Alright. So welcome everyone, to this session. We're gonna talk about, tech driven efficiency, a topic that I'm very passionate about. So a quick intro.Sabrina Rethakin, and I lead teams that build scalable, solutions that are tech driven here at Walmart. And I am passionate about a simple yet powerful idea. And it's about how efficiency is no longer about speed. It's about how do we design systems and solutions for today, Not only for today, but also for the future. So, I wanna talk a little bit about how a tech and a product mindset is going to help us do exactly that and how do we unlock modern efficiency that's sustainable, that's human centered, and also a little bit around AI. How is AI going to help us, with driving efficiency? So I did, pop in a quick poll in there, if you can see, just to see what you think about efficiency. I hope that you can see the poll.

Just trying to see if you can see it. Give me a thumbs up if you if you could see it. Awesome. Alright. So I just wanna see what your thoughts are around efficiency. Like, what are the biggest barriers that you see today when it comes to efficiencies? We have too many tools, not enough automation, confusing or outdated processes, lack of clarity and alignment or alignment, and then resistance to change. And this is also a big one. Awesome. Just giving you a few seconds to put in your thoughts around it. Alright. Let's see. Okay. Confusing or outdated processes. Okay. So that's number one. And then we see lack of clarity or alignment. Okay. Interesting. So that takes me to my next slide. So when we think about productivity, we oftentimes relate that to, hey. Let's have more tools or more technologies to solve for it.

But to be honest, these tools are not really helping us as we want them to because we have too many tools. And one of the, interesting stats that I picked up from Harvard Business Review was an average knowledge worker, like you and I, we switched between 1,200 apps, not just applications, but switches between applications. This is really mind boggling because this is time lost where you can invest that back on work that requires high value thinking or things that matter the most instead of switching between applications. So, true efficiency today is, about how do we rebuild that friction? How do we, eliminate the noise that's caused today with so many tools that we have at our disposal? I hope you can kinda relate to it, and I see it through the polls. So how do we drive efficiency right now?

So pop in, your thoughts around what you think will drive efficiency, today. So I have listed, three key, areas that my team focuses on, and this is based on what we've experimented, what had worked for us. And the first one is around automate the repeatable. Right? So if you have repetitive work and if you, have mundane processes, think about how you can automate them. And this is where, we talk about AI and the efficiencies that we are seeing with AI is your alerts, your QA reports. So if you have QA engineers, how do you, provide them the right tools to skip maybe something that took you 10 steps to get where you want to? Now you can shrink that into probably one or two steps. These will free up time from the teams, and they could focus on things that matter the most.

And then the second one is around orchestrate your workflows. So if you think about it, most of the friction that you see within the tools, it isn't in the tools. It's in the gap between them. Right? And if you think about, the broader picture of how you connect the dots instead of building technologies in silos or it's actually solving just one problem, but think about the future problems that it can and solve. And, interoperability has been a key theme. Most of us who have dealt with legacy systems, we see that pain. Right? Because your legacy systems don't really degrade with the new technologies. So when you're really thinking about workflows or solutions you're building today, think about interoperability as well. And then the third one is around data. So prioritize with data.

So no matter how many tools and solutions that are AI, ML, Gen AI based that you would unlock, if your data is not right or if you don't have the right data strategy, that will really impact your few ability to scale for the future. So, think about how you can actually have the right data strategy, the right owners for the those data assets, and you can't optimize what you can't measure. Right? So think about how are you going to really measure the outcomes when you build those solutions. So one of the, experiences that our teams, have had with leveraging tools like AIML is thinking about the business problem that we're trying to solve. Right? So within our compliance domain, we had, a specific workflow that required 200 different touch points. And it took us weeks to complete that specific workflow. So, we simplified that by creating a a simple ML model that shrunk the 200 step into a two step process, now cutting it down to two hours.

So that's huge efficiencies. Now we're able to reinvest that time back on another area where we can optimize the process. So that is through compound value. So, when we think about what's changing, in in the future, if you look at the future outlook of technology, these are the things that you can do right now based on what you're seeing. Now when you think about the future, what are some of the trends that we're seeing? Right? So, like, this entire session, you would hear me talk a lot about AI because that's, again, another area that I'm very passionate about because, definitely, AI is moving at a pace that we have never seen before. It's very unprecedented. So when you think about the next wave of productivity, it isn't just about faster or cheaper. It's intelligent. It's about personal and providing a customized experience for your employees or for your, customers.

So here are some of the six areas or trends that we are seeing that's going to really shape how we see productivity for the future. So the first one is around human AI collaboration. So AI is becoming more of a smart assistant. Of course, it's not replacing you and I, but it is cocreating with us. It's cocreating content. It's brainstorming alongside us. And if in the future, efficiency is going to be more collaborative with these AI tools and AI bots. And then the second one is personalized productivity. Now AI isn't a one size fit all. Right? It's tailoring those specific business cases we have, those specific workflows, and and how are we going to help those individual use users and their own use cases?

So it's not gonna be a a broad spectrum of problem solving. So and and the most important part is how are we enabling our teams to make smarter decisions. Right? Manage time better and be focused on those high value critical work. The third area is hyper automation. Not sure if you heard about that heard heard of that word, but we're actually moving beyond, an individual, task, right, to streamlining an entire workflow from start to finish. So this is saving time. So think about the siloed example that I gave before. Like, don't think about the one specific use case that you're trying to solve, but also think about the end to end journey of how you can reduce errors, how you can automate. So this is where we are heading. And then the the fourth one is the democratization of technology.

You and I now have access to GenAI as a general user that we didn't experience before. Right? And thanks to these tools, the no core core tools, like, anyone can code an application today. You don't need to know, coding to do that. And it's mind boggling if you watch some of those videos from Claude where one that required, like, lines and lines of codes to build a specific application. Now you can do that in couple of minutes. So we're re really moving at a pace where now more of our teams have access to the many tools I talked before. But now we have to be more intentional, more purposeful, again, focusing on how are we really going to solve those critical problems instead of in reverse, you know, using technology and looking for a problem to solve.

But now you have a problem and make sure that you're able to solve it through technology. And then the fifth one is around responsible AI adoption. Of course, when we think about AI and what it can do to the future, we also need to think about how we can scale with trust. You can't scale without really trusting the tool. Right? So this means designing with fairness, privacy, and transparency in mind. And make sure that you give the right guardrails for your two teams when they start using these tools. Right? What they can do, what they cannot do. So that's very important. So these aren't just trends. If you notice, these are really a cultural shift in how we do work. So how we design, how we decide, and deliver work for the future. And efficiency belongs to to the people who are actually prepared for the future.

And now when we think about being prepared for the future, it also brings us to the topic of upskilling. So moving on, how do we build a future where we are ready for this change, and how we can utilize these tools that we have at our disposal to the fullest. So the biggest risk in efficiency, like I mentioned before when I when I started this discussion, is that you design tools for today and not not think about the future. So instead of designing for a one specific problem, but think about how you can solve many problems with a single tool. So in the few in to be future ready, your systems need to have a composable architecture. So are you building reusable models? Can you, replace these technologies without having to do rework? So and also having the API first thinking. Right?

Tools that talk to each other equals less duplicate work equals better decision flow. And then designing, for change, not just for now, for the future is important. Like, I keep mentioning this because it's very critical that you also have the right support structure to help your teams to do this continuous change, and you keep iterating. And the third one is around focus on decision and speed. Right? So build build platforms that help enable your teams to focus on value work, the automation that we spoke about before. And and think about how you can unlock time so they can reinvest it back on things that matter the most. Now we've been talking so much about technology, but, honestly, technology is only half the equation. Right? So it's it's only one part of the puzzle.

So to make this truly work that you need to have clear goals. You need to have clear objectives of outcomes so people really know where to focus. So if you don't have clear goals, you wouldn't have clear outcomes. So that's really important. And then the second one is psychological safety. So this is where your teams have the autonomy to try, test things out, understand the risks, have the right guardrails, and they don't feel penalized when the outcome is not what they expected. So you need to provide a space for them to try as technologies, trial run. And also if it fails, now, you know, there's there's a term, you know, fail fast so you can get to the next one faster. Right? So make sure you create that sort of an environment for your team so they feel safe. They they they can try things for the future.

And then the third one is upskilling. So this is ever more critical when you think about our talent base. Not all of us, are AI experts. So prepared for the future of what technology is gonna bring, but we continuously learn about these things. So how are you helping your teams, your engineers, your QA teams, your technical leads to acquire these skills? Do you have the right support system to help them upscale? And, also, when it comes to hiring new talent, are you thinking about, the skills for the future that you need when you hire your new workforce? So these are some of the things that for you to be considerate of. So here at Walmart, one of the things that we also noticed is when you implement new technologies, if people don't understand the technology, you would face some resistance. Right?

People can't comply with things that they don't understand. So we were trying to implement, inventory management system that was based on AI to help our associates understand the inventory levels in stores. Since they didn't quite understand what we were trying to do, we did have some pushback. However, we worked with these associates. We kinda sat with them, got their feedback. We try started cocreating the solution with them, And we kept iterating the application while we got feedback from them, and we were doing test runs. And now we started getting them onboarded to what we're trying to do. So this is really critical, when it comes to implementing new solutions. Make sure that you bring your team along the journey so they feel like they're part of it.

Now as we come to the last set of this discussion, there's three things that I really want you to focus on, and, hopefully, these would be the takeaways for you. And think about tech, it's a multiplier. It's not a crutch. You can start small. Right? Automate wisely. You don't need millions of dollars to invest on the newest technologies. There's so many free tools available for your two teams, that you can try out, that you can of course, within safeguard rails that you should enforce, but make sure that you're able to use those tools that would be available for free or is not gonna cost you a lot of dollars.

And the second one is build for change. So think modular. Right? Build think about an API first application. So you're designing systems for the future as well and not just on your own silo. And then the third one is make sure that you empower your people. That is really important. So productivity source when teams feel confident about using the tools that you get them. So all in all, efficiency is no longer just operational. It's strategic. It's not about doing more, but it's doing what matters and what's going to drive the best business outcome to your teams. So let's stop thinking about efficiency as just a spreadsheet, of numbers, but also start thinking of it as a a design challenge. Right? Let's build systems not only that work faster, but also work better, smarter, and more humanly. So with that, I think we're ending, or nearing the end of this discussion.

So thank you so much for joining. I really appreciate it. Hope you enjoyed it. Let's stay connected on LinkedIn. So I think we only have a minute, so I'm not sure whether we have time for any questions. But I'll definitely answer the questions on the chat, since we are nearing, the the end of the session. But thank you so much for joining.