and suppliers. 

To counteract the threat, companies must call on the right expertise and be guided by relevant regulations and standards to ensure their IoT is secure and resilient, now and in the future.

Most digital transformation initiatives fail. That’s not speculation—it’s empirically validated. A meta-study by Michael Wade and co-authors from IMD Business School in Switzerland, puts the aggregate failure rate at 87.5%. These failures don’t stem from a lack of technology. 

They stem from a lack of first principles thinking. Worse, they stem from groupthink packaged as “best practices” due to misunderstood value creation paradigms, misaligned incentives, and instinctive gut reactions.

Groupthink is the structural rot at the core of digital transformation. It disguises itself as best practices, consensus, and risk mitigation. In reality, it’s the comfort zone of institutional “cover your ass” politics avoiding accountability. Vendors and consultants exploit this dynamic to sell solutions, either by making them so narrow they avoid all integration costs and result in no real impact or so vast they drown in abstraction and escape all responsibility. 

Either way, they make money, while you always lose.

Spray and Pray: A Controlled Path to Failure

The default corporate approach to transformation is to crowdsource use cases, prioritize them by committee, and allocate budgets based on consensus. This is what I call spray and pray. It’s a portfolio of supposedly risk-averse, disconnected initiatives that signal motion but produce no impact. Committees gravitate toward politically safe options—sevens on a scale of one to ten. Sevens don’t win. They just help avoid blame when things turn out mediocre.

Crowdsourcing sounds democratic. But unless every participant has domain expertise, independent judgment, and access to the same information, Condorcet’s jury theorem guarantees failure. In practice, these conditions are never met. The outcome is consensus driven groupthink mediocrity.

Boiling the Ocean: The Illusion of Ambition

At the opposite extreme is boiling the ocean—attempting sweeping, technology-first transformations with no grounding in customer value. This is tech consumerism disguised as strategy. Moving to the cloud, buying a new ERP, or adopting the latest AI tool might make you look busy. But if it doesn’t create measurable value for your customers, it’s a distraction and guaranteed waste of resources.

Being an early adopter is often glorified. It means you’re a participant in an unpaid drug trial or beta test. The software may be new, but the value creation logic is not. As Charlie Munger noted, the benefits of increased efficiency flow to the vendor of new technology and eventually to the consumer, but definitely not to you. Unless you’re creating and capturing proprietary differentiated value, you’re just funding someone else’s business.

Fear, Novelty, and the Emotional Antipatterns

These failures aren’t just cognitive. They are evolutionary, subconscious and emotional. When faced with complexity and uncertainty, leaders regress to the most basal of human responses. The inner reptile avoids risk, delays decisions, and clings to orthodoxy. The inner monkey reacts emotionally, chases trends, and mistakes activity for progress.

Together, the reptile and the monkey can end up dominating the boardroom. They drive decisions not from first principles, but from fear, ego, and FOMO. The result: spray and pray portfolios, boiling-the-ocean transformations, and millions wasted on initiatives with no clear customer benefit. The unaccounted for and often ignored opportunity costs often run into billions.

Thinking Like a Producer

The antidote is not more frameworks or consultants. It is first principles thinking. Start by saving. Eliminate initiatives that don’t directly tie to customer impact. Stop acting like a tech consumer. Start thinking like a producer.

Technology is a means, not an end. The only transformation that matters is the one your customer feels. Work backward from that. Avoid crowdsourced decision-making for strategic priorities. Make fewer decisions. Make them more deliberately. Focus on depth, not breadth.

Groupthink thrives where accountability ends. Break the cycle by aligning incentives, eliminating noise, and rigorously focusing on value creation. Digital transformation does not fail because it is hard. It fails because it is misunderstood.

You don’t need another vendor pitch. You need clarity, courage, and conviction. Everything else is noise.

Here’s the harsh reality: what you think you know about Gen Z is likely already out-of-date. In fact, the only constant with Gen Z is change itself. And it’s this that makes designing digital experiences that truly resonate with them as customers so challenging; but it’s far from a lost cause.

Digital overload

Extensive research with Gen Z audiences consistently reveals one clear message: they’re busy. Too busy to spend time reading your content. Too busy to try and unpick complex experiences.

But busy doing what? My strong suspicion is that Gen Z are victims to a world of digital intrusion. Alerts, messages, notifications – all competing for attention, all demanding that they must do something, all demanding they do it now.

Understanding this helps you consider how your brand enters this melee. Think you’ll be able to provide some static web pages with text on? Wrong. TLDR. Gen Z are ‘skimmers’ who mostly absorb headers and images, often missing large chunks of content if the page is too cluttered or hard to digest. This is something we’ve seen firsthand in our user testing, with some even copying web content into an AI summariser because they wanted something easier and quicker to digest. Don’t be the brand with the digital experience that pushes Gen Z to run your content through AI because it’s too much to handle.

Hyperpersonalisation cuts through the noise

Making content more relevant is where personalisation can play a huge role. No longer a ‘nice to have’, it should be in your MVP thinking and woven throughout any experience with your brand. And when we say personalisation, we mean intelligent, intuitive experiences that reflect who your user is, and that adapt and deepen as they engage. This includes algorithmic-based personalisation, where content is tailored based on behaviours and preferences; memory-driven shortcuts that recall what’s been previously done and reduce friction; and personalised tracking features, such as stats that chart progress or achievements – think reading goals on StoryGraph or personal bests on Strava. Think of a website that can bend and flex to show the user the exact content they need, fast.

Gen Z also want control. This spans customisation – whether it’s changing avatars, wallpapers or icons – but it also includes data use. Building experiences that use data or gather user input to give that exact user the exact information and next steps they need is great. But also keep in mind that Gen Z expect transparency and are highly cautious of how their data is used. This means any perceived overreach or lack of control will see them run. 

The need to stand out

Given the sheer volume of digital experiences that Gen Z encounters (are there any brands left that aren’t trying to digitise their experience?), the need to stand out from your competitors – to generate loyalty and recognition – is greater than ever. 

Our Shifting States report clearly identifies that Gen Z are fluid – don’t give them the right experience and you could lose them. And any marketer will tell you: customer retention is easier than customer acquisition. 

But here’s the paradox: How do you stand out to Gen Z when you have a smaller than ever window of ‘influence’ to earn their engagement? In this scenario, it’s tempting to fall back on established design patterns that ‘work’, but then you’re not standing out. So what do you do?

The answer here is balance. Find the balance between giving people something that’s easy to use and something that stands out. This is where there’s room for design innovation. Find ways of injecting personality and feeling to your work that helps it set sail in a sea of monotonous digital, but make sure it’s digestible – Gen Z-optimised content that’s communicating key information in a new way.

Seamless experience

Another issue that comes up time and again is fixing disjointed experiences.

Many brands have opted for a SAAS-first digital strategy – not wrong – but, if not well implemented, this can lead to friction in the experience; irritations such as multiple passwords/logins, different information in different systems, different interfaces and poor mobile experience.

This doesn’t work for Gen-Z. Think about the users I’ve already described and then imagine them in this situation. Adaptability is what Gen Z does best, but it also makes them hyper-aware of friction in a brand experience.  

Gen-Z needs seamless experience – single logins, actionable information from across systems, and a mobile-first experience. And speed and ease aren’t perks – they’re non-negotiables. In a world of rapid change, slow or clunky experiences aren’t just frustrating – they’re dealbreakers. What older generations might tolerate, Gen Z simply won’t.  

This creates both risk and opportunity: brands that deliver seamless experiences can stand out dramatically in a crowded landscape. But success requires more than isolated convenience features like free delivery. It demands a holistic approach to optimisation across all touchpoints, creating fluid pathways that anticipate and meet Gen Z’s needs. 

Keeping up with digital-first companies is essential

While the above insight is all well and good, it’s tough for traditional brands who are not purely digital. You provide all the people and infrastructure. Digital is only a small part of what you do. But you’re expected to keep up with digital-only brands, whose sole focus is a digital product providing an experience you now need to match or better. The brands digital native Gen Z are flocking to.

This exists across every sector, but to think about just three:

1. Finance (compare Monzo with Barclays)
2. Travel (compare AirBnB with Hilton Hotels)
3. Insurance (compare Confused with Admiral)

These companies are setting trends and moving with Gen Z. Adapting to their fluidity to predict and get ahead of trends, before they even exist – just look at how AirBnB has shifted its offering from providing hotels to providing a whole travel experience. And doing so through a beautifully crafted, easy-to-use, pocket-size digital interface. 

How to make this work

Moving to a brand that provides the experience Gen Z needs can require some major change. But the biggest thing is to make sure you understand and can move with the fluidity. 

A model of continuous discovery can help. Rather than conducting one-off pieces of research, think of discovery like a live stream, not just a static snapshot. If you look at the brands cited above, they’re already operating this way – defining new norms.

They’re not running sporadic research projects; they’re digital product organisations built on insight and metrics. And if you want to compete, you need to do the same thing.

You can implement this in many ways, but two examples would be to:

1. Go big and go quant using a platform to measure engagement and respond quickly to any noticeable trends in the data. AI is great for this type of data analysis, showing you trends in an instant, but you’ll need further research to understand these trends in more depth.
2. Conduct ongoing panel research to understand trends inside and outside of your sector, and regularly experiment and learn with the results.

Make sure you’re circulating research and generating a wider understanding of your audience, so everyone understands who you’re dealing with and what you’re doing about it – so they’re all bought into the mission.

Discovery isn’t always the issue

From my experience, knowing what to do – whether that’s improving a process, changing ways of working or building something new – isn’t usually the problem. Most of the brands we work with already have a good sense of the improvements they want to make. 

The real challenge is being able to do them. Things like shifting priorities, unclear strategy, budget constraints, people leaving, or internal politics often get in the way. I think organisations in this situation need to look a bit deeper at what’s holding them back, and be honest about what needs to change to actually make progress.

In general though, the issues I usually see are pace of delivery, lack of focus, people and bureaucracy. 

My advice:

• Break away from digital bureaucracy and focus on accelerating delivery speed. Adopt true agile delivery practice. Adapt to change. Bring ideas to life faster, so that you can test and learn from them quicker.
• Set clear design principles that reflect what Gen Z actually want (values like connection, speed and transparency), and hold yourself accountable to them.
• Use data dashboards to track performance – specifically among younger audiences – and test your products with real users from these cohorts, not just proxies.
• Perhaps most importantly: hire young people! No insight, no research method, no trend report can replace lived experience. If you want to build for Gen Z, bring them into the room.

It’s tough. Gen Z are a slippery fish and competition for eyeballs is fierce. But if you really want to go after this market then digital experience must be a top priority. 

And just remember: good digital experience is good for everybody so maybe, by improving things for Gen Z, you’re improving things for everyone else as well.

Depending on where you look, digital transformation initiatives are reported to have an extraordinarily high failure rate of anything up to 80%. Digging deeper, specific reasons vary from one organisation to another, but it’s not unusual for issues such as unclear strategic goals, fragmented data, an inability to scale, internal resistance, or a myriad of other problems to derail even the most well-funded efforts. In monetary terms, this adds up to an eye-watering “$2.3 trillion wasted on unsuccessful projects globally so far,” according to one estimate.

This remarkable level of underperformance belies a market that continues to boom, with one industry projection putting growth at over 25% a year and trending to over $4 trillion in value by the end of the decade. Clearly, this situation raises various fundamental questions, perhaps most importantly of which are what is going wrong with so many projects, and how can organisations get digital transformation right?

What’s going wrong?

One of the most common pitfalls is that, rather than focusing on the underlying business problem, leaders favour a technology-first approach. Among the various problems this kind of workback mindset can create is that by letting the digital element of the overall transformation dominate, by definition, people and processes must follow. Instead of seeing the efficiency gains they wanted, businesses deploy mismatched tools and workflows that don’t deliver, while employees wonder what has changed for the better.

Another significant issue is a lack of a clear, organisation-wide strategic vision. Without leadership alignment and strong communication at every stage of the process, digital transformation efforts often remain siloed within individual departments or teams, instead of being embedded across the wider business as originally intended.

Other problems, such as those associated with internal resistance to change, can also frustrate strategic objectives. It’s quite understandable, for example, that employees who’ve been burned by failed transformation efforts in the past are cautious about new digital-led change, particularly when it is not clearly explained or supported with training. In these circumstances, even the most beneficial initiatives can find it difficult to gain the support they need for success.

Getting digital transformation right

Irrespective of whether a digital transformation initiative is relatively simple or extremely complex, success depends on having a clear purpose and holistic organisational alignment. This should start by identifying the real-world business problem that needs solving, and rather than asking what a new technology can do, leaders should find out where the organisation is struggling and what outcomes need to change.

Establishing this kind of clarity helps avoid the trap of following the digital transformation hype or rolling out tools with no compelling use case. It also enables more effective engagement with the teams that you’re asking to change how they work. This is a crucial consideration because when people understand the reason behind a transformation and how it connects to their roles, they are far more likely to get on board.

Ongoing communication and feedback are equally critical. Don’t forget, effecting transformation is not a one-off event but a process. You must test, refine, adapt and, when necessary, re-transform your strategy over time. Creating the right space and processes for feedback and then adjusting the way you integrate digital technologies based on real user experience helps minimise resistance and builds support from within.

Manage your expectations and take it one step at a time

Even with the right strategy and strong internal support in place, digital transformation is rarely a seamless experience. Some organisations may see mixed initial results. They might also face early adoption figures that are lower than anticipated. Elsewhere, uptake may stall because they haven’t properly integrated new systems into existing workflows or because teams are unsure how the changes affect their responsibilities.

But low numbers at the outset are not necessarily a sign of failure. What matters more is whether those numbers improve over time, and whether the transformation is driving meaningful change. Indeed, it’s important not to define success solely by short-term return on investment. A more useful approach is to look at patterns, such as whether teams are beginning to use the new tools more effectively, feedback is improving, and workflows are evolving in the right direction. These are the true indicators of an effort that is gaining transformative traction.

It is also essential to think beyond metrics because ultimately, the wider cultural impact matters just as much. Recognising individuals or teams who embrace new ways of working, creating support communities around new tools, and reinforcing the purpose behind the change all help embed transformation into the organisation’s DNA.

Artificial intelligence (AI) is reshaping industries across the globe. In the world of facilities management (FM), where operational efficiency, occupant satisfaction, statutory compliance and sustainability intersect, AI promises much, yet as a sector, FM has been fairly slow on the uptake. 

When we conducted research in 2021, 77% FM professionals admitted that FM is ‘behind the curve when it comes to adopting smart technology’, with only 27% at the time unlocking the full advantages of smart tech in business process automation. Fast forward to 2025, and our most recent report revealed at the Workplace Futures conference, showed things are changing. 66% of respondents have AI in their 2025 budgets, but many are still hesitant due to expertise gaps and ROI uncertainty. Barriers include a lack of internal expertise, budget constraints and concerns about data security.

Despite misgivings, AI has a lot to offer, with automation of business processes and workflows leading to much greater efficiencies, saving time and money while improving end-to-end visibility using live data. What’s key is that any digital transformation, with or without AI, is managed and implemented in the right way and using the right skills as it isn’t a quick fix for everything. 

Too often businesses invest in software without first understanding exactly what problems they want to solve and what their technology needs to do, or how their organisation must prepare.

Here are some of the common pitfalls:

1. Incomplete or inaccurate data

AI is only as smart as the data it learns from. And the reality is that any AI solution needs lots of high quality data if it’s to make a lasting difference. 

In FM, the data landscape is fragmented at best. Multiple legacy systems, inconsistent reporting standards, siloed departments and service partners all contribute to a lack of clean, live, structured information. The result? AI is trained on flawed inputs, leading to faulty outputs. For instance, a machine-learning model might identify a pattern in energy consumption and suggest a change in HVAC scheduling. But if the data ignores factors like temporary occupancy surges or outdated sensor readings, the recommendation can do more harm than good.

We must begin with robust data governance. FM leaders need to treat data as a strategic asset, curated, contextualised, and continually validated. Only then can AI begin to add value, drive productivity gain and enable us to act more quickly.

2. Lack of context

One of AI’s greatest limitations in the built environment is its inability to understand why something is happening. Machines are fantastic at pattern recognition, but they struggle with nuance. Without context, AI can’t tell the difference between an anomaly and a real issue.

That’s why AI in FM must remain a tool, not a decision-maker. A hybrid approach, where machine logic and human judgement work together, is the real future of intelligent building and maintenance management.

3. Legacy systems that aren’t fit for the future

Some older Computer Aided Facilities Management (CAFM) and Building Management Systems (BMS) are not compatible with AI, and for businesses that have these systems but want to move forward, investment in ‘starting again’ is probably the only option. Trying to fit a square peg in a round hole will only cost more in the long run.

This can be achieved slowly, however, so rather than chasing full process automation, FM firms can take a phased approach. Prioritise critical systems and processes where AI can deliver the biggest ROI—like better planned and predictive maintenance for equipment, smart energy optimisation or reduced administrative burden (more about that later) —and expand from there. Hopefully, the savings made by these ‘quick wins’ will help fund future investment, whilst also allowing systems to be tried, tested and refined.

4. Forgetting the ‘human touch’

No matter how advanced AI becomes, it can’t replicate the human experience or original thinking. In FM, statutory compliance and customer service are everything. Customers value trust, and accountability; qualities that can’t be automated. Long term customer relationships are forged on more than business acumen.

5. The cost of AI

AI isn’t cheap. Between the cost of sensors, infrastructure upgrades, software licenses, and skilled leaders and staff to manage it all, the investment is significant. But the benefits: greater productivity, greater efficiency, reduced downtime, better energy efficiency, and improved occupant satisfaction, will all reap dividends overtime. 

Many of these benefits fall to the end user, which begs the question, who should pay for AI? Should it be the customer, seeking long-term savings and compliance? The service provider, looking to differentiate in a competitive market? Or should the cost be shared, perhaps built into performance-based contracts?

FM firms need to be transparent about the costs and benefits of AI initiatives. Business cases must be tailored, showing clear payback timelines and KPIs.

But FM firms must also recognise that there is much they should be doing anyway to get their own house in better order. Customers can help by structuring commercial contracts with terms and conditions that recognise, value and incentivise the investment their suppliers make into technology, rather than the staid and traditional contracts that haven’t changed in decades.

Our industry typically operates on very low margins, so expecting supply-side to do everything is neither feasible nor sustainable.   

6. Ethical concerns

The use of AI brings up important ethical concerns related to data privacy, bias, and accountability. FM companies need to assess how AI may affect employee roles. There’s a risk that it could unintentionally support discriminatory outcomes if the training data is biased. For instance, Amazon discontinued its use of AI in recruitment after the system began automatically rejecting female applicants.

To implement AI ethically, organisations must prioritise transparency, fairness, and ongoing evaluation to ensure the technology functions as intended and avoids harmful side effects.

And finally…

7. Not understanding the problem before you try and solve it

Any investment in digital transformation must begin with understanding the problem/s. Speak to everyone in your business, evaluate what’s working and what isn’t, audit assets and working practices, identify the quick wins. We did this within DMA before developing our own workflow management software, BIO®. By consulting teams across the business we got a feel for their pain points and possible areas for improvement. 

Before BIO®, our engineers were spending around 2 hrs a week filling in timesheets and writing manual claims for allowances, expenses and overtime. By fully automating this process, each engineer saves up to 80 hours per year. Combined with time saved for back-office teams manually inputting and uploading daily work record sheet information equates to around 12,000 hours annually. 

Automating admin is a key area that can have a big impact, removing spreadsheet reliance and freeing up people to turn their attentions to more visible and impactful tasks that have a positive influence on customers. When AI works well it should allow ‘people’ to bring more value and creativity to the table.