The challenge of governing AI at the speed of investments
The boards of directors of companies in the construction and real estate sectors are approving investments in artificial intelligence at higher-than-average rates. However, the same progress has not been observed in policies, oversight mechanisms, and organizational capacities capable of ensuring that these investments generate consistent results. This difference represents the so-called AI proof gap.
Grant Thornton's 2026 AI Impact survey of business leaders explains why this gap exists and what organizations need to do to turn leadership-approved investments into measurable, sustainable performance backed by effective governance.
The base has not yet kept up with the ambition
Leaders in the construction and real estate sectors are committed to the adoption of artificial intelligence. Currently, 79% of boards of directors have already approved significant investments in AI, a rate higher than the average observed in other sectors. What is still lagging behind, however, is the foundation needed to turn these investments into concrete results: governance structures and workforce training.
The challenge is amplified by the complexity of the sector itself. There is no single model for the application of AI in an industry that has relevant differences between asset classes, ownership structures, and business models. What works for a construction company may not work for a real estate investment trust (REIT). As a result, many organizations are still seeking to understand where and how AI can drive value in their specific operations.
This is where the so-called AI proof gap arises. The companies that manage to overcome it will be those that are able to clearly define where AI should be applied in their business, build the necessary infrastructure to support these initiatives, and consistently measure the results generated.
Investment approved. Pending governance
The approval of investments seems to be happening before the creation of the necessary infrastructure to sustain them. Boards of directors are among the most active in approving AI investments in the survey, but perform below average when it comes to the adoption of governance policies. This suggests that discussions are still focused on the investment and exploration phase, addressing questions such as what tools are available, where to start, and what other companies in the industry are doing.
The consequences of this scenario can be observed in the maturity level of the sector. Half of respondents are still testing AI in specific use cases, while only 4% say they have the technology fully integrated into operations, compared to 14% considering all industries analyzed in the survey. This can represent a significant cost in terms of performance, as organizations with fully integrated AI are nearly four times more likely to report technology-driven revenue growth than those that are still in the testing phase (58% vs. 15%).
Only 13% of participants in the construction and real estate sectors say they have full confidence that they would be able to pass an independent AI governance audit within 90 days. In the general survey, this index reaches 22%. In addition, 72% say they are reasonably confident that controls exist, but recognize that the evidence is dispersed among different teams and tools.
In practice, this maturity gap reflects an industry that is implementing AI in environments that were not originally designed to support it. Many vendors are already incorporating AI capabilities into the products used by companies, often without an area or internal responsible person in charge of overseeing what these tools do or how they are performing. At the same time, data remains distributed across fragmented systems and across different asset classes, with no consistent definitions or standards.
Another worrying indicator is that only 9% of organizations have a tested AI-related incident response plan, less than half of the overall survey average of 20%. In an industry where relationships are close and reputation circulates quickly, the cost of a mistake without a structured containment plan can be significant. Each new investment made without strengthening governance increases exposure to risks and increases the possibility of capacity gaps emerging in the future.
Boards approve of AI. They still do not govern it.
Question: How did the board of directors participate in the oversight and development of the AI strategy?
AI Coherence is the New Business Performance Gap
The governance gap highlighted earlier may have an explanation in the survey data itself: the industry has not yet implemented AI at a scale where governance becomes the main limiting factor. Competitive movements are the main external pressure driving AI adoption, cited by 40% of respondents. However, the sector has responded to this pressure without having the necessary foundations to obtain the best results.
Insufficient training is pointed out as the main cause of the low performance of AI in the construction and real estate sectors, being mentioned by 41% of respondents, against 31% in the overall average of the survey. In comparison, barriers related to governance and compliance occupy the second position, but present lower rates than those observed in the total set of companies (39% against 46%).
Nearly half of industry leaders say they need more outside knowledge about AI's capabilities, eight points above the survey average. In addition, training and talent development appear as the second largest need identified, with an index 11 points higher than that observed in the general sample. In other words, the industry is still trying to understand what AI can do, before focusing on how to govern what it's already doing.
This issue is more complex for construction and real estate than for most industries. A construction company that uses AI to optimize schedules faces challenges different from those of a real estate fund that applies it to asset valuation. There is no easily replicable AI implementation across different asset classes. As long as organizations do not clearly define the problems that AI must solve in their business contexts, training programs and governance structures tend to generate results below expectations. The governance under construction in the sector will only be effective if it is underpinned by strong capabilities.
"AI is present in every board meeting these days. Everyone is asking questions. But there is no single application that works for the entire industry. What drives results in data centers isn't necessarily what works in a high-end corporate building. The companies that succeed will be those that move from asking what AI can do to asking where AI fits into their business," said Greg Ross, Construction and Real Estate Market Leader, Grant Thornton Advisors LLC.
Three actions to turn AI ambition into scalable success
The survey data clearly shows that many organizations are widening their exposure to artificial intelligence at a faster rate than they are strengthening their control mechanisms.
Each new implementation carried out without a proper governance structure increases the risks. Similarly, each capability gap that remains unaddressed reduces the time available for companies to keep up with organizations that are already moving faster. The gap between those that are managing to close the AI proof gap and those that are still in the planning phase is already noticeable and measurable.
- Govern what has already been implemented
Governance cannot wait for the next investment cycle. It needs to be applied to solutions that are already in use, with documented decision processes, clearly defined responsibilities, and a tested plan for responding to incidents or failures.
In an industry where vendors are incorporating AI capabilities into existing workflows and data remains fragmented across different asset classes and systems, the risks associated with utilizing solutions without proper governance are real and immediate.
Organizations that structure these mechanisms now will be better prepared to scale up the use of AI with confidence, ensuring scalability without proportionally increasing their exposure to risk. - Build capabilities ahead of the next wave of implementation
Generic training programs will not be effective in such a diverse and complex sector. Organizations need to invest in the development of specific skills for each function, integrated into the workflows and reality of each area of the business.
This approach should answer the main question for employees: what does AI mean for the way I do my job, in this asset class and in this specific role?
It is this level of targeting and precision that differentiates the simple adoption of technology from the effective generation of results. When professionals clearly understand how to apply AI in their day-to-day activities, organizations increase the chances of turning investments into performance, productivity, and business value. - Define where AI generates value before scaling what it doesn't
There is no one-size-fits-all formula for implementing AI. A use case that generates results in one asset class may not have any relevance in another.
Organizations that achieve better results move from asking what AI can do to clearly identifying what specific problems AI should solve in their business.
This definition is essential to direct investments, prioritize initiatives, and concentrate efforts where there is greater potential for return. It is this clarity that transforms pilot projects into concrete results, and concrete results into solid justifications for new investments.
Final considerations
These are the challenges that Grant Thornton helps companies in the construction and real estate sectors to face on a daily basis: creating governance structures for the AI solutions already embedded in operations, developing capacity building programs aligned to the reality of different asset classes, and supporting leaders in identifying where AI really generates value before large-scale expansion.
The insights presented in this report reflect both the company's practical experience and the work developed with organizations in the sector.
A successful AI journey may begin with board approval and interest in the technology, but real value will only be achieved when companies can demonstrate that their implementations are understood, properly governed, and capable of generating measurable returns for the business.
Methodology
Between February 23 and March 18, 2026, Grant Thornton surveyed 950 business leaders, including CFOs, CIOs/CITOs, COOs, vice presidents, area leaders, and directors reporting directly to senior management.
Of this total, 100 respondents belonged to the Construction and Real Estate sectors, forming the specific sample used for the analyses presented in this report.
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