Every large project has a gap between knowing something is wrong and doing something about it. Few go as planned. Of the more than 16,000 projects that Oxford professor emeritus Bent Flyvbjerg studied, only 8.5% met both their budget and their schedule. [1] On a contract, a change order is how a change in scope or cost becomes official. AI can now spot the conditions behind a change order much sooner, but most of it stops at the alert. A person still has to read it, decide what to do, and do it. Much of the warning's value leaks away in between. A newer kind of tool is starting to close that gap. We call it AI project change order management.

The newer tools are agentic, and it helps to be precise about what that means. An assistant answers questions. An agent takes steps. Procore, a construction management software company, frames the difference this way: most AI tools in construction “behave like chatbots on top of disconnected data,” while its agents are built to understand what is happening in the platform, decide what needs to be done, and act on it. [2] In practice, that means updating a record, generating a document, or responding to an event such as a new submittal, request for information (RFI), or change order, according to rules the customer sets. [2]

Platforms are moving in the same direction. Procore completed its acquisition of the agent company Datagrid in January 2026, then released its first agents on May 21, for document search, submittal review, RFIs, daily logs, and contract review. [2][3] At Autodesk University 2026, Autodesk previewed agents across its industry clouds. In construction, they find answers in project documents, turn bid proposals into structured data, and check submittals against specifications. [4]

A project manager reviews data across multiple screens. Photo: seventyfourimages/Envato Elements

AI Project Change Order Management

Procore's library has grown to 20 pre-built agents. [5] Some are aimed squarely at the conditions that lead to change orders. A Change Analysis agent looks across changes, RFIs, and drawings for scope shifts, cost exposure, and schedule risk. A Financial Analyst agent studies commitments, change events, and cost trends to flag overruns early. [6] Procore also says its agents can create and update records in the platform, including change events. [7] In Procore, a change event is where the change order process starts. It records the reason for a change before it becomes an actual cost. [8] From there, the team requests quotes from subcontractors, reviews the responses, and creates a potential change order. [8] So while the software now flags a condition and opens the file, a person still approves what comes next.

Maker-Checker Workflows: Where the Human Stays in Control

The industry should not get carried away about autonomy. Procore describes its agents as “human-in-the-loop,” meaning people stay in charge. The agents gather data, cross-reference, and draft, but do not make final decisions on their own. [6] Autodesk describes its construction agents the same way, with people reviewing the results and making the final call. [4] This pattern is often called maker-checker, in which one party prepares an action and another approves it. Here, the agent prepares, and a person approves. Capital projects run on contracts and regulatory obligations, and software cannot be held accountable the way a licensed engineer or a signing authority can. So the agent's job is narrower. It shortens the distance between a change appearing in the project data and a draft sitting in front of the person authorized to approve it. The decision stays human.

AI Adoption Pace: From Under 5% in 2025 to 40% by 2026

The pace of adoption is fast, though much of the evidence is forecast. Gartner predicts that 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% in 2025. [9] The same firm expects over 40% of agentic AI projects to be canceled by the end of 2027, citing rising costs, unclear value, and weak risk controls. [10] It also warns of “agent washing,” where vendors rebrand existing assistants and chatbots as agents. [10] Both cautions apply in construction. Here, agents are added on top of the software teams already use, not in place of it. What is new is something that reads, writes, and notices things faster than the people watching the same dashboards.

The Agent Is Only as Reliable as the Data It Reads

The remaining constraint is the data an agent acts on. If it creates a change event from a spec deviation, its work depends on the quality of the records it reads. Construction data has long lived in silos: scheduling tools that do not talk to the document system, or a field app that does not update cost tracking in real time. The fastest progress is coming from intelligence added on top of the existing system of record. Some platforms mirror each user's role-based permissions, so an agent only sees what that person is already allowed to see. [6] That is a lower bar than migrating to new software, which helps explain why adoption has moved quickly. It also means the output can be no more trustworthy than the project data underneath it. That should temper how much autonomy a team gives an agent on day one.

Bridges and tunnels run over budget more than roads do. Photo: zeffss/Envato Elements

Go back to the change order math. McKinsey estimated in 2015 that bridges and tunnels overrun by an average of 35%, and roads by 20%. [11] Now take an illustrative case. A system flags a condition, but it takes two weeks for anyone to act on it. Much of the early warning's value is gone by then. This generation of tools is closing that distance. Given the right rules, they can open the change event and prepare the analysis the moment a condition appears, so the warning arrives with the paperwork already started. A person still decides what happens next.