← ASU+GSV Summit Sessions
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- [00:03]
A
This session was recorded live at the 2026 ASU GSV summit in San Diego.
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B
My name is Lawrence Stephens. So I lead our skills company wide skill strategy and sort of the learning tech stack and the ecosystem that powers the learning experience at Cisco. And Marcy and I have both been at Cisco sort of coming up on two years and doing our best to bring a little bit of change into the space and help sort of take us forward in this era of lots of unknown and uncertainty. Maybe we'll just introduce panelists and then I'll sort of kick us off and hand over to my panelists.
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C
I'm Lydia Logan. I'm the Vice president for global education and workforce development in corporate social responsibility at IBM, which means I'm responsible for our external education workforce programs and partnerships.
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D
Anshul Sonak. I am a first principal engineer without an engineering degree. 30 years in education and workforce around the world. Primarily responsible for large scale public private partnerships with education, government workforce, ecosystems around AI, semiconductor skills and digital trust cyber security related skills.
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E
Thank you, Anshul. Vishal. Vishal Gupta and I lead for Pearson the enterprise learning business. And that really means is everything we do with enterprises with governments all over the world in terms of helping them upscale reskill their people, their partners, their customers. We have a set of products and solutions there and that's what I lead. Thank you.
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A
Thank you. Shal Hi, I'm Guna Jayaraman. I'm the Chief AI Officer in Cornerstone. I'm excited to be here. I don't know about the packed host, but I really appreciate the intimate setting so I'm looking forward to the discussion.
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B
So just a little pulse in the room, sort of show of hands. Who of you are from sort of the corporate kind of world up on the corporate world? Okay, lots in sort of academia. Anyone?
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F
Okay.
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B
Hello. Hi. Anyone in the sort of government sector? Hi, welcome. There you go. Very cool.
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D
Anybody from media so that at least we are very careful? Nobody?
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B
So just a couple of headlines on the workforce consortium. It's sort of a collective of 11 global technology leaders. Companies like Accenture, Cisco is part of this as well. Cornerstone Eightfold, Google, ipm. And the mission is, and I need to make sure I get this right, prepare the global workforce with actionable insights and scalable frameworks to leverage AI's transformational opportunities. And then the consortium members together have pledged to upskill 95 million individuals over the next 10 years across the globe. So pretty cool just seeing this come together. Companies reaching across the aisle, working together sort of corporations, vendors, suppliers, and really exciting time. I've sort of got like a kickoff teaser question. So the challenge for you is in one word, what's the greatest advantage of the AI workforce Consortium's cross industry collaboration compared to working independently? Yeah, I've got sort of along the
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C
line, can I use two words better together?
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B
Okay. I love it.
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D
Common voice.
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B
Okay.
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E
Solve big rock problems.
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B
Okay.
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A
I'm going to do one word and one sentence. Velocity. To me I think that's a big thing. The speed of change that's happening is so huge that none of us, however AI forward we are, can navigate this in an isolated way. So that's the mission that I really get excited with the consortium.
- [03:57]
B
Very cool. Velocity. Definitely. I think probably all of us are experiencing rate of change just kind of unheard of. So I think working across the aisle and across companies helps us tackle that and work together. So okay, onto the sort of bigger meteor questions. I'll just kind of work down the line, so if you bear with me. So beyond technical infrastructure, what concrete steps are your companies taking to build the leadership, commitment, cultural openness and the governance frameworks that make AI adoption sustainable?
- [04:33]
C
So I'm also going to give a nod to our Chief Learning Officer, Josh Clark, who's here. So we work across IBM internally with our learning teams and we take what Josh and his team lead for the company. We also take that outside. So the importance of making sure that there's ethical and responsible use of AI in what we develop and of the learning programs that incorporate our technology, cybersecurity, right. Data science, AI. So we look at the skills build program that my team runs and we offer around the world for free. And we incorporate these, you know, the foundational commitments to make sure it's safe and responsible and industry aligned. And we offer that externally. So what we do internally, we're also doing externally. But it is a, you know, trust and safety and ethics are really key.
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D
Us, since we are a semiconductor manufacturing company, really building up a robust, secure, resilient, US friendly semiconductor or a silicon supply chain is very important for us. So working with communities K to 12, vocational schools, community colleges, higher education, current workforce, small medium businesses, large customers, that entire value chain for digital readiness, that's a flagship program which means more scale, more trust and most importantly, responsible outcomes for them. That's what we are focused on.
- [06:02]
E
Thank you. And at Pearson, just like most other companies, last year we did a whole set of experiments, if I may, a lot of poc, so on and so forth. This year I think the thinking has been we could always use AI to improve productivity, to have more efficiency. Obviously automation is a big story there. But how do we fundamentally change the operating model of the company to be AI first in everything we do? And we think about our value chain, it's really about obviously our products and how do we obviously bring in AI natively there and then everything around business development, sales and delivery, operations, the whole thing. And as a part of that, one of the things we found is that AI adoption at the end of the day comes down to leadership and how do we get our leaders, our top 100 leaders, really onto that journey. And so we have been doing a bunch of work with our leaders running of course, not only programs and training programs to get them to really build agents, do stuff so they really deeply understand the transformational impact of AI, but then also creating a performance framework which actually includes everything that we do around AI and the outcomes we can deliver and as front and center part of the performance and how we measure our leader. So there's been a ton of activity on the AI front in terms of accelerating adoption on our side.
- [07:24]
A
Really fascinating to hear. Thanks for sharing. So if you don't mind, I'll take you through my own journey in the last three years or so since ChatGPT came in November 2022. I love the title of the session here, Productivity Paradox. I'm living that every day. So I'm sure like you all can confidently say how much AI is helping our everyday life. Like for me writing in my emails to building applications end to end, to preparing for a board meeting, to all the way planning my vacation, planning my daughter's school charity event, AI is, I can say confidently and openly that it's making me at least 50% like I'm doing double the things that I used to do a few years ago. Can I say that for my team in Cornerstone, like for my organization, I worked in Amazon for 13 years. Can I say that? Absolutely not. So there is a huge, it's not incremental delta, huge delta between what we experience technology individually versus the company setting. So I've been wondering what is going on and it might not be a surprise to any of you. It's not the foundational technology. So the technology is the same, the models are the same, they are much more advanced. There is unlimited use in the company setting. It's everything around the tech is what I see. The biggest barriers are like one trust. I think you all mentioned the number one questions employees ask me as an employee if I wear the company setting, I ask how Is my data going to be handled? What's it going to do to my performance rating? Am I going to have my job a year from now? So we as leaders in this room, we as leaders in the company setting, we have a huge responsibility to provide clarity to our employees. And I think we can do that. Like governance, auditability, traceability, responsible AI everybody is considering as stable stakes. The other two topics, I think it's more of a moral, ethical question on where is your conviction, like I am of the camp that it's human plus AI. AI will replace tasks, more tasks, like unbelievable number. But then humans are going to be innovating much more strategically. They are going to shape the AI. They're going to build the next generation of artificial general intelligence or whatever that might be. So if we have that vision clear, if we communicate, I think the friction goes down a lot. Instead of resisting change, now I'm going to be part of the change and then it's going to trickle down to the employee. So I feel like that building the trust is important. The second thing, I think you all are the key. And when I say chief learning officers like hr, organizational leaders, how do we implement this? I think it's not typically we go start small, change management, continue to iterate and improve. That still holds good. But we have an opportunity to think big and then start small. So the once in a lifetime opportunity with the tooling again, we all have experienced individually, like what can I do to our company setting? Like if it's an opportunity to blow out the mission, what we want to accomplish for our customers, for the business, then redesign and rethink what our roles are going to be. What are the roles going to be? I've been following the ASU conference for the last few years. Skills last year was a big, big topic. I think it's even more important this time around, but in a different context and setting, it's no longer static. What was considered a skill for a role last year is completely not relevant anymore. But still the idea of redefining and rethinking what that means to your workforce could really help. So I feel like there is a big delta between individuals and company setting. And if we can figure out as leaders and communicate this vision clearly to the employees, then we can bring them on board.
- [11:22]
B
Okay, a little bit into the sort of a bit more of the ecosystem side of the world. So we're rapidly moving from gen AI as a productivity assistant to agentic AI as a automative autonomous collaborator that can execute lots of steps, help with workflows and sort of make decisions, sometimes scarily, without human oversight. How are you thinking about the rise of agentic AI Sort of helping us sort of fund the opportunity to fundamentally rethink about learning. And do we think organizations are ready?
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C
No, they're not ready. Yes, we think about it every day, all the time. One of the things that we talk a lot about is we are the last people who will work on exclusive teams, teams that are exclusively humans. So blended teams of humans and agents means that's the norm. It's the norm for us at IBM. We're expected to use agents, whether they're ones that have already been created for us to use, whether we build them ourselves, whether we're using some from, you know, from our partners that have been approved and safe. But the expectation is that we are using them. We are using them to make. To automate regular workflows or to completely restructure to your point, how we would approach doing something. So some of it is completely blowing something up. Use an agent to do it in a new way. Others, how do you expedite what you already do? When I talk to universities and deans will say to me, how should we be changing what we're teaching? And I say, if you're not teaching your students to build agents, use agents and think about their team projects that they do. If they create an agent and allocate some part of that team project to the agent, and then think about how do they oversee what the agent is producing, how does it make their project better, and what are they going to present at the end? Because that's more like how we work. So if we're not saying those kinds of things to universities about how we expect people to come in ready to do what we do, it's hard for them to make sure they're prepared, if I may add.
- [13:39]
A
Very well, said Lydia. So, Lawrence, you and I were chatting before the session, meeting employees or users where they are one. I think the pane of glass is important. But also, Lydia, like you mentioned, bringing AI into your flow of work, again, translating whatever people are using outside of work as individuals, how do you bring that to enterprise setting? Given that we have tools that are safe to use, whitelisted. But then how do we get people to really embrace that human plus AI in their own work, and then it gets encapsulated into the teams and then organization level. So meeting users where they are both in terms of the experience as well as the everyday work is very critical.
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D
Okay, now I want to kind of add A new dimension or layer of complexity. So this is a true leadership challenge. Now since most of you are in enterprise, you can associate. You're breaking the work into task and then task to workflows. Now you have choices. You have human agents with human oversight, human in the loop or human on the loop. And then you have something new coming up, physical AI. So robotics and IoT. So you have three choices of workforces for every single task. So it's truly a leadership judgment moment. What can human alone do? What can robot alone do? What can agent alone do? What can human plus agent or agent plus robot or robot plus human or all three. So seven combinations. Now how do I decide? So this is exactly where. What is not evident in an organizational culture is what's the team dynamics? Who's deciding? These are the tough questions which are surfacing out. I'm a great cosmology believer, so I'll give you cosmology analogy. In that sense, in the universe we understand what's physics and what's matter, but what we don't understand is what's dark matter and dark energy, which is remaining 95% actually. So MIT actually did a whole sustainable competitive advantage research and they came up with this term called residual heterogeneity. What is truly unique left? I think leaders need to now really pause and really understand what is truly unique left. If AI can do everything and is available everywhere for everyone, and with these seven choices, what are described? And to your point, you're absolutely right. I mean there is a huge paradox. We are seeing individuals can, but organizations are not. So what's failing is this dark energy which is are not seeing it. So this is where leaders have to really spend a lot of time in the enterprise. Where are the decision making choke points? You know, it's typically layered under three level of organization hierarchy or a chart, someone intern or a young guy who knows that tool very well, while the decisions or the power dynamics are somewhere else at a VP level. Now how do we change this? These are tough questions. So this is exactly where. This is a new reinvention of leadership. I'm imagining in one or two years down the line you will have a whole new leadership playbook around things like this.
- [16:50]
E
So I actually build on what Anshul was just saying. So, you know, at Davos, this company called Workday, which is a major HR tech company, they released a survey and as a part of the survey they had gone and asked CXOs, a number of C suite people, how do they feel about working with agents around Them this idea that you're going to be surrounded by 20 agents and you know, you'll have different agents from different tasks, so on and so forth. And I think the number was 70% of them are super anxious about it. They don't. On one side, they're all saying, we all need to be agentic. We all need to be agentic. But the reality is today, human beings have not figured it out. I think I am, I am anxious about it. I really don't know that world. And I'm going to be. Not with. I'm not going to have colleagues, but I'll have this hybrid thing where I'm going to be getting work done by a bunch of agents. And this is important work. The topic of trust, how much, again, this thing, how much human in the loop, human in the lead. I mean, all of these topics become really, really critical. And this is why when you think about agent tech, it goes way beyond technology, as you're rightly saying. It's a leadership topic, it's an operating model topic. You know, how do we look at, look at the future in a very, very different way? And I think the one thing which will elevate the conversation, which I think is one of the reasons all of us are together, is really around skills. Because I think you will have technology, but the skills that human beings need. And that's not just tech skills. You will have a lot of tech skills. We at Pearson have been really obsessing a lot about human skills, as we call them. There is a new skill we recently released called learning to learn. It's called learning agility skill. A really important part of the puzzle because today it's agentic, tomorrow it's going to be quantum. And there's so much going on. So how do you bring human beings on that, on that journey with you? So that skills and the capabilities you need to build, we think it's kind of an obligation. And this is why I think working together with the ecosystem becomes so important with such a complex problem to solve. So the more people come in to solve the problem, we can elevate millions, tens of millions of people who could, who could be on that journey with us.
- [18:55]
B
Some of the consortium findings, 78% of the roles or the tasks for roles that have been analyzed through the Consortium, Keep me honest, I think it's 50 or so roles of the top roles have some sort of AI orchestration or impact or connectivity with dependency on AI for us. Just sort of, as I've listened to sessions here, and what's really interesting is sort of a bit of the resurgence of the humanities and arts side of critical thinking is sort of suddenly that's becoming really important now. And even though it's kind of ironic, because AI is a super tech thing, right? And requires really complex infrastructure, but then to wrangle it, you've got the critical thinking, the sort of human skill side of things, something just interesting. You know, what we're seeing as we look at labor market insights and data internally in the work that we're doing. This concept or the skill of sort of AI orchestration is becoming quite a hot topic at the moment. And what I mean by that is AI in and of itself, an LLM, a tool will do almost whatever you want it to do, but you have to tell it what you want it to do. And also in the sort of ecosystem universe that we have at the moment, so many parts of the ecosystem are disconnected from each other. So if you want AI to go look at a, an organizational strategy and say, okay, here's my labor market data, here's the company strategy or the org or the functional strategy, give me some signal and help me think through the learning, this need. All those things aren't connected unless the human connects all of those gives the context to the AI. And then the AI or the agent then helps you think through that together. So sort of you need to front load the AI with the human thinking. And another thing that's kind of interesting is for those, you know, I live every day sort of thinking about requirements and the clarity around what do I need tools to do. But a lot of folks don't think deeply about requirements. And guess what? If you don't have well thought through requirements, you don't get clarity and specificity out of AI. So I think that's sort of an emerging skill that whether we like it or not, you need to get good at thinking about the requirements of what you want your problems to solve or your tools to solve.
- [21:13]
A
So no, I totally agree, Lawrence. I'll go a step further. Even if we go two years, three years past, the continuum is going to be there, right? So innovation is not going to stop. Even if artificial general intelligence can do end to end, then we get to go do something more. We get to create more applications out of that. So I feel like there is a narrative that SaaS is winding down or learning is commoditized if ever. Like now is the time where learning is going to be extremely relevant because it's not static. Everyday things are changing every day. Your role is Changing companies, growth targets are changing, missions are changing. So how do you adapt, make your workforce adapt to that is very, very critical.
- [21:56]
C
I think the half life of skills, I think this, you know, at this conference over the last several years, we've talked about the half life of skills, and it used to be around three years, and now it may be months. Right. The session Anshul and I were in before this, the moderator said, well, the time to get to 100 million users for the Internet was eight years. The time for ChatGPT to get to 100 million users was eight months. So he quoted a general he had worked for, and he said, you have to move at the speed of the fight that you're in. And I think we are in a fight we've never seen before or experienced before. And so we're moving at a pace to try to figure out how to solve this for our own companies, how to solve it for the partners we work with, how to solve it for society. And I think we have a responsibility to work together to solve. What does that look like? What does it look like for individual people? What does it look like for teams, cultures, organizations? We work with governments, we work with university systems, we work with nonprofits.
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G
Right.
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C
We work with other enterprises. We have to sort of share the best thinking that we have about this around leadership, around development of people, and figure out how do we tackle this at the speed of the fight we're in.
- [23:15]
E
Yeah, one of the topics. Absolutely agree, Lydia. I think one of the topics, which is also an elephant in the room, at least for us at Pearson, is everything around data, because I think when you speak about, obviously knowing the business requirements, so on and so forth on the other side, at least for us, and we have petabytes of data. Right. But it's not in a good place. So it's very siloed. We don't have kind of. We have not had traditionally sort of a clearly articulated data architecture. We're trying to get better at that. It's a very complex problem to solve. So on one side, there is the whole sexy world of AI, but on the other side, if your data foundation is not right, none of this works. Right. And this is where companies get bogged down, because you have this whole thing on the top. But, but. And that's, that's, that's, that's one of the things that I want to mention, because it's an unsexy piece, but very important to think about.
- [24:07]
B
I want to do a little plug. My partner in crime over there, Sandra, and I and a couple of others and we actually did a session on, on Monday where I'm happy to chat separately because it's not the topic of this conversation. But many of us are trying to solve the same problem around sort of a data architect. It's a common data architecture and sort of foundational layers. So we've been thinking together, is it possible to create a framework that everyone could follow independent of company, independent of where you are in your maturity. So if you'd love to have a chat about that and also help us think through and pressure test it, come find us. So we think it's gonna help everyone, including suppliers because if, if we can have a consistency of an architecture, it makes it easier to partner with other people as well.
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F
So.
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B
All right, third question. So we're committed to this 95 million sort of global reach, but it's not uniform and the adoption sort of globally is not, not fully uniform. How do we, how do you think about balancing the need for sort of a standardized global scalable framework when the reality is different companies move at different speeds for different reasons. Regulations, opportunity, resourcing. So how does that, how do you think about that?
- [25:35]
A
I'll start with a plug for the consortium. So I start shared with started with the whole velocity how individually we cannot navigate the change. I also think individually we cannot get to this 95 million. So again with AI, unlike any other technological advancement, there is also a social responsibility communities to come together. So I think it's a fantastic way for us to standardize the baseline and then navigate the progress, measure the progress and then inform each others on what to be doubling down on. I think consortium plays a big role the the company level. I think we as leaders have a huge responsibility too. It's very tempting to get put the bias hat on and say that these are the roles I'm going to be automating or AI is going to replace knowledge workers and then we go after that. The risk of doing this is one, the concentration of AI fluency within your organization is going to be in that teams who are automating that work and then the other teams whose work are getting automated are going to be left behind. So we're not going to be able to embrace them in the thought process. I would highly recommend to resist that urge to jump in with piecemeal automation. Let's look at the big picture of what is the art of possible with AI. If we look from there, then we have an opportunity to think each role. How does it look with human plus AI like Lydia Mentioned every role doesn't matter if it's a corporate desk job or it's a frontline job. Like every day we read about robotic innovations in the manufacturing, in operations and logistics. So if we look at what does each role have to do with AI embedded, then we include them in the thought process. 2 I think also important is building AI for the business. Also has to start with building AI for the employees. So every day if I am not seeing the same experience that I see with Cloud or ChatGPT on my personal experience, if I'm not getting the internal AI to be on par, I'm going to be left behind and I'm going to lose interest in it. So I feel like the internal AI strategy for employee experience needs to be tailored for different Persona. HR leader, a manager, a frontline worker need to see in their mode of modality that they're comfortable with, they need to experience AI. So I believe taking a methodical approach, again that's where consortium can definitely come help with the standardization. But approaching it so that the entire workforce get the equitable advantage of learning and keeping up pace with AI is important.
- [28:16]
E
Yeah, I mean a little bit to build on what Guna was saying. So I think there has been a bit of over obsession, I call it about white collar jobs when we talk about AI and skills and so on and so forth. I don't know how many of you know, when you think about the blue collared work you mentioned frontline workers, that's 10 times the size of white collar. Right. It's a very huge, I mean when you, when you're talking about, you're talking about nursing, construction, you know, hospitality, tourism, there's a whole bunch of those sectors and I think one of the things that we have been really obsessing about is what changes for those people when it comes to AI. How are their workflows changing and, and what can we do to enable them to get more efficient and get better productivity. The other side of it of course, is that is this really just a productivity game? And as I said in the beginning, is AI truly a growth driver? So when you think about manufacturing companies, automotive companies, retail companies today, a lot of thinking around it with the CEO and the CIO and the Chro is, is about getting more productive. Right. That you're going to take cost out. But I believe that's a little bit of you're not doing justice to AI by doing that. Right. I think it can fundamentally change the growth trajectory of your company. But I think very few companies, especially in the growth, especially in the traditional industries. Tech companies have tried to figure it out, but I think the companies in the traditional industries have not figured it out what that looks like. So I think thinking about that in a more wholesome way, as you said gona is really, really critical. Otherwise we will create I think this whole we call it the two speed world. Right. Where there are bunch of people who will know how to do it, who will be enabled, who will have the skills. But then there's a huge communities of people who will probably get left behind if we don't look at it holistically. Yeah.
- [30:02]
D
On the left behind part, I mean there are a lot of divides which already exist. If you think about it AI as a topic, people land up talking about AI researchers, AI engineers, AI developers and that kind of a career which in a broader scheme of labor market worldwide it's only 2% of the work rest 98% is all the white collar, blue collar, other color, pink, green and others. And then if you talk about normally entry level jobs and the impact which is a sexy topic to talk but there is a whole mid level and there is a gray level or a senior level. Right. So the disruption is happening in all the nine boxes. If you paint this, that's a huge learnability to your point. Right. Or a lifelong learning opportunity. There are very few policy levers available right now. The world is discovering this. I mean since I sit in the policy side of the work, I can tell you one example which from small businesses and I'm assuming you guys most of your enterprises in the learning cognitive infrastructure site but not like a big tech companies yet. Right. So you're still discovering this time is an issue, money is an issue, energy is an issue. So how do you really jump start your organization journey in this Singapore government actually last month when they announced a budget, they gave 400% tax credit. So if you put one $1 for a reskilling upskilling via government approved program, which is a huge opportunity for a reskilling upskilling company, training providers and so and so forth. You put one, you can get four back. Now we really need a lot of new such out of the box models to break this. I can give you many examples on women entrepreneurship as an example. Right. Or a gray economy or a silver economy models of lifelong learning. To me, I mean as a education industry professional, all these are unknown opportunities and hence a big business potential. Now depending on how you guys are thinking about as your own business, there's tons of opportunities which are lying. Governments are Equally struggling with this. So somebody need to just come up with the new models and really educate. Large companies will figure it out, but that's a 2% of the work, right? What you guys are doing. When we saw the raise hand, you have a much, much bigger opportunity. That's how I would want to address this. Right. Anyway, I can go on and on, on the AI divide and AI paradoxes, but I would be jealous. Right. Of you guys. Right. Because the opportunity is massive.
- [32:39]
B
All right, do we think we have maybe a couple of minutes for a question or two from the audience? Anyone? Anyone have. Go ahead. I don't know if we have. Do it. Okay, the mic's coming over. Being helicoptered in.
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F
Thank you. Hello, everyone. My name is Idril Ogbuna. I'm actually a student at Houston Tilliston University. I'm a double major studying computer science and mathematics. I'm from a more technical background. After my first summer, freshman year, I was. I went to App one. I was building like Free Robotics. And then last summer I was working as an AI engineer at aws. We are building AI agents for them. So I'm kind of very interested in this field, to be honest. So you mentioned something that I thought was really interesting about how AI is it really just like a productivity tool. And I think there was something. I worked on this for a hackathon about two weeks ago, and it was something about how AI could be used as more of a creativity tool than a productivity tool. And I could talk about that more, but maybe just later. My actual question is, I have seen a lot of people speculating about two things. Either AI and AI agents become more of individuals themselves. Like AI agents become more of like things that help individuals achieve their goals, or it becomes more of its own entity that works with people. I'm really interested in hearing your thoughts concerning what exactly you think the future is going to look like in this field.
- [34:11]
A
I can share this great question. I think both go hand in hand, in my opinion. So already we are seeing the evolution of automation has existed for a long time now. We started seeing generative AI, now agentic AI, and then there is talk of artificial general intelligence. Definitely you can think of digital twins like now. Like I said, if AI is making me 50% better, then it's doing a lot of my job. So I can see it evolve as another entity, human workforce, agent workforce in a company setting. But also it is important to understand that again, there is a builder who is building those agents too. So seeing them together as a productivity tool, as an accelerator for your own productivity, for teams productivity, as well as what the company is set out to achieve. I think it's a bright model, so it's going to play both the roles.
- [35:09]
B
Oh, question at the back and then one at the front. And I'm keeping on the time as well.
- [35:16]
A
Quick question, one comment. This has been exceptional, this panel, so thank you very much. You talked about uniqueness with leaders needing to spend time on what's truly unique. And if people across different companies are using, whether it's an agent, human, physical AI or some combination,
- [35:37]
D
how do leaders
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A
best think about what's truly unique? What's separating companies when everybody's doing this?
- [35:42]
D
Yeah, since I made that comment, I would take that. I think there is a lot of hype. So people use AI for the sake of using AI without answering that question. What's organizationally truly unique? Right. You are seeing extreme hype or horror. But if you have to real, have a hope. We need to answer that question first. What's your organization truly standing for? There's no one standard template for that that requires a real organization DNA or a culture discovery process. AI is not going to give you that answer. But people are. To your point, people are jumping on AI for AI and that's not going to solve that.
- [36:18]
C
I think the other thing to think about is you should use the technology to do what the human is trying to do, right? Not just the technology for the technology's sake. That's probably a waste of time and money. And then you have to think about what's in it for me. Humans are not going to start doing something differently if they don't understand how it will benefit them. So there's a cultural change that needs to happen with people first before you have the change of technology being successful.
- [36:46]
G
Hi, I'm Hannah. I run a social startup in Mexico to enable people in vulnerable situations access fair jobs. Actually the kinds of blue collar jobs that you were just talking about, which for those people actually means a 3.5x salary increase. So we're talking about some steps below the ladder. It was so interesting to listen what you all were just saying. And I have two questions. One is something that we are struggling, like working through right now is those people that we are working, we have to work with them 100% through WhatsApp, the kind of training that we do, we can't send them to a platform. It's just not going to work. The level of preparation and also just confidence in using technology is not there. So I'm really curious if you have seen ways probably beyond the US like more in global south countries to do learning that's way more accessible and really meets people where they are. Because all that we are seeing in Mexico and across Latin America is is companies trying to rely on NGOs or then organizations like us. So now we are partnering with Microsoft to do AI skilling for frontline workers and at the same time we don't see yet companies being able to really invest in that. So I'm really curious to hear if you're finding ways to do that because that would enable your workforce. And then on the other side, I think the truth is there will be layoffs in those groups of people. Right. And I think it's just a question of like talking about productivity. It's inevitable that companies are going to lay off people. And I just wonder like what you think in your roles but also as human beings just seeing what's going to happen. There's such a risk of us slipping back into a higher like a much higher level of poverty and inequality. What do you think that companies can do to do something about that and kind of counteract that? Yeah, very, very possible future.
- [38:42]
A
I give a 60 second very rapid fire and then I'll pass it over. Great question. So one meet where users are. Absolutely. I think you're going to see more of that I can give. I'm sure everybody is working too like cornerstone, like everything is like headless. Not just in terms of integrating with other applications but different modalities like ease of use, text based learning. We are absolutely there. The second one, my thoughts. I think there is a lot of noise here. We always have had limited budget, we have had staffing to that budget. Maybe we over hide or maybe we are being myopic and thinking that like we are going to lay off. But ultimately the questions on like productivity versus creativity. There is a immense opportunity for every company to actually do more grow their top line. So I don't see this as an excuse to lay off. But you're going to see that noise. But it'll settle down.
- [39:37]
E
Yeah, I mean I'll just add something. So we are very, we do a bunch of things in the global south. I mean countries like Mexico and Brazil, India, so on and so forth. I think the one thing I will add there to whatever Guna said is that I think the public private partnership becomes very important in those countries. So private companies cannot solve for all of it. I think having that very strong sponsorship from the governments there and what you can bring in there is critical. When you launch these programs. On your second question, I am more an optimist, I would say. I mean, I believe obviously in the short term you may see some layoffs, but there are going to be a lot of more jobs getting created as well. I think Lydia was talking about that. So I do believe that in the end this is going to be great for humanity. And we all have. I believe we should all believe that. I think that's a good way to look at this problem instead of a doomsday scenario.
- [40:24]
C
We're out of time, but we do have partnerships across the global south and Latin America, and some are also using WhatsApp. So I'm happy to connect you with our LATAM leader.
- [40:35]
E
Do I have time?
- [40:36]
B
We might get. We're probably going to get run out. Do you want to just find us for a few minutes afterwards? So thank you for joining. We'll sort of wrap the one final thing I'll leave you all with. There is an AI Workforce Consortium Hub sort of website. You can find learning recommendations, all of the roles and the skills that have been identified. And there's actually a webinar coming up that you can sign up for called the AI Agents and the Impact on Cybersecurity. All the dates and the registration are on the hub. So thank you again for joining us. Thanks for the panel and thanks for humoring me to come in and jump in and help facilitate this.
- [41:15]
A
Thank you for stepping in.
- [41:16]
D
Thank you, Marcy.