44%
planned to increase AI investment
AGC / Sage · 1,100+ U.S. contractorsAI can help a painting company move faster—but only when the estimate, job, labor, vendor, and accounting data can move with it. Start with the workflow costing the most time, trust, or margin visibility.
Buying an AI tool is easy. Connecting it to a dependable operating process is the adoption work.
Construction companies are increasing AI investment, but broad interest has not yet translated into deeply connected operations.
The surveys below measure different groups and define “use” differently. They are directional construction and trades indicators—not painting-only adoption rates.
44%
planned to increase AI investment
AGC / Sage · 1,100+ U.S. contractors74% / 28%
use AI somewhere / use it across many or all areas
Houzz · 722 construction & design businesses12%
said AI was embedded in business processes
ServiceTitan · 1,032 contractors across tradesAGC/Sage also found that implementation time, employee resistance, field-office communication, and software integration remain material IT challenges. The bottleneck is rarely access to another tool. It is making the stack work as one operating system.
They are trying to stop the same job from being copied across six systems, keep handoffs moving when one person is out, and see what changed before the review becomes a reconstruction project.
The opportunity is not to remove judgment. It is to stop spending judgment on copying, chasing, and reconciling information.
A sold job is copied into the CRM, phone system, production board, photo app, accounting, and reporting.
One person knows what to check, who to ask, and where the missing information lives.
Someone must notice a status change, write the message, and remember to send it.
Labor, material, estimate, and accounting data reconcile after the decision window closes.
The owner carries exceptions across systems because the software does not share enough context.
“Use AI more” is not a project. “Match material purchases to active jobs and flag exceptions before closeout” is.
Human checks the money. Automation moves the repeatable work and makes uncertainty visible.
Pick one repeating workflow with a clear owner and observable result.
Name where every critical fact starts and which system controls it.
Automate repeatable rules; turn uncertainty into a visible exception.
Keep a human on ambiguous matches, payments, and money-sensitive messages.
Prove the operating result before connecting the next workflow.
The right first workflow depends on where your operation loses time or confidence. The checkpoint matters as much as the automation.
Create records, assign follow-up, and carry source and customer details forward.
Confirm qualification and scope.
Create the production record, transfer approved scope, and notify the right roles.
Confirm dates, crew, and unusual requirements.
Trigger internal or customer updates when approved stages change.
Review delays, sensitive messages, and exceptions.
Organize job context, summarize notes, and flag missing documentation.
Validate quality, change orders, and customer commitments.
Match labor and material activity to jobs, compare actuals with estimates, and surface drift.
Review uncertain matches and act on margin signals.
WayMark’s Automated Job Costing Agent is built around a narrow operating outcome: a current, traceable view of labor and material cost without replacing the systems your team already uses.
The target operating model is a roughly 15–20 minute weekly exception review after configuration. Actual review time depends on data quality and unresolved exceptions.
See the Job Costing Agent →Available accounting, vendor, estimate, labor, purchase, and active-job context.
Transactions to the right job and cost category, with source context preserved.
Actual labor and material cost against the estimate while work is active.
Duplicates, missing job matches, budget drift, and other exceptions.
Questions to the person closest to the work, then files the answer with the record.
Current owner views by job, crew, estimator, subcontractor, and job type.
If the team cannot agree on the steps, source records are consistently incomplete, or no one owns the exceptions, the first project is workflow definition and data cleanup—not full-process automation.
That does not mean “wait on AI.” It means build the operating foundation AI needs.
Not necessarily. Many useful automations connect the CRM, estimating, production, accounting, and reporting tools already in place. The better question is whether each system has a clear role and whether the data can move between them reliably.
Start with a narrow workflow and make uncertainty visible. Define required fields, source-of-truth rules, and an exception queue. An AI system should not hide missing or conflicting information behind a confident answer.
That is not the goal of this approach. The goal is to reduce copying, checking, and chasing so office and operations staff can focus on exceptions, customers, crews, and decisions that require context.
It depends on the source. Some systems expose events or transactions as they happen; others update on a schedule or limit access. Every implementation should define the refresh behavior for each source instead of promising instant updates everywhere.
Choose one repetitive, cross-system workflow with a clear business result. If current labor and material cost is difficult to reconcile while work is active, automated job costing is one concrete starting point. See how automated job costing works.
Connect estimating, labor, vendor, and accounting data to surface job-cost exceptions while work is still active.
See the Job Costing Agent →