Top Tech Tools: AI's expanding role in successful store expansion
The emergence of artificial intelligence has been one of the biggest stories of the 2020s.
While modern machine learning began to take shape in the early 2000s, recent investments and developments have propelled AI to new heights. The technology is now used by consumers to do everything ranging from making shopping lists to finding more precise answers to difficult questions.
Retailers, of course, have taken major steps toward implementing AI in one aspect of their business or another. A recent survey from AI technology provider Nvidia found that 91% of retailers and CPG companies are either actively using or assessing AI. Companies noted that AI is helping decrease annual costs (95%) and increase annual revenue (89%).
The commercial real estate world is also beginning to utilize the emerging technology in a number of ways – with only more integration expected in the coming years as AI continues to evolve and become more precise.
“What AI means today and into the future is being able to make better, smarter decisions quicker,” said Stephen Polanski, global business director at retail intelligence firm Kalibrate, speaking at a panel at the recent ICSC show in New York City. “Adoption will be key, and we still need human interaction to be able to maximize those decision making processes.”
For Chain Store Age’s latest Top Tech Tools feature, we spoke to four companies using AI to make life easier for commercial real estate teams in a challenging macroeconomic environment.
GreenLite
Due to economic headwinds including labor constraints and the cost of materials, new commercial real estate construction is at a well-documented standstill. Factor in complying with local building code regulations, and retail brands can be stuck waiting extended periods for shovels to make it into the ground for new builds.
That’s where GreenLite comes in to help. Launched in 2022 with AI at the forefront, the company leverages the technology to streamline the permitting process for new construction, making it easier for developers to meet local regulations and to have permits issued as quickly and as efficiently as possible.
Founded by James Gallagher and Ben Allen, GreenLite seeks to streamline what is often thought of as a complicated, laborious process using archaic software for retailers, as well as fitness chains, banks and other categories looking to expand their physical footprints.
“Building code review is a necessary compliance process,” said Gallagher, who serves as the CEO of the company. “What has happened over the last couple of decades, and has been exacerbated post pandemic, is that there is a major talent gap and shortage of people working in the public sector for cities and development agencies. This has impacted the speed to which developers meet demand.”
GreenLite serves as a private regulatory agency and allows retailers and developers to directly apply for permits through the platform. A retailer’s project team will start the process with GreenLite with an intake procedure that includes the architectural specs of the new build. GreenLite’s AI then “ingests and categorizes” the building plans, extracts the key data and generates city-specific checklists. The up-to-code building designs are then analyzed and approved by the company’s in-house experts.
“The difference between us and the city process is we do that in about five days, versus 45, 60 or 90 days,” said Gallagher. “Once the necessary corrections are made to the building plans, we will approve the permit for issuance. GreenLite sells not only speed, but predictability and transparency.”
With the permitting process for new builds sped up, retailers can get a faster start on the construction and related hiring processes, as well as the in-store labor hiring for when the location opens. GreenLite also works with chains, including quick-serve restaurants, to remodel spaces and update store fleets to modern standards.
Looking ahead, GreenLite expects its use of AI to only expand in the future. In particular, Gallagher noted that the company’s tools can be integrated directly into a retailer’s design process so that they are up to code from the start, streamlining the approval process even further.
Kolena
Just as local regulations can tie up a retailer from expanding, synthesizing and analyzing lease agreements can be a burdensome process for commercial real estate operators and managers when acquiring a new retail property.
With the help of Kolena’s AI-powered platform, companies can review, validate, generate and act on claims, leases, financial packages and more.
Founded in 2021 by three AI engineers, Kolena set out to “100% automate” document workflow. The company published a list of use cases on where AI can best be adopted, with the lease abstraction process, often outsourced to third-party companies, consistently being mentioned as a sticking point for commercial real estate players.
“There is cost and time spent on the lease abstraction process, and real estate firms lose opportunities by taking weeks on that process,” said Mohamed Elgendy, co-founder and CEO of Kolena. “Even with a standard AI assistant, firms would still spend time uploading the lease documents and asking it questions.”
With Kolena, hundreds of lease agreements from a property can be uploaded onto the platform, and with the help of AI, the data, including rental rates, renewal terms, maintenance responsibilities and more, is synthesized into an abstract, greatly speeding up the investment cycle.
Kolena’s proprietary AI model works alongside existing tools, benchmarking prompts and tasks that specific AI tools excel at, such as understanding legal terms, mathematics or converting text into data. Notably, Kolena assigns what it calls an AI architect to each customer, in addition to a customer success professional, meaning that users have a hands-on, AI expert to support them when navigating the automation process.
Elgendy says that Kolena’s accuracy, transparency and ability to make citations of key data points are the features that set it apart from other workflow documentation platforms.
“We make sure that whatever the user’s workflow looks like, including what documents they are uploading, that the process is 100% automated,” he said. “Users don’t need to jump between tools or tasks. They also don’t need to know anything about the AI.”
Buxton
Founded in 1994, site selection firm Buxton has long sought to help retailers better understand their customers, with the goal of providing precise data sets that guide expansion. But times have changed since the mid-90s, and now, the company puts AI at the center of its offerings, giving clients an advanced location intelligence outlook.
“Buxton has been using machine learning for 20 years, and obviously we have evolved that as the methodologies have changed and improved, but it has drastically accelerated in this modern era of the large language model,” said Philip Crow, senior VP of product strategy at Buxton. “Today, we have had a very intense focus on embedding AI in our internal processes as a company to make us more efficient. Whether it’s how we build software or how we train sales people, we have embedded AI into just about everything we do.”
AI is directly integrated into Buxton’s platforms both in the form of support tools like chat bots, as well as in strengthened analytical capabilities that give the user an advanced view of a specific data set. At the core of its platform, Buxton uses AI to compile first- and third-party data from thousands of in-store and online transactions, which are then interpreted into a massive consumer profile that gives insights into key demographic information.
From there, retailers can learn more about their target customers, and better optimize their store footprints as a result. As an example, on Buxton’s Scout platform, a user might try and run 10 different reports for one site, including the demographics and psychographics of a trade area, and then compare the location against their existing network of stores.
With AI integrated into the platform, the most valuable insights from each report can be highlighted and synthesized into a comprehensive report that otherwise would have been a labor-intensive process.
“AI is only as good as what you feed it,” said Crow. “It requires input and data, and we sit at the intersection of those two things – and did even before AI existed. Our data sits on top of AI layers that add additional analytical capabilities that maybe in the past would have required weeks of an analyst’s time to go do on behalf of a customer. Now, the customer can get answers instantly.”
Crow added that Buxton’s AI advancements were largely focused on improving internal processes until last summer, when the company shifted towards focusing on how the technology can best be offered to its customers. This came as AI models continued to become more advanced in just a short period of time.
“The technology has shifted from ‘help me understand this’ to ‘go do this for me,’” said Crow. “We’ve made a lot of advancements, and have really tried to focus on increasing the efficiency for our users.”
Kalibrate
Kalibrate is another location intelligence platform that leverages AI to give its users – whether they be in the retail, hospitality, banking or fuel industries – the most accurate customer dataset possible.
Using survey data from 2024, Kalibrate developed its proprietary AI model to offer insights and analysis faster results, speeding up development timelines and decision making.
“We heard overwhelmingly from executives that AI isn’t just expected from their team members, it’s going to be expected from their vendors as well,” said Dustin Stancil, senior VP of location intelligence at Kalibrate. “We have been leveraging AI tools for quite a while now. But while traditional geospatial analytics tools are effective, what our end users wanted was to reduce the number of clicks it takes to find an answer. We wanted to slowly put AI into our intelligence tools to make them easy to grasp, and then take feedback from there and build on top of that.”
To meet customer needs, Kalibrate introduced the K.AI chatbot companion to assist with the data search process, as well as AI summaries of a trade are based on specific attributes, such as demographics like income, age or education. Using the chatbot, users can compare locations and ask which would be the best location for a new store or restaurant.
Kalibrate also uses AI to summarize sales forecast reports, giving users a better sense of the financial prospects for a future location. By utilizing advanced technology, Kalibrate clients can get the best insights available when making real estate decisions, whether it be expansion or rightsizing a portfolio, either on a DMA, state-wide or nationwide level.
“Through forecasting capabilities that we build for customers, we can run projections on potential sites to see what will be a good fit for the brand,” said Stancil. “Guests can ask us ‘how many locations could we fit in Palm Beach County, or the greater Los Angeles area?’ We help come up with those answers and target opportunities.”
In the future, Kalibrate aims to continue using new developments in AI to hone in even further on location intelligence.
“AI has moved at such a rapid pace,” said Stancil. “We want to be able to shift more towards the agentic side, which includes asking specific questions like ‘can you suggest three store locations for me in Dallas, Texas, or in Wilson, N.C.,’ and have it be able to spit out an answer for you that is logical and that takes into account both internal data and third-party data.”