Solutioneering the Transformation from Digital to AI Facilities

solutioneer the ai facility

Technology-first solutioneering serves facility operators navigating the transformation of Digital into AI facilities.

Facility Management Transformation

Facility management and maintenance have undergone significant transformations over the past decades, from labor-intensive manual work to automated smart buildings, driven by technological advancements and changing operational needs.

AI and machine learning technologies are poised to accelerate this transformation, putting further pressure on facility operators and facility solution providers to drastically expand their services and deepen their skill set in order to keep physical and digital assets up to date.

The Olden Days

Before the rise of modern technology, facility maintenance was primarily manual, physical labor with rudimentary tools. Devices and equipment were repaired or replaced as they broke down and inventories were kept track of with pen and paper (or on a whiteboard, as below).

analog solutioneer
Superintendent of Buildings and Grounds Harold M. Wadsworth (left) and custodian foreman Dean Gardner of Utah State College select the crew staff for training in 1955. (facilitiesnet.com)

As industrialization and urbanization accelerated, the introduction of mechanical and electrical systems revolutionized facility management. HVAC systems, automated elevators, centralized electrical grids, and early security systems were integrated into buildings, requiring more specialized skills to maintain these systems. During this period, computerized maintenance management systems (CMMS) began emerging, allowing facilities to track and schedule maintenance more efficiently.

This evolution demanded an initial shift in services offered by facility solutions providers, moving away from providing hands for manual labor to bringing the expertise to operate electrical systems and computers.

The Digital Revolution

With the rise of advanced and connected computing, facility management saw another major shift towards digital solutions. The integration of enterprise resource planning (ERP) systems, sophisticated CMMS platforms, and building automation systems (BAS) now allowed facility managers to monitor and control multiple building systems remotely.

smart building
Advantech WISE-PaaS iBuilding Solution (advantech.com)

Energy efficiency became a growing concern, leading to the introduction of energy management systems that optimize heating, cooling, and lighting based on usage patterns. This decision-making forced facility operators to increasingly rely on digital information and data systems, which facility solution providers would develop and maintain for them.

The skills required to manage facilities dramatically shifted from craftsmen to skilled professionals familiar with digital technology stacks. The fundamental shift to technology-first facilities solutions and maintenance laid the foundation for an accelerated path towards smart and connected buildings.

The Internet of Things (IoT) has transformed facility maintenance, making real-time monitoring and predictive analytics an industry standard. IoT sensors collect data on equipment performance, environmental conditions, and occupancy patterns, which in turns enables us to develop maintenance strategies to anticipate equipment failures.

AI Transformation

Facilities are poised to be transformed by the impact of artificial intelligence in the coming decade. Aside from cloud computing, digital twins, and smart energy further enhancing energy efficiency, reducing operational costs and minimizing downtime, on-premise “AI workers” will take over more and more facility floor space as they assist companies to grow and expand more cost-effectively.

ai solutioneering
An AI generated image of a modern office where a human and a humanoid robot are interacting (dall-e)

These “AI workers” aren’t just robots sitting alongside the production assembly line, but are also security drones monitoring the premise, call center agents solving customer problems, internal sales assistants providing training to sales staff, quality assurance teams ensuring faulty products are caught before shipping out, and so on. This is challenging facility solution providers once again as it requires a new layer of skills to be offered to facility operators.

These new skills include not only assembly and maintenance of AI systems, but also providing AI solutions that ensure the “AI workers” continuously learn from their human counterparts working at the company. To remain competitive, it’s no longer good enough to rely on an off-the-shelf software solution or SaaS; proprietary business processes that provide a competitive edge in the market must be encoded within AI models and reinforced with your team’s best practices, innovative ideas, and lessons learned.

That’s the particular challenge MTS Solutions is tackling on behalf of our customers as we help our customers navigate the exciting transformation of Digital into AI facilities.

Is AI Labor-Serving or Labor-Saving?

friendly labor-serving ai

Is AI supposed to be labor-serving or labor-saving?” is one of the most often asked questions when talking about AI transformation. Time and time again, we (at MTS Solutons) emphasize the same point:

AI is meant to be labor-serving, not labor-saving.

Henry Ford – founder of the 121-year old Ford Motor company – addressed a similar concern a century ago. As the mechanized assembly line revolutionized industry, many feared machines would replace workers entirely. In 1929, Ford put it plainly [1]: “For unless machinery is labour-serving, it has no excuse for being.” We can adopt that sentiment and say:

“For unless artificial intelligence is labor-serving, it has no excuse for being”

We see the same fears today with AI. Some predict that artificial intelligence will inevitably take over many human jobs, leaving workers with no place in the economy. But history tells a different story.

When stronger, faster, higher-powered machines emerged in the early 20th century, it didn’t eliminate work — it transformed it. Businesses became more productive, industries expanded, and new jobs emerged that were previously unimaginable. Furthermore, the new jobs required more human intelligence, not less.

Workers at River Rouge, Courtesy: The Henry Ford Museum

Ford also noted that the power generated to run the machines by itself, is meaningless. You need people to extract the benefits from the abundance of power through the use of machines.

The same applies to AI today. The raw computing power of modern AI chips is useless unless it’s used to develop algorithms that enhance human productivity, helping people perform their jobs better, not eliminating them.

That said, of course some tasks and jobs will be fully automated by artificial intelligence — but only where automation makes sense. A good rule of thumb is this: if a task requires no judgment, no creativity, and no decision-making, then it’s better handled by AI. There’s no point in having a person spend time on repetitive, mindless tasks when they could be focusing on higher-value work — work that requires out of the box thinking, intuition, problem-solving, or relationship-building.

“The old method works just fine—so why change it?

… is probably the biggest hurdle we face when introducing new technologies. It’s not the technology itself that holds companies back — it’s our natural resistance to change. Many hesitate to shift away from familiar ways of working, often because they see new systems as disruptive, unnecessary, or at that moment even performing worse. But adopting technology for the sake of novelty isn’t the goal. The real question isn’t whether something is new, but whether it’s better.

The only way to break through this resistance is through results. Time and time again, we must prove that new technologies aren’t about replacing people or cutting corners — they’re about making work more effective, reducing inefficiencies, and allowing teams to focus on what truly matters: serving the public.

Why AI?

If we look beyond the buzzword, AI is really just about using the immense raw computing power of modern computer chips to mimic and automate human activities — whether it’s writing stories, painting pictures, answering customer questions, or making sense of trends and data. The automation makes sense if past events accurately predict future events.

friendly labor-serving ai

The reason why we can do today what we couldn’t do twenty years ago is the availability of immense, raw computing power. With the help of modern AI chips we can define and describe human activities using billions or trillions of parameters – something impossible by humans. So, no matter how complex we find an activity, if past events accurately describe future behavior, we can use chips to turn the process into a (very big) algorithm, and then leverage the algorithms to automate the process.

Now, imagine applying that same idea to running a business. In theory, any process — from hiring employees to managing inventory to responding to customer inquiries — can be learned and automated by AI. We’re already seeing this in action. Chatbots handle customer service, AI-powered tools draft emails and reports, and smart software helps businesses detect fraud, optimize supply chains, and predict sales trends. But right now, most of these AI tools work in isolation—each one doing its own specific task.

That’s about to change. Over the next couple of years, Agentic AI will evolve from handling individual tasks to managing entire workflows. Instead of just answering customer service chats, it’ll track orders, schedule follow-ups, highlight anomalies, and recommend actions — all without direct human intervention.

Think of it like the early days of factory automation—first, machines replaced individual manual tasks, and eventually, entire assembly lines became automated. This transformation isn’t just about efficiency — it’s about re-imagining how businesses operate. Just like factories in the 20th century scaled up production like never before, AI-driven businesses will be able to scale decision-making, creativity, and problem-solving at levels we’ve never seen. And that’s where things start to get really exciting.

[1] Ford, H. (1930). Moving forward. Doubleday, Doran & Company.