Construction leaders evaluating AI in construction often begin by asking how much time it will save. They want to know whether it can analyze bids faster, simplify drawing management, identify unusual payroll activity or help finance teams review project performance.
Those questions matter, but they reflect the same function-by-function approach that has shaped construction technology investments for decades.
As AI becomes part of everyday construction workflows, leaders should look beyond the time it saves within a particular function and consider whether it helps teams understand how decisions made during preconstruction, project delivery, workforce management and finance affect one another.
At Sage, we see the greatest opportunity where operational and financial data come together, giving AI the context to help teams understand not just what is happening, but why it is happening and what it could mean for the project.
How is AI used in construction?
AI is used in construction across estimating, project management, document review, workforce management and financial forecasting. It can help teams analyze bids, find project information, identify unusual labor or cost activity and review project performance more efficiently.
A project’s financial performance starts long before finance sees it
Consider a subcontractor bid that is well below the expected range. While flagging an outlier highlights a potential issue, additional analysis is required to determine whether the contractor achieved genuine savings or the subcontractor failed to capture the full project scope in their bid. That requires comparison with expected pricing, similar projects and the subcontractor’s past performance.
After work begins, locating a drawing faster has value, but teams still need to know they are working from the current revision and understand what has changed. A revision may affect field activities, labor requirements, the schedule and project cost.
Payroll may then identify an unusual increase in hours, while project information shows whether crews are responding to revised work, schedule pressure, or an activity taking longer than estimated. Finance can calculate the effect on the forecast but deciding what to do requires understanding the cause.
By the time margin pressure appears in an updated forecast, the underlying issue may have begun with an estimating assumption, scope issue, drawing revision, or changing labor requirement. Identifying an issue and understanding what caused it are two different things.
How does connected project data improve decision-making?
Connected project data helps construction teams understand how changes in one part of a project affect another. When estimating, schedule, labor, cost and financial information can be considered together, teams can identify risks earlier, understand what is driving performance and respond with greater confidence.
When project developments are reviewed separately, contractors may still have to compare reports, speak with colleagues and retrace decisions through spreadsheets, emails and separate systems to understand what has changed.
An AI tool may automate work within one department without necessarily helping a contractor understand why a project is moving away from plan or how to respond.
Estimating assumptions should be carried into budgets and commitments after award, while drawing and schedule changes should be considered alongside labor activity and cost. When finance investigates a margin change, the team should be able to review the project activity that preceded it without rebuilding the job’s history from disconnected records.
ACT Construction experienced this challenge when estimating and job setup were managed separately. After connecting those processes with Sage Intacct Construction, estimates carried into project creation and teams gained a more consistent view of project and financial information. CEO Joe Murray described the result as supporting the business “from initial lead through project completion.”
How should construction leaders evaluate AI?
Construction leaders should evaluate AI by asking whether it can connect the right project context, surface relevant operational and financial information and help teams act before issues affect the outcome. The goal is not simply faster task completion, but more informed action across the project lifecycle.
Can a low bid be evaluated against expected pricing and prior performance? Can a drawing revision be considered alongside its schedule and labor implications? Can an overtime variance be reviewed in relation to current field activity and the project forecast? Can a margin issue be traced to the assumptions and changes that contributed to it?
The construction industry already has technology that describes individual parts of a project. At Sage, we believe opportunity for AI in construction is connecting those parts into a complete project story, helping teams understand how decisions across the project affect outcomes, identify risks earlier and act while there is still time to influence the result.
Julie Adams is Senior Vice President of Construction, Product at Sage. Based on a deep understanding of customer needs, she and her team define product strategy and deliver market leading solutions that elegantly solve complex business requirements using technology. Julie has extensive experience building high performing teams and has held various product leadership positions for cloud applications at large companies as well as smaller startups.
