Artificial Intelligence (AI) is rapidly advancing in the construction industry. According to Fortune Business Insights, the global AI in construction market is expected to grow at a rate of 24.6% annually. However, here is the uncomfortable truth: most contractors are not ready to benefit from AI. The problem is not that contractors are resistant to technology; it is that most businesses lack the necessary data structures and processes for AI to work effectively. Let us examine a familiar process, change orders, and explore its progression across three stages of readiness: centralized data, process, and automation.
1. Centralize and Standardize Data
Every contractor has change orders. The question is, how are they managed in the business? In most companies, it is a mess. Billy the PM keeps his change order log in an Excel sheet on his laptop. Joe tracks his in a notebook. The accounting department has an entirely different set of numbers in the ERP system. AI cannot help that. It cannot flag trends, catch missing data, or predict risks when the information is inconsistent, incomplete, or stored in multiple locations.
The first step is to centralize and standardize the data. Every change order should reside in a single platform, whether that is an ERP, a Project Management tool, or a Smartsheet on a SharePoint site. The inputs should have uniform fields, such as project number, client name, scope, price, status, date, and approvals, as examples. Once that consistency is in place, leadership can begin to trust the information. Moreover, when companies can trust the data, automation and AI have a solid foundation on which to build.
2. Standardize The Processes
Continuing with the change order example, companies should establish a process to manage the identification, validation, and authorization of changes. Every project team and office should handle change orders in the same manner. This way, there is no confusion about which change orders are approved, what is pending, or what has been billed. Standardizing the process is not about adding red tape or slowing people down; it is about bringing clarity to the work, accountability to the numbers, and consistency that can enable the adoption of AI. Without standardized processes, companies cannot scale technology effectively because every project and/or team member runs on a different playbook.
Change orders are a perfect example. In many organizations, the workflow depends entirely on the project manager’s personal habits. Some people meticulously document their tasks; others rely on memory until the end of the month. The result can be inconsistent billing, missed revenue, and constant back-and-forth between the field and accounting.
A standardized process transforms the inconsistencies into control. As stated earlier, every change should move through defined checkpoints:
- Identification: There is an office-to-field handoff, so the field is aware of what is in scope and what is out of scope. The Forman documents the change, attaching supporting photos, sketches, and notes in accordance with a checklist.
- Validation: Operations confirms scope alignment and cost impact, logging the data into the single platform with uniform fields before client submission.
- Authorization: The client approves, and accounting integrates the adjustment into both the job budget and forecasting model.
What matters is not the number of steps, but the consistency of execution. When every project follows the same rhythm, patterns emerge, data becomes reliable, and performance becomes measurable.
At that point, technology can finally amplify what is already working. Without a stable process, AI and automation only accelerate the confusion.
3. Automate and Apply AI
Once the data is clean and the process is consistent, automation can take over repetitive tasks. Imagine a system that captures every change order as soon as it is submitted, uses OCR (optical character recognition) to read attached documents, automatically routes it for approval, and updates project costs in real time. That is not science fiction; it is available now.
AI then adds another layer of value. It can analyze hundreds of past projects to predict which types of changes are most likely, flag unusual cost patterns, or even recommend negotiation points with specific clients based on historical behavior. What was once reactive and tedious becomes proactive and strategic.
Why Most Contractors Are Not Ready
Here is the reality: most contractors are still in the first step. Project data is fragmented. Teams operate with tribal knowledge, and their workflows often depend on individual expertise. That model has worked for many of these contractors, but it will not prepare them for AI and will not be effective at all in the next decade. AI thrives on accurate data and consistency. Without those, it is useless. Contractors who skip the foundational work will find themselves spending money on tools that collect digital dust.
Building the Foundation for What is Next
Change orders are just one example. The same principles apply to every process that drives profitability: accounts payable, job costing, project scheduling, service management, and more.
Before AI can transform construction, contractors must first standardize how work gets done, digitize how information is captured, and automate where it makes sense.
Once those steps are in place, AI becomes a multiplier, not a distraction.
The Takeaway
AI is not a replacement for human expertise; it is an amplifier for it. The construction contractors that succeed in the coming years will be those that are ready for modern technology and can integrate and operate in their customers’ technological environment. Contractors who take the time now to centralize their data, standardize their workflows, and build automation-ready systems will be positioned to lead. Those who do not will find themselves reacting to an industry that is moving faster than they can keep up with.
The construction industry is changing. The question is not whether AI will reshape it; it is whether the company will be ready to benefit when it does.
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Leigha Menjivar