Ballast Nedam builds and maintains roads, bridges, tunnels, housing and industrial facilities across the Netherlands. Robotics is already part of our daily work. Bricklaying robots are active on our projects, which raises a practical question: where should the next robot be deployed?

The construction industry often struggles to answer that question. A supplier presents an impressive demonstration, enthusiasm builds, a pilot is launched, and a few years later it is still unclear whether that was the best place to start. There is no widely accepted view of which activities are truly suitable for automation, and there is little agreement on what makes an activity worth automating in the first place.

That is the challenge we are giving you. We are not asking you to build a robot. We are asking you to determine where robotics can create the most value.

Your scope covers the full lifecycle of our business: design and engineering, factory production and prefabrication, construction sites, infrastructure maintenance, inspection, demolition and material reuse. Across these activities, identify where machines should replace manual work, where they should support people, and where automation is unlikely to add value.

To arrive at a defensible conclusion, we expect you to:

1. Survey the field

What robotics and autonomous systems already exist in and around the construction sector? Consider solutions that are deployed today, currently being piloted, or still under development.

The available information is too extensive to analyse manually in a short time. Use AI as a research tool to explore patent databases, academic research, supplier information, procurement data and market reports. Where is progress accelerating, where has it slowed down, and where are opportunities still largely unexplored?

2. Define what "worth automating" means

There is no single accepted definition. Develop your own framework and explain the reasoning behind it.

Which criteria should matter? How should they be weighted? How do you account for factors that are important but difficult to quantify? Make your assumptions explicit.

3. Apply your framework

Use your methodology to assess relevant construction activities and identify the most promising opportunities for robotisation.

Be transparent about what is supported by evidence and where you are making assumptions or extrapolations.

4. Challenge your own conclusions

No framework is perfect. Which high-ranking opportunity could still fail in practice, and why? Which lower-ranked opportunity may become highly relevant in the coming decade?

One aspect we ask you to keep in mind throughout the challenge is the role of AI itself. AI is not only a research tool; it is also one of the technologies that makes modern robotics increasingly capable. A machine that follows a fixed script is fundamentally different from a machine that can perceive its environment, make decisions and adapt to changing conditions. Consider this distinction when evaluating future opportunities.

Helpful public sources include patent databases such as Espacenet, academic literature on construction automation, CBS statistics on labour and productivity, TenderNed procurement data, and publicly available maintenance and asset documentation from Dutch infrastructure authorities.

By the end of the day, we would like to see:

1. A reusable assessment method

A clear and practical approach for evaluating robotisation opportunities in construction. The reasoning behind the method should be visible and understandable so it can continue to be used after the event.

2. A substantiated ranking or opportunity map

An overview of where the strongest opportunities exist, including the evidence, assumptions and uncertainties behind the conclusions.

Equally valuable are areas that appear unsuitable for automation today, together with the reasons why.

3. A deeper analysis of the most promising opportunity

Provide enough detail for us to understand what such a system would need to sense, decide and do, how much autonomy it would require, and what value it could potentially deliver.

4. A list of data we should start collecting

Your analysis will likely highlight information that is currently unavailable. Tell us which data points would improve future decision-making.

5. At least one unexpected insight

Fresh perspectives are often the most valuable. Teams from outside the construction industry may identify opportunities or blind spots that practitioners overlook.

We are deliberately not directing you towards a specific problem. The research and the choice of focus are central to the challenge. If you convince us to investigate an area we were not seriously considering before, you have succeeded.