5 things construction professionals should know about AI in 2026
Are you prepared for what’s next in AECO?
According to the 2026 Bridging the Gap report, 90% of AEC firms are hitting a wall when it comes to deriving real value from artificial intelligence — despite the fact that it's one of the most discussed topics across every market and vertical in the industry. In an episode of Bridging the Gap: The Conversation — 2026 Reality Check, Ian Besford, Global Digital Delivery Leader at Mott MacDonald, shared his perspective on where AI is genuinely useful, where it falls short, and what the industry needs to do differently to close that gap.
1. The time problem isn't really about time
Ask anyone in construction why their team hasn't fully adopted AI tools and the answer is almost always the same: we don't have time. Time to learn the tools, time to experiment, time to figure out where they actually add value on a live project.
Ian Besford has a different read on that answer.
"We all know we work in this world where the pressure's always on to deliver. So people prioritize delivering over learning new ways of doing things. It's very much that tale of the woodcutter who's too busy chopping down trees in the forest to spend his time sharpening his axe." Ian Besford, Global Digital Delivery Leader, Mott MacDonald
The woodcutter keeps chopping because stopping feels like falling behind. But the axe gets duller with every tree, and the work gets harder. The same dynamic plays out across construction teams every day. The pressure to deliver the current project makes learning new ways of working feel like a luxury, even when those new ways would make every subsequent project faster.
The firms making real progress with AI aren't the ones that suddenly found more hours in the day. They're the ones that made a deliberate decision to treat learning as part of delivery, not a break from it.
2. AI's value is in replacement, not addition
The reason most AI pilots stall is the same reason most construction technology pilots stall. The tool gets added on top of an existing workflow rather than replacing part of it. Teams use the AI tool because someone asked them to, continue doing everything else the way they always did, and conclude after a few weeks that the AI didn't save them any time.
It didn't save them time because nothing was removed. Addition never creates efficiency. Substitution does.
"The real issue is how AI can help you by replacing elements of your work rather than adding to it as something else you have to do." Ian Besford, Global Digital Delivery Leader, Mott MacDonald
The construction teams getting genuine value from AI right now are using it to eliminate specific, time-consuming tasks. Summarizing meeting minutes. Producing first drafts of documents. Searching across large volumes of project information. Research and data analysis. These aren't glamorous use cases, but they're real ones — tasks that previously took hours and now take minutes, freeing up the time that the woodcutter never had.
The same principle applies to automation more broadly. Connecting the platforms your team already uses so that data flows automatically — rather than being manually extracted, reformatted, and re-entered — removes a category of work that nobody notices until it's gone. Revizto's integrations and API capabilities work on exactly that basis, connecting coordination data to the wider project environment and eliminating the manual overhead that sits between systems.
3. ChatGPT is not the ceiling
For most construction professionals, the mental model of what AI can do was formed somewhere between their first ChatGPT conversation and their most recent Copilot experience. That's a reasonable starting point, but it's a dramatically limited view of where the technology is heading.
"You can do things today which you couldn't even imagine 18 months ago. Most people have experienced ChatGPT or Copilot over the last year or so, and that's kind of their view of what AI can do." Ian Besford, Global Digital Delivery Leader, Mott MacDonald
The gap between where most people's AI expectations sit and where the technology actually is — let alone where it's heading — is significant. In software engineering, bespoke AI tools are already making developers two to three times more productive than they were before. That level of productivity gain hasn't hit the engineering and construction space yet, but Ian's view is that it's coming.
The firms that will be best positioned when it arrives are the ones that have been experimenting with the current generation of tools, building the habits and the workflows that will absorb the next wave of capability rather than scrambling to catch up with it.
For now, the practical opportunity is clear: use AI for research, data analysis, document drafting, and information retrieval. Get comfortable with what it can do today, and stay curious about what it will be able to do next.
4. Human accountability cannot be outsourced to AI
One of the most important questions to come out of the Bridging the Gap: The Conversation — 2026 Reality Check webinar came from the audience: when you connect AI to BIM, whose responsibility is it to know good data from bad data?
Ian's answer:
"When it comes to the use of AI, everything we have to do still has to be signed off at the end of the day by a human, and there has to be an accountable person. It can't just happen in a black box where you don't see anything that's going on, you just see the output of it. You have to understand what it's doing and what the process is doing." Ian Besford, Global Digital Delivery Leader, Mott MacDonald
This matters particularly in construction, where the output of design and coordination decisions gets built. If it goes wrong, people can get hurt. The accountability cannot disappear into an algorithm. It has to sit with a person who understands what the AI did, why it did it, and whether the output can be trusted.
The practical implication is that AI in construction workflows needs to be implemented with the same quality assurance logic applied to any other design input. You wouldn't put a graduate with no experience on the most complex structural design without oversight. The same principle applies to AI generated outputs. Human review is not optional.

5. Curiosity is the real competitive advantage
The firms that will get the most from AI over the next five years are not necessarily the ones with the biggest technology budgets or the most sophisticated current implementations. They're the ones that have built a culture of curiosity — where people are comfortable not knowing how they'll do things next and genuinely want to find out.
"Perhaps the real issue is how we encourage people to be curious, and to get comfortable not knowing how they're going to do things next, and wanting to learn how to do it in a different way. For me, that curiosity is really where we need to focus our efforts." Ian Besford, Global Digital Delivery Leader, Mott MacDonald
That curiosity is hard to mandate and impossible to buy. It grows from an environment where experimentation is encouraged, where learning from a failed pilot is valued as much as a successful one, and where the people closest to the work are given permission to try new approaches rather than being held to the way things have always been done.
For construction firms thinking about their AI strategy, the most important investment might not be in a specific tool or platform. It might be in creating the conditions for their people to engage with AI genuinely, to find the use cases that matter in their specific context, and to build the habits that will compound into capability over time.
The technology is moving fast. The firms that stay curious will keep up. The ones that wait for certainty before engaging will find themselves exactly where they've always been — too busy chopping down trees to sharpen the axe.
FAQs
According to the 2026 Bridging the Gap report, 90% of AEC firms are hitting a wall when it comes to deriving real value from AI. The most common reason is that AI tools get added on top of existing workflows rather than replacing part of them, creating additional workload rather than reducing it. Sustainable AI adoption happens when teams identify specific tasks the technology can eliminate, not just augment.
The most practical current use cases are task-level applications: summarizing meeting minutes, producing first drafts of documents, searching across large volumes of project information, and data analysis. These use cases are well proven, reduce genuinely time-consuming work, and build the habits that will support more advanced AI adoption as the technology develops.
Human accountability cannot be outsourced to AI in construction workflows. Every AI generated output that influences a design or coordination decision needs to be reviewed and signed off by a person who understands what the AI did and whether the output can be trusted. The accountability sits with the human, not the algorithm, regardless of how the AI was used in the process.
Start with replacement rather than addition. Identify the most time-consuming, repeatable tasks in your current workflows and evaluate whether AI can eliminate them rather than sit alongside them. Build habits with current tools rather than waiting for more advanced capabilities. And create an environment where experimentation is encouraged — the firms building AI capability now will be better positioned when the next generation of tools arrives.
Not yet, and not without significant human oversight. AI tools are advancing rapidly and are already making software engineers two to three times more productive in some contexts. That level of capability hasn't reached construction design and coordination at scale. The direction of travel is clear, but the timeline is uncertain. Firms should be experimenting and building capability now rather than waiting for the technology to mature before engaging with it.
AI and BIM are increasingly connected, with AI being applied to analyse model data, identify clashes, and automate coordination tasks. When connecting AI to BIM workflows, the quality of the underlying data is critical — AI amplifies both good and bad data. Human review of AI generated outputs remains essential, particularly for decisions that will be built from. Data governance and model quality are prerequisites for effective AI in BIM environments.

