By Glenn Brouwer – CEO & Founder of Inspech

What Road Managers Actually Need from Technology

By Glenn Brouwer, CEO & Founder of Inspech

Summer is usually the first opportunity I have to step away from the day-to-day pace of the business. The calendar slows down, there are fewer flights and meetings, and it creates space to reflect on the conversations that have shaped the first half of the year.

Over the past months, I've spoken with road authorities, concessionaires, pavement engineers and inspection teams across several European countries. Although every organisation operates in its own context, I noticed an interesting pattern emerging.

When we talk about digitalisation, the conversation rarely starts with technology anymore.

It starts with operations.

The organisations I meet are generally not looking for another way to measure their roads. Most already have mature processes in place. They use LCMS, IRI, FWD, friction and texture measurements, supported by well-established Pavement Management Systems. Their engineers understand these methods, trust the data and know how to translate it into maintenance strategies.

In other words, they are not lacking engineering capability, what they are asking is something much more practical:

How do we maintain an up-to-date understanding of what is happening across the network between those measurement campaigns?

That may seem like a subtle distinction, but I believe it changes the discussion entirely.

A conversation I had recently with a concessionaire responsible for several thousand kilometres of motorway illustrates this well. Their inspection programme is sophisticated, their engineering teams are highly experienced and their decision-making process is mature. They weren't looking for someone to explain pavement management to them. Instead, they described a challenge that every large road organisation recognises:

The moment a survey has been completed, the network starts changing again.

A crack begins to develop. A repair is carried out. Drainage problems become visible after heavy rainfall. Traffic loads accelerate deterioration in one location while another section remains remarkably stable. By the time the next scheduled survey takes place, thousands of small changes have already occurred.

The challenge is not that these organisations lack information. The challenge is maintaining visibility between the moments when information is collected.

That observation has stayed with me because it highlights an important distinction. Measurement and visibility are not the same thing. You can have excellent engineering measurements and still struggle to maintain a current understanding of what is happening across your network. The obvious response, of course, is to inspect more frequently.

Fortunately, that has become much easier. Cameras are more affordable, survey vehicles are increasingly capable and collecting road imagery is no longer the expensive exercise it once was. But this is where another bottleneck appears.

Capturing more imagery also means creating more work. Every additional survey produces thousands of images and hours of video that still need to be reviewed, interpreted and incorporated into existing workflows. The constraint gradually shifts from data collection to data processing. That is a challenge I hear repeatedly. It also explains why my own view on AI has evolved over the past few years.

When AI first entered our industry, much of the discussion revolved around automation. Could algorithms detect defects? Could they classify pavement distress? Would inspections eventually become autonomous?

These are interesting technological questions, but after spending time with inspectors and asset managers, I have become convinced they are not the most important ones.

Road inspections are ultimately about judgement.

An experienced inspector does far more than identify a crack or a pothole. They interpret what they see in context. They consider previous inspections, understand local conditions and recognise patterns that influence maintenance decisions. Pavement engineers combine those observations with structural measurements and deterioration models. Asset managers balance technical priorities against budgets, planning and operational constraints.

That chain of expertise is precisely what makes road management effective, and technology should strengthen it, not replace it.

For me, the value of AI lies in helping people work through growing volumes of information without becoming overwhelmed by them. AI can identify locations where visible conditions may have changed, highlight observations that deserve attention and organise inspection data in ways that make review significantly more efficient. The decision itself still belongs to the professional.

Perhaps that sounds less ambitious than fully autonomous inspections. Operationally, however, I believe it is far more valuable.

Another thought has emerged from these conversations, and I suspect it will become increasingly important over the coming years: Traditionally, inspections answer a straightforward question: What does the road look like today?

But once inspections become more frequent and consistently structured, a different question becomes possible.

What has changed since the last inspection?

That shift may appear small, yet it fundamentally changes how visual information supports asset management.

Instead of looking at isolated observations, organisations begin building a visual history of their network. They can see where deterioration is progressing, where repairs are holding up well and where unexpected changes deserve further investigation. This does not replace structural measurements or engineering analysis, nor should it. It simply provides additional context that helps teams decide where their expertise is needed most.

Looking back on the first half of this year, I increasingly believe that this is where the greatest opportunity lies. The road sector does not need to replace the engineering methods it has spent decades refining. Those methods remain essential. What it does need is a better connection between them.

More frequent visual inspections can improve network awareness. AI can help inspection teams process growing amounts of information. Historical inspection records provide context about how conditions evolve over time. Engineering measurements continue to provide the evidence required for diagnosis and intervention planning. Each plays a different role, but together they create something more valuable than any single technology could achieve on its own.

That is also how we think about Inspech.

Our ambition has never been to replace LCMS, Pavement Management Systems or the expertise of pavement engineers. Our role is much simpler than that. We want to help road organisations maintain a current, structured and traceable understanding of visible road conditions between the moments that matter most.

If there is one conclusion I take away from the conversations I've had this year, it is this: the future of road intelligence is unlikely to be defined by yet another measurement technology.

It will be defined by how well we understand what has changed since the last one.