TL;DR: Most large retailers have used AI in some capacity when making lease decisions. Adoption is strongest among those that use integrated third-party solutions for lease management and accounting, and those with more consistent lease terms. While AI adoption correlated with greater satisfaction with lease tools and decisions, it also correlated with a higher rate of lease-related problems.
- Only 11% of large retailers we surveyed said they never use AI in lease-related decisions.
- 60% of retailers that use AI in most or all lease-related decisions have an integrated third-party solution for lease management and accounting.
- 47% of retailers that use AI for most or all lease-related decisions reported being “very satisfied” with their lease decisions in the past 12 months, compared to just 18% of all other respondents.
- Retailers that use AI were nearly 1.5x more likely to report downstream issues stemming from lease decisions than those who never use AI.
In the spring of 2026, Tango commissioned a survey of retail real estate leaders overseeing 200+ locations. We asked them how often their organization used AI in lease-related decisions and processes—and the vast majority were using it to some degree.
Nearly half (45%) said they use AI in some lease-related decisions. More than one in four (28%) use it in most or all lease-related decisions. And just 11% said they never use it.
Years ago, when Tango announced we were developing AI capabilities for use cases like lease abstraction and document intake, it was hard for large organizations to imagine letting an LLM scan and analyze their lease documents—even a purpose-built one like ours.
Now that AI has had a few years to mature, however, most retailers are clearly finding opportunities to leverage its analytical and time-saving capabilities. But is AI adoption leading to better portfolio outcomes? Our survey explored the challenges of modern CRE portfolio management. Here’s what we found by digging deeper into responses.
AI adoption is strongest among retailers with third-party lease systems
Survey finding: More than half (60%) of respondents who said they use AI in most or all of their lease decisions were using a third-party solution for lease management and accounting. Adoption was strongest among those using integrated third-party solutions.
Respondents who relied on spreadsheets were most likely to say they “rarely” or “never” use AI in lease-related decisions (40% compared to 25% who didn’t rely on spreadsheets).
Third-party lease software solutions have been racing to incorporate AI capabilities—9 out of 10 of Gartner’s top lease software solutions currently offer AI features that could be used to support lease decisions. While some third-party solutions have simply bolted-on AI capabilities, others, like Tango, have built AI into the foundation, training their LLMs on lease documents and the lease software itself.
Retailers that use these solutions have the best access to AI capabilities they can trust in lease-related decisions. Spreadsheet users, on the other hand, are least equipped to leverage AI—the best they can do is plug their spreadsheets into an enterprise version of a generic LLM, which won’t have enough context to accurately interpret their data and avoid AI hallucinations. Custom software users can either do the same, or build and maintain their own LLMs in addition to the software.
In other words, everyone has access to using AI in lease-related decisions, but some segments clearly have more reason to rely on it.
Greater AI adoption correlated with higher satisfaction with lease decisions and tools
Survey finding: Respondents who used AI for all or most of their lease decisions were far more likely to report being “very satisfied” with their lease tools and processes (47% compared to 21% of all other respondents). This group was also most likely to report being “very satisfied” with their lease decisions from the past 12 months (47% compared to 18% of all other respondents).
Retailer satisfaction with lease decisions varied based on AI adoption level. Organizations with greater adoption reported higher satisfaction with their decisions:
The same proved true for respondents’ satisfaction with their lease tools and processes, with greater AI usage corresponding to higher satisfaction with tools and processes.
This doesn’t necessarily mean that AI usage corresponded with better outcomes. In fact, the opposite may have been true. But clearly, AI usage at least shaped how respondents felt about those outcomes and the tools that contributed to them.
Over-reliance on AI may increase lease-related problems
Survey finding: Every retailer that used AI in most or all lease-related decisions reported missing or nearly missing lease deadlines, and 94% of this group had at least one instance of a lease decision creating a downstream problem in the past 12 months, compared to just 66% of those who never used AI in lease decisions.
LLMs are designed to provide clear rationale for a choice, whether it’s supporting a decision you’ve already made or helping you choose between options—even if that rationale isn’t rooted in reality. And that’s the risk of using AI that’s bolted on your lease system, rather than built in. A generic LLM (or one that isn’t completely integrated into your software) doesn’t have context about how documents and data relate to each other. It fills in the gaps by predicting the most likely relationships, and lies just as confidently as it tells the truth.
So, depending on how AI is incorporated into a retailer’s lease system and how they use it, a retailer that relies heavily on AI may be more likely to overlook conflicts because their AI didn’t understand which projects would be impacted by a decision. Retailers may also assume that the AI has analyzed options it didn’t have the ability to see—like lease terms that recently activated due to changes in conditions (such as poor sales performance, declining traffic, or a co-tenant’s departure).
This is why Tango has been so selective about where and how we incorporate AI into our portfolio management solutions, and why we’ve spent the last couple of years fine-tuning outputs to remove interpretive gaps. AI has immense potential to improve access to the information that’s buried in your lease portfolio and analyze possibilities, but it needs three things to be effective:
- A thorough understanding of how your lease system works
- Visibility into all of the data and documents that inform lease decisions
- Human oversight
Otherwise, as with the retailers we surveyed, heavy reliance on AI may lead to worse real estate outcomes.
Inconsistent lease terms correlated with lower AI adoption
Survey finding: Just 9% of respondents who had “somewhat inconsistent” or “extremely inconsistent” lease terms said they use AI for most or all lease-related decisions, compared to 46% of respondents with “extremely consistent” lease terms.
Inconsistent lease terms introduce additional variables into real estate decisions. The options available at one location may be completely different from another. And even exercising the same option at two locations could have dramatically different costs and implications depending on the terms of each lease. While AI can certainly help retailers navigate greater complexity, the problem is that inconsistency also complicates visibility.
In lease portfolios, a retailer often needs to piece together data from multiple sources and analyze it within the context of a lease before they can see which conditional lease options (such as co-tenancy clauses, performance-based exit options, or expansion and contraction clauses) are available. If the options and conditions that trigger them vary significantly from one lease to another, the process may require more manual effort to see the full range of possibilities and understand the trade-offs.
Consistent lease terms lower the barrier to entry for incorporating AI by reducing the number of gaps to navigate in the decision-making process. Still, more advanced, specialized LLMs like Tango’s can help simplify complex decisions too. Retailers can use our AI assistant, Ask Tango, to find leases that meet specific criteria and have particular terms. You might ask questions like, “Which leases in Austin, Texas have break options available?” Even if your leases use different terms and language, Tango’s lease-trained LLM understands how lease terminology translates into actual options, and which leases contain the types of opportunities you’re looking for.
Summary: Retailers need to be intentional about where and how they implement AI in lease decisions
AI adoption obviously isn’t the only factor that influences a retailer’s satisfaction with their tools, processes, and decisions. Nor is it the only variable that correlated with a higher risk of missed lease deadlines or downstream issues. But there are some clear differences in both outcomes and perceptions when we compare retailers that heavily rely on AI in lease decisions and those that have yet to use it at all.
AI may play a key role in helping retailers make decisions faster, but as it stands, the way retailers are using it in lease-related decisions may be doing more harm than good.
In our full report, The 2026 Retail Real Estate Portfolio Execution Index, we examine more of the factors that impact real estate outcomes and explore broader challenges with modern retail CRE portfolio management—particularly, how retailers are navigating the increased pace and volume of store changes, and the greater complexity of today’s retail leases.
