What Capterra’s New report reveals About AI in Construction Management
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The schedule or margin of a construction project doesn’t usually go off track all at once. It happens gradually over time with activities such as missed updates in the field, overlooked costs, or risks that have been growing unnoticed for several days. This gap in construction management processes is exactly what Capterra set out to address in its research released in 2026 titled “Can AI in Construction Management Software Help Keep Projects on Time and Budget?”
As part of its research, Capterra surveyed 10 leading vendors of construction management software - viAct, Appenate, Bridgit, ConstructConnect, Cority, InEight, Planyard, Sage, STACK Construction Technologies, and Workyard – to find out the role of AI in the current scope of operations, the efficiency of AI in construction projects, the responsibility for making the final call on AI-assisted decisions, and how far autonomous workflows are likely to go by 2027.
The findings of the research are much more practical and detailed than previously available information about the advantages of AI in the construction industry because the data is provided directly by the vendors of construction management software.
This blog aims at discussing what the findings in the report really mean and how they can help construction companies evaluate AI in construction management software right now.
Where AI is Already Live in Construction Projects?
When it comes to the adoption rates of technologies in the construction industry, some stages are more successful than others. Large is the volume of repetitive information moving between people, the more is the adoption rate of these technologies.
According to the Capterra survey, the highest adoption rates can be seen in the field of bid management and pre-construction, along with field and site progress tracking. These are being used by 4 out of 10 surveyed providers. Estimation and take-off, scheduling, documentation and RFI management, and risk detection technologies – each sit at 3 out of 10. Neither cost prediction nor budget monitoring technologies are being used by more than 2 out of 10 companies, which have moved to the beta stage of their development. Payment, billing, cash flow prediction trails are furthest behind, with live at just 1 of 10.
The honest answer depends on where AI is applied. AI take off improves the accuracy of the bid. Whether that translates to on-budget delivery depends on everything that happens after construction starts. What AI does well in preconstruction is handle tedious, repetitive work, so experienced estimators can focus on the judgment calls that actually win jobs. ConstructConnect, in Capterra's 2026 research report
The response from Appenate indicates a correlation between the same method being applied to a jobsite with the aim of tackling a different need: delays in processing paperwork. With the use of voice-to-text data entry combined with automated form generation, workers on the field can now record their data while they continue working, thus giving the opportunity to know what is happening on the site without having to wait for days before receiving the information.
In fact, this have proven to be true across the whole survey. AI gains ground first where any repetitive data exchange takes place, and not where the largest spending decisions are made.
How Construction Management Software Providers Measure Real Impact of AI?
The rate of adoption and measurement of AI are not moving at the same pace. Providers are getting better at tracking what AI changes inside a workflow, but far fewer can yet connect that change to a project’s final result.
5 out of 10 companies that were surveyed measure cost variation against budget at the end of the project, and another group of 5 companies, including viAct, can track forecast accuracy. However, only 3 companies track schedule variance against baseline, change-order volume and cycle time, or cash flow and payment-delay outcomes. On the other hand, it is interesting that 5 out of the 10 providers do not measure project outcomes at all. They gauge AI by usage or productivity instead, which is a meaningfully lower bar.
So, does AI keep projects on time and on budget? Not by itself. But by helping teams identify risks sooner, make better-informed decisions, and intervene earlier, it can materially improve project outcomes. InEight, in Capterra's 2026 research report
That distinction, between AI that changes a workflow and AI that changes a project’s outcome is the single most useful filter for any team evaluating a construction AI vendor’s claim in 2026.
Who Makes the Final Call: Humans or AI?
Capterra reached out to all the 10 vendors to find out who makes the final call across five consequential workflows: accepting an AI-generated schedule, adjusting cost forecasts, flagging project risks, approving pay applications, and committing a bid to a client. The results draw a clear line.
As per the results, in 8 out of 10 companies the decision making is completely in the hands of humans in terms of approving pay applications. Regarding the approval of bidding applications of its clients and the acceptation of changes in schedule, these procedures are controlled by humans in 6 out 10 companies. Concerning the adjustment of cost forecasts, half of the organizations (5 out of 10) trust humans while the other half let AI take control of this process. Lastly, flagging and escalating risks is where AI dominates, with 5 out of 10 providers already letting AI recommend the flag and 1 of them even let AI escalate it without human in the loop at all.
It helps, but it doesn't drive. AI-powered construction software keeps projects on time and on budget by giving estimators and PMs better information faster, not by taking the wheel. The estimator or PM makes the final call, always. That's not a limitation; it's how you build trust in it. STACK Construction Technologies, in Capterra's 2026 research report
The pattern is not really about what AI is capable of. It is about who absorbs the consequences if AI is wrong. Every workflow where money or a client relationship is irreversibly on the line stays human-led, no matter how mature the AI is elsewhere in the same platform.
How Far Agentic AI Actually Goes?
Agentic AI, meaning autonomous agents that complete multi-step tasks without a human triggering every step, has moved out of the experimental stage, though the adoption is still early.
3 out of 10 providers surveyed have it live for customers today, 1 is in beta, 2 have it on the roadmap for the next 12 months, and 4 have no plans to go autonomous at all.
Workyard’s description of what “live” actually looks like in practice is a useful reality check: agents that continually analyse performance and flag emerging variance before it becomes a budget surprise, not agents that commit money or make client-facing calls on their own. That distinction lines up exactly with the human-in-the-loop pattern in the decision-ownership findings above.
One of the providers in this group is viAct. In our response to Capterra for this report, we described how our AI monitoring platform continuously monitors site progress, safety, productivity, and compliance through a site’s existing CCTV, IoT devices, and Computer Vision, so that project teams can step in before small issues turn into costly delays.
That is the same live Agentic AI pattern described above: flagging and surfacing, not committing or deciding on its own.
Alongside that response, we shared our own operational figures with Capterra:
Deployment across 400+ construction sites,
50% reduction in the Total Recordable Incident Rate (TRIR)
65% fewer Lost Time Injuries (LTIs)
Over USD 2.5M in accident-related savings.
A recent Singapore construction case study we cited also reported a 10X improvement in safety score, 7,000+ working hours saved, and on-time project delivery. What they reflect is the same mechanism the wider report keeps surfacing across the 10 providers: earlier detection changing the outcome, not automation replacing the decision.
What AI is Expected to Automate by 2027?
Even for the times to come, the construction management software providers were cautious about automation, that is, what should and should not run without a human-in-the-loop. Schedule updates generated from field progress data and progress documentation from photo or reality capture analysis were the joint most selected workflows that is expected to go autonomous by the end of 2027, each chosen by 40% of the providers. Next safety hazard detection and reporting was followed by 30% of vendors. First-pass estimates and bid assembly landed at 20%, and quality take-off and invoice matching at 10% each.
5 out the 10 providers said no consequential step should be fully autonomous by 2027, regardless of workflow. None of the providers named a workflow that resembles a financial or contractual commitment, which lines up exactly where human control is strongest in the survey today.
The Verdict: What the Evidence Actually Shows?
Capterra's own conclusion is very direct: AI alone cannot guarantee a project stays on time and budget, but it does improve the chances. At all 10 providers, AI is getting involved with processes of estimating, filed visibility, scheduling, cost management, safety, and project management. Direct measurement of final project outcomes is still developing, and human oversight remains firmly in place for the decisions that carry real financial or contractual weight.
AI-powered construction management software does not guarantee that a project will finish on time and on budget, but it can materially improve the decisions that determine those outcomes. By identifying cost, schedule, and project risks earlier, and giving teams faster access to accurate information, AI helps contractors act before small issues become expensive problems. Sage Construction and Real Estate, in Capterra's 2026 research report
Capterra frames the practical standard for evaluating any construction AI platform as looking for the shortest path from an emerging project risk to an actionable signal to a measurable outcome. The graphic below lays that path out step-by-step.
Conclusion and Key Takeaways
Read together, this report points to one underlying pattern: AI in construction is not spreading evenly across the project lifecycle, and it is not replacing judgment anywhere it has reached. It is spreading fastest wherever information needs to move faster between people, and it is staying firmly under human control wherever a decision carries real financial or contractual weight.
Put simply: AI is proving itself first as a faster, earlier warning system, not as a decision-maker. Providers are not holding AI back out of caution alone, they are holding it back because the outcome measurement itself is still immature, with half the providers surveyed unable to tie AI activity to a project result at all. That is the honest state of the industry that Capterra's research surfaces, ahead of any single provider's story.
viAct's own reported outcomes fit inside that same pattern rather than standing apart from it. What we shared with Capterra, earlier detection through existing CCTV and IoT infrastructure leading to fewer incidents and faster intervention, is one working example of the exact mechanism the report identifies industry-wide. That alignment confirms that the earlier-detection approach is not just a claim we make about our own platform, it is the direction the wider industry data is also pointing toward.
For construction teams evaluating AI platforms in 2026, that leads to a concrete standard. The useful question is not whether a vendor has AI. It is how early that AI surfaces a signal, and how directly that signal reaches someone who can still act on it. That is the test this report's own evidence supports, and it is the same test worth applying to any AI construction platform, including ours.
Key Takeaways
AI adoption is concentrated in bid management, pre-construction, and field progress capture, and thins out sharply as workflows get closer to payments and cash flow.
Half of the providers surveyed still don’t measure project outcomes at all, only usage or productivity, which makes outcome measurement itself a competitive differentiator.
Human control stays strongest whenever a decision is financially irreversible, such as pay approval and bid commitments, and weakest wherever a decision is just an early warning, such as risk flagging.
Most Agentic AI still asks for permission before it acts. It flags and recommends rather than commits money or makes client-facing calls on its own. That is the real state of “autonomous” AI in construction right now.
The most useful test for any AI construction platform is the shortest path from an emerging risk to an actionable signal to a measurable outcome; not how autonomous the platform claims to be.
FAQs
1. How can a construction team tell if an AI platform is actually improving project outcomes, or just adding a dashboard?
The real test is whether a vendor can show outcome data, not just activity data. Usage statistics, like how many reports were generated or how many alerts were sent, only confirm the tool is being used. They do not show whether incidents dropped, schedules held, or costs came in on target. Platforms that report outcome-level metrics, such as measurable reductions in incident rates, like viAct, are answering a different and more useful question than platforms that only report usage.
2. How is Agentic AI different from the AI features construction platforms already have?
Older AI features typically responded to a single request, like generating a report, scoring a bid, or flagging one anomaly, and then waited for the next input. Agentic AI is designed to keep working across a sequence of steps on its own, continuously watching a site or a workflow and acting on what it finds without a person restarting the process each time. The shift is less about new capability and more about the AI staying active in the background rather than being called on demand.
3. What does viAct AI actually monitor on a construction site?
viAct uses computer vision and video analytics layered on a site's existing CCTV and IoT infrastructure to continuously monitor safety compliance, productivity patterns, and progress against plan, without requiring new hardware installations.
4. Does viAct AI replace the role of a safety officer or site supervisor?
No. viAct's AI monitoring system is built to flag risks and irregularities early so a safety officer or supervisor can act on them faster, not to make enforcement or personnel decisions on its own. The judgment calls stay with the site team.
5. How does viAct measure whether its AI is actually working on a site?
Through outcome tracking rather than usage tracking, primarily incident rate reduction, lost-time injury frequency, and cost savings tied to avoided accidents, measured against a site's baseline before deployment.
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