Conversational AI revolutionizes the customer experience landscape

In the ever-evolving landscape of customer experiences, AI has become a beacon guiding businesses toward seamless interactions. While AI has been transforming businesses long before the latest wave of viral chatbots, the emergence of generative AI and large language models represents a paradigm shift in how enterprises engage with customers and manage internal workflows.

“We know that consumers and employees today want to have more tools to get the answers that they need, get things done more effectively, more efficiently on their own terms,” says Elizabeth Tobey, head of marketing, digital & AI at NICE.

Breaking down silos and reducing friction for both customers and employees is key to facilitating more seamless experiences. Just as much as customers loathe an unhelpful automated chatbot directing them to the same links or FAQ page, employees similarly want their digital solutions to direct them to the best knowledge bases without excessive alt-tabbing or listless searching.

“We’re seeing AI being able to help uplift that to make all of those struggles and hurdles that we are seeing in this more complex landscape to be more effective, to be more oriented towards actually serving those needs and wants of both employees and customers,” says Tobey.

The capacity for AI tools to understand sentiment and create personalized answers is where most automated chatbots today fail. Enter conversational AI. Its recent progression holds the potential to deliver human-readable and context-aware responses that surpass traditional chatbots, says Tobey.

“We’re seeing even more gains that no matter how I ask a question or you ask a question, the answer coming back from self-service or from that bot is going to understand not just what we said but the intent behind what we said and it’s going to be able to draw on the data behind us,” she says.

Creating the most optimized customer experiences takes walking the fine line between the automation that enables convenience and the human touch that builds relationships. Tobey stresses the importance of identifying gaps and optimal outcomes and using that knowledge to create purpose-built AI tools that can help smooth processes and break down barriers.

Looking to the future, Tobey points to knowledge management—the process of storing and disseminating information within an enterprise—as the secret behind what will push AI in customer experience from novel to new wave.

“I think that for me, one of the exciting things and the challenging things is to explain how all of this is connected,” says Tobey.

This episode of Business Lab is produced in partnership with NICE.

Full Transcript

Laurel Ruma: From MIT Technology Review, I’m Laurel Ruma and this is Business Lab. The show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.

Our topic is creating great customer experiences with AI, from the call center to online, to in-person. Building relationships with customers and creating data-driven but people-based support teams is critical for enterprises. And although the technology landscape is ever-changing, embracing what comes next doesn’t have to be a struggle.

Two words for you: foundational AI.

My guest is Elizabeth Tobey, head of marketing, digital and AI at NICE.

This podcast is produced in partnership with NICE.

Welcome Elizabeth.

Elizabeth Tobey: Happy to be here. Really excited to talk about this today.

Laurel: Great. Well, let’s go ahead and start. To set some context for our conversation, what is the customer experience landscape like now? And how has it and will it continue to change with AI?

Elizabeth: Well, to start, I think it’s important to note that AI isn’t a new technology, especially not in the customer experience (CX) era. One of the things that is quite new though is generative AI and the way we are using and able to use large language models in the CX paradigm. So we know that consumers and employees today want to have more tools to get the answers that they need, get things done more effectively, more efficiently on their own terms. So for consumers, we often hear that they want to use digital solutions or channels of their choice to help find answers and solve problems on their own time, on their own terms.

I think the same applies when we talk about either agents or employees or supervisors. They don’t necessarily want to be alt-tabbing or searching multiple different solutions, knowledge bases, different pieces of technology to get their work done or answering the same questions over and over again. They want to be doing meaningful work that really engages them, that helps them feel like they’re making an impact. And in this way we are seeing the contact center and customer experience in general evolve to be able to meet those changing needs of both the [employee experience] EX and the CX of everything within a contact center and customer experience.

And we’re also seeing AI being able to help uplift that to make all of those struggles and hurdles that we are seeing in this more complex landscape to be more effective, to be more oriented towards actually serving those needs and wants of both employees and customers.

Laurel: A critical element of great customer experience is building that relationship with your customer base. So then how can technologies, like you’ve been saying, AI in general, help with this relationship building? And then what are some of the best practices that you’ve discovered?

Elizabeth: That’s a really complicated one, and I think again, it goes back to the idea of being able to use technology to facilitate those effective solutions or those impactful resolutions. And what that means depends on the use case.

So I think this is where generative AI and AI in general can help us break down silos between the different technologies that we are using in an organization to facilitate CX, which can also lead to a Franken-stack of nature that can silo and fracture and create friction within that experience.

Another is to really be flexible and personalize to create an experience that makes sense for the person who’s seeking an answer or a solution. I think all of us have been consumers where we’ve asked a question of a chatbot or on a website and received an answer that either says they don’t understand what we’re asking or a list of links that maybe are generally related to one keyword we have typed into the bot. And those are, I would say, the infant notions of what we’re trying to achieve now. And now with generative AI and with this technology, we’re able to say something like, “Can I get a direct flight from X to Y at this time with these parameters?” And the self-service in question can respond back in a human-readable, fully formed answer that’s targeting only what I’ve asked and nothing else without having me to click into lots of different links, sort for myself and really make me feel like the interface that I’ve been using isn’t actually meeting my need. So I think that’s what we’re driving for.

And even though I gave a use case there as a consumer, you can see how that applies in the employee experience as well. Because the employee is dealing with multiple interactions, maybe voice, maybe text, maybe both. They’re trying to do more with less. They have many technologies at their fingertips that may or may not be making things more complicated while they’re supposed to make things simpler. And so being able to interface with AI in this way to help them get answers, get solutions, get troubleshooting to support their work and make their customer’s lives easier is a huge game changer for the employee experience. And so I think that’s really what we want to look at. And at its core that is how artificial intelligence is interfacing with our data to actually facilitate these better and more optimal and effective outcomes.

Laurel: And you mentioned how people are familiar with chatbots and virtual assistants, but can you explain the recent progression of conversational AI and its emerging use cases for customer experience in the call centers?

Elizabeth: Yes, and I think it’s important to note that so often in the Venn diagram of conversational AI and generative AI, we see an overlap because we are generally talking about text-based interactions. And conversational AI is that, and I’m being sort of high level here as I make our definitions for this purpose of the conversation, is about that human-readable output that’s tailored to the question being asked. Generative AI is creating that new and novel content. It’s not just limited to text, it can be video, it can be music, it can be an image. For our purposes, it is generally all text.

I think that’s where we’re seeing those gains in conversational AI being able to be even more flexible and adaptable to create that new content that is endlessly adaptable to the situation at hand. And that means in many ways, we’re seeing even more gains that no matter how I ask a question or you ask a question, the answer coming back from self-service or from that bot is going to understand not just what we said but the intent behind what we said and it’s going to be able to draw on the data behind us.

This is where the AI solutions are, again, more than just one piece of technology, but all of the pieces working in tandem behind the scenes to make them really effective. That data will also drive understanding my sentiment, my history with the company, if I’ve had positive or negative or similar interactions in the past. Knowing someone’s a new customer versus a returning customer, knowing someone is coming in because they’ve had a number of different issues or questions or concerns versus just coming in for upsell or additive opportunities.

That’s going to change the tone and the trajectory of the interaction. And that’s where I think conversational AI with all of these other CX purpose-built AI models really do work in tandem to make a better experience because it is more than just a very elegant and personalized answer. It’s one that also gets me to the resolution or the outcome that I’m looking for to begin with. That’s where I feel like conversational AI has fallen down in the past because without understanding that intent and that intended and best outcome, it’s very hard to build towards that optimal trajectory.

Laurel: And speaking of that kind of optimal balance between everything, trying to balance AI and the human touch that many customers actually want to get out of their experiences with companies like retail shopping or customer service interactions, when they lodge complaints, refunds, returns, all of these reasons. That’s a fine line to walk. So how do you strike the balance to ensure that customers enjoy the benefits of AI, automation, convenience, and availability, but without losing that human aspect to it?

Elizabeth: I think there’s many different ways to go about this, but I think it is again about connecting a lot of those touch points that historically companies have kept siloed or separate. The notion of a web presence and a marketing presence and a sales presence and a support presence or even an operations’ presence feels outdated to me. Those areas of expertise and even those organizations and the people working there do need to be connected. I feel in many ways we’ve gone down this rabbit hole where technology has advanced and we’ve added it on top of our old processes that sometimes date years or decades back that are no longer applicable.

And until we get to the root of rethinking all of those, and in some cases this means adding empathy into our processes, in some it means breaking down those walls between those silos and rethinking how we do the work at large. I think all of these things are necessary to really build up a new paradigm and a new way of approaching customer experience to really suit the needs of where we are right now in 2024. And I think that’s one of the big blockers and one of the things that AI can help us with.

Because some of the solutions and benefits we’ve been seeing are really about identifying gaps, identifying optimal flows or outcomes or employees who are generating great outcomes, and then finding a way to utilize that information to take action to better the business and better the flow. And I think that that’s something that we really want to hone in on because in so many ways we’re still talking about this technology and AI in general, in a very high level. And we’ve gotten most folks bought in saying, “I know I need this, I want to implement it.”

But they do need to take a step back and think about what are they looking for as a success metric when they do implement it, and how are they going to vet all of the different technologies and vendors and use cases to choose which one to go after first and how to implement it and how even to choose a partner. Because even if we say all solutions and technologies are created equal, which is a very generous statement to start with, that doesn’t mean they’re all equally applicable to every single business in every single use case. So they really have to understand what they’re looking for as a goal first before they can make sure whatever they purchase or build or partner with is a success.

Laurel: So how can companies take advantage of AI to tailor customer experiences on that individual level? And then what kind of major challenges are you advising that they may come across while creating these holistic experiences?

Elizabeth: I do think that change management within an organization, understanding that we’re going to have to change those muscles and those workflows is one of the biggest things you’ll see organizations grapple with. And that’s going to happen no matter what partner or vendor you choose. That’s something you’ll just have to embrace and run with and understand it’s going to happen. And I think also being able to take a step back and not assume you know the best use case, but let AI almost guide you in what will be the most impactful use case.

Some of the technologies and solutions we have can go in and find areas that are best for automation. Again, when I say best, I’m very vague there because for different companies that will mean different things. It really depends on how things are set up, what the data says and what they are doing in the real world in real time right now, what our solutions will end up finding and recommending. But being able to actually use this information to even have a more solid base of what to do next and to be able to fundamentally and structurally change how human beings can interface, access, analyze, and then take action on data. That’s I think one of the huge aha moments we are seeing with CX AI right now, that has been previously not available. And the only way you can truly utilize that is to have AI that is fully connected within all of your CX workflows, tools, applications and data, which means having that unified platform that’s connecting all of these pieces across all interactions across the entire customer journey.

And I think that’s one of the big areas that is possibly going to be the biggest hurdle to get your head wrapped around because it sounds enormous. But it’s actually a very fundamental and base level change that will then cascade out to make every action you take next far simpler and faster and will start to speed up the pace of the innovation and the change management within the organization.

Laurel: Since AI has become this critical tool across industries for customer interactions and experiences, how does generative AI now factor into a customer experience strategy? What are the opportunities here?

Elizabeth: We always go immediately to those chatbots and that self-service. And I think the applications there are wide and broad and probably fairly easy for us to conjure up. That idea of being able to on your own time in the channel of your choice, have a conversation in the future state, not know and not care if you are speaking to an artificial intelligence or a human led interaction because both are just as quick and just as flexible and just as effective for you. I think the ways that are more interesting to talk about now that maybe aren’t top of mind to everyone right now are around how we help agents and supervisors.

We hear a lot about AI co-pilots helping out agents, that by your side assistant that is prompting you with the next best action, that is helping you with answers. I think those are really great applications for generative AI, and I really want to highlight how that can take a lot of cognitive load off those employees that right now, as I said, are overworked. So that they can focus on the next step that is more complex, that needs a human mind and a human touch.

And they are more the orchestrator and the conductor of the conversation where a lot of those lower level and rote tasks are being offloaded to their co-pilot, which is a collaborator in this instance. And so they’re still in control of editing and deciding what happens next. But the co-pilot can even in a moment explain where a very operational task can happen and take the lead or something more empathetic needs to be said in the moment. And again, all of this information if you have this connected system on a unified platform can then be fed into a supervisor.

And we do now have a co-pilot in our ecosystem for supervisors who can then help them change from being more of a taskmaster of coming in and saying, “What do I need to do today? Who do I need to focus on?” Answer that question for the supervisors so they can become far more strategic and impactful into not diverting crises as they appear. But understanding the full context of what’s happening within their organization and with their teams to be able to build them up and better them and be far more strategic, proactive, and personalized in giving guidance or coaching or even figuring out how to raise information to leadership on what is going well.

So that again, they’re helping improve the pace of business, improve the quality of their employees’ lives and their consumers’ lives. Instead of feeling like they are almost triaging and trying to figure out even where to spend their energy. Their co-pilot can actually offload a lot of that for themselves. And this is always happening through generative AI because it is that conversational interface that you have, whether you’re pulling up data or actions of any sort that you want to automate or personalized dashboards.

All of this can be done without needing to know how to code, to have to write a SQL query, anything like that, that used to be a barrier to entry in the past.

Laurel: So this is sort of a follow-on to that, which is how can companies invest in generative AI as a way to support employees internally? There’s a learning curve there, as well as customers externally. And I know it’s early days, but what other benefits are possible?

Elizabeth: I think one of the “a-ha” moments for some of the technology we’re working on is really around, as I said, that conversational interface to tap into unstructured data. With the right knowledge management and with the right purpose-built AI, you’re going to be able to take a person like me. It’s been decades since I’ve written any code or done anything that complex, and you’re going to be able to have me be able to interface with the entirety of our CX data. Be able to pull it, ask questions of it through a conversational interface that looks a lot like a search engine we know and love today, and get back personalized reports or dashboards that will help inform me.

And then again, after seeing all of that information, I can continue the conversation that same way to drill down into that information and then maybe even take action to automate. And again, this goes back to that idea of having things integrated across the tech stack to be involved in all of the data and all of the different areas of customer interactions across that entire journey to make this possible. I think that’s a really huge moment for us. And I think that that’s where… At least I am still trying to help people understand how that applies in very tangible, impactful, immediate use cases to their business. Because it still feels like a big project that’ll take a long time and take a lot of money.

But actually this is just really new technology that is opening up an entirely new world of possibility for us about how to interact with data. That we just haven’t had the ability to have in the past before. And so again, I say this isn’t eliminating any data scientists or engineers or analysts out there. We already know that no matter how many you contract or hire, they’re already fully utilized by the time they walk in on their first day. This is really taking their expertise and being able to tune it so that they are more impactful, and then give this kind of insight and outcome-focused work and interfacing with data to more people.

So that they can all make better use of this information that before was just not able to be accessed and analyzed.

Laurel: So when you think about the future, Elizabeth, what innovations or developments in AI and customer experience are you most excited about and how do you anticipate these trends emerging?

Elizabeth: I think you’re going to hear from me and folks within our organization talking a lot about how knowledge management is at the core of artificial intelligence. Because your AI is only as good as the data that it is trained on and how your data is presented and accessible to AI is a huge game changer in whether your AI projects are going to really work for you or falter and not meet your goals. And so I think that for me, one of the exciting things and the challenging things is to explain how all of this is connected.

And that while in many ways we’re talking a lot about large language models and artificial intelligence at large. That sometimes some of the things that we’ve been discussing for a long time in CX, knowledge management is the secret behind all of this that’s going to take us from novel and interesting and a fun thing to demo to something that’s actually really impactful and revenue generating for your business.

Laurel: Thank you so much Elizabeth for joining us today on the Business Lab.

Elizabeth: Thank you for having me. This was a great conversation.

Laurel: That was Elizabeth Tobey, who is the head of marketing, digital and AI at NICE, who I spoke with from Cambridge Massachusetts, the home of MIT and MIT Technology Review.

That’s it for this episode of Business Lab. I’m your host, Laurel Ruma. I’m the Global Director of Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can find us in print on the web and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.

This show is available wherever you get your podcasts. If you enjoyed this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review. This episode was produced by Giro Studios. Thanks for listening.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

Providing the right products at the right time with machine learning

Whether your favorite condiment is Heinz ketchup or your preferred spread for your bagel is Philadelphia cream cheese, ensuring that all customers have access to their preferred products at the right place, at the right price, and at the right time requires careful supply chain organization and distribution. Amid the proliferation of e-commerce and shifting demand within the consumer-packaged goods (CPG) sector, AI and machine learning (ML) have become helpful tools in enabling efficiency and better business outcomes.

The journey toward successfully deployed machine learning operations (MLOps) starts with data, says global head of machine learning operations and platforms at Kraft Heinz Company, Jorge Balestra. Curating well-organized and accessible data means enterprises can leverage their data volumes to train and develop AI and machine learning models. A strong data strategy lays the foundation for these AI and machine learning tools to use data to detect supply chain disruptions, identify and address cost inefficiencies, and predict demand for products.

“Never forget that data is the fuel, and data, it takes effort, it is a journey, it never ends, because that’s what is really what I would call what differentiates a lot of successful efforts compared to unsuccessful ones,” says Balestra.

This is especially crucial but challenging within the CPG sector where data is often incomplete given the inconsistent methods for consumer habit tracking among different retailers.

He explains, “We don’t know exactly and we don’t even want to know exactly what people are doing in their daily lives. What we want is just to get enough of the data so we can provide the right product for our consumers.”

To deploy AI and machine learning tools at scale, the Heinz Kraft Company has turned to the flexibility of the cloud. Using the cloud can allow for much-needed data accessibility while mitigating compute power.  “The agility of the whole thing increases exponentially because what used to take months, now can be done in a matter of seconds via code. So, definitely, I see how all of this explosion around analytics, around AI, is possible, because of cloud really powering all of these initiatives that are popping up left, right, and center.” says Balestra.

While it may be challenging to predict future trends in a sector so prone to change, Balestra says that preparing for the road ahead means focusing on adaptability and agility.

“Our mission is to delight people via food. And the technology, AI or what have you, is our tool to excel at our mission. Being able to learn how to leverage existing and future [technology] to get the right product at the right price, at the right location is what we are all about.”

This episode of Business Lab is produced in partnership with Infosys Topaz and Infosys Cobalt.

Full Transcript

Laurel Ruma: From MIT Technology Review, I’m Laurel Ruma, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.

Our topic is machine learning in the food and beverage industry. AI offers opportunities for innovation for customers and operational efficiencies for employees, but having a data strategy in place to capture these benefits is crucial.

Two words for you: global innovation.

My guest is Jorge Balestra, global head of machine learning operations and platforms at Kraft Heinz Company.

This episode of Business Lab is produced in partnership with Infosys Topaz and Infosys Cobalt.

Welcome, Jorge.

Jorge Balestra: Thank you very much. Glad to be here.

Laurel: Well, wonderful to have you. So people are likely familiar with Kraft Heinz since it is one of the world’s largest food and beverage companies. Could you talk to us about your role at Kraft Heinz and how machine learning can help consumers in the grocery aisle?

Jorge: Certainly. My role, I will call, has two major focuses in two areas. One of them is I lead the machine learning engineering operations of the company globally. And on the other hand, I provide all of the analytical platforms that the company is using also on a global basis. So in role number one in my machine learning engineering and operations, what my team does is we grab all of these models that our community of data scientists that are working globally are coming up with, and we grabbed them and we strengthened it. Our major mission here is the first thing we need to do is we need to make sure that we are applying engineering practices to make them production ready and they can scale, they can also run in a cost-effective manner, and from there we ensure that in my operations hat they are there when needed.

So a lot of these models, because they become part of our day-to-day operations, they’re going to come with certain specific service level commitments that we need to make, so my team makes sure that we are delivering on those with the right expectations. And on my other hand, which is the analytical platforms, is that we do a lot of descriptive, predictive, and prescriptive work in terms of analytics. The descriptive portion where you’re talking about just the regular dashboarding, summarization piece around our data and where the data lives, all of those analytical platforms that the company is using are also something that I take care of. And with that, you would think that I have a very broad base of customers in the company both in terms of geographies where they are from some of our businesses in Asia, all the way to North America, but also across the organization from marketing to HR and everything in between.

Going into your other question about how machine learning is helping our consumers in the grocery aisle, I’ll probably summarize that for a CPG it’s all about having the right product at the right price, at the right location for you. What that means is on the right product, their machine learning can help a lot of our marketing teams, for example, even when they are now with the latest generative AI capabilities are showing up like brainstorming and creating new content to R&D, what we’re trying to figure out what is the best formulas for our products, there’s definitely now ML is making inroads in that space, the right price, all about cost efficiencies throughout from our plans to our distribution centers, making sure that we are eliminating waste. Leveraging machine learning capabilities is something that we are doing across the board from our revenue management, which is the right price for people to buy our products.

And then last but not least is the right location. So we need to make sure that when our consumers are going into their stores or are buying our products online that the product is there for you and you’re going to find the product you like, the flavor you like immediately. And so there is a huge effort around predicting our demand, organizing our supply chain, our distribution, scheduling our plans to make sure that we are producing the right quantities and delivering them to the right places so our consumers can find our products.

Laurel: Well, that certainly makes sense since data does play such a crucial role in deploying advanced technologies, especially machine learning. So how does Kraft Heinz ensure the accessibility, quality and security of all of that data at the right place at the right time to drive effective machine learning operations or MLOps? Are there specific best practices that you’ve discovered?

Jorge: Well, the best practice that I can probably advise people on is definitely data is the fuel of machine learning. So without data, there is no modeling. And data, organizing your data, both the data that you have internally and externally takes time. Making sure that it’s not only accessible and you are organizing it in a way that you don’t have a gazillion technologies to deal with is important, but also I would say the curation of it. That is a long-term commitment. So I strongly advise anyone that is listening right now to understand that your data journey, as it is, is a journey, it doesn’t have an end destination, and also it’s going to take time.

And the more you are successful in terms of getting all the data that you need organized and making sure that is available, the more successful you’re going to be leveraging all of that with models in machine learning and great things that are there to actually then accomplish a specific business outcome. So a good metaphor that I like to say is there’s a lot of researchers, and MIT is known for its research, but the researchers cannot do anything without the librarians, with all the people that’s organizing the knowledge around so you can go and actually do what you need to do, which is in this case research. Never forget that data is the fuel, and data, it takes effort, it is a journey, it never ends, because that’s what is really what I would call what differentiates a lot of successful efforts compared to unsuccessful ones.

Laurel: Getting back to that right place at the right time mentality, within the last few years, the consumer packaged goods, or you mentioned earlier, the CPG sector, has seen such major shifts from changing customer demands to the proliferation of e-commerce channels. So how can AI and machine learning tools help influence business outcomes or improve operational efficiency?

Jorge: I’ve got two examples that I can say. One is, well, obviously we all want to forget about what happened during the pandemic, but for us it was a key, very challenging time, because out of nowhere all of our supply chains got disrupted, our consumers needed our products more than ever because there were more hunkered down at home. So one of the things that I tell you, at least for us, that was key was through our modeling, through the data that we’ve had, we’ve had some good early warning of certain disruptions in the supply chain and we were able to at least get… Especially when the outbreak started, a couple of weeks in advance, we were moving product, we were taking early actions in terms of ensuring that we were delivering an increased amount of product that was needed.

And that was because we had the data and we had some of those models that were alerting us about, “Hey, something is wrong here, something is happening with our supply chain, you need to take action.” And taking action at the right time, it’s key in terms of getting ahead of a lot of the things that can happen. And in our case, obviously we live in a competitive world, so taking actions before competition is important, that timing component. Another example I can give you and is more of something that is we’re doing more and more nowadays is this piece that I was referring to about the right location about product availability is key for CPG, and that is measured in something that is called the CFR, and is the customer field rate, which means is when someone is ordering product from Kraft Heinz that we are able to fulfill that order to 100%, and we are expecting to be really high with high 90s in terms of how efficient we are filling those orders.

We have developed new technology that I think we are pretty proud of because I think it is unique within CPG that allows us to really predict what is going to happen with CFR in the future based on the specific actions we’re taking today, whereas it’s changing our production lines, whereas changes in distribution, et cetera, we’re able to see not only the immediate effect, but what’s going to happen in the future with that CFR so we can really act on it and deliver actions right now that are in the benefit of our distribution in the future. So those are, I would call it, say, two examples in terms of how we’re leveraging AI and machine learning tools in our day-to-day operations.

Laurel: Are those examples, the CFR as well as the supply chain and making sure consumers had everything on demand almost, is this unique to the food and beverage industry? And what are perhaps some other unique challenges that the food and beverage industry faces when you’re implementing AI and machine learning innovations? And how do you navigate challenges like that?

Jorge: Yeah, I think something that is very unique for us is that we always have to deal with an incomplete picture in terms of the data that we have in our consumers. So if you think about it, when you go into a grocery store, a couple of things, well, you are buying from that store, the Kroger’s, Walmart’s, et cetera, and some of those will have you identified in terms of what is your consumption patterns, some will not. But also, in our case, if you are going to go buy a Philadelphia [cream cheese], for example, you may choose to buy your Philadelphia in multiple outlets. Sometimes you want more and you go to Costco, sometimes you need less, in my case, I live in the Chicago land area, I go to a Jewel supermarket.

We always have to deal with incomplete data on our customers, and that is a challenge because what we are trying to figure out is how to better serve our consumers based on what product you like, where you’re buying them, what is the right price point for you, but we’re always dealing with data that is incomplete. So in this case, having a clear data strategy around what we have there and a clear understanding of the markets that we have out there so we can really grab that incomplete data that we have out there and still come up with the right actions in terms of what are the right products to put, just to give you an example, a clear example of it is… And I’m going back to Philadelphia because, by the way, that’s my favorite Kraft product ever…

Laurel: Philadelphia cream cheese, right?

Jorge: Yes, absolutely. It’s followed by a close second with our ketchup. I have a soft spot for Philadelphia, pun intended.

Laurel: – and the ketchup.

Jorge: Exactly. No, but you have different presentations. You have the spreadable, you have the brick of cream cheese, within the brick you have some flavors, and what we want to do is make sure that we are providing the flavors that people really want, not producing the ones that people don’t want, because that’s just waste, without knowing specifically who is buying on the other side and you want to buy it in a supermarket, one or two, or sometimes you are shifting. But those are the things that we are constantly on the lookout for, and obviously dealing with the reality about, hey, data is going to be incomplete. We don’t know exactly and we don’t even want to know exactly what people are doing in their daily lives. What we want is just to get enough of the data so we can provide the right product for our consumers.

Laurel: And an example like cream cheese and ketchup probably, especially if a kid is in the house, it’s one of those products that you use on a fairly daily basis. So knowing all of this, how does Kraft Heinz prepare data for AI projects, because that in itself is a project? So what are the first steps to get ready for AI?

Jorge: One thing that we have been pretty successful on is what I would call the potluck approach for data. Meaning that individual projects, individual groups are focused on delivering a very specific use case, and that is the right thing to do. When you are dealing with a project in supply chain and you’re trying just to, for example, say, “Hey, I want to optimize my CFR,” you are really not going to be caring that much about what sales wants to do. However, if you implement a potluck approach, meaning that, okay, you need data from somebody else, and it’s very likely that you have data to offer because that’s part of your use case. So the potluck approach means that if you want to try out the food of somebody else, you need to bring your own to the table. So if you do that, what starts happening is your data, your enterprise data, becomes little by little more accessible, and if you do it right eventually you pretty much have a lot and almost everything in there.

That is one thing that I will strongly advise people to do. Think big, think strategically, but act tactically, act knowing that individual projects, they’re going to have more limited scope, but if you establish certain practices around sharing around how data should be managed, then each individual projects are going to be contributing to the larger strategy without the largest strategy being a burden for the individual projects, if that makes sense.

Laurel: Sure.

Jorge: So at least for us that has been pretty successful over time. So we have data challenges absolutely as everybody else has, but at least from what I’ve been able to hear from other people, but Kraft Heinz is in a good place in terms of that availability. Because once you reach a certain critical mass, what ends up happening is there’s no need to bring additional data, you are always now reusing it because data is large but it’s finite. So it’s not infinite. It’s not something that’s going to grow forever. If you do it right, you should see that eventually, you don’t need to bring in more and more data. You just need to fine-tune and really leverage the data that you have, probably be more granular, and probably get it faster. That’s a good signal. I have the data, but I need it faster because I need to act on it. Great, you’re on the right track. And also your associated cost around data should reflect that. It should not grow to infinity. Data is large but is finite.

Laurel: So speaking of getting data quickly and making use of it, how does Kraft Heinz use compute power and the cloud scaling ability for AI projects? How do you see these two strategies coming together?

Jorge: Definitely the technology has come a long way in the last few years, because what cloud is offering is more of that flexibility, and it’s removing a lot of the limitations, both in terms of the scale and performance we used to have. So to give you an example, a few years back I had to worry about “Do I have enough storage in my servers to host all the data that we are getting in?” And then if I didn’t, how long is it going to take for me to add another server? With the cloud as an enabler, that’s no longer an issue. It’s a few lines of code and you get what you need. Also, especially on the data side, some of the more modern technologies, talking about Snowflake or BigQuery, enable you to separate your compute from your storage. What it basically means in practical terms is you don’t have people fighting over limited compute power.

So data can be the same for everyone and everybody can be accessing the data without having to overlap each other and then fighting about, oh, if you run this, I cannot run that, and then we have all sorts of problems so definitely what the cloud allowed us to do is get out of the way in terms of the technology as a limitation. And the great thing that happened down there now with all the AI projects is now you could focus on actually delivering on the use cases that you have without having to have limitations around “how am I going to scale?”. That is no longer the case. You have to worry about costs because it could cost you an arm and a leg, but not necessarily around how to scale and how long it’s going to take you to scale.

The agility of the whole thing increases exponentially because what used to take months, now can be done in a matter of seconds via code. So definitely I see how all of this explosion around analytics, around AI is possible, because of cloud really powering all of these initiatives that are popping up left, right, and center.

Laurel: And speaking about this, you can’t really go it alone, so how do partners like Infosys help bring in those new skills and industry know-how to help build the overall digital strategy for data, AI, cloud, and whatever comes next?

Jorge: Much in the same way that I think cloud has been an enabler in terms of this, I think companies and partners like Infosys are also that kind of enablers, because, in a way, they are part of what I would call an expertise ecosystem. I don’t think any company nowadays can do any of this on its own. You need partners. You need partners that both are bringing in new ideas, new technology, but also they are bringing in the right level of expertise in terms of people that you need, and in a global sense, at least for us, having someone that has a global footprint is important because we are a global company. So I will say that it’s the same thing that we talked about earlier about cloud being an enabler: that expert ecosystem represented by companies like Infosys is just another key enabler without which you will really struggle to deliver. So that’s what I’ll probably say to anyone that is listening right now, make sure that your ecosystem, your expert ecosystem is good and is thriving and you have the right partners for the right job.

Laurel: When you think about the future and also all these tough problems that you’re tackling at Kraft Heinz, how important will something like synthetic data be to your data strategy and business strategy as well? What is synthetic data? And then what are some of those challenges associated with using it to fill in the gaps for real-world data?

Jorge: In our case, we don’t use a lot of synthetic data nowadays because at least from the areas that we have holes to fill in terms of data is something that we’ve been dealing with for a while. So we are, let’s put it this way, already familiar on how to produce and fill in the gaps using some of the synthetic data techniques, but not really to the same extent as other organizations are. So we are still looking for opportunities when that is the case in terms of what we need to use and leverage synthetic data, but it’s not something that least for Kraft Heinz and CPG at all we use extensively in multiple places as other organizations are.

Laurel: And so, lastly, when you think ahead to the future, what will the digital operating model for an AI-first firm that’s focused on data look like? What do you see for the future?

Jorge: What I see for the future is, well, first of all, uncertainty, meaning that I don’t think we can predict exactly what’s going to happen because the area in particular is growing and evolving at a speed that I think is just honestly dazzling just because of the major things. I think at least what I would say is the real muscle that we need to be exercising and be ready for is adaptability. Meaning that we can learn, we can react, and apply all of the new things that are coming in hopefully at the same speed that they’re occurring and really leveraging new opportunities when they present themselves in an agile way. But at least from the how to prepare for it I think it’s more about preparing the organization, your team, to be ready for that, really act on it, and be ready also to understand the specific business challenges that are there, and look for opportunities where any of the new things or maybe existences that are happening can be applied to solve a specific problem.

We are a CPG company, and that means the right product, right price, right location, so anything boils down to how can I be better in those three dimensions leveraging whatever is available today, whatever’s going to be available tomorrow. But keep focusing on, at least for us, we are a CPG company, we manufacture in Philadelphia, we manufacture ketchup, we feed people. Our mission is to delight people via food. And the technology, AI or what have you, is our tool to excel at our mission. Being able to learn how to leverage existing and future to get the right product at the right price at the right location is what we are all about.

Laurel: That’s fantastic. Thank you so much, Jorge. I appreciate you being with us today on the Business Lab.

Jorge: Thank you very much. Thank you for inviting me.

Laurel: That was Jorge Balestra, global head of machine learning operations and platforms at Kraft Heinz Company, who I spoke with from Cambridge, Massachusetts, the home of MIT and MIT Technology Review.

That’s it for this episode of Business Lab. I’m your host, Laurel Ruma, I’m the director of insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can also find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.

 This show is available wherever you get your podcasts. If you enjoyed this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review. This episode was produced by Giro Studios. Thanks for listening.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.