MicroStrategy: In your experience, what are the most pressing challenges facing retailers today, and how can AI help solve them?

Michael Relich: The most pressing problem in retail is the highly competitive environment and the pressure on margins. With inflation compressing margins even further and the complexity increasing with Omni-channel, it’s becoming more challenging. We’re dealing with a much more competitive and complex environment. There’s an overwhelming amount of data being generated, and the challenge is how to make sense of it to make the right decisions.

How do you approach the problem of too much data? What technology do you think is the prevalent solution now?

Michael Relich: One of the biggest issues is that retailers have been collecting data for years, even decades, but the data has often been siloed. Tools like Snowflake or Google BigQuery—cloud-based databases—are potential solutions, but there’s a considerable effort and investment needed to pull that data, scrub it, and organize it into a data lake or data warehouse to make it useful.

I’ve been in too many meetings where we spend the first 45 minutes arguing about what the data means.

Even with clean data, retailers often struggle with making good use of it.

Michael Relich: Yes, another significant challenge is accessing that data in a meaningful way. How do you make that data accessible to your employees while maintaining a level of governance?

The problem with tools like Tableau or QlikView is that people can generate data, but I’ve been in too many meetings where we spend the first 45 minutes arguing about what the data means.

Proper governance and a semantic layer are crucial to define what the data represents. Once that’s in place, you can give people access to the data so they can spend less time debating and more time generating insights.

What tools do you find most valuable in addressing these challenges?

Michael Relich: I think MicroStrategy is incredibly valuable because it offers a level of governance and a semantic layer, which helps organizations agree on what the data means and use it to generate insights.

What MicroStrategy has done with its AI approach is particularly impressive. It’s ahead of competitors because, while people used to complain about the effort required for the semantic layer, it now drives AI efforts by passing that structured data to an LLM (Large Language Model) to enable conversational BI.

How does conversational BI impact the adoption of these tools?

Michael Relich: Adoption has always been a significant issue with BI tools. For many, interacting with BI tools is daunting, but if you can ask natural language questions and get immediate answers, that’s extremely powerful.

The current model in most retailers involves analysts and planners spending their Sundays anticipating every possible question from executives during Monday business review meetings, only to find out in meetings that they’ve missed something. This leads to a cycle of reactive work throughout the week.

With MicroStrategy and conversational BI, executives can ask questions directly in a non-intimidating way, which addresses the adoption issue. It allows for quicker decision-making without the need to rely heavily on analysts.

High turnover and poor training often result in a terrible customer experience. AI can help streamline this process and improve service quality.

What do you see as the most promising use cases for Gen AI-powered analytics in retail?

Michael Relich: Generative AI can make a significant impact on personalization in retail. With all the horizontal data on customers, AI can define segments much more accurately and market to them in a more meaningful way. This is particularly important as digital marketing costs rise, especially with privacy issues making first-party data more critical. Generative AI can help identify customers and communicate with them in the way they prefer.

The second obvious use case is in customer service, through chatbots or enhancing the productivity of customer service agents. High turnover and poor training often result in a terrible customer experience. AI can help streamline this process and improve service quality.

You are known for being a technology pioneer in retail in many ways. Can you share an example of how you’ve leveraged technology in retail?

Michael Relich: Years ago, when the first iPad came out, I had this idea—since we have all these images in the cloud for e-commerce, but buyers often work with style numbers like WM345, which may not be meaningful to them—why not combine rich media with data and put it all on an iPad? It was ironic that I was still using a BlackBerry at the time, but I emailed my team back in LA to order three iPads. I told the product manager, "I’m going to be your first customer."

You were the first to launch a mobile retail app?

Michael Relich: Exactly. We worked on it and made about 60 enhancements. We launched our first mobile app, and it was incredible. An MIT professor even studied it, and I was a finalist for the MIT Innovation Award.

When did you know that mobile BI will take off?

Michael Relich: A defining moment for me was when I was in the President of GUESS's office. She was not known to be the most computer-literate person in the company. One day, a buyer walked in and asked her about a particular style. To my surprise, she effortlessly pulled up her iPad, found the style, and said, "Oh, this one? It’s selling well in the Northeast."

At that moment, I thought, "Wow, we've succeeded." We had finally solved the adoption issue. Previously, in best-seller meetings, we would be looking at sales data that was two weeks old because it took someone that long to compile it into a spreadsheet. But with the new system, everyone had an iPad, and they were looking at real-time data from just the day before. It was a game-changer for us.

AI won’t be a rip-and-replace solution; instead, it will be about finding where AI can be injected into current processes to improve efficiency and generate incremental sales.

Looking ahead, what do you think the role of AI will be in retail in the future?

Michael Relich: AI is going to transform retail in a big way. We’re only just scratching the surface. Retailers are overwhelmed with inquiries about AI, and boards are demanding to know how it will be used. AI won’t be a rip-and-replace solution; instead, it will be about finding where AI can be injected into current processes to improve efficiency and generate incremental sales.

You’ll see AI agents implemented in various parts of the retail process. The real value will come from these agents working together, which will change the retail workforce by replacing tedious, repetitive tasks with AI-driven processes.

For someone just beginning their AI adoption journey, what advice would you give?

Michael Relich: Understand that AI can make a huge difference in avoiding lost sales. It can help identify trends, guide purchasing decisions, and optimize product allocation. Retailers need to move away from broad approaches and use AI to tailor assortments to specific locations based on consumer demand.

What do you think the future of e-commerce will be in retail? Will it drive out brick-and-mortar stores in favor or online?

Michael Relich: E-commerce’s role will vary depending on the product. Basics will see a significant percentage of e-commerce orders, but fashion might lean more towards in-store shopping due to the importance of fit and fabric feel. I think a 30% to 35% e-commerce penetration in apparel retail is fairly healthy. Omni-channel is the future—customers expect a seamless process across online and in-store shopping.

MicroStrategy: Empowering the Intelligent Enterprise

Michael's insights highlight the transformative power of AI in retail. It's not just about staying competitive; it's about creating a truly intelligent enterprise that thrives in the face of change. Ready to embark on that journey? 

Embark on your AI-powered retail journey today. Learn more about MicroStrategy's AI-powered analytics solutions and download our free Market Brief, "Next-Gen Retail: Harnessing AI for Advanced Analytics."