AI, retail, and the future of real estate

Coming up for air from ICSC Las Vegas 2026 - what a terrific show! It was great to spend a few days in warm weather, putting faces with names and getting deals across the finish line.

My overall take of the market sentiment: leasing activity is high and exciting throughout major cities. Asian influence is continuing its expansion across shopping destinations and has brought a new sense of innovation and experiential elements to retail.

This trip was very fruitful for Dan AI! It was awesome meeting with organizations across the country in person, debuting our forthcoming enterprise solutions, and spending extra time hosting some of the Danimals at XS.

At a time of fast-paced AI adoption and ever-increasing screen time, attending events like ICSC proves how important the human element will always be in our business!

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Retail real estate has always rewarded people who could read a location. The difference today is that artificial intelligence can read thousands of locations at once - and back every read with data. From site selection to lease structuring, AI is turning retail real estate from an art built on experience into a discipline built on evidence.

Site selection becomes a prediction problem

The core question in retail real estate - will this location perform? - is exactly the kind of problem machine learning is built for. Models trained on foot traffic, demographics, competition, and co-tenancy can estimate how a given storefront is likely to perform for a specific concept, then rank candidate sites accordingly.

Instead of touring a shortlist and guessing, teams start from a data-ranked map and tour with intent.

Smarter leasing and tenant mix

AI is also reshaping what happens after the site is chosen. Landlords use models to understand which tenant categories would lift a center's overall visitation and dwell time, and to price space in line with the demand a location actually commands.

The result is a tenant mix designed to compound - each store drawing traffic that benefits its neighbors - rather than assembled ad hoc.

What it means for landlords and tenants

For landlords, AI means sharper pricing and a defensible story for every space. For tenants, it means walking into site decisions and lease negotiations with evidence instead of instinct. The winners in retail real estate will increasingly be the ones who pair local expertise with the data to prove it.

It was a blast hosting a Dan AI happy hour at Bella Union last night with the Young Retail Brokers group and meeting users in person!

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Retail is quietly becoming one of the most aggressive adopters of artificial intelligence. The headlines focus on chatbots, but the real transformation is happening across the back office and the sales floor - in how retailers forecast demand, personalize marketing, run stores, and decide where to open next.

Demand forecasting and inventory

The oldest problem in retail is having the right product in the right place at the right time. AI-driven demand forecasting uses sales history, seasonality, weather, and local trends to predict what each location will sell, cutting both stockouts and overstock.

Better forecasts mean healthier margins and less waste - a direct hit to the bottom line.

Personalization and marketing

Retailers use AI to tailor recommendations, promotions, and messaging to individual shoppers based on behavior. Done well, personalization lifts conversion and loyalty; the same models help time campaigns and target the customers most likely to respond, so marketing budgets work harder.

Store operations and location decisions

On the operations side, AI supports staffing, layout, and shrink reduction. And when it comes to growth, retailers increasingly lean on location intelligence - including foot traffic data - to decide where to open, relocate, or close.

A new store is a large, long-term bet, and AI helps de-risk it with evidence about how a trade area actually behaves.

The physical-store payoff

The through-line is that AI is not replacing physical retail - it is making it smarter. The retailers pulling ahead treat their stores as data-rich assets, using AI to squeeze more performance out of every location and every decision.

For real estate brokers and leasing professionals, prospecting has always been a grind: hours of research to find the right owners, tenants, and opportunities before a single conversation happens. Artificial intelligence is compressing that grind - and quietly changing what it means to be a productive broker.

Prospecting used to be manual

Traditionally, building a pipeline meant stitching together public records, listing data, news, and a lot of cold outreach. The brokers who won were often the ones who simply worked the phones longest. That advantage is eroding as software takes over the repetitive research.

What AI prospecting tools actually do

• Surface opportunities: They scan large volumes of property, ownership, and market data to flag leads that fit a broker's criteria.
• Summarize fast: They condense a property or a market into a quick, readable brief, so a broker walks into a call already informed.
• Draft outreach: They generate first-pass emails and call scripts tailored to the prospect, turning hours of writing into minutes of editing.
• Qualify leads: They score and prioritize prospects so brokers spend time on the conversations most likely to convert.

Faster comps and market reads

Beyond prospecting, AI speeds up the analysis that used to bottleneck deals - pulling comparable properties, summarizing trends, and answering "what is this block worth?" in seconds rather than an afternoon. Brokers can respond to clients faster and cover more ground.

The broker's new edge

None of this removes the broker; relationships and judgment still close deals. But it shifts the edge from who can grind the longest to who can move fastest with the best information. The professionals who adopt these tools early get more at-bats - and more time for the human work that actually wins business.

Thank you REBNY (Real Estate Board of New York) and Next Generation for partnering with Dan AI in hosting an awesome breakfast event this morning! It was great having a full house of the city's top young retail leasing professionals across the industry and fielding thoughtful questions.

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Dan AI was selected for the latest Foundations cohort from the Center for Real Estate Technology & Innovation (CRETI) - an AI group organized around growing net operating income through sharper underwriting, leasing, and transaction workflows.

The cohort brought together four early-stage companies, each focused on the day-to-day work behind real estate deals rather than adding another layer of reporting.

Within the group, Dan AI is focused on helping retail leasing professionals source and close deals, pairing proprietary data with workflow tools for tenant matchmaking and modern brokerage.

Founder and CEO Josh Siegelman called CRETI "a key partner as we scale," pointing to the network, market positioning, and operator insight the program brings.

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Image: CRETI

Predicting technology is a good way to look foolish in hindsight. But the direction of travel in commercial real estate is clear enough: over the next five years, artificial intelligence will move from a novelty in a few firms to the connective tissue of how the industry operates. Here is where it is heading.

Underwriting becomes continuous

Today a property is underwritten at a moment in time and revisited periodically. As models ingest real-time demand and market signals, underwriting will become continuous - assets constantly re-scored as conditions change. The annual appraisal will start to feel as dated as a paper map.

Prospecting and brokerage compress

The research that fills a broker's day will keep shrinking toward instant. Finding opportunities, drafting outreach, and running comps will be near-automatic, freeing professionals to focus on relationships and negotiation. Expect smaller teams doing more, and a premium on the human skills software cannot replicate.

Buildings that sense demand

Location intelligence and foot traffic data will become standard inputs, not specialist add-ons. Owners will treat buildings as data-rich assets, tuning tenant mix, pricing, and marketing to the demand a location actually generates - and measuring the results in near real time.

The human role that remains

AI will not remove judgment from commercial real estate; it will raise the floor on what a data-backed decision looks like. The firms that thrive will pair machine speed with human relationships and instinct. The ones that do not adopt will find themselves competing on evidence they simply do not have.

The next five years will not replace the people in commercial real estate - but they will reward the ones who move first.