AI in construction: How to keep your firm competitive

AI in construction — it’s already having an impact. 65% of construction executives Wipfli surveyed for our 2026 report on the state of technology in the construction industry reported that AI has led to operational improvements at their firms.

While AI adoption is accelerating, only 17% of executives report that AI capabilities currently influence how their firms bid, staff or differentiate their services in the market. This is a key state, because not all AI use is the same. Isolated use cases don’t have the same value as enterprise-wide AI adoption backed by a data lakehouse. That’s why forward-thinking firms are making a tiered, multi-year effort to integrate construction-specific tools that leverage AI as a crucial component.

Let’s explore how your business can implement an AI strategy that will keep it competitive as the industry becomes more technology-driven.

How is AI used in construction?

AI can streamline workflows and reduce administrative burdens both in the office and on the job site. Examples of valuable use cases in construction include:

Estimates

Estimates can be a common bottleneck for construction firms. AI can accelerate the process by analyzing previous bids, identifying comparable projects, highlighting pricing anomalies and helping teams evaluate potential risks before a proposal is submitted. With AI handling the routine analysis, experienced estimators can dedicate more time to higher-value decision-making.

Engineering

Engineering teams can use AI to review specifications, compare design documents, identify inconsistencies and accelerate review processes.

Document organization

Construction firms are up to their eyeballs in documents. Most of which are PDFs, including RFIs, specifications, contracts, submittals, drawings and more.

AI can help pull information from unstructured documents, create a summary of the content and answer questions about project records. It can also link these documents with project schedules, financial data, procurement records and other operational systems.

Scheduling and project planning

AI for planning can help construction firms build more accurate schedules by evaluating weather forecasts, labor availability, material delivery timelines and project dependencies. It can also identify potential delays early and recommend adjustments to keep projects on track.

Building information modeling (BIM) and progress tracking

When integrated with BIM platforms, AI can compare project plans with real-time jobsite data to track progress, identify discrepancies and improve decision-making. This helps teams maintain quality standards and quickly address issues before they become costly rework.

Procurement and supply chain management

AI can help predict material needs, monitor supplier performance and identify potential procurement risks. By improving visibility into inventory and supply chain disruptions, firms can avoid delays and better control project costs.

What are the benefits of AI in construction?

Creating and executing an AI strategy takes time, energy and financial investment. So, what makes the cost worth it?

• Safety: Many of your team members actually risk their lives at work. Pilot studies have shown AI can help improve job site safety by as much as 30% by monitoring safety cameras and analyzing incident data to assess project risks. For example, if you have a big concrete pour coming up, you may be able to find out ahead of time if it’s high risk and help your team prepare accordingly.
• Technical precision: Job requirements are growing ever more precise, especially for in-demand projects like data centers. AI can help you reach a level of precision you might struggle to achieve without it, avoiding costly mistakes and delays.
• Real-time error correction: AI can track your progress on a site in real time using data from job site cameras. This can help you catch errors as they happen.
• Labor shortages: Construction will likely always have labor issues, but AI can help you allocate your work more efficiently. In our report, 48% of executives reported using AI for task and workflow automation
• Predictive analytics: AI can track patterns over time across all elements of your work to help you avoid potential problems ahead of time and make costs more predictable.
• Adaptability: For complex, high-value projects like data centers, project requirements can frequently change midstream as technologies evolve. New AI tools like edge AI can allow you to make faster on-site decisions, more easily adapt your designs in the field and update to meet changing needs.
• Sustainability: You can use predictive analytics to optimize energy-efficient materials or reduce carbon footprints in projects like data centers. AI integrations with drones for site surveys or 3D printing for rapid prototyping can also help you not just move faster but also create less material waste.

In construction, safety is its own essential goal. But what all the rest of these elements add up to is the ability to stay competitive in a construction business that is changing more rapidly than many firm leaders realize.

Tech, AI-related and otherwise, is only going to continue to displace old ways of doing business in construction. What’s coming down the pipeline isn’t just better design tools or analytics, but even robots to physically take over certain riskier jobs.

In five years, your competitors may be using a robot to handle site layout. Will you be ready for that day?

What are the risks and challenges of AI in construction?

Change isn’t always easy. Construction leaders looking to better use AI should consider barriers or risks like:

• Caution:Construction firms tend to be cautious about making significant changes in how they work — and with good reason, given the safety risks inherent to the job.
• Understanding:Teams may lack awareness of how construction-specific AI solutions can solve problems.
• User adoption: Technology adoption remains a significant challenge across the industry. 45% of executives identified employee struggles with existing technology as their top infrastructure challenge, making workforce training and change management critical components of any AI strategy.
• Interdependency:Because construction firms are integrated into a web of trade partners, architects, engineers and subcontractors, it can be hard to get your firm using AI tools if your partners aren’t doing the same. Try developing common AI standards for you and all your partners, as well as using shared BIM models to create a single source of truth among all parties.
• Regulatory hurdles:You’ll need to comply with new laws on AI data use.
• Cost:Implementing your AI strategy can require a significant upfront investment, although you will likely see long-term ROI from efficiency gains.
• Job displacement:Although AI or automation can help construction firms navigate labor shortages, these technologies could also displace certain job roles. If a particular role is being moved over to AI, it’s essential to offer upskilling opportunities or training programs for any team members affected so that they can move into a new position.
• Ethical and risk considerations:You’ll need to carefully monitor predictive analytics for signs of bias (for example, labor forecasting tools may unfairly impact certain demographics). Jobsite cameras and real-time monitoring also give rise to data privacy concerns.
• Cybersecurity: Cloud-based systems, including AI, are at risk for cybersecurity issues like data breaches.

How should your construction firm start thinking about AI?

For many construction firms today, AI is just a buzzword. You may be encouraging your employees to play around with chatbots, experimenting with AI for document management or using AI to help with scheduling.

Doing these things can be useful. But simply playing with AI tools doesn’t mean you have a real enterprise-level AI strategy in place — and can even distract you from creating one.

Instead, think about AI from the perspective of problems and solutions. What problems does your business face? And can AI help you solve any of those problems?

As you begin to answer this question, you can rank your ideas in two ways: ease of implementation and impact. In other words, identify low-hanging fruit and big fish. The former offers opportunities to get comfortable using AI, while the latter can deliver major value for your business.

Implement AI in 3 stages

Start to think about timelines here, too. Executing an AI strategy typically takes several years and can be broken down into three stages: Solutions that take less than a year to implement, those that need one to three years and long-term projects that will require three years or more.

Here are examples of what each stage might include:

• Stage 1: Moving from paper or spreadsheet project management to a cloud-based construction management system (CMS), using AI schedule management tools that can predict delays based on weather and supply data.
• Stage 2: Implementing a BIM platform that incorporates real-time worksite data with instant AI analysis.
• Stage 3: AI-powered analytics to help with labor forecasting and shortages, real-time insights into project health, better workforce management, generative AI for design optimization and advanced automation.

Create an AI strategy

Once you’ve identified business problems that AI could help you solve, you can start developing an overall AI strategy. Here are five key steps in that process:

1. Assess where you are today.

Before you can go where you want, you need to be honest about where you are. This means conducting a digital maturity assessment to identify any gaps in your current systems from a technology perspective.

For example, you may already be using tech, including AI, but via several unconnected systems that don’t talk to each other. Your data may be dirty, highly fragmented or not properly categorized.

And as a result, you may think that you’re a forward-thinking, AI-friendly business, when in reality, you’re missing out on value or even actively hindering your own success.

However, an honest assessment will help you better understand any current shortcomings around tech and AI so you can set about solving them.

2. Define success metrics.

As you implement your AI strategy, think about how you define success so you can measure whether you’re working toward it. This should be an ongoing process, not just something you do once or at a certain point in the journey.

For example, as you first begin creating your strategy, set some KPIs for what you want to achieve. These can be quantitative, like reducing the amount of time you spend on tasks like scheduling by a specific percentage or number of hours, or more qualitative, such as transitioning your firm onto a cloud-based CMS within the next 12 months.

Then, as you move forward, evaluate whether you’re hitting your existing KPIs or need to establish new ones. The more sophisticated your AI adoption gets, the more you may be able to tie AI use into metrics like revenue, job safety and other high-level indicators.

3. Start small.

Once you’ve done a digital maturity assessment, you can start taking action to improve your tech and AI usage. The easiest way to do this is to start small.

Think about the low-hanging fruit you’ve identified: Easy-to-implement AI or tech solutions to current business problems. Pick one or two of these and give them a go.

Figuring out how you can automate grunt work is often a good place to start here. Found an AI tool that could help you with scheduling or time sheets? Give it a try and see if that tool can help take a tedious task off someone’s plate.

4. Build your foundation.

After your team gets more comfortable using some simple AI tools to solve problems in your business, you can begin creating a more impactful foundation for future growth. For construction firms, specifically, this often looks like moving to a cloud-based CMS platform.

A cloud-based CMS will allow you to centralize all your data and operations in one place. Rather than struggling with fragmented data or disconnected systems, you’ll have a central source of truth from which to work.

As you do this, make sure that your data is clean and properly categorized. With anything AI-related, garbage in equals garbage out.

Once you’ve put your business onto a cloud-based CMS, you’ll be ready to integrate other, more complex AI tools into your processes in the coming months and years. At this point, you may want to partner with advisors to figure out which tools might best fit your specific business needs.

5. Bring your team along.

AI adoption is a team effort. Successfully carrying out your AI strategy will involve getting your team to buy in, learn new tools and be willing to embrace new ways of doing things.

One idea that can help here is asking specific team members to serve as AI champions within your organization. These team members would offer internal leadership in this area and help guide the rest of your team in implementing AI into their work.

Add metrics to track team buy-in as well. Surveys on how comfortable your team feels using AI can help you understand whether your efforts to integrate AI into your workflows are succeeding.

6. Adopt additional AI tools over time.

After you have your foundation in place, you can work to incorporate other, more sophisticated AI tools into your business. This process will likely take years, but it will help you stay competitive for the long run.

For example, BIM platforms have been around for a long time. However, new iterations are adding AI and data from job site cameras to give you real-time analysis on the work your team is doing.

In other words, you won’t just be able to build a 3D model of a project but also actually use live data to compare the actual construction against that model. As construction projects grow ever more exacting in their requirements, this can help you meet the level of precision needed to do the job.

What comes next for AI in construction?

To learn more about how the construction business is changing, read the “State of the construction industry” report prepared by Wipfli’s research team. You’ll get insights gleaned from 308 executives at firms with revenue ranging from under $50 million to over $250 million, on areas including not just AI but also cybersecurity, enterprise tech strategy, future investment and more

Read more

• AI Agents in construction: The new path to operational efficiency
• For smaller or mid-sized construction firms, AI automation offers a bright spot — and a warning
• It’s time for construction leaders to get serious about technology upskilling