Brick-and-mortar is back—with AI as a key sidekick

In-store retail is enjoying a renaissance—and with it, familiar challenges. Judicious use of AI in retail can help businesses thrive in this new era.

Remember the retail apocalypse? In 2017, nearly 8,000 stores shut down, setting a grim record that was only surpassed in 2020 during the height of the COVID-19 pandemic when almost 10,000 stores closed their doors. Rising costs, leveraged buyouts, the lingering effects of the Great Recession, and increased inflation all contributed to the near-death of brick-and-mortar retail.

Well, against all odds, brick-and-mortar retail is experiencing a powerful revival. In a promising sign of recovery, only 3,818 stores closed in 2023, and 4,548 in 2024—about nearly half the numbers seen in 2017 and 2020.

This resurgence is more than just a trend; it's a testament to the unique advantages of physical retail and the rise of omnichannel digital innovations, such as curbside pickup and pick-up-in-store services. Retailers are using technology to enhance in-store experiences, creating immersive and personalized shopping environments that digital platforms can't replicate.

However, as retailers enjoy this revival, they encounter everyday challenges. Timely store launches, maintaining store asset uptime, and empowering employees to maximize productivity are all priorities. To overcome these challenges, many retailers are turning to technology partners for solutions.

Here we’ll explore how artificial intelligence is emerging as a tool to help transform the way brick-and-mortar retail operates, from the back office to the front. We’ll show how AI makes it possible to integrate and optimize data from disparate systems, enabling core stakeholders—store associates, customers, suppliers and AI agents—to operate on a single platform to ensure consistent, connected experiences.

New stores, new obstacles

As retailers expand their footprint, they encounter a host of new challenges, including an increase in truck rolls (that is, on-site visits), inefficient reactive maintenance, uncoordinated repair scheduling, and installation delays. Addressing these complexities requires innovative solutions and strategic partnerships to ensure optimal operations and sustained growth.

Let's explore three key areas of store operations to see how retailers are using AI in store operations to integrate and optimize their data for improved operational efficiency.

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1.    Opening new stores

On average, big-box retailers require six months to open a new store. This process involves managing fragmented systems, coordinating schedules with multiple contractors and stakeholders, and dealing with scheduling and installation delays. These hurdles, compounded by insufficient technology integration, often lead to missed target opening dates.

Now, retailers are turning to AI to not only speed up the store opening process but also to gain deeper insights into demographics, competition, complementary businesses, operational considerations, and buying behaviors at potential new store locations. This helps them confidently select the best sites for new stores.

For example, areas with strong economic growth, low unemployment, and high purchasing power are prime indicators of a successful site location. AI-powered tools provide retailers with current data and analyze historical trends to forecast the future state of an area. This enables retailers to predict where they will get the best return on investment before they even begin the process of launching a new store. This proactive approach positions stores to be more successful, with improved accuracy in site selection leading to higher traffic and better overall store performance.

Once a store site is selected, AI can also automate and enhance CAD floor design, optimizing staging of the IT assets, streamlining app configuration, and facilitating comprehensive asset mapping, while also ensuring efficient store network setups, pilot execution management, store readiness testing, staff training, launch campaigns, and go-live support. Ultimately, this empowers retailers to scale faster, meet deadlines, and successfully launch new stores more efficiently.

2.    Managing store asset IT health

As the line between digital and physical shopping blurs, retailers and their associates must manage an increasing number of systems, tools, and devices. The complexity of operating these assets has grown, making it more challenging to maintain their health and optimal performance. And the cost of asset downtime can be devastating.

This highlights the importance of upgrading legacy technology with new, self-healing capabilities. Enterprise IoT observability platform (E-IoT) offer real-time monitoring, predictive auto-resolution, intuitive self-help tools, and preventive health checks for things like energy and HVAC management, ensuring optimal performance and efficiency. Additionally, they introduce predictive capabilities to anticipate potential asset breakdowns and incorporate self-healing features to automatically resolve issues before they arise.

Alternatively, automated health-check solutions deliver real-time audits, and comprehensive dashboard reports for front-office, back-office, and field service engineers. With robust device lifecycle management, retailers can ensure optimal efficiency through meticulous asset tagging, tracking, and end-of-life refresh strategies. Finally, with a centralized inventory view, procurement, technology operations, service desk and IT asset management teams can work from a unified platform, thereby enhancing data accuracy across stores.

While the obvious benefit is that retailers save money by avoiding downtime and increasing asset visibility, properly managing store asset IT health also prevents data breaches. This is particularly crucial given the rising costs associated with breaches, which reached an average of $4.88 million in 2024. Investing in robust asset management practices not only enhances operational efficiency but also safeguards retailers against potential financial losses and reputational damage.

3.    Addressing store associate churn

While seeking to maximize associate productivity, retailers must contend with an average annual employee turnover rate of 60%. This high turnover rate exacerbates the challenge of maintaining operational efficiency, as constant training and onboarding are necessary.

To solve for this, many retailers are turning to virtual agents to streamline the work of both store associates and managers. These apps use generative AI to provide real-time support and guidance, helping employees tackle tasks effectively and maintain store health. They can also facilitate self-help resources and standardized playbooks to ensure consistency and best practices across all stores.

Other retailers have established gen AI-powered bot-based service desk and field service tools for remote troubleshooting. These enhance the speed and accuracy of problem resolution while significantly reducing the need for on-site visits truck rolls.

With over 64% of employees believing AI has positively impacted their job performance and overall satisfaction, an increasing number of retailers are likely to adopt labor management tools to boost productivity, morale, and customer service. While employee churn will always be an industry concern, these tools offer a promising solution to mitigate its effects by providing quicker onboarding and training resources, and by enabling remote troubleshooting of operational issues.

Steps to accelerate AI maturity in store operations

So how do retailers embark on this transformative AI journey? Here are steps we’ve developed through extensive work with clients in the sector:

1. Identify the right data strategy. The foundation of any successful AI implementation is a robust data strategy. Retailers should assess the quality and quantity of their data, ensuring it is accurate and relevant to their customers. By establishing a clear data governance framework, retailers can ensure data integrity and security, a crucial first step.

2. Pick the right use case. There are several use cases that retailers must weigh before deciding on the AI implementation that’s right for them. They should start by identifying use cases that align with their business goals and offer the highest potential for ROI. By identifying specific business challenges and bottlenecks, retailers can pinpoint use cases that will optimize operations, reduce costs and drive growth.

3. Ensure responsible AI practices are being used. As AI becomes more integrated into retail operations, it's essential to prioritize ethical and responsible practices. Leaders should ensure their AI systems are transparent, fair and unbiased. This involves regularly auditing AI algorithms for potential biases, ensuring compliance with data privacy regulations, and maintaining transparency with customers about how their data is used. Additionally, retailers should invest in training so their workforce can manage AI technologies responsibly.

The key to operating brick-and-mortar retail AI as efficiently as possible

Here’s the good news: brick-and-mortar retail isn’t dead. In fact, it’s back and has the potential to be better (and more efficient) than ever before.

However, as retailers become more agile, they will need to increasingly rely on trusted systems, processes, and partners to successfully run their operations. By embracing these changes, brick-and-mortar retail stores can ensure their continued success and solidify their presence at the center of the retail ecosystem.