A Big Retail Tenant Kept Saying No. Then AI Closed the Deal.

Why It Matters

AI‑powered leasing accelerates deal velocity and improves tenant‑fit analysis, giving landlords a competitive edge in a tight retail market. The approach signals a broader shift toward data‑driven decision‑making in commercial real estate.

Key Takeaways

• NewMark Merrill leverages LLMs to rank and pitch retail tenants
• AI turned a repeat‑no fashion tenant into a signed lease
• AI recommended a higher‑impact department store over an off‑price retailer
• Human teams still verify AI output to avoid rabbit‑hole errors
• AI adoption is reshaping leasing strategy across the CRE industry

Pulse Analysis

The commercial‑real‑estate sector is witnessing a rapid infusion of artificial‑intelligence tools, especially large‑language models, that can parse tenant rosters, market data, and foot‑traffic patterns in seconds. At the ICSC conference, landlords highlighted how AI platforms generate ranked tenant lists and draft persuasive decks, cutting the time needed for traditional market research. This shift mirrors broader proptech trends where predictive analytics and machine learning are becoming standard for site selection and lease negotiations.

NewMark Merrill’s recent successes illustrate the practical upside of AI‑assisted leasing. By feeding the model details of a Southern California shopping center, the system identified a fashion tenant that had previously declined and produced a customized pitch that convinced the tenant to sign. A similar workflow evaluated a Chicago property’s vacancy risk and suggested a discount department chain that would boost overall traffic more than a national off‑price retailer. In both cases, the AI supplied data‑rich arguments, while the leasing team refined the presentation, demonstrating a hybrid human‑machine workflow.

Industry observers see these early wins as a harbinger of wider adoption, but they also warn of pitfalls. AI can lead analysts down “rabbit holes” of endless optimization, and the final 30 percent of deal closure often hinges on nuanced judgment that machines lack. Consequently, firms are investing in governance frameworks that require human verification of AI‑generated insights. As AI models become more sophisticated and integrated with real‑time market feeds, the balance between speed, accuracy, and oversight will define which landlords capture the most value in an increasingly competitive retail landscape.