Achyuta Ghosh — Executive Research Leader, HFS Research[00:06]
Hello and welcome back to the HFS GCC Advantage. Today I have Sunil Gopinath with me, who’s the CEO of Albertsons Companies India, for a conversation on what it means to build an AI-first GCC in retail. Albertsons is a fascinating story because grocery is one of the most operationally intense industries. Factors like stores, supply chain, pricing, availability, customer behavior, e-commerce, frontline execution — all have to come together perfectly every day, and this makes this discussion extremely important. Sunil, welcome to the show.
Sunil Gopinath — CEO, Albertsons Companies India[00:40]
Thank you. Glad to be on the show, Achyuta. Thanks for inviting me.
Achyuta Ghosh — Executive Research Leader, HFS Research[00:43]
So Sunil, before we get into the AI story, can you give us a brief overview of Albertsons, the role it plays in grocery retail and e-commerce in the US, and your own journey leading the India GCC?
Sunil Gopinath — CEO, Albertsons Companies India[00:55]
Albertsons is one of the largest food and drug retailers in the US, serving customers across stores, digital channels, pharmacies and our local communities. We have over 2,240 stores, around 22 distribution centers, 19 manufacturing plants, around 275,000 associates, and we have over $80 billion in sales. And we have been consistently delivering for the last 100 years. So we say we are one of the major players, very sustainable, and continuing to grow.
Now our ambition is to become one of the most loved grocers in our communities, and that means making it easy, making it fresh, making it worth it, and making it for our customers.
Our own journey in the GCC — we’ve been here for almost a year. We are now close to a thousand employees. And along with the US and Manila, India is going to be one of the three key centers for technology innovation and AI-based transformation for Albertsons.
Achyuta Ghosh — Executive Research Leader, HFS Research[02:12]
Thanks, Sunil. As you rightly pointed out, Albertsons is a 100-year-old-plus company with a large store footprint — more than 2,000 — and e-commerce is becoming an important growth area. So how is Albertsons thinking about AI in this environment, especially when customer expectations are evolving quickly around personalization, recommendations, pricing, availability, and convenience?
Sunil Gopinath — CEO, Albertsons Companies India[02:36]
Retail and grocery are highly complex, high-volume operating environments where thousands of decisions are being made every day across our stores, our DCs, our digital channels, our supply chain, our merchandising and customer engagement. So AI is most valuable when it helps associates make better decisions, simplifies our workflows, improves efficiencies, and creates seamless customer experiences.
As you know, digital is a growth area for us. At Albertsons we are growing at around 13% year-over-year in our digital channel. And so in this context, personalizing the experiences for users on the digital channel, having conversational experiences — all of this is going to keep enhancing our value proposition to our customers. And at the end of the day it should be a customer-centric experience, balanced value, winning footprint. These things matter to us.
So in short, for us AI is a huge enabler of the business. It’s not a technology end in itself. And so we pay a lot of attention to make sure that AI works for us, and not the other way around.
Achyuta Ghosh — Executive Research Leader, HFS Research[03:54]
I think that’s very well put, Sunil. I believe the India GCC is playing a central role in Albertsons’ AI transformation. Can you go a little bit deeper on this? How is the India team helping build and deliver AI solutions for the enterprise? How are these solutions supporting areas such as insights, decision making, personalization, store operations and business optimization? Can you bring this to life through some examples?
Sunil Gopinath — CEO, Albertsons Companies India[04:21]
Yeah. See, there are two layers to how we build products at ACI India. One is, of course, we are deeply integrated as one team with our US technology organization and our Manila center. Second, we have built intact teams here as product units. Product management, data sciences, AI, AI engineering, design, user experience — they all come together as one seamless team. And having them all co-located here just makes it all the more easier for us to deliver unified solutions.
And we have been grateful to have had the good fortune of being able to hire some of the top talent in data sciences and applied AI and generative AI. And also, because we have the connect to product and business, we have roadmaps for each of our verticals — whether it’s supply chain, whether it’s retail, or customer experience — where we have AI-enabled roadmaps for the long term. And that makes it very, very sustainable.
We also have horizontal platforms — our infrastructure as well as enterprise AI platforms. Those teams are also here in ACI, and they are also AI-first. For example, our infrastructure team is building things like AIOps, where we are more anticipatory in nature. Can we predict issues before they happen, in our cloud, in our infra? And even if things do get into a challenging situation, instead of throwing people at problems, can technology and AI go really deep and find out the root cause analysis of what actually led to this situation? So some very, very deep technology is being built right here. So this is how ACI is contributing to the overall tech and AI strategy of Albertsons.
Achyuta Ghosh — Executive Research Leader, HFS Research[06:23]
Thanks, Sunil. You’ve been doing a lot of work around AI, but many pilots do not make it into production, right? Many initiatives probably do not see the light of day at an enterprise level. So from your experience, what are the biggest pitfalls that stop AI from becoming pervasive in an enterprise context, and what should GCC leaders get right early?
Sunil Gopinath — CEO, Albertsons Companies India[06:48]
See, one of the biggest challenges organizations face — or rather, mistakes that they end up making — is treating AI itself as the end goal or an objective. How quickly, or how broadly, do we deploy AI in the company? That’s like putting the cart before the horse. For us it’s always very, very clear: we want to make AI work for the business. Is it a business enabler? Does it move the dial for all of our key business metrics? If we start from that, that makes things a lot easier, and we’re in control of our destiny a lot better.
And we are very, very clear that anything we do for enterprise AI should either be helping improve decisions — that could be our store directors, it could be our demand planners, it could be our engineers who are building customized user experiences for our mobile app. It has to lead to improved decision-making, improved customer experience, and associate productivity. And finally, of course, business outcomes. If it’s not contributing to any of these four dimensions or outcomes, then it becomes a theoretical exercise, and that’s not of interest to us.
Third, for AI to be effective, data is foundational. Getting your data right, having high-quality data, labeled data, business semantics, correct ontology, knowledge graph, so that you understand the relationship between data. If these foundational elements are not right, AI cannot do magic on it. AI in the consumer world, where it’s trained on public data, is a very, very different beast from AI in an enterprise context like ours. So you have to be very clear on: is the data ready for AI?
And then, how do you make it enterprise-grade? Which means making sure it adheres to the policies of the company — governance, security, privacy, cost management. We can’t have anybody and everybody using AI to do whatever they want, and it compromises our values, or it compromises costs, or it compromises policies, guidelines, company branding. So you have to really put in an enterprise-grade security and governance layer to make sure it conforms to all of your own policies.
And finally, you have to continue to watch adoption metrics. Every time you launch something, you take it to production — how are users using it? Are they giving you feedback? Is it being useful? Or is it making their everyday workflows, which were all manual before — has it really automated them and saved time? So watch all of those metrics. Not everything needs to be a revenue metric, but have your own L1, L2 and L3 metrics that you constantly track, so that you build a comfort level that AI is working on your behalf. So I would say these are some of the pitfalls, slash important things to continuously monitor. So you’re building a responsible AI, a fully governed AI, and that really helps unlock value for the organization.
Achyuta Ghosh — Executive Research Leader, HFS Research[10:04]
I think these are great points. And just to close — if you had to summarize your experiences into an AI-first blueprint for GCCs, what would that blueprint actually look like across operations, tech development, decision making? You covered governance also. So how do you think the playbook, or the blueprint, will look?
Sunil Gopinath — CEO, Albertsons Companies India[10:28]
Yeah, I think a few things. One is the teams need to have a strong sense of ownership. If the team owns particular platforms, solutions, business outcomes, that will lead to really fantastic outcomes, because now you know what the business problem is. You know the path to adoption and scale, not just building it. One of the biggest challenges is if the GCC team thinks that they’re only responsible for building it, but not necessarily for deployment, scale, and seeing it work in the business — then it becomes a big challenge. So that sense of end-to-end ownership is very, very critical.
Secondly, we are part of a much larger global company, so making sure that we integrate really well — both with the business counterparts, with our technology counterparts, the product counterparts — so that we work in lockstep with the rest of the company to enable successful product launches and go-to-market, becomes very, very important.
Thirdly, you have to build it holistically. You can’t just build an AI team and say we’re going to do AI. As I mentioned, you’ve got to have data engineers, AI engineers, product managers, scientists, machine learning experts, application engineers, designers working very, very closely hand in hand. Because just delivering AI to the world is not going to pay the bills. Solutions that solve business and customer problems in their context pay the bills. So making sure all of these things integrate and land well becomes very, very critical. And yeah, I would say having a very outcome-focused, high-ownership organization that is very data-driven and business-context-sensitive is the key to success.
Achyuta Ghosh — Executive Research Leader, HFS Research[12:24]
Thanks, Sunil, for joining in and sharing your thoughts. What I found very valuable in this conversation is the way you grounded AI in real retail decisions — demand, pricing, personalization, and so on — and how a GCC can help the enterprise make better choices. The Albertsons story is a strong example of where the GCC model is headed: closer to business context, closer to decision-making, and closer to enterprise outcomes. Thanks again for being a part of the HFS GCC Advantage show.
Sunil Gopinath — CEO, Albertsons Companies India[12:55]
Absolutely. My pleasure. Thank you so much. Great talking to you.
