AI maturity in construction: Moving from experiments to impact

Artificial intelligence (AI) is on everyone’s lips. News headlines alternate between hype and fear, but on a job site, the questions are more practical: Where do we start? and What does progress look like? Right now, AI adoption is growing in construction – roughly a third of firms already use some form of AI or machine learning, up from just a quarter two years ago, according to a Deloitte report.

Yet the ways companies use AI are uneven and often elementary. Many lean on generic tools for administrative tasks like note‑taking and document editing. Others are hesitant to experiment at all because there’s no clear framework for moving beyond those basics.

To help contractors make sense of this landscape, let’s explore the AI maturity curve – a simple framework showing how to progress from basic tools to more sophisticated applications that unlock real value.

1. The state of AI in construction

AI adoption in construction is accelerating, but most firms remain in the early stages. Early findings from ProcurePro’s 2025 AI in Construction survey show that the majority of adoption is still at a basic level: 73% are applying AI to general admin tasks, while only 25% are leveraging it for complex tasks or decision-making.

How AI is used today reveals the sector’s immaturity. The most common applications are administrative and marketing tasks - note‑taking, document formatting, drafting social posts, and project estimates.

These small wins save time but don’t yet tap into the deep operational data that makes construction projects so complex. In short, there is broad awareness but only moderate adoption and limited depth of usage.

2. What is the AI maturity curve?

The AI maturity curve is a roadmap for progressing from general‑purpose tools to integrated, domain‑specific systems. It loosely aligns with research on enterprise AI maturity from MIT’s Center for Information Systems Research, which describes five stages of AI capability.

In their model, 28% of organisations are still experimenting and preparing, 34% are piloting and building capabilities, 31% are industrialising AI across the enterprise, and only 7% are truly AI‑future‑ready.

For contractors, the stages look like this:

1. Generalist tools - using off‑the‑shelf AI to write emails, summarise meetings, or search the web. These tools offer immediate efficiencies but little job‑specific context.
2. Contextual tools - feeding your own historical pricing, subcontractor lists, and drawings into AI so it understands your business. At this stage, AI can structure bids and surface missing coverage, but humans still drive decisions.
3. Assisted intelligence - integrating AI within procurement workflows. The software compares quotes, flags anomalies, and recommends negotiation strategies. People focus on relationships and judgement while AI crunches the numbers.
4. Autonomous systems - AI-driven systems that manage the process with minimal human intervention. These systems rely on high-quality data to deliver optimal recommendations.
5. Self-learning AI - the future of AI in construction, where AI can continuously learn from new data, improving its predictions and decisions over time without constant human input. This stage is still in its early days but holds the potential to revolutionise the industry further.

This progression, from general tools to self-learning systems, shows how contractors can gradually build AI capabilities into their processes and benefit from more sophisticated AI tools over time.

3. Why maturity matters

Different stages yield different outcomes. Early‑stage tools deliver “quick wins” like reducing the time spent on emails or formatting invoices.

As companies move into the contextual and assisted stages, AI starts to leverage proprietary data. Tools can structure messy quotes, compare supplier pricing, or identify risks, supporting more informed negotiation and risk management.

Research indicates that each additional technology adoption is associated with a 1.14% revenue increase, and construction businesses using AI save about 10.5 hours per week, according to this industry study. Higher maturity translates into real productivity gains, margin protection, and better decision‑making.

At the same time, maturity demands good governance. The MIT research notes that organisations progressing through stages must simplify processes, build scalable data architecture, and determine where humans stay “in the loop,” as detailed in MIT’s findings.

Without investment in data quality and change management, AI initiatives plateau.

4. The procurement maturity curve

Construction procurement has followed a similar trajectory. Historically, procurement in construction has been manual, time‑consuming, and prone to errors. As systems became more fragmented, technology emerged to manage tasks like tendering and invoicing. However, only by connecting procurement processes can firms take full advantage of AI-driven tools.

The procurement maturity curve demonstrates how construction procurement has evolved:

1. Manual processes - reliance on spreadsheets, emails, and paper-based processes.
2. Fragmented systems - adoption of individual tools for bidding, procurement tracking, and communications.
3. Connected procurement - integration of procurement data across platforms (e.g., ProcurePro), ensuring better data flow and collaboration.
4. AI-powered procurement - advanced AI-driven procurement tools that optimise supplier selection, pricing, and project outcomes.

In order to benefit from the full capabilities of AI in procurement, construction businesses must first ensure that their procurement systems and processes are fully connected. This creates a data lake where AI tools can draw on contextual information, enabling smarter decision-making. Without these systems in place, AI tools can only provide limited value.

5. The opportunity for contractors

Construction is ripe for AI because of its repetitive, data‑rich workflows and thin margins. Procurement is a particularly promising starting point: bid levelling, price comparisons, and subcontractor management generate large datasets and involve time‑consuming tasks. By pairing AI with human expertise, contractors can reduce wasted hours, improve accuracy, and protect margins.

Importantly, AI does not replace the human elements that make construction successful. In commercial real estate, analysts note that AI “is more than just a set of tools - it’s a way to enhance human expertise”.

AI-driven valuations and pricing models must still be adjusted for on‑the‑ground factors like regional variations or client relationships. Over reliance on automation can erode the intuition and negotiation skills that seasoned professionals bring to the table.

By viewing AI as a powerful assistant rather than a replacement, contractors can unlock efficiencies while retaining control. Early wins build momentum; contextual tools integrate data; assisted intelligence streamlines procurement workflows. Each step along the AI maturity curve prepares teams for the more autonomous systems of the future.

Conclusion

AI is no longer a futuristic concept for construction - it’s a present‑day opportunity. Adoption is accelerating, yet most firms remain in the early stages, using general tools for basic tasks. The AI maturity curve offers a roadmap to progress: start with small, quick wins, then layer in contextual data and assisted workflows.

Along the way, keep humans firmly in control. Judgement, negotiation, and relationships remain irreplaceable.

Ready to learn more? Download our Practical AI Use Cases cheat sheet to understand the five phases and see practical examples of how contractors can apply AI to procurement today - and what’s coming next.