AI in construction: why only 11% use it (and how to catch up)

Only 11.4% of Spanish construction companies use artificial intelligence today, far behind the 17.5% seen in industry, 25.7% in services, or 60% in the ICT sector. The figure comes from the First Congress on Innovation in Construction, Building, Infrastructure and Concessions (IC2) and lines up with what CaixaBank Research has been flagging in its sector-by-sector AI adoption reports: construction, alongside metallurgy, transport and logistics, is falling behind. But that same number reads differently if you run a construction firm or engineering practice: if 89% of the sector hasn’t started yet, moving now is a way to stand out, not a way to catch up with the crowd.

Why construction lags behind

The bottleneck isn’t technology, it’s structural. Only 1% of construction professionals have advanced technical training in digital tools, according to the same IC2 congress data. Add to that a culture of thin margins where any investment without immediate payoff reads as risk, plus a site that’s still run on processes that haven’t been revisited in decades: manual budgeting, quality control done by eye, and subcontractor coordination over WhatsApp and spreadsheets. It’s not that the sector doesn’t want to digitize; it’s had few good incentives to do it well, and plenty of examples of digitization that went nowhere.

What early adopters are already doing

91% of construction firms that have adopted AI plan to increase that investment through 2026, and 89% of early movers report improved profitability, with operational efficiency gains topping 44% in some cases. The use cases actually moving the needle aren’t futuristic — they’re concrete:

• AI-assisted budgeting and bidding: firms using an agent to draft and adjust proposals are submitting 30% to 50% more bids in the same time, without growing the technical office team.
• Computer-vision quality control: cameras and AI inspecting welds, concrete finishes, or deviations in real time during execution, instead of catching the defect weeks later at final review.
• Concrete production optimization: platforms that cross-reference weather, technical and logistics variables to adjust mix design and delivery timing to each day’s actual conditions.
• Site paperwork tracking: automatic data extraction from work orders, delivery notes and certifications that today sit unstructured in paper or PDF, giving traceability without relying on someone to type it in by hand.

How to start without a big-contractor budget

None of these use cases require the infrastructure of a listed conglomerate. For an SME builder or engineering practice, the order that works is:

1. Pick a single process that’s visibly bleeding time or money — usually budgeting, quality control, or paperwork — instead of launching a generic “digital transformation” nobody knows how to start.
2. Measure your starting point: how many technical-office hours a budget takes today, how many defects get caught late, how much time gets lost hunting down one delivery note.
3. Pilot it on a narrow scope, one use case with a small team, before committing budget to a full platform.
4. Scale only what proves a return, following the same logic we use when choosing between a chatbot, an AI agent, or classic automation for any business process: the tool gets picked once the problem is well scoped, not before.

What shouldn’t be missing: your own data, no lock-in

A common mistake in the sector is adopting a closed platform that promises “AI for construction” without explaining where site data actually lives, or what happens if you switch providers next year. Drawings, certifications, and project history are a company asset, and any applied-AI project in construction should be built on that premise: your own code and infrastructure, with no dependency on a single platform. At Evicron we work on applied AI for the construction sector on exactly that basis, from the specific use case through to integration with the tools your technical office already uses.

The takeaway

Spain’s 11.4% AI adoption rate in construction isn’t a ceiling — it’s a snapshot of a sector starting late, with use cases already proven by the ones who moved first: faster bids, real-time quality control, and paperwork traceability that no longer depends on paper. The advantage of moving now, while it’s still a minority, is that standing out from the competition takes far less effort than it will in two years, once the 60% adoption rate the ICT sector has today becomes the norm on site too.

Want to identify which AI use case would move the needle most for your construction or engineering firm? Get in touch: the first consultation is free and we reply within 24 hours.