For decades, deciding where to open a store or which tenant to recruit for a vacant space was as much art as science — a blend of broker relationships, market experience, and static census data. That approach is increasingly giving way to a model built on a far larger and richer set of data points. A new generation of AI-powered location intelligence platforms is transforming how shopping center operators, leasing managers, and retailers understand shopper behavior, evaluate sites, and curate their tenant mixes. Over time, these tools may also influence how retail leases are drafted and negotiated, giving both landlords and tenants access to data-rich insights that could inform issues such as rent, co-tenancy, exclusive use rights, and other operating covenants.
Emerging AI Intelligence Tools
A growing number of location analytics platforms, such as Placer.ai, GrowthFactor, and Pass_by, are offering landlords and tenants greater visibility into retail activity, site performance, and market dynamics. These tools use location data, demographic information, machine learning models, and other market intelligence to analyze visitation patterns, trade areas, customer behavior, development activity, competitive positioning, and tenant-level performance. While Placer.ai emphasizes broad retail-property and market insights, GrowthFactor focuses on transparent retail site-selection due diligence, and Pass_by provides predictive foot traffic and tenant-finder tools designed to assess shopping center performance and identify prospective tenants for vacancies.
These tools are particularly relevant today as landlords and tenants navigate a retail leasing environment shaped by changing consumer behavior, hybrid work patterns, evolving trade areas, and rapidly expanding universe of data points now available to inform real estate decisions. For landlords, location analytics can support leasing strategy, tenant mix decisions, rent positioning, marketing of vacancies, and negotiations with prospective tenants by providing evidence of traffic patterns and customer demographics. For tenants, these platforms can improve site-selection diligence, help validate projected sales potential, identify competitive risks, and support negotiations over rent, concessions, exclusivity, and co-tenancy protections. In practice, these tools can make the leasing process more informed and efficient, while also shifting negotiations toward measurable performance data rather than assumptions about location quality.
Impact of AI Tools on Retail Lease Negotiations
Beyond site selection, these AI-powered due diligence tools are beginning to reshape the retail lease negotiation process. Some shopping center owners are using foot-traffic analytics and predictive modeling to support asking rents, renewal rents, and proposed rent increases by presenting tenants with visitor-count estimates and trade-area data. Placer.ai, for instance, explicitly markets its platform as a way to “support effective lease negotiations” and “command premium renewal rates with data-driven reporting” by proving a center’s performance through visit trends, local and national rankings, and trade area comparisons. On the tenant side, these same tools give retailers the ability to evaluate or challenge a landlord’s traffic claims before signing a letter of intent or lease — a development that may reduce the informational advantage landlords have traditionally held in some negotiations.
The rise of AI-driven tenant mix curation may also prompt a meaningful rethinking of exclusive use covenants, which are often among the most heavily negotiated provisions in retail leasing. Traditionally, these clauses have been drafted as rigid protections, granting a tenant the right to be the sole operator of, or to restrict others from engaging in, a specified retail use within a shopping center and potentially exposing the landlord to contractual remedies such as damages, rent abatement, injunctive relief, or, in some cases, termination rights.
As AI platforms generate more granular data on shopper behavior, cross-shopping patterns, and co-tenancy synergies, landlords and tenants may come to view exclusive use provisions less as static prohibitions and more as calibrated tools that can evolve with the center’s retail ecosystem. For example, a specialty grocer that historically insisted on an exclusive use clause prohibiting other food retailers may learn from foot traffic and cross-shopping data that a complementary food-and-beverage concept, such as a fast-casual restaurant or artisanal bakery, could increase visits to the grocer by attracting a broader base of food-oriented shoppers. In that scenario, the rigid exclusive use restriction is not protecting the grocer so much as insulating it from a tenant mix change that would benefit everyone, including the grocer itself.
This insight could lead to more nuanced, data-informed exclusive use provisions. Rather than blanket prohibitions on broad retail categories, future clauses might define exclusivity by reference to specific product lines or customer segments, incorporate thresholds tied to measurable impacts on the protected tenant’s foot traffic or sales, or include review mechanisms that allow the parties to revisit the scope of the restriction when analytics indicate that a proposed new tenant would enhance rather than cannibalize the protected tenant’s performance.
Landlords could benefit from this flexibility by preserving their ability to act on data-driven tenant recommendations while reducing the risk of breach-of-contract claims and related disputes. Tenants, meanwhile, would gain access to a data-driven framework for evaluating whether a new entrant genuinely threatens their business or, counterintuitively, strengthens it. Other retail lease covenants, including radius restrictions, operating covenants, and prohibited-use clauses, may undergo a similar evolution. As AI platforms make it possible to estimate, often with increasing speed and granularity, the potential revenue impact of these changes to a center’s tenant mix or operating patterns, these provisions can be calibrated with a precision that was previously impossible, moving the industry toward covenant structures that are anchored in empirical performance data rather than static assumptions about competitive harm.
As location analytics and AI-driven forecasting tools become more widely used in retail leasing, parties may increasingly need to consider whether leases should address reliance on algorithmic projections. “In future negotiations, a landlord that supports a rent proposal with an AI-generated sales or traffic forecast, or a tenant that relies on its own site-selection model, may seek contractual language addressing how the parties will treat projections and allocate the risk that actual performance materially diverges from projected outcomes. Although these issues are not yet a common feature of lease negotiations, they could become more prevalent as landlords and tenants increasingly rely on sophisticated, and potentially conflicting, data sets to support leasing decisions, making contractual frameworks for data sharing, algorithmic reliance, and forecast risk allocation more important over time.
Conclusion
Taken together, platforms like Placer.ai, GrowthFactor, and Pass_by reflect a broader shift toward a far richer, more granular data environment in the shopping center industry. Traditional leasing judgments based on broker experience, historical performance, and static demographic snapshots are increasingly being supplemented by a deeper, continuously refreshed picture of visitation patterns, trade areas, dwell times, and cross-shopping behavior. For shopping center operators, these tools may help reduce vacancy risk, support lease negotiations with additional data, and curate tenant mixes that better reflect local shopper behavior. For retailers, they can streamline aspects of site evaluation and provide another basis for assessing rent, co-tenancy, exclusivity, and other lease terms.
As these tools become more integrated into the leasing process, landlords and tenants should also consider the legal and practical risks associated with their use. Mobile-location data, demographic data, and related analytics may raise privacy, data licensing, consent, confidentiality, and consumer protection issues, including compliance with applicable state privacy laws. The retailers and operators that thoughtfully adopt these tools, while accounting for their limitations and legal constraints, may be better positioned to make smarter, faster, and more defensible leasing decisions in an increasingly competitive retail market.
For landlords, the practical takeaway is to build these analytics into leasing strategy—supporting rents and marketing vacancies with concrete performance evidence, treating exclusive use and other restrictive covenants as calibrated rather than blanket protections, and accounting for privacy and data-licensing obligations. Tenants, in turn, can use independent analytics to scrutinize a landlord’s performance claims, press for exclusivity and co-tenancy protections calibrated to specific products or measurable thresholds, and seek lease terms that govern how AI-generated projections and forecast risk are allocated.
This alert is for informational purposes only and does not constitute legal advice. The outcome of the pending litigation remains to be determined and is not guaranteed.
This information is provided for educational purposes only. It should not be construed or relied on as legal advice. It is not intended to create, and receipt of it does not constitute, an attorney-client relationship. If you have specific questions regarding a particular fact situation, we urge you to consult the authors of this publication or other legal counsel.
