Column Editor’s Note: In this Active Industrial Learning column article, I interviewed Todd James, Chief Data and Technology Officer at 84.51° to discuss the rapid evolution of AI and its application in business, particularly in retail. We explored how data science and AI are transforming decision-making processes, enabling personalized shopping experiences, and optimizing operations at 84.51°. The conversation highlights the importance of responsible AI deployment, the need for upskilling across organizations, and the evolving role of chief data officers in bridging the gap between business and technology. The article underscores the significance of integrating AI into business strategies to deliver value and improve operations
Keywords: artificial intelligence, retail industry, responsible AI, chief data officer, upskilling, predictive analytics
The power of data science in the business realm is tremendous, but the hype and complexity surrounding it can be overwhelming. The Active Industrial Learning column focuses on applications of data science to businesses, with an eye toward practical considerations that can make data science initiatives more likely to succeed. The aim is to translate business concerns into the language of data science, and vice versa, to empower data science leaders at companies of all sizes and data maturity levels.
84.51° is a retail data science, insights, and media company. It helps the Kroger Co., consumer packaged goods companies, agencies, publishers, and affiliates create more personalized and valuable experiences for shoppers across the path to purchase. 84.51° utilizes first-party retail data from 62 million U.S. households sourced through the Kroger Plus loyalty card program to fuel a more customer-centric journey using 84.51° Insights, 84.51° Loyalty Marketing, and retail media advertising solution Kroger Precision Marketing. The company’s name is based on the longitude of Cincinnati. It harkens to ‘longitudinal analysis’ and the home of both 84.51° and the parent company, the Kroger Co.
Over the past few decades, as data became more abundant, investments by organizations into data infrastructures and data science capabilities have increased significantly, and AI is now promising a wholesale augmentation of organizational decision-making. 84.51° is very much a product of this data-fueled era we are living in. Of course, business decisions are still made all the time without the aid of data. So where does the rubber meet the road?
As the chief data and technology officer at 84.51°, Todd James understands the importance of the human element. He has led numerous business units in advanced analytics and is well versed in establishing an organizational foundation for AI. For him, it is all about seeding the business with value-driven solutions—from HR to marketing to IT and everything in between. And that requires upskilling.
I sat down with Todd to discuss his experience leveraging AI to drive value and optimize retail experiences for customers, as well as what the future holds for the AI-driven C-suite.
Hamit Hamutcu: When you look at AI and how it’s developed, what do you think are the implications on business and, more broadly, on society, particularly when you also consider the necessity of responsible deployment of AI?
Todd James: As I look at it, I do think we are at a pretty significant pivot point in the capability of technology. It’s been called a once-in-a-lifetime replatforming; it’s been called a techno-industrial revolution. But no matter what you call it, if you look at what AI is capable of, it’s converting a judgment question into a prediction problem. Over the past several decades, companies have certainly made significant improvements based on process and based on automation. But there were still a lot of gaps, where there were judgment calls that were not impacted by data and not activated through analytics. That's the new capability we have. You could look anywhere in an organization or in society where a decision is made, and now through advanced analytics you have the ability to use data and analytics to drive a better informed outcome.
I think that has pretty significant implications. It creates opportunities to make decisions easier on people and on customers. We within 84.51° like to say that our job is to make people’s lives easier by creating more valuable and relevant shopping experiences. We get excited about that, and I think you can see it across every aspect of business. But with great power comes great responsibility. I’m happy to see that we’re having the right active dialogue across companies, across society, on the role that we want artificial intelligence to play, how decisions are made, and how we operate as a society. In my organization, we want to make decisions that are consistent with our collective and individual values.
Hamit Hamutcu: You said that you’ve seen a positive dialogue with people trying to figure out how to move more responsibly—taking a broader perspective and not just advancing technology but looking at how it’s impacting people’s lives. Can you elaborate on that? How can we further that dialogue? There are obviously companies, but there are also other stakeholders, regulators, NGOs, and other groups that represent different parts of the society.
Todd James: I think that’s a great point. When I say that it’s a positive dialogue, it’s positive that we’re having the dialogue. I think if you look on a societal level, positive and perfect aren’t necessarily the same thing. What’s important to me is that we get a diversity of perspectives, not weighted to a particular industry, cause, or ideology, but a balanced, principle-based approach that we can look at collectively and say, ‘This is acceptable to us. This is something that we as a society believe will be appropriate.’ As I look at it, the most important thing is to get as inclusive a voice as possible.
Hamit Hamutcu: In an earlier conversation, you mentioned three keywords in terms of setting up the foundation for data and AI technologies: scale, democratization, and engagement. Can you comment on those three words? What do they mean for you, your organization, and the adoption of AI in general?
Todd James: To have an impact across an organization, you need to think beyond some of the point solutions that a lot of organizations have been working with. At the end of the day, it’s very important that you’re able to impact a large range of decisions through improved insights and activations. To be able to do that, you have to start to think a little bit differently about how you want to build artificial intelligence, and shift from thinking about it as a point solution to more of a new layer of infrastructure. I call it a layer of algorithmic infrastructure, where you’re able to reuse components to create consistency in experiences across an organization, but also speed of delivery.
As for democratization, the advent of generative AI made it so things that were very hard and very expensive are now much more accessible. You see it in the rapid growth of OpenAI: I think it was just a couple months for ChatGPT to get to 100 million users, and it was just under 4 years for Facebook. I think there’s a materiality in that pace around putting very advanced artificial intelligence in front of a large group of people, many of whom are not technical. AI is becoming more accessible, so you need to be able to put processes in place that allow people within your organization to use it, but to do it in a way that is efficient and responsible.
The third one is around education and awareness. When you start enabling decisions with increased data, with advanced analytics, that has a direct impact on people. It’s very important to educate them so that they can play a role. From an organizational perspective, it’s very important for us to engage the people that are doing the work along the way, as well as the people that will be the users of the technology.
Hamit Hamutcu: I know you run the data analytics at your organization, and you prefer to upskill the entire company, not just your advanced teams. How does that work? Is that under your purview? How do you partner with HR or talent development in general?
Todd James: It’s very hard to get anything executed across a large organization by yourself. Within our organization, clearly around the technical teams, there’s probably a more direct line of upskilling. But within my organization, we’re working with business leaders, human resources, delivering high-touch training and experiences to groups. We’re also looking at broadly available self-directed training.
I’ll give you a few examples. Within Kroger, it’s everything from providing exposure and experiences and having sessions with the executive leadership team, to the art of the possible education and opportunity generation with individual business units, to technical training around data science and various technologies within the tech teams, to also launching and expanding the data literacy training across the organization. So you’re doing some of this in partnership with the business. Any of the learning that you’re doing across mass audiences, HR is an incredibly important partner. My guidance for anyone looking to impact, you want to approach it on multiple fronts. You want to drip-feed solutions that are happening to get people excited; you want formal education; you want informal sessions. But you’re really trying to give people exposure and awareness across different mechanisms, because we all learn differently, and all of us need to hear things a few times before it starts to sink in.
Hamit Hamutcu: I am sure the number one question you get as a chief data officer is, what works? What’s a successful use case where you’ve leveraged advanced analytics, data science, and AI and it’s made an impact—not a pilot, but something that’s ingrained within the organization?
Todd James: We’re a large organization. We have an in-house data science team within 84.51°, which is a subsidiary of Kroger, and we run millions of automated algorithms and about 950 billion forecast runs per year. So we are impacting a lot of decision points.
One of the areas that we look at is in the grocery space. When you want to do an e-commerce order and that order is going to be for pickup at one of our stores, we actually have a store associate that moves around the store and picks those items. As you look across all our stores, we have different layouts, different setups, but you can use advanced analytics and deep learning to optimize your route. If you have data assets to tell you what's on the shelf, if you have maps that show you the layout of the floor, you can optimize each one of those routes to make it faster, more efficient. It has a very material impact, and what it allows us to do is reduce the lead time that shoppers have to make the order, which is a really great experience for the shopper as well as a more straightforward and simplified approach for our associates. We’re calling it dynamic batching and routing, and we have it in all our high-volume stores across Kroger.
Now, what we learned from that is, not only can we drive improvements for the customer and for our associates to make their lives easier, filling and making orders, but underlying that is a routing center of excellence. We were able to deploy the underlying routing approach to support distribution center–to-store truck routing, and our initial tests are showing about a 3–8% reduction in miles. That’s a reduction in cost. That's a reduction in emissions. That is more flexibility within our supply chain. But as we look to the future, that same algorithm can do return routes. That same algorithm can look at the routing-type decisions that happen inside warehouses.
Hamit Hamutcu: I love that example. I started my career in analytics at FedEx, where it was all about those 1%, 2% improvements.
Todd James: Small changes in a big organization can drive a tremendous impact. It’s a mindset shift for your teams and how you organize them. But we’re seeing great impact that’s better for the associate, better for the customer. And the teams are excited because they’re able to see their work have a tremendous impact, and they’re working with the business and the technology and the analytics people together. And each project contributes a capability, service, or process that makes the next project easier.
Hamit Hamutcu: I want to end with some observations and projections about the role that you’ve grown into over your long career in data and analytics. A lot of organizations for the last say 15 to 20 years have been installing chief data officers, but the definitions have always been a bit different. Sometimes engineering was part of it, sometimes it wasn’t. There was data governance, there was privacy and security. But then, with machine learning, big data, and data science, you needed different kinds of talent. And now there’s AI. What does this evolve into? Do we have a chief AI officer for organizations? Does this become simply part of the chief data officer’s job? Or are we looking at something completely different?
Todd James: The chief data officer grew out of a pretty tactical data management, data governance, data architecture role. I don’t think that definition is a fit for where we are today—because of the velocity of data, because of the analytics that are able to drive an impact. It really has to be more about the impact on the business, because this technology is transformational. You're not doing it by yourself, but that leader needs to be the catalyst to drive that change across the board. And by the way, you still have to be technically sound to be able to make this happen. I think it’s become a much more robust role, but it's also laced with technical complexity.
I think to be effective the role needs to be combined, and if it’s not combined, it needs to be a power of two between the chief data officer and the chief analytics officer. If you split the roles, the chief data officer becomes more about enabling data services, and the AI officer becomes the value creation arm. But if you put them together, value creation, moving in step with the enablement of data—data activated through analytics at the service of business—is really what you want. And that’s more of a Chief Data and Analytics Officer (CDAO) role.
For the future, I believe it’s a transitional role. When I was up at the Chief Data Officer and Information Quality (CDOIQ) Symposium at MIT, I made the statement that this job, if properly executed, goes away. If you're doing the right thing to educate and raise awareness around how to effectively and responsibly utilize advanced analytics, how to think of data as an asset—just as our leaders today have an understanding of finance or operations and measurements—then the exciting part of the CDAO role becomes part of the business.
Hamit Hamutcu: I heard that sentiment maybe 15 years ago. I was talking to someone who was newly appointed to be the first chief digital officer of a large retail bank. What he said at the time was, “If I’m successful, I should be the first and the last chief digital officer, because our business should be digital and it should be everywhere.” Similar to what you were saying, there should be an understanding by the leaders of the company on these new technologies that it’s blended. It’s not a separate, standalone capability.
Todd James: I think, too, the biggest hurdle to value realization is you've got to connect the business and the data science together. That’s where the magic really occurs—when you bring business intimacy with technical knowledge. You can see it in our teams that are cross-functional today.
I’m also starting to see people changing how they’re crafting their careers: undergraduate in supply chain, graduate in data science, or vice versa. You’re starting to get people that are gearing their careers, and they’re not coming out, in that example, to be a data scientist in supply chain. They’re going to do that as their first job, but ultimately, they want to be the head of supply chain, and they’re equipping themselves for this environment. I think if that trend picks up, we’re going to find ourselves in a situation where more and more people are able to couple an understanding of analytics with business competency. When you have people like that running business, it is much easier to think about federating the analytics capabilities. What you have central are data, process, oversight for risk, but the execution can be more distributed. I think that’s where organizations will head over the next few years. I don’t think any of us are there now, but if you look at the trajectory, that’s my hypothesis.
AI and data science are at the core of what 84.51° does, enabling the company to deliver customer insights and shopping experiences that were previously impossible. But as much as AI helps to automate core functions of the enterprise, leaders and decision-makers like Todd James recognize the importance of upskilling and education in delivering anticipated ROI. AI, in that sense, is akin to the digital revolution, in that successful integrations will involve an organizational understanding of its role and potential, rather than a slapdash attempt to force it into specific use cases.
Hamit Hamutcu and Todd James have no financial or nonfinancial disclosures to share for this article.
©2025 Todd James and Hamit Hamutcu. This article is licensed under a Creative Commons Attribution (CC BY 4.0) International license, except where otherwise indicated with respect to particular material included in the article.