How construction teams can move past entry-level AI adoption
AI in construction has moved quickly from curiosity to commitment. According to the latest industry survey, 82% of firms now report having an AI strategy.
That should be a sign of maturity. But instead, it reveals a new challenge: most of these strategies remain firmly at the entry-level. That means firms are running pilots, testing tools, and trialling dashboards, but very few are embedding AI into the flow of day-to-day delivery. There’s movement, but not yet momentum.
To understand why, we need to look beyond the headlines and into the patterns that keep construction firms stuck in this test-and-stall cycle.
Why is construction stuck at the entry-level AI adoption
When firms first try AI, they often approach it as an experiment. A pilot with an estimating platform. A trial of an AI-powered construction tool for site inspections. Maybe a dashboard that forecasts risk. These projects look good in reports, but many remain side projects. They don't scale into the day-to-day.
The picture is more complex than a simple reluctance to change. Even among companies with annual revenues above $250m, 12% still rely mainly on manual reporting. Look closer and you’ll see the same patterns repeating, shaped by the pressures construction teams face every day.
1. Data silos create trust issues
Construction data lives across project management systems, procurement records, BIM models, and field reports. But AI needs clean, connected data. Industry experts at Digital Construction Week 2025 call data preparedness a prerequisite, not a detail.
Think about it this way: If an AI system is asked to predict delays but only has scheduling data (with no view of material deliveries or weather impacts), the prediction becomes less useful. Site teams quickly learn to second-guess outputs that don’t reflect what they see on the ground.
2. The procurement bottleneck
Procurement is still weighed down by slow and manual processes. Chasing quotes by phone, juggling long email chains, and working with a narrow supplier base are still the norm for many.
That creates a problem when AI tools are layered on top. They promise faster, smarter decisions, but the data they need is stuck in inboxes or call notes. Instead of improving outcomes, AI gets blocked by the limits of outdated procurement workflows.
3. Skill gaps create adoption anxiety
AI literacy isn’t spread evenly across teams. Digital specialists may be comfortable with algorithms, but site managers, engineers, and project leads often see them as “black boxes.” Without a clear context, adoption remains shallow.
If teams don’t understand why a tool is suggesting they change something, they default to methods they know. That feels safer than acting on something they can’t explain.
4. Leadership framing shapes expectations
How leaders talk about AI sets the tone. When it is described as “something we’re trying,” teams might feel less committed because it sounds uncertain. But AI has already moved past the trial stage and is becoming part of standard practice.
The shift comes when AI is framed as a core part of decision-making, not a side project. When leaders link AI directly to business goals (cost control, risk management, project reliability), adoption becomes stronger and more sustained.
5. Short-term thinking misses the bigger picture
Too often, AI is seen as a tool swap. A replacement for existing software rather than a way to rethink decision-making across the project lifecycle.
AI-driven project management platforms can analyse huge datasets, improve scheduling, and flag risks early. But these gains only stick when workflows are redesigned. If you drop AI into old processes, you get movement without momentum. Companies can say they are “doing AI,” but the impact doesn’t reach critical areas like budgeting, reliability, or cross-team coordination.
What maturity in AI for construction really looks like
So what does it mean for your team to move beyond experiments? Maturity in AI for construction isn’t about stacking more tools on top of each other. It’s about weaving AI into the way your projects actually run.
Some signs of maturity you can look for include:
Integration. Your AI systems connect with core platforms like BIM, procurement tools, and project management software. Cost predictions from an AI estimator, for instance, feed straight into resourcing decisions instead of sitting in a separate spreadsheet.
Decision support. Rather than generating reports that sit unused, AI actively shapes strategy. You and your team rely on predictive insights to adjust resource allocation, re-sequence phases of work, or change subcontractor schedules.
Continuous learning. Every project you deliver improves the model. Risk forecasts become sharper, schedules more realistic, and cost estimates closer to the mark.
Trust from the field. Your site teams see AI as a partner, not a distant experiment. When a safety-monitoring model flags a risk, they act because they trust the insight.
You know you’re moving past entry level when AI influences how decisions get made at every level, from the project office to the site cabin.
Practical shifts to move forward
To move past entry-level adoption, you must be deliberate about where AI supports your workflows and how your teams are prepared to use it. Here are four shifts to guide you.
1. Start with your workflows, not the tools
Before choosing a platform, take time to map out where the bottlenecks are. Maybe your design reviews drag on because information passes through too many hands. Or your site progress slows because compliance documents aren’t complete. AI can help in both cases—but only if you know exactly where the friction lies.
When your workflows are clear, AI pilots align with real pain points instead of abstract ambitions. That’s when adoption feels useful and your teams see the benefit right away.
2. Build AI literacy across your teams
Adoption stalls when only a few people understand how AI works. To move forward, you need literacy to spread beyond IT or innovation departments.
This doesn’t mean every site manager becomes a data scientist. It does mean they feel confident asking: What data went into this forecast? What assumptions drive it? How should I balance it against what I see on-site?
Short, practical training sessions work best. You can start by weaving AI literacy into meetings you already hold, using real outputs as learning moments.
3. Frame the purpose clearly from the top
When you introduce an AI initiative, the “why” matters as much as the “what.” If your aim is faster scheduling, say so. If it’s risk reduction, explain how the AI contributes.
Clarity helps your teams connect each tool to outcomes. It also sets a standard for what success looks like. Without that framing, pilots tend to drift, and outcomes are harder to measure.
4. Pilot with purpose
Early pilots often fail because their goals are too vague. A stronger approach is to define measurable KPIs before you begin. For example:
Achieve a 5% cost overrun reduction on one project.
Reduce average delay days on scheduled tasks by 10%.
Improve safety incident prediction accuracy by 15%.
When your pilots are tied to numbers, they generate evidence. That evidence builds the case for scaling.
From entry level to everyday value
AI in construction has moved from hype into cautious experiments. The real opportunity now is to turn those experiments into capabilities that support how projects are delivered. That requires clear workflows, broader literacy, purposeful leadership framing, and pilots tied to measurable outcomes.
Entry-level adoption may tick the box of “having an AI strategy.” But everyday value comes when AI shapes decisions, integrates with the systems your teams already use, and builds trust on site.
The firms that make this shift won’t necessarily be the ones with the most tools. They’ll be the ones whose people know how to use AI insights confidently, turning predictions into better planning, safer sites, and stronger project outcomes.
Curious where your firm sits on the AI maturity curve? At Adapt, we help construction leaders connect AI to real workflows, not just pilots. Let’s talk about how we can support your team in moving past entry-level adoption.
