Author: Shelby Golan

Today we welcome Todd James, Chief Data & Technology Officer at 84.51º, to The CXO Journey to the AI Future podcast. He joined 84.51º in August 2021, where he paved the way for continued growth, building on its rich data, science, and technology capabilities. Previously, Todd spent 15 years at Fidelity Investments where he held a variety of key strategic leadership roles. As an innovative leader, Todd built the global data and analytics organization for Fidelity’s Workplace Investing and Health Care business units. He also led efforts to modernize servicing and operations through applied artificial intelligence and automation and directed Fidelity’s Cross-Enterprise AI Center of Excellence (COE).

Question 1: First Job: Could you talk a little bit about your background and how you got to the position where you are now?

We’re a retail data science organization that is part of The Kroger Company. As I like to think about it, what we do is we help Kroger run a media operation that goes to market.

We also have an insights business that helps CPGs and partners understand the path to purchase, and a venture capital fund called PearlRock Partners that invests in emerging food services businesses. Additionally, we’re the advanced analytics arm of the parent company of Kroger. So when it comes to AI and advanced analytics across Kroger, we’re tasked with driving a lot of those results across the organization.

It’s interesting that throughout my career I’ve moved between business leadership roles and technology leadership roles. So while I was at Fidelity, I held a variety of responsibilities from running business units to strategizing the organization. The tail end of my tenure there was really around building out data and advanced analytics capabilities for the workplace, investing in healthcare businesses, and setting up the global capabilities for that. So I had a great opportunity working at Fidelity to do a lot of different things, including help build out the advanced analytics and AI journey that they’re on today.

Question 2: Generative AI: Everybody’s talking about it. You’ve spent a lot of time with data and analytics for years. Could you put a little context around Generative AI? How is the Gen AI movement different?

It’s different, but a lot of the capabilities aren’t necessarily new. There are things happening today that we’ve already been doing for 5-7 years. The difference is that what was very difficult to do (and on a limited scale from a usage POV), has become a lot easier to implement. It’s far more democratized.

The capabilities are greatly expanded relative to some of the natural language solutions that we were working with several years ago. As I look at it, I think the democratization of AI is what has been unlocked. We put incredible power, in terms of prediction, into the hands of more people. And I think that will open up all kinds of opportunities to simplify and enhance customer experiences, making the lives of our associates easier. So I do think there’s a bit of a shift, and a lot of it has to do with accessibility.

Question 3: What are some of the early learnings in Generative AI for you?

First of all, if you talk to most people, I think the rate of change we’ve become accustomed to continues to compress. What we’ve seen over the last year, and even what we’ve seen with some of the recent announcements over the past couple of weeks, is that the acceleration of capabilities and the acceleration of benefits that you can get from this technology, is unprecedented.

So how you think about your business, how you think about managing it, and how you think about up leveling it has put a lot of pressure on existing structures. I think from my perspective, our goal has been to solve how you shift from a world in which the majority of your analytics are developed, supported, and maintained within an analytics organization, to one in which the analytics organization is part of a much bigger ecosystem in which businesses are using out-of-the-box capabilities with AI, third-party vendors are coming in, and the capability is being deployed far more  broadly. So that’s a bit of a learning curve.

The good news is, if you have solid processes in place, and you have solid controls, then you have a basis around which you can start to build things out. I would imagine that if you don’t, you’re probably feeling rather exposed right now.

Today, everywhere we have a decision point being executed either by a person, through automation, or via AI (through prediction), we always talk about how to have the right control process and the right control framework. If it’s in a manufacturing facility you may have in-line quality checks. If it’s technology, we have deployment checks and monitoring around that. It’s no different, I think, with AI. You need to embed your solutions within a control framework. And that was probably a little bit easier to manage when everything was coming out of an analytics organization. Now you have to think about that at a different scale. So that’s definitely one of the learnings.

Another big one for us was engaging the business on the journey. To some extent, the exhilaration and excitement in the media around Generative AI has grabbed a lot of attention. And it’s grabbed a lot of attention, not just for Generative AI, but for how advanced analytics and data can have an impact and transform the business in general. So there’s been a real opportunity to have more discussions around the organization that enables us to work on some of the change aspects and educational awareness with business leaders.

The third learning is that you have to scale. It’s very hard to generate value through individual point solutions. But…if you build a capability in a way that’s beneficial on one decision point, and then another decision point, and another decision point…you can do that at scale. And that’s where you start to see some of the opportunities.

Some of the solutions out there are inherently built to scale, but I think a lot of the use cases basically amount to: solve this problem here, solve that problem there. But what you come to realize is that these solutions are just text summarization around a particular set of information that could potentially be used by a much broader range of employees. Ideally, you want to make that available so you can tailor it to individual populations. That’s where you can start to see some of the real benefits.

Bonus: This concept of ‘Buy versus Build’ is often a technology decision that you, as an IT leader, have to make. And with Generative AI, it feels like we’re at the beginning of a whole new set of technologies. What’s the ‘buy versus build’ thesis that you believe we should be thinking about?

I think it’s a little bit of a forcing function because of the speed and pace in the space today. The underlying fundamentals on this haven’t really changed. We should be building things in-house that are truly proprietary and differentiate us on a competitive level. That’s where I would prefer to put our assets and our professionals who are focused on building data and analytics products.

However, what I’m seeing is that the platforms are accelerating a lot of the capabilities that they’re offering from a Generative AI point of view, especially around commoditized capabilities, and we want to be able to leverage where they’ve already put in the work. So we’re additionally having discussions with our partners about what’s on their roadmap. For example, there have been a few cases where we saw tremendous value in leveraging Generative AI to better equip people to answer questions. However, after talking to our partners, we realized that was already on their roadmap. So we’ll apply our resources in other areas for the time being.

The third consideration around “buy vs. build” is that there are certain cases where we see unique emergent capabilities that we don’t currently possess the skill to advance. For example, startups that sit outside the large platform providers. Many startups have very unique capabilities on the outside that can accelerate a niche area where we want to drive value. So we’re working with emerging technologies as well.

I think because of the pace at which everything is moving, some things we thought were pretty novel ideas, we realized, were cases that just about every company wants. So we need to figure out just how much of the world is already on the large providers’ roadmaps. This enables us to assign our resources to other tasks that help us become more competitive.

Question 4: What are some of the issues, or maybe even gaps, in technology that you believe need to be addressed? I know this isn’t a technology-only issue. What are the headwinds that you have to address?

You have to think about scale to really understand the gaps. Point solutions are easy. You can go online and say: “Give me a recipe for a rustic Italian meal that is inspired by this part of the Italian coast.” Anyone can do that. It makes it seem very easy. It sets everyone’s expectations super high, before they start to see the gaps.

The real challenge is when you want to evolve it at scale in a way that can support your business. First, the tech stack starts to evolve. We went from a traditional tech stack that we had become fairly accustomed to, to a data and analytics tech stack. Now, we have to figure out how we want to incorporate it into both market-based and proprietary LLMs, and additionally fit everything into our architecture and the supporting set of capabilities around it. A lot is still being defined, it’s very new. The majority of companies, unless you’re actually a tech services provider building these capabilities, probably have a gap now because it’s novel.

For the other gaps, I think we need to take a hard look at changing the way we work. One of the discussions that we’ve been having with the business is: How are these capabilities going to change how we work? What are the skills and competencies that we need to start building within our organization? How do we help our people on that journey? Where are we incorporating new tools? Where are we working with our teams to grow?

You have to shepherd people along the path. Not because people don’t have the capability, but because it’s a chain. You need coding, but you also need prompt engineering. To some extent we now have solutions that we’ve deployed inside the business, and we’re working with people that are not technologists. So we have to ask: How do you leverage some of the Gen AI capabilities that we provided? How do you write prompts? How do you write good prompts? How do you evolve? How do you get the right information out? So initially it’s going to be about raising everyone’s skills and knowledge on the business side.

The final gap is going to be ensuring that this technology, like any manual or technical process that you’re building out, has the right, and most efficient, controls.

At the end of the day, the mindset needs to be less “I’m going to build an individual chair as a craftsman” and more “I need to build a factory that can produce furniture at scale.” The same thing is true here. I don’t need a use case. I need a system that can support multiple use cases in a way that is efficient, effective, reliable, and responsible.

Question 5: Responsible AI: How do you think about it? What does it mean to you?

It means a few different things. One of which I would say is that no matter what industry you’re in, you’re in the business of trust. You need to make sure that you’re thinking about responsibility and you’re thinking about how you’re using this technology in the right way.

Additionally, we want an environment where we’re bringing in and hiring people who can feel confident about the processes we’re going to exhibit and sustain, and the ethics around how we’re doing work. So we’ve put a lot of time and effort into refreshing some of the practices that we had around responsible AI. This includes enabling them to be ported across the broader organization, which brings us back to that concept of democratization. We have a framework in place that’s pretty focused on making sure that our AI is reliable and performative. It does what it’s supposed to. It’s compliant with privacy and trust. It’s secure. But also: It’s safe. We’re in the business of food. We want to ensure that our algorithms are performing in a way that accounts for the safety of our associates, our customers, and society at large. There needs to be accountability as we deploy these solutions, which goes along with transparency and explainability.

Finally, the other key aspect that we take a hard look at is around fairness. At the end of the day, our goal at 84 51º is to make people’s lives easier.

I’m very fortunate to be in an organization where we want to have a positive impact on the world. We want to make sure that what we’re doing from an analytics perspective reflects all those values, and all those ideals, both inside the company, but also with regards to the people we attract to come work here, who have similar standards and similar expectations.

Todd James is currently the Chief Data & Technology Officer at 84.51°, a retail data science, insights, and media company helping The Kroger Co., consumer packaged goods companies, agencies, publishers, and affiliated partners create more personalized and valuable experiences for shoppers across the path to purchase.

A driver of digital transformation, Todd spent 15 years at Fidelity Investments where he held a variety of key strategic leadership roles. An innovative leader, he built the global data and analytics organization for Fidelity’s Workplace Investing and Health Care business units. He also led efforts to modernize servicing and operations through applied artificial intelligence, and automation and directed Fidelity’s Cross-Enterprise AI Center of Excellence (COE).

Prior to Fidelity, Todd led a strategy consulting practice at Deloitte, directing strategic engagements with global Fortune 500 and government clients. As a director at Resource Consultants, Inc. he built and led a technology services business unit while also overseeing corporate IT. Prior to his business career, Todd was an officer in the U.S. Coast Guard where he held leadership roles in IT, information security, and shipboard operations.

Aside from a B.S. in Mathematics and Computer Science from the U.S. Coast Guard Academy, Todd also earned an MBA from The College of William and Mary, and a M.S. in Computer Science from the University of Illinois. He is an editorial board member for CDO Magazine.

Today we welcome Sathish Muthukrishan, Chief Information, Data, and Digital Officer at Ally to “The CXO Journey to the AI Future” podcast. He joined Ally Financial in January 2020 with a focus on modernizing processes, teams, and technology and driving transformative business outcomes across multiple Fortune 500s.

Question 1: First Job: Could you talk a little bit about your background and how you got to the position where you are now?

Today I serve as Chief Information, Data, and Digital Officer, and it’s a really huge privilege to be serving under that title at a digitally native bank. Because fundamentally, everything we do centers around technology. It’s a dream job for any IT leader who wants the accountability and responsibility to make everyone else around them look extremely successful.

I originally grew up in the airline world, starting with Singapore Airlines and then United. I later moved onto other verticals: American Express and Honeywell before joining Ally Financial.

Question 2: Generative AI: Everybody’s talking about it. Undoubtedly you have been doing AI analytics and other things in and around automation for years. Could you put a little context around Generative AI? Is this the highest priority for you right now?

I may have a slightly different philosophy and take, but I’ll get to your question. I’ve believed for a long time that we should never lead with technology. If you lead with technology, you take your eye off what’s important, which are the capabilities you’re building for your customers. The technology must be relevant to the customer, and must drive business impact.

So we’ve always led with asking whether or not this is the right capability for the customers. Is it creating the right business impact? And then you figure out what technology you have to use to build that.

However, your question is very important right now, because sometimes a technology will come around that can disrupt every industry and every company out there globally. Gen AI may accomplish exactly that. So, there’s an extreme focus on how we can securely (and scalably) utilize gen AI, while still keeping in mind our two core principles:

• What business impact are you creating?
• What customer value are you producing?

Question 3: New Metrics: There are lots of use cases where Generative AI could be used. How will you go about deciding where to put attention? How do you go about defining the right metrics for an AI deployment? Is it productivity? Is it business impact? What are those metrics that matter?

Everybody is thrilled and excited to start using gen AI as you can imagine, and we have taken a very simplistic approach: Anything that we do with gen AI should either drive efficiency or drive effectiveness. Efficiency means an additive technology to make your everyday job better, easier, or more productive. Effectiveness is driving real business impact and results.

So how do we go about that? We know that this isn’t a technology that can be executed within IT – the entire company will have to participate, and it will have to become a revolution. So we created what we call an Ally AI Playbook. The AI playbook provides a platform for anybody who has an idea to take that idea to production. This could mean finding a path, figuring out the governance, learning who the right people to engage with are, getting access to the tooling, and moving everything forward.

In order to bring this playbook to life, we’ve created a step-by-step process:

1. It starts with what we call AI days. These are half-days where we talk about AI to the entire company. We’ve had thousands of people participate from within the business, across all organizations. We bring in outside speakers. But more importantly, we bring in use cases that we’re working on to show everyone what success looks like
2. Afterwards, people will come up with ideas, and we’ll guide them to the AI playbook by using a persona
3. We’ve created a central AI factory team that gives them access for 30 days. So, they can take their idea and start experimenting with it. At the end of the 30 days, they prepare a whitepaper to demonstrate whether it’s hitting the efficiency or effectiveness mark. If it is, we take that through the production runway.
4. Once on that runway, we figure out the elements around risk and security, and whether or not the idea is useful. Then, it can be taken to production.

That’s how we’re approaching our AI use cases.

Bonus: Everyone has been moving towards SaaS and no one is building their own custom solutions anymore. Do you believe that to be true? What’s the ‘buy versus build’ thesis with regards to AI that we should be thinking about right now?

Oh, what a great question to ask! And the right one to ask right now.

My take is that it’s buy and build, not buy versus build. Now I’ll tell you why. There are several reasons:

The first one is if you go buy generative AI and use it, you’re going to produce the same outcome as every other company. So how are you going to differentiate yourself?

And that leads to number two: If you go and buy off-the-shelf software or any SaaS offering for that matter, they’re using AI. And if you don’t play with it yourself and understand how it works, how are you going to leverage the AI capability that someone else is providing?

Finally, cybercriminals have the same access to this technology that we have. They’re trying to use it for proactive measures. They’re trying to use it to get a compromise from any company. So how do you then protect against that?

For all of these reasons, if your organization does not fully understand the impact that AI can provide, and if your technology team does not have a hands-on understanding of the technology, you will be left far behind.

And that’s why we say: buy and build. The way it has come to life at Ally is that we quickly understood that this technology is moving way too fast and new competitors are coming every day, so we created what we call an Ally AI Platform. You can think of it as a bridge between the internal Ally and all its applications, and the external AI capabilities. It gives me access to go interact with a model, let’s say, OpenAI, which is what we’re connected with today. If I don’t like it, or if I want an additional model to compare it to, I can go and connect with a LLaMA model or a Bard, and we’re doing that in our lower environments. But in our production environments, we are just connected to OpenAI. So the Ally AI platform gives me the ability to point to multiple models and either choose the best option or get all the outputs and compare (or even combine them) and make it more relevant to Ally.

Question 4: What are some of the issues, or maybe even gaps in the technology, that you believe need to be addressed? I know this isn’t a technology-only issue.

There are different buckets and each of them has their own weight in terms of how strong these headwinds are.

The one that I particularly worry about is third-party risk. We have an existing footprint in terms of using third-party technologies. Do we feel comfortable? Have they secured their tech environment the way we would secure ours given the advancement in security threats?

And like I said, gen AI has democratized cyber attacks. The same cyber attack that’s going after the financial industry can go to the healthcare industry. So, how do we protect ourselves?

The second is the sophistication of these cyber threats. It’s become emotional. And it’s turning into parallel attacks, where you have to be on your toes and keep educating your entire organization all the time. That’s the second headwind that I think about.

If I look at it internally, what do I have to do? First is education. You can’t leave the organization behind. So you have to be patient in educating everyone and showcasing the success that gen AI brings. Show them that this is not rocket science. Everybody can use it. English is the most popular language in gen AI, so everyone can play a part.

I come from a regulatory organization. The regulators control partners’ risk compliance and legal audit and help them understand what we’re implementing and proactively identify risks that come along with it. This way they can twist their brains to figure out what other risks there are.

We need to push the organization forward, but if we’re upfront about all the risks that we’ve identified, they can help us mitigate those risks. So that’s another headwind in terms of educating and bringing the organization along.

Then finally, there’s talent. How can you keep your talent challenged and give them better opportunities? You want them to stay with your company and not leave to join someone more sophisticated.

Question 5: Responsible AI: How do you think about it? What does it mean to you?

As a regulated institution, I have to think, breathe, speak, and eat Responsible AI.

One, because we need to have consistency of service. Our goal is to be a fair and responsible bank that mitigates model drift and bias, and this all starts with data cleanliness and accessibility. Further, we focus on testing our models ourselves, as well as the outcomes across various customer segments – to ensure that bias is not introduced.

Then, our independent risk organization tests the models out. For the first use case that we launched, they looked at 60,000 outcomes to make sure that there wasn’t model drift or bias introduced.

That’s from a business perspective.

From a social perspective, I worry even more. In this decade and this era, you have a lower chance of succeeding if you are not digitally connected.

You and I probably don’t think twice before we pick up our smartphones to connect and do what we do every day. But millions of people don’t have access to the internet, they don’t have access to a smartphone, or don’t have the education or opportunity to learn how to efficiently use them.

Gen AI, if not appropriately executed, implemented, and integrated within society, is going to widen the digital divide. And chances are it will become a digital abyss.

So, tech leaders are responsible for modernizing their organization with gen AI because they understand it the best. They can connect the dots. The public-private organizations have to make sure that they pay it forward to create a society that doesn’t become more divided on account of these digital advancements.

Sathish is currently the Chief Information, Data, and Digital Officer at Ally, a leading digital financial services company. Reporting to the CEO and a member of the company’s Executive Committee, he is responsible for leading product, user experience, data, digital, technology, security, network, and operations – an end-to-end role in the midst of everything that happens in the digital native organization.

Most recently, he was Honeywell Aerospace’s first CDIO with a charter to digitize the Aerospace businesses. As an example, he successfully transformed the used serviceable airline parts into a digital business powered by blockchain. He was also responsible for leading and transforming Honeywell AERO’s 10,000-plus global engineering organization.

Sathish was one of the pioneers of American Express’ digital transformation. He led the launch of several unique, industry-first, groundbreaking digital products enabling strategic partnerships with Foursquare, Facebook, Twitter, Microsoft, Apple, Samsung, and TripAdvisor steering the American Express journey from payments to commerce. He delivered the ability for Card Members to Pay with Points creating a new currency for payments.

Sathish’s experience in several technology leadership positions with Honeywell, American Express, United & Singapore Airlines has led to over 30+ patents in the manufacturing, payments, and digital technology space.

Today we welcome Balaji Viswanath, Managing Director of Emerging Tech, Enterprise Architecture, Software Engineering and RPA at Tyson Foods to The CXO Journey to the AI Future podcast. He joined Tyson in June 2018 as Senior Director of IT Architecture. Balaji is a Technology and Data Science executive with 25+ years of experience in his field.

Question 1: First Job: Could you talk a little bit about your background and how you got to the position where you are now?

I’ve had the privilege of being in the technology industry for about 26 years or so now. The first twenty of which I spent in a core technology company, and the last six in Tyson Foods. If I look back at my career, I’d say I spent the first third or so building technology, products, and solutions that we could use in-house. The latter portion was predominantly spent trying to deploy technology in a way that added business value.

If you look at core technology areas like e-commerce, digital marketing, big data, or very recently, Generative AI, you see over and over again that everyone is spending a ton of time in analytics. Everyone wants scalable analytics platforms that enable the business to gain actionable insights from their data.

Over the years it’s been a privilege to be part of high-performing teams. Recently, everyone is very passionate about what Gen AI can do for the industry and what Gen AI can do to add value at Tyson.

Question 2: Generative AI: Everybody’s talking about it. But you, as a business leader, have seen other cycles before. Could you put a little context around Generative AI? Is this the highest priority for you right now

I’m glad you asked that, Gamiel. From my experience and understanding, a very significant amount of prioritization has to be applied to AI today. During my day job, I speak to at least ten vendors a week or ten new product companies a week that are talking about AI in some shape or form. I do believe that the predictive components of what we do in our day-to-day jobs are starting to be omnipresent and very ubiquitous.

AI is different primarily because it’s evolving so rapidly. There’s a sense of “Oh, am I missing out?” So there’s an evolution, a paradigm shift. If you look back at the last year after the introduction of ChatGPT and its capabilities, I think the leap that Gen AI has taken as a technology has led to new products and new offerings every day. So, it’s starting to become a huge priority for most of the companies I speak to, including ours.

Extra question: You’ve been in technology for a while now. Could you talk about some early learnings? What lessons or even advice would you give to somebody who’s in an IT leadership position when it comes to taking advantage of Generative AI?

There are a few things I could talk about.

First: Start with the business problem. AI and data science, in an unprecedented manner, are forcing a convergence between what was traditionally IT and what was traditionally business. I think data science is bringing together these two disciplines very efficiently. So, start with the business problem and don’t go after tech for the sake of tech, or AI for the sake of AI.

The second thing I would say is to chew in “bite-size chunks.” One of the ways we try to explore Gen AI and try to bring it into our company is by starting with a small business problem. Engage very closely with your business partners, deploy something as quickly as you can, and see if you can get value out of what you deploy. And if you see value, then you expand on it a little more and make it big. But if you don’t see value quickly, it’s easier to fail fast and move on to the next thing.

And keep your options wide open. Today, every hyperscaler that we work with has a significant AI offering. It would be, in my opinion, short-sighted to think that we go with one and only one. I think all these offerings that we need are very nuanced in themselves, and I think having MLOps and foundational platforms that allow us to exploit the best capabilities from each and every one of these offerings would also be a key learning that I’ve gathered over the last few months.

Question 3: New Metrics: You and I talked a little bit about your way of measuring impact or metrics. There are lots of use cases where Generative AI could be used. How will you go about deciding where to put attention? How do you cut through all of the potential use cases with a metric that matters? And what are those metrics that matter?

That’s a very good but very complex question. This is where I probably have the greatest number of discussions in my day job. How do you determine the value of something once it’s deployed? Today, there are broadly two kinds of value that we look at.

One is the value that impacts the bottom line, which is around efficiencies and manufacturing…anything we can do to get more efficient as a company. And what AI and Gen AI can do to help us get more efficient.

And the other area of value that we look at is what we call “top-line value and growth.” Does this help us improve our top-line growth?

There’s also a significant amount of thought around how we can employ AI to improve our employee experience across the company.

I met a few people recently that were specifically focused on using AI to do opportunity sizing. So we can say, “Hey, even before you embark on the opportunity, what AI can we use to size this opportunity?”

So that’s our approach. We engage with our business partners and try to get as close to articulating value as possible (in terms of a dollar figure). We approach problems in small iterative chunks, fail fast, move on to the next thing, and hopefully, we’ll arrive at a good value proposition for a large AI project.

I will tell you, though, that an integral aspect of value is essentially the benefit minus the cost. So one of the things that we’re working on actively with all our partners is: “What does Generative AI really cost?”

We have still yet to meet a company that has truly figured this out. Because there’s a computing aspect to it. There’s a training aspect to it. There’s a big data aspect to it. There’s an analytics aspect to it. So we’re hyper-focused on trying to understand what Gen AI really costs and use that to understand its true value.

You’ve been in an industry that has been in the process of transforming its landscape to a cloud-based, SaaS model, and that mostly doesn’t build custom solutions anymore. Do you believe that to be true? What’s the ‘buy versus build’ thesis that you believe you should be thinking about?

Being in enterprise architecture, this is a question that we are confronted with very frequently. Do you buy? Do you build? We started off with a very traditional base layer type approach where we said: “Hey, if it’s a system of record and if somebody in the industry already does it extremely well, then why don’t we go buy it instead of trying to build it?” Versus anything we think is differentiated, we try and build.

When you talk about this traditional tenant, as far as AI is concerned, especially for Generative AI, it’s too early for us to see if we have to buy, or if we have to build. I do think there are use cases where it makes a lot of sense for us to build. I think there are two ways of looking at this. One is either we send our data out or we bring the AI in.

So, there are a few use cases where it makes sense for us to bring open source or other models inside the company, train them with our data, deploy, and see value.

There are a few other bigger use cases where we need the firepower of externally well-trained, hyper-scaler hosted, managed models. We typically build a strong decision tree around this.

Question 4: What are some of the issues, or maybe even gaps in technology that you believe need to be addressed?

Like I already mentioned, I think the first thing we’re trying to figure out is: What is the true cost of Generative AI? What is the true cost of deploying and scaling a Gen AI product inside a company like ours and seeing value? So that’s our first area we’re trying to understand.

And from a skills standpoint, prompt engineering is starting to become an extremely important skill. I did speak with a few people recently who were starting to invest in AI through utilizing prompt engineering which I thought was very, very interesting.

Prompt engineering is a skill that we’re starting to understand and is starting to be a multi-disciplinary thing. It’s not so much only a pure technology play. It’s not like you get the best programmer to write the best prompts. It’s starting to become a very interdisciplinary thing.

And last, but not the least, compute. We have been in discussions with a few people who are willing to deploy AI models for us. The cost at this point is quite untenable without understanding the value. So, I believe that achieving a balance between cost and value is another area or a gap that the industry will need to focus on and address pretty quickly.

Responsible AI: How do you think about it as an IT leader? What does it mean to you?

There’s a lot of discussion about this, I’m sure, in most companies. For us, there are a few things that we go off of.

First, anything that we build, we very clearly say, “Do not let this be the only decision point. This is but an input into overall decision making.” So, in a sense, AI can possibly do a task, but a job is a very different thing. A job includes cognitive aspects and we don’t believe we are there yet with AI as an industry. The cognitive aspect isn’t close to where human beings are at.

So, we don’t put AI in places where we really need to make decisions. We look at AI as an augmented decision-maker as opposed to being a decision-maker in itself.

The second reason for concern is that we don’t indiscriminately send data outside the company to external AI models. We are very careful about how we use our data and treat it as an asset.

Finally, we focus a lot on explainability. We try to understand why an AI model, or a Generative AI model, provides the inferences that it does. There is a team that’s starting to look at explainability in AI.

Balaji Viswanath, Managing Director of Emerging Tech, Enterprise Architecture, Software Engineering & RPA at Tyson Foods to our CXO of the Future Podcast (“The Three Questions” edition). He joined Tyson in June 2018 as Senior Director of IT Architecture. Balaji is a Technology and Data Science executive with 25+ years of experience in his field.

He is a technology leader with a passion for applying technology to transform businesses at fundamental levels. He is particularly passionate about bringing in new technologies to solve business problems and build high-performing teams. Balaji has extensive experience working with business groups across verticals to envisage best-in-class digital experiences and translate them into robust Technology solutions.

His core competencies lie in working with business partners to identify large value-add opportunities by applying Data Science, Machine Learning, and other cutting-edge technologies.

He has led several Digital Transformation and Multi-country/Multi-Channel global platform rollouts in E-commerce, Digital Marketing, Web, Sales IT, CRM, and Supply Chain domains.

During his career, he has implemented state-of-the-art technologies such as Adobe Experience Manager, Intershop Enfinity, Drupal, etc. across large-scale enterprise IT. He is also a proven Change Agent, and has led several teams through grassroots organizational changes and transformations including transitions to managed services.

Today we welcome Maria Latushkin, a technology and product executive who has spent over 20 years leading Product, Engineering, and Operations teams transforming technology for global brands including Walmart, Peet’s Coffee, and most recently Albertsons.

As Group Vice President of Technology and Engineering at Albertsons, Maria is responsible for all aspects of technology for retail, payments, and supply Chain, thinking through the future of retail technology and spearheading the digital transformation of the supply chain space.

Prior to her current position, Maria held CTO roles at various consumer technology companies including Omada Health and One Kings Lane, and led technology and product teams at Peet’s Coffee and Walmart.com.

Question 1: First Job: You’ve had a very technical background for a long time. It’s awesome to have a female leader in technology. Could you talk a little bit about your background and how you got to the position where you are now?

I’ve actually had quite a meandering career. But I like to think about it as all of the stars aligning for this specific purpose in life. I’ve been in large companies like Walmart, and really small Series B companies, with most of my focus around some sort of consumer experience.

I’ve also ventured out into healthcare. And now working for Albertsons, I feel like this is all coming together. There are both retail and healthcare aspects in my current role, within wellness and the pharmacy. And it’s really about trying to create better value, improve consumer lives, and develop relationships with the customer through the technology that we leverage.

It’s really exciting to be where we are, where the whole industry is being redefined.

Question 2: Generative AI: Everybody’s talking about it. But you, as a business leader, have seen other cycles before. Could you put a little context around Generative AI? Is this the highest priority for you right now?

I’ve seen these different innovation hype curves, but this one feels different for me. And the reason it feels that way is that it’s not just a new technology, it has the potential to fundamentally transform how we work (as opposed to just doing things faster). And that’s why I find it both exciting intellectually, but also transformative from the business perspective.

It can create or amplify the business models that we have right now and unlock new opportunities and capabilities for us. So it’s something that’s a very high priority.

And, as always, when you have something new, you also want to be careful about how you introduce it into the workplace, and the enterprise. You want to be responsible about it. We have this joke about Generative AI: “With great power comes great responsibility.” It’s a very powerful technology with a very powerful capability that must be governed appropriately. So there’s a lot to think about, and a lot to do. And then it’s: How do you scale it? How do you make it a fundamental piece of your enterprise business?

Maybe you can unpack that for us. I hear a lot of CIOs are doing experiments internally, but at the same time are focused on employee education. Could you talk about some early learnings?

There are a few different areas we need to focus on: first, investment is really hot right now. We’ll need to invest ourselves, and also become better and smarter through partnerships with other companies that have made their own smart investments. When you think about all the copilot capabilities, I do believe this will help enterprises to be much more effective.

And then finally, you need to think about the solutions that you create as a development organization. These will likely be more unique, more bespoke. Something that has specific value at a given company, and every company will be different.

Generative AI is still new and it’s already wonderful at certain things (while still learning others). There are going to be more “Aha” moments and early findings. So it’s key to get started where it’s strong, and not just look at it as a magical unicorn. Gen AI is going to be able to solve certain things, but not others, just yet. But as time goes by, more and more opportunities will become available.

So we have to think about being deliberate and thoughtful around what it’s already good at right now, what’s coming down the pipe, and what might be further out. Then, you try to figure out the phases of engagement.

The other optic here is thinking through quick wins and balancing those with impactful change. It’s not just prompt engineering, right? You also have to figure out the technology, the architecture, the security, the data governance…etc. You have to educate your teams. And so you want to both prove the value and also create a space within the use case to do all those things.

And you want to make sure that you pick the right use cases and the areas that have the highest feasibility. Do you have the data to make it happen? Can you really stand it up and get it off the ground?

And at the same time, you also want to start thinking about the areas that will provide the greatest impact: something that’s meaningful change. That will probably take longer.

Question 3: New metrics: You and I talked a little bit about your way of measuring impact or metrics. I’m curious, as you think about planning for next year, there are lots of use cases where Generative AI could be used. How will you go about deciding where to put attention? I love the idea of quick wins, but ultimately you said this could be quite transformative. So how do you balance all of that?

We try to evaluate two things: One is productivity. Is this something that will help us? Will it amplify what we’re doing as an organization? Will it help us achieve something in an easier and or faster way?

Those initiatives very often fall in the quick wins situations, by the nature of the fact that they give you productivity. It’s something that’s supposed to give you results faster.

And then you always want to pick something more strategic. And yes, it will take longer, and needs to be evaluated against the long-term impact.

You have this opportunity to start building things. I presume you’ve got new use cases. But then we’ve been in an environment for the past decade or so where we’ve been buying solutions versus building. And now we’re in a struggle where you may have to build things that are unique to your use cases. What are some of the technology gaps or issues you need to address? Where would you like more investment? On technology? Or is it in people and processes? And how do you balance the traditional ‘Buy’ versus ‘Build’ equation in this new sector of AI?

It’s interesting, I actually don’t look at it as a struggle. To me, it’s an extension of the same thought process. When you identify something that provides a utility to your organization, it makes sense to see if someone else is doing it and doing it well. In that case, you can buy it, and then you wind up seeing the results and benefitting very quickly.

But if you have things related to your IP, or unique to your knowledge or business model, you may not find something readily available in the market as a generic solution. In that case you’re going to have to build.

It’s the same logic that a lot of companies apply to the regular buy versus build.

In order to build, you will need to acquire more skills. You will need to train your team on this and find ways to be proficient. In some cases, you may be able to shoot for a hybrid solution that extends some off the shelf models that are already close. So maybe you don’t build things from scratch, but it’s also not a SaaS product right away. You’re able to take a platform and extend that platform, making it a little bit more unique, and a better fit for your needs.

Question 4: What are some of the issues or roadblocks, or maybe even gaps that you believe need to be solved before you can fully execute on this? What are the headwinds you’re facing?

I don’t look at them as roadblocks in the sense that we have to solve all of them before we can make progress. To me, they are those forces that sometimes play against you, and other times you just have to keep in mind while making progress towards your goal.

As this is a new discipline, some of these roadblocks will be new and unique, or even use case by use case. So as an example, when I think about the financial aspect of being able to educate people on FinOps, it would make a big difference in certain use cases, but in others may not be necessary at all.

The same applies to data quality and availability: depending on the use case that you’re trying to address, it may be a really big roadblock, or it may not matter at all. That’s part of the way you want to try to evaluate. Do I have all the things that I need to make my use case successful?

And then you need to think about evaluation and validation. How do you know that your use case will produce results you’re willing to stand by? And how are you planning to approach it? This is the whole idea and the whole discipline of data governance. You have to get this right from the very beginning and you have to understand your point of view. What are your guardrails, ethics, and social responsibility? A lot of education needs to happen, and the regulatory side of the house is constantly evolving. But this part is important, new, and ill-defined.

Finally, you must educate your team and upskill them in a variety of ways. It can feel daunting because it’s so new, so figuring out ways to be practical is probably the most important part. People may be excited, but they also may be scared, so you must make sure that you educate, create awareness, train, and figure out how to reach all of the right audiences in the manner that would be important to them.

How do you think about responsible AI as an IT leader? Do you educate your board? How does Responsible AI come together in your mind?

I don’t think it’s one person’s job to figure this out. I do believe it’s important enough that leaders of different disciplines within a company will come together and make it their priority. And so, depending on the structure of the company, it could be different people. But I do feel that this is one aspect that must be addressed early, and people have to be really thoughtful about it in executive leadership.

Maria Latushkin is a technology and product executive specialized in Retail, Payments, and Supply Chain. She is GVP, head of Technology and Engineering at Albertsons, a famous grocery short chain all around California, and the West Coast, where she is responsible for all aspects of technology for Retail, Payments, and Supply Chain, thinking through the future of retail technology and spearheading the digital transformation of supply chain space.

She is a high-energy, innovative technology executive accomplished at aligning technology strategy with business goals and delivering new business capabilities through technology.
Maria has spent over 20 years leading Product, Engineering, and Operations teams transforming technology for global brands including Walmart, Peet’s Coffee, and most recently Albertsons.

Before her current position, Maria held CTO roles at various consumer technology companies in the consumer and healthcare space, including Omada Health and One Kings Lane, and led technology and product teams at Peet’s Coffee and Walmart.com
 

Today we welcome Ekta Chopra, a Visionary IT Executive and Digital Expert who has invested her entire career in transforming private equity-backed companies with cutting-edge technologies, high-performing teams, and digital technologies.

With over 20+ years of Technology experience from private equity and retail companies, she joined E.L.F. in 2016. Before having her current job as Chief Digital Officer, she worked as Director of Technology of the James Irvine Foundation for three years, where she was recruited to drive a complete transformation of IT team, systems, and operations for this foundation committed to enhancing the quality of life for people in California. Before that, she served as VP of Technology for Charming Charlie, a specialty retailer that grew from 17 to 120 stores nationally.

Ekta holds a B.A. in technology & operations management and a minor in finance from California Polytechnic State University. She is also a member of Silicon Valley CIO/CTO groups as a thought leader.

Question 1: First Job: You’ve been a digital officer for a while, could you talk a little bit about your background and how you got to the position where you are now?

I was born in technology; a tech geek from the beginning. But I had the opportunity to work in many different domains from aerospace to private equity, to women’s fashion accessories to reporting to the chief investment officer for a foundation, to getting my dream job at E.L.F. Beauty, which truly has been like a rocket ship. And that’s what we like to call ourselves: we’re sort of on this rocket ship with a lot of superheroes doing some really amazing things.

So, you know, I moved away and pivoted a bit from information technology to digital transformation in an era where e-commerce was really pivoting from tech to digital. And that’s something that I’ve been able to embrace and really grow at. So that’s, in a nutshell, my journey.

Question 2: Generative AI: You’ve had a long journey working digital strategy, could you put a little context around Generative AI? How does all this fit together? It isn’t necessarily a brand-new thing, but an evolution, right?

Absolutely. I’ve been doing what we call power-up sessions at E.L.F. to educate the whole company. And I’d say 1956 is when generative AI was born, essentially. It has had many evolutions over time. So, AI has been there for a long time. ChatGPT just made it very accessible in 2020. And today it almost seems like, you know, every conference, every boardroom, every investor is thinking about it. This is an evolution. Not a revolution, but an evolution.

Now is the time to find a way to really embrace it, because it is something that I see has a lot of potential from a consumer lens, which, as you know, is very dear to my heart. But then, as a public company, I also must think about it from an employee efficiency and employee experience perspective. I think it has tremendous potential.

It sounds like it’s becoming a higher priority for you and your business. Where does it fit in the prioritization you mentioned? You’re getting looked at by Wall Street, I’m sure your board is asking for more things. How high priority is it?

It’s really high priority for us. But I would say that we have already been doing a flavor of it. So, if you think about our digital business, it’s doing extremely well year over year. E.L.F. has had 18 consecutive growth quarters. We’re now the number one favorite Gen Z brand, and you don’t become the number one favorite Gen Z brand unless you really understand your consumer. So that comes with offering personalized experiences. But AI will allow me to offer hyper-personalization, which I’m really excited about when it comes to E.L.F.’s products.

And if I think about that and put it in perspective, the amount of creative, the brand messaging, and the content that you need, is exponential. How do you do that? You know, it’s not humanly possible without hiring a bunch of folks. So how do you do that effectively with either co-pilots or for your everyday content? You need a content supply chain that’s driven by AI.

I think that’s where I feel a huge opportunity, from a productivity perspective and really engaging our consumers. So that’s been our focus area as I think about our journey and acceleration.

Extra question: Could you talk about some early learnings, or what you have experienced in the effort of having more data and getting closer to your customer and understanding their needs?

I think the early learnings really are: ‘You have to be grounded in reality.’ There is, of course, all the excitement and the innovation that’s been possible since 2020, but I think there’s also the reality of us being a public company.

So, if I think about our focus areas and how the learnings have shaped them, it’s leveraging this concept of prompt engineering. And it’s also about education: getting everyone in the company comfortable with co-piloting using AI. It’s really important, right? If they don’t know how to ask the right questions or prompts, they’re not going to get the right results. And you’re not going to be able to train the AI the way you want to on your brand, voice, tone, or your own persona as an employee. So that’s number one: educating and getting our employees trained on it.

Second, whether it be Microsoft, Slack, or Salesforce, everyone is coming out with co-pilots. So, we need to test and learn. We can work with existing vendors to be the guinea pig in some cases, because as long as they’re trusted vendors and we’re being mindful about our use cases, it’s a good use of our time.

The third piece is that we have to consider where it makes sense to plug in some of platforms that are already built for certain use cases versus building new things ourselves. For example, why do we need data analysts? Why couldn’t we build something to provide access to our data at the fingertips of different people? This would have the potential to create a lot more speed an actionability.

Furthermore, there’s not just one model. You have Llama, you have Bison, you have Open AI. Each one needs to be trained differently, so they won’t necessarily train the same answers, or react to the same use cases in the same way. So how do you play around in that sandbox?

And finally, AI governance and security enhancements are critical. You can’t afford to not be thinking about them, especially as an enterprise leader. So, we need to stay grounded in those realities. There are things where we’ll go deep and things where we’ll just tiptoe a little bit and just test and learn.

So, is that a planning exercise? Or is it really just better communication throughout the entire project?

Great question. It’s a combination of both. You start with the planning, and then you take it all the way through execution. And you build the muscle memory so that it becomes almost second nature that you must communicate. So, it’s a planning thing as well as a communication thing.

Question 3: New metrics: How will you measure success? You’ve got both internal productivity opportunities and then you’ve got consumer brand, and of course, customer adoption. So, what are the unique metrics that generative AI is creating for you?

I think it falls into three buckets.

One is: “What impact is it having on our business?” And that could range from increased revenue, new revenue streams, reducing cost of how you serve, or your consumer, or things like improved consumer experience through the implementation of chatbots. The impact on your business is very real and quantifiable.

Then there is technical feasibility. Is it really technically feasible? Is it costing you too much? You know, as you try to build these cases, you have to also take that into consideration.

And I think the third is availability of data. You have necessary data models that you’ve trained to get solutions that are actually making an impact. So, you have to be able to measure it. You know, we have many use cases right now that we’re trying out, and some of them frankly don’t even make sense. They’re not even AI use cases. I think it’s about evaluating where the juice is worth the squeeze.

Bonus: What are some of the technology gaps or issues you need to address when thinking about all this from a venture capital perspective? Where would you like more investment? Is it more about technology or are people and process more of the gaps for you?

I would say it’s not one thing you know? My Head Developer or Engineer would say there’s always a caveat. So, in this case, there’s a great degree of complexity. There are technical challenges, of course, which are huge. How can you really make this technology fit into your ecosystem and work with many of the other platforms?

Then, there are data challenges. You’re working with a huge amount of data. How do you build something that is going to move at the speed of the business? We work at E.L.F. speed. So, for me, it’s really important that whatever I bring in can actually move quickly.

And then there are ethical challenges. As a brand we’re extremely grounded in our purpose. E.L.F. is for every eye, lip, face, and skin concern. I cannot discriminate. So, how do I make sure that the models I’m building are ethically right, that we’re serving the consumer the right color shade, matching, and so forth? And we really want to make sure that we’re very sensitive to that.

And of course, the nice bow on top is security and privacy. That’s always a concern as a public company. I would never even consider prototyping or doing something that already inherently might have security concerns around it.

And then of course, an issue I know my employees are concerned about is how this is brought into the organization. Are you doing this to get rid of jobs? And the answer should be no. It’s to make sure your smartest people are as effective as possible, and you’re actually making their jobs easier. So, the framing of that is so important as well.

Question 4: What’s a generative AI takeaway. If you had to tell Grandma what Generative AI is, how would you go about describing it?

I would say it’s like a Magic 8 Ball because you don’t know what’s happening on the inside. But it’s a more effective Magic 8 Ball that is capable of providing you precise information, depending on what you’re asking. And the more you engage with it, the more fine-tuned it is to what you’re looking for. It’s going to improve the broader quality of the work individuals are doing inside the business, and can serve in many new revenue streams, while creating new ways to engage with consumers.

Ekta Chopra is a Visionary IT Executive and Digital Expert who has invested her entire career in transforming private equity-backed companies with cutting-edge technologies, high-performing teams, and digital technologies.

Digital Expert, Metaverse and Web 3.0 enthusiast, Board Advisor with experience in private equity and retail, she has over 20+ years of Technology experience. She has invested her entire career in transforming private equity-backed companies with cutting-edge technologies, high-performing teams, and digital technologies. She also leverages cutting-edge technical solutions to maximize operational efficiency and drive unprecedented results.

She currently works at E.L.F. Beauty as Chief Digital Officer. Ekta holds a B.A. in technology & operations management and a minor in finance from California Polytechnic State University. She is also a member of Silicon Valley CIO/CTO groups as a thought leader.