For many real estate and construction firms, artificial intelligence (AI) is less about sweeping transformation and more about targeted gains, such as automating repetitive work and helping organizations make better-informed decisions. Leaders in the industry are focused on using AI in ways that are measurable and aligned with business needs.
According to the RSM Middle Market AI Survey 2026, the vast majority (89 percent) of the respondents from real estate and construction firms reported that AI is fully or partially integrated into their operations. Firm leaders don’t intend to slow down, as 80 percent plan to increase their spending on AI within the next fiscal year, with 62 percent planning to invest $1 million or more.
However, industry leaders are not just spending money on AI and hoping for the best. They expect results.
The conversations about AI have evolved from interest to practicality. Executives now have more pointed questions about return on investment (ROI) and the actual use cases for AI. They want to benchmark applications and implement AI that achieves operational wins.
As the construction and real estate industry has moved from curiosity about AI to closer scrutiny, leaders are assessing their AI choices and investigating how their peers are applying these tools in real-world operating environments.
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Organizations are at different stages in the AI journey. Common strategies across the industry include the automation of routine tasks, mitigation of risk, and unlocking of strategic data.
When asked what metrics their organization uses to assess AI’s return on investment, real estate and construction respondents’ most common answer was cost reduction (38 percent). But close behind were improved process efficiency (36 percent), enhanced or faster decision making (35 percent), and improved productivity (35 percent). Instead of expecting AI to reinvent their organizations, industry leaders are folding the technology into existing efficiency initiatives to enhance day-to-day processes.
Matt Riccio, National Real Estate Consulting Leader at RSM US, said firms are using AI to take repetitive, low-value tasks off employees’ plates, especially the kinds of work that are necessary for the business but difficult to staff. In real estate, that can include generating analysis or combining information to support forecasting and planning. In construction, that may mean streamlining process-heavy administrative work or improving the handling of project information.
Real estate and construction respondents were asked what outcomes in the next year would signal success for their AI initiatives. The top two indicators, each garnering 52 percent, were improved employee efficiency and improved decision making.
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“One CFO [Chief Financial Officer] told me about multiple projects they had in motion,” said Mac Carroll, Construction Industry Senior Analyst at RSM US. “They all address tedious tasks that people historically don't want to do, so the firm is rolling in AI to take that burden off people.”
That pragmatic mindset is strong among middle market companies, where leaders are often skeptical of large, expensive implementations that promise sweeping change. Carroll said real estate and construction firms are more likely to start with a defined pain point, test a practical solution, and expand from there if the results justify it.
Among those industry respondents whose organization initiated AI pilots in the prior 24 months and who reported moderate or limited success, a majority (51 percent) said that when their AI pilots failed to scale, the main reason was data quality issues. In addition, one-quarter (25 percent) said data quality issues were the greatest inhibitor to AI deployment within their organization. Although this sample size was small (43 respondents), the results indicate that data governance and confidence in core systems remain essential for real estate and construction firms.
Without accurate data, deploying AI comes with risk. Organizations need to identify how data is managed and make sure there are guardrails in place before pushing broader AI adoption.
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For firms that are just beginning their AI journey, Riccio advised leaders to first build confidence in the technology stack. From there, leaders should establish a roadmap that reflects the realities of the business and industry.
Carroll advises deploying enterprise-approved AI tools, creating clear internal policies, and empowering a small group of engaged users to explore practical applications that fit the organization’s governance standards.
Carroll and Riccio agree that the way forward is clear: Real estate and construction firms will likely operate in an environment with more automation, more accessible data, and more AI-assisted decision making. Over time, AI may become less of a stand-alone initiative and more of a standard part of how companies compete.
In fact, the vast majority (87 percent) of real estate and construction respondents completely or somewhat agreed that their workforce size and composition will look fundamentally different in the next two to three years because of AI. Furthermore, an overwhelming 92 percent completely or somewhat agreed that within that same time frame, humans and AI systems will work together as integrated teams.
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Industry leaders anticipate a future where AI is prominent. Almost all (98 percent) real estate and construction respondents said they have plans for their firm’s next phase of AI development.
Riccio said that in a market still sorting out what works, successful AI adoption will belong to firms that combine experimentation with governance, ambition with realism, and innovation with a clear understanding of business value.
“The best approach is disciplined curiosity,” Carroll added. “Organizations should do their research, talk with trusted advisors, and learn from peers that are testing similar tools.”
In real estate and construction, AI’s biggest impact may not come from immediately replacing how work gets done, but from steadily making processes smarter, faster, and more informed.
