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Bridging the digital divide through AI and skills training
This week on What's At Stake, Penta Partner Andrea Christianson is joined by two executives from Cognizant Technologies: Senior Vice President of Legal and Chief Corporate Affairs Officer Tobi Young and Chief Technology Officer for AI Babak Hodjat. The group discusses AI in the context of the workforce and how it could help companies operate more dynamically. Additionally, they highlight how tools like generative AI are bridging knowledge gaps and democratizing tech use.
Tobi and Babak also spotlight Cognizant's Synapse Initiative, a global AI upskilling pilot program. Cognizant launched the Synapse Initiative last October with the goal of preparing one million people worldwide over the next three years to become the workforce for the high-tech, AI age. Whether you're an AI skeptic or optimist, this episode promises to offer a fresh perspective on how AI is reshaping industries and empowering the workforce.
Welcome to a special edition of Artificially Intelligent Conversations. I'm your host, andrea Christensen, a partner at Penta and head of our AI task force. Today, I'm speaking with Toby Young and Babak Hojjat from Cognizant, a Fortune 200 digital services and tech company that is a leader on AI in the corporate world and was recently named again to Fortune's list of most innovative companies. So Toby is Cognizant's Senior Vice President for Legal and Chief Corporate Affairs Officer, where she oversees regulatory, legal and sustainability issues. Toby started his career as a press secretary on Capitol Hill and then became a lawyer, where she held roles in the White House and the DOJ. She also clerked for Supreme Court Justice Neil Gorsuch and was the firstJ. She also clerked for Supreme Court Justice Neil Gorsuch and was the first enrolled tribal member to serve as a Supreme Court clerk in US history. So kind of a big deal. Thanks for joining us.
Speaker 1:Toby Bobak is Chief Technology Officer for AI at Cognizant, where he leads the Advanced AI Lab, which conducts cutting-edge AI research. Bobak is an expert in numerous fields of AI and has founded multiple companies in these areas. He is the former co-founder and CEO of Sentient, responsible for the core technology behind the world's largest distributed artificial intelligence system. Also, he's the primary inventor of the technology behind Apple's Siri. So, again, not a big deal. Thank you for joining us. We're really excited to talk to you today. Anything I missed?
Speaker 2:No, but, Andrea, I have to tell you a funny story. My daughter has met President Bush, Justice Gorsuch, and the person she's most excited that I know is Babak Mr Siri himself. So this is a lot of fun for me.
Speaker 1:That's really impressive. I'm really excited. So let's dive in. So there are three general camps on Gen AI right now. I'll say there's the existential threat camp, where people think AI is going to destroy humanity. There's the utopian camp that sees AI taking humanity to its zenith. And then there's kind of the middle ground no opinion camp, where people don't actually understand what they should think, what the latest innovations are and how they affect them. So what camp are you both in and why?
Speaker 2:Well, Andrea, I will go first so that we can let Rebecca tell the real answer. But I've never been a camper myself, so I don't think that any of them have it exactly right. You know, I think there is a lot of potential with AI. There's a lot of opportunity for our daily lives, you know, to take some of the administrative burdens that we don't want to do off of our plates, to make it more where we get to do the thinking and the fun stuff and get to the meat of the problem.
Speaker 2:There is a democratization of AI. More of us can participate. We don't have to be these brilliant, deep coders. We can use our natural language and my Oklahoma dialect to code and come up with problem-solving solutions, which is amazing and it's a huge leap. But you know, there's also responsibility in this and if we don't treat this correctly, the computers are never going to have the thought processes that we as humans do. You know, our CEO also always tells us that the AI can tell us that it's going to rain. Companies and those who are pushing AI don't build trust in communities people won't use it.
Speaker 2:They'll be afraid of it. So there's a lot of education that has to happen to really give it the potential it has to impact our lives.
Speaker 3:And so now, Babette can give the more actual answer and go straight into one of the camps. Couldn't have said it better. Toby, I totally agree. Ai is a tool. It's a very powerful tool and it's up to us. It's inherently neutral, so it's up to us how we put it to use. And so, yes, actually, the more powerful our tools, the more responsible we are in how we utilize them. So, at the end of the day, it comes down to us. It really is. You know, ai is less to blame for anything that goes wrong. It's us.
Speaker 3:But I think the empowering sort of nature of AI and, as Toby mentioned, the democratization that comes with its use will actually benefit us all much more.
Speaker 3:So I don't know if that puts me squarely in the utopian camp, but I'm an optimist. I don't think we have much of a choice but to be an optimist, and when you're an optimist, you take responsibility and you constructively think about how to use these technologies and how to put them to use. I think the biggest fear with AI, and what is kind of reflected in our culture and our science fiction, is if we give AI autonomy and allow it to make decisions somehow, we. At that point we always think, oh, it's only going to make the wrong decisions. In many cases, in the past, with technology that is not AI, we have delegated autonomous decision making and it's worked to our benefit, and now we have tools that are even smarter. So I don't see why it should do that, unless we, as humans, give it the wrong goals, basically, or forget to put the off button on them. So, yeah, I'm very optimistic about where we're headed with AI.
Speaker 1:Thank you and I appreciate the touch on the philosophical that we have a choice on how we train and use and engage these tools. So you know, I do want to pivot to workforce because we've seen a lot of headlines already about job loss tied to AI and polls show most Americans are concerned. They think this will continue and Cognizant is doing a really exciting program called Synapse that I want to get into a on how AI will change the workforce landscape as we know it today and how companies, how educational institutions and policymakers should be thinking about adaptation to the age of AI.
Speaker 2:Absolutely. This question is perfect timing. Cognizant recently partnered with Oxford University on a really interesting study about AI's impact on the workforce and we found a few things that about AI's impact on the workforce and we found a few things that I think you'll think are interesting. Ai can improve productivity by three and a half percent, add about a trillion dollars to the US economy alone in the next 10 years. It's not even looking at global. There's a lot of possibility for AI in a more prosperous world. But we also found that over the next 10 years, 90% of the jobs from entry level all the way to the C-suite will experience a degree of AI-related disruption. That doesn't mean they'll go away, but they will change in some sense. So the study predicts that Gen AI will displace more than 9% of the current workforce. Over a 10th of those displaced might never find work again. So there's no denying there's a big disruption coming, and one of the things we're looking at at Cognizant is what do we do about that? Right, we've seen disruptions throughout history and I think it's time to learn the good and the bad from those changes and disruptions.
Speaker 2:One of the things I see a little bit different in this disruption than, say if we take some of the change to that affected blue collar workers is back. Then people said don't worry, your kids will be better off, things will be better. And I think we've learned. You have to look at the families from the very beginning. You have to reskill and upskill the parents working right. Then it's not just about the next generation will be okay, it's about the dignity of work, it's about the dignity of going and participating. We can take training that used to take a year With AI we can do it in three months now.
Speaker 2:So companies, I think, look at this problem a little differently now. We know that we are corporate citizens. We know we have a responsibility in this transition, in this trust building to bring people along. So you don't just conduct the Oxford University study, you take that knowledge and you say, okay, I sense this universal fear people have about this transition. What are we going to do to ease that fear? What are we going to do to explain this technology, prepare the workforce for it?
Speaker 2:So it's one of the things we're looking at is how do we partner and we can get into that with Synapse but how do we partner with our other corporate clients, ngos, to take people through this and not say your kids will be fine. No, your kids need to see you doing fine, your kids need to see you. Part of this and again it goes back to AI is democratizing STEM. You're not left behind if you're not a good coder. Toby is still going to be able to potentially have a job in this if I learn how to think a little bit differently and just task orient differently. So it's a fear. We have to know what the problem is and then we move quickly towards a solution.
Speaker 1:Yeah, rebecca, I'd be curious your take on the Oxford findings and what they mean. I mean, we've talked a little bit about how this means more critical thinking. This means more a different way of thinking, and so how should we take these findings of these Oxford studies and adapt how we're thinking about training people?
Speaker 3:Yeah, I mean the way the study was done is fascinating. We took all the jobs in the US job report and then we looked at breaking each job into its constituent tasks and there's more than 18,000 tasks that were reviewed and for every task we looked at the level of disruption that AI and generative AI is going to bring to that task. And then we kind of reconstructed it back into the jobs and that's the number that Toby just quoted, like 90% of jobs. And you're talking across the board like the CEO job is going to be disrupted. It's not just you know the worker in the factory going to be disrupted, it's not just you know the worker in the factory. And based on that, you know we looked at, you know what are the mitigations. So this wasn't just about looking at you know the upside of the economic impact, the trillion dollars over eight years and so forth but it was also you know what are the mitigations. How do you soften this disruption? Disruption is going to happen. How do you reduce the number of jobs that will have a problem, kind of reskilling and upskilling.
Speaker 3:And there's good news here as well, which is the barrier to entry is much lower with generative AI, because the language of choice to get it to do things for us is our mother tongue, english or whatever language.
Speaker 3:Like you know, our CEO always talks about like an illiterate farmer in India now can actually direct a generative AI system to do something for them, because, using speech recognition and just natural language and a generative AI system to do something for them, because using speech recognition and just natural language, the system understands that and so, because the barrier to entry is lower, it behooves us to look at how it would elevate us in what we do, because now we can express our intention and we can work together with the AI to achieve the goals that we're looking for, which means we can do more, and we can do more and more productively, which actually, I think means we will all be busier if we can sort of elevate ourselves and think of these as knowledge workers in a box that we direct and we orchestrate to get things done.
Speaker 3:And, yeah, I think we have a much better chance at every level of the job spectrum to be able to elevate ourselves. This is not going to be limited to those who know how to use the system, because knowing how to use the system is very, very easy. Just tell it what you want it to do. And so, from that respect, I think you know going into this with an open eye, you know, reviewing what those disruptions, what those tasks are, how do you upskill on those tasks and how do you elevate yourself. You know, would be would give us a roadmap to kind of weather, this massive disruption that's coming our way.
Speaker 1:Yeah, I like both the lowering barriers to entry I think that's really important and also this idea of an orchestra conductor. And you're thinking about how are you bringing in all these tools differently to create what you are envisioning to create? And to Toby, to your point about kids, I did want to ask a question because you know, I've been going to a bunch of forums and events and you hear people ask the question pretty regularly should my kids still learn to code? And I've heard people say yes and I have heard people say no, with equal confidence, and they're both smart people and experts. And you just had the CEO of NVIDIA say he thinks programming degrees are a thing of the past. So let's settle this. Should parents teach their kids to code?
Speaker 2:Well, as A fellow mom, I will tell you that, even though calculators can do math faster, my first grader still comes home practicing addition and subtraction and number bonds, and so I would say the gifting in coding is no longer required, but it's still important to understand some of the background and have a basic working knowledge in what this technology is in the future. Now, what is neat about it is, yes, go to coding camp, but don't forget the humanities, don't forget the critical thinking skills. Send your kids to those camps to have them understand the broader problem solving. You can work better with things when you understand some of the basics of coding. But what we're going to be doing now is thinking critically.
Speaker 2:What is the problem we really want to solve? How do we go about it? Understanding the world around us. So you know, I guess unfortunately for your budget, andrea you need two camps. You need coding camp and the arts and humanities to put all of these together. But I don't think and Bebek can correct me if I'm wrong I don't think you have to go as deep into coding as we once thought you would have to. But it's like anything, understanding how it works can make you better and stronger and smarter and more successful. So I'll leave it to the back to answer.
Speaker 3:I totally and just I totally agree with that. I also say that coding is not just about the syntax and the writing of the exact order. You know, it is about an overall talent to put together an algorithm with steps and roles. And you know, when we talked about the orchestration, that's basically what we do as humans. Often we code without knowing we're coding. You know, we're setting up like we're project managing something, and we're we're setting up the, the steps, and you know the gates and you know all that, um, and that's that's all. To me that all looks like coding.
Speaker 3:You don't need to actually know a programming language to code.
Speaker 3:When you think back to the history of of coding, um, you know it was all in hardware and then it became zeros and ones and yeah, and then assembler language, and then you, you, you got to lower order languages like Fortran, which is really really hard to put together, and then higher order languages kept abstracting.
Speaker 3:So you don't have to think about the nitty gritty of what's happening in the bits and in the hardware and you can think about your intention, and so that trend is already happening anyway, and now it's moved to us being able to express that in our own language, but it's still coding, it's still expressing a goal and an algorithm and the steps that you need to take and the what-if scenarios and building that in there. I think it's a talent that's that's very much needed. Also, to Toby's point, I think this kind of multidisciplinary thinking and borrowing from different disciplines you know, from the humanities and so forth and bringing that into how we imbue our machines with intention and put them to work for ourselves becomes increasingly important. It's something that we do very, very well as generalist intelligent beings and I think that's what is going to be needed for the foreseeable future in working with technology.
Speaker 2:One example, maybe to kind of break this down down that actually came up when I was on a call yesterday with one of our client partners is let's think about a hospital. Probably the riskiest time in a hospital is the nurse shift, the change from one caregiver to another and making sure the vital information from the morning team is transferred to the evening team. Right, what if we had AI doing some of this in the busy time when they're running and trying to make sure all their notes got in? The nurses need a little bit of programming skills to tell the computer what's the most important things that happen during a nursing shift transition. What do I need the next shift to understand what has to happen? So they have to have the order in their head of what they need to do. They could potentially use this direction, which is coding.
Speaker 2:As Beck said, it's organizing things in our head to make that shift more seamless, to make sure the person replacing them knows the special care that patient needed that day. The stress level goes down. You feel a seamless transition. These are places in real life critical life and death situations where we can see AI benefiting, where you can kind of imagine your mind okay, they're coding, because they're prioritizing what needs to happen and moving forward. So just throwing that out there as an example of where this is practical. It's not too scary, I think. A little bit of training, because, you know, sometimes nurses are like, well, I am used to writing these notes down and this is how I want to do it, but it could make their lives a lot better and they could feel better as they transition to their partner.
Speaker 1:Yeah, I think that's really great, and I was even thinking personally. I never deeply learned Excel and formulas in Excel, but I've had to review a lot of Excel documents, and what's been fascinating to me is I know exactly what I need Excel to do for me. I just don't know the formula to do it. And so the other day I just asked ChatGPT for a formula and it worked, because I know exactly what I need to do, and so it's the order of the thinking. You need to know what you're asking and the steps that need to happen. But it's OK that I didn't know the exact formula to use, because someone can help me with that now.
Speaker 1:But I now am that lowered the barrier to entry for me for answering a complex question that required Excel versus just looking at a document? And so I want to come back to these lowering barriers and Cognizant's Synapse initiative, and you launched this last year and the goal is to train 1 million people globally by 2026 to work in the age of AI, and so obviously there's been a lot of training and reskilling programs over the years, and we've talked a lot about how the landscape is changing and how we need to have multidisciplinary thinking, and so I'm really curious about how you've approached this Synapse initiative and why you think it's different and it's going to be a game changer for workers for workers.
Speaker 2:So our CEO, ravi Kumar, is dedicated to skilling and changing people's lives. I think he's lived a life like that. He's seen people who haven't had opportunity or access. In the United States, we might say somebody hasn't had access to coding, but in India, cognizant volunteers are teaching kids to code without computers right, I mean, the trajectory is so important. And so he challenged us to think about how can we stand up as corporate citizens, as cognizant, in this transition and answer some of the angst and anxiety. And so thus Synapse was born.
Speaker 2:And Synapse is really about two groups One, people who are in the technology industry, whose jobs are going to change, and we want to make sure we're helping them upskill and reskill. And this goes back to the dignity of work that we talked about and staying in your training and your field. Yeah, we're looking at a little bit more sophisticated skilling there for people and looking to partner with some of our clients and say, hey, we'll take some of your people whose skills might be a little obsolete with generative AI, we'll retrain them, we'll redeploy them to help them do the things you need so that communities aren't losing jobs. They're just repurposing jobs. But there's also a huge group of people in the United States right now who are not in technology. How do you deal with that? So Synapse looks to partner with some of the NGOs that we have, like, for instance, nasscom in India or Code Platoon we do here, or several groups that we work with to bring skilling curriculum to people who have not had the entry. And again, I think a lot of companies are going to be willing to partner with us, invest more because you can train faster. Right Law firms are losing a lot of money on first-year associates. You just can't train them very fast, but we now, with AI, know that you can shrink this training to about three months out of a year, as we discussed earlier. So what we're doing is we're saying we're going to give you the skills, but we don't stop there. I think a lot of people will go online. They take a skills course, they have a certificate Great, what do I do now? The end, all goal of Synapse is to connect the dots for people, to build these consortiums with our clients, our NGOs, to help people then take these skills and find a place to work, to embed them back into a company, to give them skills for the future. That's the big dream is building these consortiums, connecting the dots.
Speaker 2:As you know, just coming out of college, I have a degree. What do I do with it? You sort of wanted a college counselor to tell you the things that are out there in the world. I grew up in a very small town. I had no idea the jobs that are out there. So what we want to do is skill build those consortiums with smart partners who are already doing this great work and say and here's how you go, deploy those skills, here's some opportunities for work, here's where you go. So that's what we're looking to do. We're invested in it. We have a skills accelerator at Cognizant where we're training people. We currently do apprenticeships. We work with the Department of Labor for apprenticeships. We're doing some pilot programs with some of our clients to take on some of their teams. Teach them these skills, accelerate them and get them repurposed to make their jobs easier. And again, like we talked about, use the critical thinking that you need for the next generation of technology.
Speaker 1:Yeah, and I think what's interesting is the global lens you all are taking here, and so talk to me a little bit about when. Because Gen AI is a global revolution. It's not. It doesn't really have borders, and so just talk to me a little bit about your choice to really make this a global initiative.
Speaker 2:Well, cognizant is a global company. We have about 350,000 employees across the globe, and so we see firsthand, in all of our localities, the anxiety, the fear of where do I belong, and you know the need to bring people along in this technology and the need to bring people along in this technology. So it was an easy decision for us to jump in and say we're going to tackle this all over the world and we're starting in India, where we have a lot of associates. In the United States, where we have partners. So since 2018, for example, cognizant's given about $69 million in grants for STEM education and broadening access to people, and we've done this with 77 partners throughout the world.
Speaker 2:The one thing we know you know as a company is when we partner with local organizations who know where the people are, who know what the people need, we get better results. So we look for really good partners who are training. You know, for example, in Texas, we are working results. So we look for really good partners who are training. For example, in Texas, we are working with a trainer who's teaching veterans skills as they leave the military service to redeploy and learn these skills. Having worked for President Bush, he always said companies aren't often looking for someone who puts sniper on their resume. But a sniper obviously has a whole lot of skills that they can deploy and be super useful in the private sector and the economy. And so, taking that and knowing the focus, the loyalty, the camaraderie, the smarts it takes to do these jobs they've done, set them up with a skill set to redeploy, help them find apprenticeships, partnerships this is a global issue. We know the world is better when everybody feels safety within their own communities and that they have a good livelihood.
Speaker 1:And Bebek, you were talking a little bit in our prep session about some of the examples you've seen of how certain organizations are deploying AI really effectively. Do you maybe want to share a little bit about that?
Speaker 3:Yeah, we see a lot of low-hanging fruit, basically what they're calling high-impact, low-risk adoption of generative AI, which is natural. That's where things start, and the biggest applications that we see is companies coming to us and saying, hey, we love what we see with ChatGPT, but we want a ChatGPT-like interface to our proprietary document repository in-house or our structured data in-house, and we don't want that data to leave. We want it to be secure. There's an approach to doing that. It's called RAG Retrieval, augmented Generation. It's quite popular and we're very good at it and we do that for all our clients. That's a very common application. So, basically, I want a chat interface, a very smart chat interface, but I want it to know what I know that's proprietary to me and house in my company. The other common uses are things that we've experienced with ChatGPT ourselves, such as, for example oh, it can translate language, so great, we can use it for machine. Great, we can use it for machine translation. We can use it for summarization. It writes code to your point so we can express what we want and it'll write the code for us. And so for any development environment where we want to make our developers more productive or the quality of their work better or have the system automatically generate test cases. These are very, very common usages.
Speaker 3:But let me actually move us to other use cases where, as Cognizant, we're advocating this and using it in-house and for our clients, which is saying, well, all that is well and good and we're really good at that and we'll help you bring those types of applications in, but how are you going to disrupt your own business? And when you start thinking about it like that, ai moves from being in the periphery of what you do and making you more productive to the center of tackling your KPI. And so that's where you start, like, what is my KPI? What am I solving? For? It's usually more than one. You know, for example, I don't know saving lives in COVID and saving the economy. That's a false choice. You want to do both and, yes, they're opposing. You know goals, but we can task AI to find us that happy medium. And the same goes for a company that is trying to increase its revenues while being I don't know environmentally responsible, while you know, having good margin and cutting costs. So it's all of the above, not one. You don't have to pick one. It's all of the above. And so we start from there and we help our clients.
Speaker 3:And you know one of the things, one of the funny things that happens is that when you broaden it that way, the client then sits in front of you going wait, but then OK, we can use AI for that. So, for example, we have an AI agent. We literally type in the name of the company or the name of the division within the client company that we're talking to and that AI agent reviews what's publicly available about that company and takes input from us and comes back to us with five use cases that are central to the business of that company where we can disrupt using Gen AI. And then it does some analysis on those use cases, on the impact, and says, well, this is the one that I would recommend and you can go with that, or you can go with another one, or you can interact with it.
Speaker 3:And then we move on to another Gen AI based agent that we interact with to scope that use case. So it helps us interactively think about you know what are the actions that we want to take, how are we going to optimize the outcomes, the KPIs that we're looking for, what data is available and what data can we provision, let's say from publicly available data sources or third-party data sources, to help with this decision. And once the scoping is there, we then move on to another Gen AI-based agent that helps us generate synthetic data that resembles the data that's running and is available in our client company. And we take that synthetic data and we develop the use case for them in front of them and, believe it or not, that takes about 20 to 25 minutes.
Speaker 3:So we go in front of a client and start from not knowing what use case we want to build to actually having an end-to-end real use case, albeit on synthetic data, but synthetic data that's very closely resembling the data we know the client has, and we show them with an interface that is again Gen AI, so they can actually interact with it and it helps them with their business. I think that gives you a glimpse of what is the art of the possible. First of all, how much more productive we are, how much interaction is there between us and Gen AI in helping us formulate what we want to do and throughout, at every point along the way, we're using AI in collaboration with us to build the system and we can iteratively create this. So gone are the days where we're going to be talking to the client and spending a month just scoping out what needs to be done and then going through this waterfall approach of a five-year project to get something. We can get the POC in front of the client in that first encounter.
Speaker 1:And so just to give you an example of how we look at AI enablement at Cognizant yeah, it's really fascinating and it's kind of amazing that you are already embedding Gen AI so upfront and center with your clients right now, and I love that you're able to just get to action faster. And I think that's what everybody wants is to not spend the time thinking about what should the plan be? It should be let's execute the plan, but we can help get a really good plan a lot faster, and that's really exciting. Toby, anything you want to add before we wrap?
Speaker 2:No, I just want to say, you know, kind of as Bebek said, I think two distinguishing things to leave a policy audience with is one you know our Oxford research shows us there's disruption and so it's time to do something. But two, what we're trying to do in skilling, and what Synapse is dedicated to, is that it's not that AI is going to take your job. It's people who use AI are going to have the jobs. So it's actually just a bridge of getting people used to using AI. There's not the barriers there used to be, and I think that's really important for people to think about as they start.
Speaker 2:You know, play around, go on chat GPT, ask it to write you a song about your favorite basketball team, or you know a speech you're about to give. But I think that's you know. That is a way we are looking at it is we have a responsibility. There is a skill set that needs to be bridged. We can do it Rural Americans, urban Americans, black, white, whatever color you are. It's not that AI will do your job. It's that someone is going to use AI to do the job better, and that's what we can do for our clients, as Vivek just pointed out, and it's an exciting future, I think.
Speaker 1:Yes, yes, well said Well. Vivek and Toby, thank you so much for coming on to what's at Stake To our listeners. Remember to like and subscribe, wherever you listen, and follow us on X, formerly Twitter at Pentagroup, I'm your host, Andrea Christensen, and, as always, thanks for listening.