Picture a technician tasked with fixing a fault in an installation he’s unfamiliar with. His name is Kevin. He has his tools with him and the knowledge in his head, but the app he’s forced to use was designed by a project team that has never set foot in a boiler room. Navigating through five different screens, fields that don’t match reality, and installation photos stored in a separate system that requires a completely different login.

Kevin fixes the fault, because that’s what he always does, but it takes him an hour and a half instead of forty-five minutes. Multiply that across the rest of the team, and you’re no longer talking about a minor annoyance; you’re looking at a structural productivity killer that won't show up in any report.

Three Simultaneous Developments 

Engineering and technical services are currently facing a perfect storm of compounding challenges that, together, create a much larger problem than any single issue alone.

It goes without saying, but the first issue is the tight labour market. Heading towards 2030, the Netherlands faces a projected shortage of over 121,000 technical professionals. Demand for technical expertise is rising due to the energy transition, sustainability initiatives, and an increasing need for maintenance and service. Meanwhile, the influx of new technicians is structurally failing to keep pace with those leaving the industry. This is not a temporary blip that will resolve itself as soon as the economy cools down.

The second problem is the silent drain of expertise. In 41% of organisations, critical operational knowledge resides solely in the heads of skilled workers set to leave within the next five years. This is knowledge that has never been documented because there was never an explicit reason to do so: the person who knows which faults occur with which specific configurations; the engineer who can tell from the sound of a pump that it will need maintenance in three months; the service technician who knows exactly why a particular client site always causes trouble. When they retire, the next generation starts from scratch.

The third challenge is the increasing technological complexity of the work. Installations are getting smarter, systems are becoming more interconnected, and a service engineer today must not only understand how machinery works mechanically, but also how software configurations, sensors, and data streams impact its performance. The volume of information required to resolve an issue effectively is growing, while the time available to process that information is shrinking.

Together, these three developments lead to the same outcome: more work, fewer people, higher knowledge requirements, and mounting pressure on the productivity of everyone out in the field.

Current Digitalisation is Missing the Mark 

The obvious solution is digitalisation, which is already well underway or fully implemented in most organisations. There are scheduling systems, field service apps, ERP integrations, and reporting tools. The financial investments are substantial.

Yet, of the many dozens of frontline professionals we speak to and observe in the field each year, the vast majority share one common sentiment: the digital tools they are forced to use daily do not feel like a support tool, but rather an additional burden. At best, they take up more time than they save; at worst, people simply refuse to use them.

This gap between investment and user experience is no accident. It is the direct result of an approach that consistently starts from the wrong end.

Decisions about digital tools in technical services are almost always made by people who rarely use them themselves: IT departments executing a platform strategy, operations managers justifying a business case, or implementation partners applying the exact same methodology they’ve used for dozens of previous clients. These are legitimate perspectives, but they fail to capture what actually happens when someone in the middle of a site inspection needs to open a work order, take photos of an anomaly, and save them in the correct folder, all while holding a phone in one hand and managing an expectant client on the other.

The system you develop might be functionally flawless, but it doesn't align with the reality of the person using it every single day. And frontline workers are resourceful when official processes become too cumbersome: WhatsApp groups replace scheduling systems, custom Excel sheets do what the app was supposed to do, and photos are dumped into a shared folder because the system requires three extra steps to achieve the same result.

These workarounds are not a sign of defiance or unwillingness. They are signals. They show exactly where the design has lost touch with practical reality.

The Costly Consequence 

The most visible consequence is that the data the system is supposed to generate either doesn't exist or is inaccurate, simply because no one enters it consistently. As a result, the management information needed to improve operations is based on assumptions rather than facts. Opportunities spotted by technicians in the field never reach the people who can act on them, and operational deviations only come to light during a complaint or an audit, rather than in real time.

But there is also a less visible consequence, which might actually be the greater issue. Let’s look back at Kevin. As an engineer spending his days with his tools in plant rooms, he has a very clear picture of what slows him down, what would help him, and what could be improved. Yet, his insights are rarely gathered systematically. Not because no one cares, but because the framework to do so simply isn’t there.

As a result, an invaluable source of insight remains untapped, right at the very heart of where operations happen.

Technology Offers More Potential Than Ever 

The technological landscape has changed dramatically over recent years. Solutions that were once complex, prohibitively expensive, or only accessible to large enterprises have become much more attainable today. This has significantly lowered the barrier to digitalising the shop floor.

IoT enables real-time monitoring of equipment and automatically captures technical data. AI can support workers on the job, for instance by explaining procedures step-by-step or making relevant knowledge instantly accessible. At the same time, smartphones and tablets have become an intrinsic part of daily work, and from a technical standpoint, we can build far superior user interfaces compared to a decade ago.

What required a massive capital investment a few years ago is now often both technically and financially viable for organisations of almost any size. This fundamentally shifts what is possible.

Yet technology alone will not solve the problem. Indeed, the biggest challenge is not a technical one; it is a translation problem. On one side, you have the people performing the work day in, day out. They know exactly where processes stall, what information is missing, and where the opportunities lie to work smarter. On the other side are those who understand what is technologically possible, how systems integrate, and which solutions can realistically be delivered.

These two worlds meet surprisingly rarely. This results in solutions that are technically excellent but fail to align with daily practice. Conversely, valuable ideas from the shop floor frequently go to waste because there is no mechanism to translate them into a viable technical solution. The inevitable consequence is poor adoption.

If you only understand the technical landscape, you end up building something nobody asked for. The true value lies in connecting the two sides.

Getting the Sequence Right 

The question is no longer whether digitalising the frontline is possible; that has already been answered. The question is whether the approach is correct. The single differentiator between an approach that succeeds and one that fails is the sequence: start with the people doing the work, understand how they structure their day, what slows them down, and what would actually help them. Then, translate that into what is technically feasible and meaningful, and design from there. This should not be a token user test at the very end of a project, but the core foundation upon which every tooling decision rests.

Organisations that adopt this mindset build tools that professionals actually use, and the operational benefits are clear: faster onboarding for new hires, less wasted time per job, superior data quality, and engineers who can do their work without having to bypass the system.

The first step is also the simplest: ask the people on the shop floor what their day really looks like. Not via a survey, but out in the field. What goes well, what wastes time unnecessarily, and what would they change tomorrow if they had the choice? Combine those answers with an honest assessment of what is technically available and feasible, and you have the foundation for a digital strategy that truly serves the people it was built for.