Shopping in 2025: A Retail Leader's Perspective on AI's true potential
Interview with Justin Mennen, Chief Digital & Technology Officer
Justin Mennen is a recognized digital transformation leader. He has over 24 years of experience leading enterprise technology modernization and large-scale business transformation globally. Most recently, he was the CDO & CTO at Rite Aid, CDO & CIO at CompuCom, and held executive leadership positions at Estee Lauder and Dell.
Justin is passionate about leveraging technology to transform businesses and has a proven track record of driving digital transformation and implementing innovative solutions.
In today’s interview, we talk about:
Generative AI's impact on retail in the next 5 years
Three popular use cases
Data challenges and hype around AI
Phased approach to AI adoption
Blending physical and digital retail with AI
What follows below is a condensed and lightly edited version of our interview.
Justin, let’s start with the big picture. How do you envision Generative AI shaping the overall landscape of the retail industry in the next 1-5 years?
Justin Mennen: Mustafa thanks for having me. I really enjoy following your work on both product management and AI. And I appreciate you inviting me to share my thoughts.
As to your question, when we talk about the next one to five years, I see Gen AI as a game changer for the retail industry. It's not just about driving innovation; it's about fundamentally reshaping how we operate and how customers experience shopping.
There are countless ways that Gen AI will impact our industry with regards to customer interactions, content generation, automation and operational efficiency, but there are three immediate use cases that I’m particularly excited about.
Personalization At Scale: With Gen AI, we're looking at unprecedented levels of personalization. Think about product recommendations and marketing – AI allows us to understand and cater to individual customer preferences like never before.
Visual Product Search: Visual product search and recommendations are on the horizon. AI, with its image recognition and analysis capabilities, opens up exciting new avenues. Customers will be able to find products through visual search, uploading images in a way that wasn't possible in the past. This takes personalization to the next level.
Supply Chain Optimization: Gen AI's predictive capabilities are crucial for optimizing inventory, forecasting demand, and streamlining logistics. It's not just about cost reduction; it's about improving efficiency and sustainability. So, I believe AI in the supply chain is going to be a significant focus for retailers moving forward.
I love all three of your examples. Lets take the opposite point of view. What is some of the hype you are seeing in the industry? Use cases that are just vaporware?
JM: Great question. Where I see a bit of hype is in overlooking the data challenges. Many organizations struggle with unstructured or unusable data and for generative AI to drive hyper-personalization, you need a solid data foundation. Achieving that deep, nuanced understanding of an individual customer is a challenge, and not as straightforward as it might seem.
Agree 100%. Data is a very real challenge. May I ask what are some of the other challenges associated with the above use cases? And how do you overcome them?
JM: I think there are three critical challenges:
Data Privacy and Security: I think data privacy and security are significant challenges. The issue is collecting customer data to the level you need for AI to work effectively, while also ensuring proper privacy and security controls are in place. The solution lies in implementing a robust data protection system that complies with regulations like GDPR, uses anonymization, and considers what a customer has opted into or out of.
Integrating With Existing Systems: Incorporating AI into current infrastructure without disrupting what's already working well. I think the best approach, as with any disruptive technology, is a phased one - piloting, testing, learning, iterating, ensuring compatibility and seamless integration. Using APIs, microservices and the like enables smoother integration.
Human-AI collaboration: There's a lot of discussion around humans versus robots. With generative AI, ensuring it complements rather than replaces human judgment for critical decisions is key. The solution is establishing clear protocols on how humans will work alongside AI systems - where will humans make decisions versus AI - and putting defined rules and processes in place.
I think you're completely right. There's a lot of fear that AI will take away jobs, which happens with every new technology. It's a very natural human reaction.
Now, two more questions. As you see retail organizations adopting these technologies, what has their approach been so far? And looking ahead to 2024, what do you think is the best approach?
JM: I see a lot of interest from organizations, but also hesitation because they don't know where to begin.
In my view, the best approach treats Generative AI like other disruptive technology initiatives:
Define objectives and scope - identify specific problems/opportunities where AI will add value.
Assess capabilities - evaluate infrastructure, data, and staff skills to identify gaps needing development.
Develop strategy - align with business goals, don't just do AI for its own sake.
Start small - pilot in controlled environments to minimize risk and demonstrate ROI.
Ensure robust data governance and compliance - adhere to privacy laws and ethical guidelines.
Build/acquire talent - most organizations lack generative AI talent at scale, so complement internal teams with external experts.
Measure outputs - show stakeholders results.
Iterate and expand on success.
Retail is unique in having both digital and physical footprints. Many still go to physical stores to shop. How do you see AI influencing both the in-store and online shopping experiences?
JM: I think generative AI will significantly enhance both physical and online retail experiences, ushering in the next generation of omnichannel personalization.
For in-store, AI can provide personalized recommendations via interactive displays or apps based on shopping history and preferences. Virtual try-on powered by AI will improve in-store experiences for clothing and cosmetics retailers. Personalized chatbots can help shoppers find products, get information, and receive suggestions, merging online and in-store interactions.
And for the back of the store, AI algorithms can better predict stock requirements to minimize overages, shortages, and stockouts.
Justin, this has been great. Thanks for being our guest.
JM: Mustafa, this was fun. Thanks for having me.
Friends,
My AI BootCamp for PM kicks off today in a few hours.
Frankly, I was unsure if we'd even get 5-students. To my amazement, the BootCamp is not only full but also has over 30 eager individuals on the waiting list.
Thank you all for your support. Your emails, LinkedIn re-posts, and word of mouth played a pivotal role in getting us here. Much appreciate it.
I asked DALL-E to create an image of how I feel - both nervous and excited. Here is what it came up with.
You know what is scary, it automatically picked a bald man, in his 50s with glasses. Go figure!!
Thanks once again.
-Mustafa