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When construction companies start using AI, the entry point is almost always the same: tools like ChatGPT. Drafting RFI responses. Summarizing meeting notes. Writing emails faster. Useful, but a long way from the operational impact the industry has been promised.
Most firms are still early in their AI journey. Industry research across AEC and construction technology consistently shows that adoption is still concentrated in experimentation and point use cases rather than fully integrated workflows, with most organizations still testing or selectively piloting AI rather than embedding it into core operations.
At the same time, sentiment is strongly forward-looking. Multiple industry surveys, including research from firms such as Autodesk and construction technology reports from organizations like Dodge Construction Network, consistently show that most construction leaders expect AI to materially reshape project delivery, cost management, and planning over the next few years.
The firms getting the most value from AI are not the ones with the biggest technology budgets. They are the ones that have moved from AI as a writing assistant to AI as a workflow layer, connecting systems like Procore, CMiC, and Microsoft Teams.
They reduce the manual coordination that consumes skilled labour hours. They get cost visibility earlier. They make fewer decisions based on outdated information.
This post covers what that actually looks like in practice, and one risk most firms haven't addressed yet.
Why Most AI Use in Construction Stays at the Surface
The barriers to deeper AI adoption in construction are not primarily financial. Across multiple industry reports, the most cited obstacles are complexity, cultural resistance, and disconnected systems.
That’s consistent with what we see in the field. Construction operations are inherently fragmented. Work moves through texts, phone calls, emails, site visits, spreadsheets, and project management platforms, often without a clean handoff between any of them.
A superintendent places an order on site. The coordinator finds out when the invoice arrives. The project manager reconciles the cost at month end.
By the time anyone has a full picture, the decision has already been made. This is why surface-level AI adoption is both understandable and insufficient. It's easy to layer AI into existing workflows. It's much harder to connect those workflows so information moves cleanly from field to finance. That's where most firms stall — and where the real efficiency gains are hiding.
Where the Real Savings Are: Four Construction Workflows Worth Solving
Based on the operational patterns we see most consistently across Canadian general contractors, these are the areas where construction workflow integration tends to produce the fastest, measurable results.
1. Purchase Orders and Procurement Visibility
This is one of the most common and costly manual burdens in GC operations.
In many environments, a large share of purchase orders are created after the purchase has already happened. Accounting is working off delayed information, coordinators spend hours reconstructing what occurred, and by the time a cost variance surfaces, the margin impact is already embedded.
We saw this directly at a Canadian construction firm running 1,100 purchase orders per month across 22 active sites. 90% of those POs were being created retroactively. Their accountants were spending up to 33 hours per month on PO creation alone. The problem wasn't a lack of systems. They were running CMiC and Microsoft Teams. The problem was that the field-to-office handoff was broken.
By connecting the Teams messages and voice notes superintendents were already using directly into CMiC, PO capture moved to the point of purchase rather than days later. Retroactive PO creation dropped from 90% to 25%. Accounting recovered 20 to 30 hours per month. No new platform. No training program.
The shift is simple but material: coordinators move from data entry to review. Accounting sees costs before invoices arrive. Audit trails build automatically.
2. Data Entry Into Project Management Systems
Platforms like Procore and CMiC only work as well as the data going into them. And getting data into them is one of the most time-consuming parts of a coordinator’s role.
Field updates come through texts and calls. RFIs arrive in inconsistent formats. Daily reports are filled in after the fact. The result: your system reflects yesterday’s reality.
AI integration changes that by connecting incoming communication directly into structured systems. Emails, notes, and updates are parsed and used to populate fields automatically, with human review layered on top. For firms not operating on a fully integrated platform, which is still the majority, manual re-entry between systems is a hidden cost that compounds across every project.
3. Scheduling and Site Coordination
Scheduling remains one of the most under-optimized areas in construction.
Industry data consistently shows that:
- A minority of contractors use advanced scheduling tools or automation
- Schedule quality and consistency drop significantly over the life of a project
The issue isn’t the schedule itself. It’s the coordination required to maintain it.
Project managers spend significant time chasing updates, confirming availability, and reconciling changes across trades and sites.
AI doesn’t replace that judgment. It reduces the manual coordination required to support it.
When scheduling workflows are connected to existing communication tools, status updates surface automatically, reducing the need for constant follow-up and freeing up time for higher-value decisions.
4. Change Order Identification and Processing
Late change order identification is one of the most consistent sources of margin erosion.
Changes happen in the field. They’re communicated informally. By the time they’re formally processed, the cost impact is already embedded.
AI-supported workflows can monitor incoming communications, RFIs, and site instructions to flag potential scope changes earlier.
The project manager still makes the call, but they make it sooner, with better information.
The Data Risk Most Construction Firms Haven't Addressed
Here's something that doesn't come up in most conversations about AI in construction, but should.
The same teams adopting AI to save time are often creating an unmanaged data exposure in the process. A coordinator pastes subcontractor pricing into ChatGPT to draft a response. A PM uses a free AI tool to summarize a contract or pull key clauses. These feel like small productivity wins, and in isolation they are. But depending on the tool's data retention policies, sensitive information like subcontractor rates, contract terms, project budgets, insurance certificates may be leaving your controlled environment entirely.
Consumer AI tools are not built for construction data. They were not designed with your client confidentiality obligations or your subcontractor relationships in mind. And unlike a misplaced spreadsheet, you may have no visibility into where that data went or how it was used.
For Canadian GCs already running Microsoft Teams and SharePoint, there is a straightforward path. Microsoft 365 with Copilot keeps your data within your existing tenant — the same environment your team is already working in. AI assistance happens inside your infrastructure, not outside it. If you've already integrated Teams into your field workflows, as many Canadian GCs have, this is a natural extension rather than a new system to introduce.
For firms integrating AI into CMiC, Procore, or other project management platforms, the same principle applies: build controlled connections between your systems rather than routing operational data through general-purpose AI tools. The efficiency gains are real and achievable. The question is whether they're built on a foundation that protects your data, your clients, and your subcontractor relationships.
This is one of the practical considerations that gets missed when AI adoption happens informally, workflow by workflow, without a governing framework. For a deeper look at why AI projects fail at the organizational level see our other post Why AI Projects Fail: How to Avoid Costly Mistakes.
How to Approach AI in Construction Without Overcommitting
The most common mistake is trying to solve everything at once. A company decides to invest in AI, selects a platform, and attempts a broad rollout. Months later, adoption is low and the original inefficiencies remain.
A more reliable approach:
- Start with one workflow that has a measurable cost today. Not the most exciting use case. The most painful one.
- Map how it actually runs. Identify where information is delayed or lost. Build a connected version. Measure the outcome. Then expand.
- If a new interface needs to be introduced, ensure that it is straightforward and easy for teams to learn to use
The firms seeing real results from AI in construction in 2026 are not the ones experimenting broadly. They are the ones solving specific operational problems, with clear baselines and controlled data environments.
If you’re exploring where to start, we can help you identify the highest-impact workflow to focus on first. Learn about our Discovery process.
Sources
- Dodge Construction Network. AI in Construction: Builder Survey. 2025. dodgeconstruction.net
- Royal Institution of Chartered Surveyors (RICS). AI in Construction: Global Survey Report. 2025. rics.org
