Cushman & Wakefield’s report, AI Impact on Commercial Real Estate: The Next 10 Years, presents a significant opportunity: in its baseline scenario, AI could generate approximately 330 million square feet of additional U.S. commercial real estate demand through 2035, compared with its pre-AI forecast. Industrial accounts for most of that increase, while retail contributes approximately 6.7 million square feet.
For Vynes CEO Richard Kim, the retail outlook raises a question that goes beyond how much space the industry will need: How can we create retail environments that offer quality and fairer prices to a broader range of customers?
That question connects the report’s wider economic findings to the everyday work of landlords, retail partners, and tenant delivery teams.
AI could expand demand through productivity
The report’s baseline links AI productivity gains to economic expansion and additional demand for space. It also considers alternative scenarios, including weaker adoption and greater labor displacement. The projected uplift is conditional, rather than a guaranteed outcome.
Our takeaway is that the practical value of AI needs to be demonstrated through the work it improves. For a retail landlord, that might mean helping a team review documents more efficiently, identify issues earlier, or coordinate a growing portfolio with the resources available.
These improvements matter because they can influence how readily a property accommodates new tenants and changing retail concepts. A long-term demand forecast becomes more useful when it prompts a near-term operational question: where could better processes help us serve that demand?
A polarized retail market puts the middle under pressure
Cushman & Wakefield identifies a K-shaped consumer dynamic, with stronger performance at the high and low ends of retail and pressure on the middle. It also highlights the growing role of food and beverage, health and wellness, and entertainment.
For Richard, this finding invites the industry to think more carefully about accessibility. A compelling retail experience should have the opportunity to reach customers across a wider range of budgets.
Our interpretation is that landlords have a role in creating the conditions for that opportunity. Location, leasing strategy, and tenant mix all matter. So does the cost and complexity of getting a retail concept into an operating space.
More efficient tenant delivery will not resolve every affordability challenge. It could, however, reduce avoidable friction for retail partners and leave more room to invest in the experience customers ultimately receive.
Experience-led retail brings new demands for space
Cushman & Wakefield’s companion retail analysis explores how stores could become more adaptive environments, supporting discovery, consultation, personalization, fulfillment, and returns. These possible changes have implications for layouts, fit-outs, and back-of-house requirements.
For Vynes, the implication is practical: landlords need processes that can accommodate different uses while maintaining consistent property standards.
Consider a unit being adapted for a wellness concept or a food and beverage operator. The delivery team needs to understand what the proposed use requires, how it fits the property’s requirements, and which issues need clarification before work moves forward.
Our view is that the opportunity to create more engaging destinations depends partly on the industry’s ability to manage those details. Innovative concepts still need a workable path through design review, approvals, and coordination.
Adaptability depends on how teams operate
A central finding of the broader report is that AI’s effects will vary across property types, locations, and assets. Productivity improvements can also arrive before revenue growth and hiring, delaying their translation into real estate demand.
Our takeaway is that owners and operators should build the ability to respond as conditions change. That includes how information moves between leasing, tenant coordination, design teams, and project managers.
For example, a review process becomes harder to scale when requirements are scattered across documents and decisions depend heavily on individual team members’ knowledge. A more structured approach can make it easier to understand what has been reviewed, what remains unresolved, and where human judgment is needed.
This is where we see a practical role for AI in commercial real estate: helping teams work through information consistently and making relevant findings easier to assess.
What this means for the Vynes roadmap
Richard’s question about quality and fairer prices is informing how we think about the Vynes roadmap. We are exploring how more efficient landlord processes could benefit retail partners and create room for innovation.
ADR Pro addresses one part of that workflow through AI-assisted design review. It supports a structured first pass, with source citations and review comments that teams can assess and refine.
The broader ambition is to improve the journey from a signed lease to an open store. We see potential in reducing repetitive work, clarifying requirements earlier, and giving teams more capacity to focus on decisions and tenant relationships.
Whether those efficiencies eventually translate into lower costs or fairer prices will depend on wider commercial decisions. The opportunity worth pursuing is a retail ecosystem where bringing a quality concept to market becomes more achievable.
Our key takeaway: AI’s contribution to retail will be shaped by how the industry applies it. For Vynes, that starts with improving the processes that help landlords and retail partners turn plans into places customers can use and enjoy.
