Shaping the Future of AI

Rachel Cossar: And welcome to the sixth season of conversations in the future of work. I am your host, Rachel Cossar, and I am thrilled to kick things off with a big focus on the future of AI. And our first guest, for this season is the incredible, Malvika Jethmelani. And so, Malvika, I’ll pass it over to you to share more about yourself. Yes. Thank you, Rachel. I’m Malvika.

Malvika Jethmalani: I’m a three time CHRO and the founder of Akvis Group, which is a human capital advisory firm driven by the core belief that to win in the marketplace, businesses must first win in the workplace. And I’m also the recent, founder of a community called Women Shaping AI. So that’s my newest job. That’s incredible.

Rachel Cossar: And, I am a totally honored founding member of that community. So we’ll get into some of that in a little bit. I’d love to start just taking a step back, you know, from your day to day. I know that you’ve had a big journey as a CHRO, and now you’re leaning into some of these other spaces, advocacy and consulting. And so just tell us a little bit more about that that journey. Yes.

Malvika Jethmalani: So when I left my last, CHRO gig in the summer of twenty three, took a step back to figure out what I wanted to do next and kind of, you know, what is the best way for me to use my strengths and my skills going forward. And, I’ve always wanted to build what I call a portfolio career. So doing a little bit of advisory and consulting work, doing some speaking and teaching, and then doing some writing and thought leadership. And that’s exactly what I’ve been, working on building over the past year. But also I, a couple years ago, I started on this journey to kind of educate myself on everything AI when when ChatGPT was just a few months old. And it became very clear to me very quickly that there’s a glaring gender gap in AI, that not too many people are talking about. And as a CHRO and and with my HR background, I’ve always been, someone who believes in inclusive and equitable work and workplaces. And, when I saw that we have this gender gap, in data science and AI, you know, I started to kind of wonder what I can do about it. And, of course, as a single, you know, person, there’s not much I can do to solve this problem. It’s a systemic issue and systemic problems require systemic solutions. And the best solution that I could think of is put together a community of really smart, talented leaders like yourself to, put our heads together to try to make a dent in this problem. Yeah.

Rachel Cossar: It’s so interesting because, so you just shared. Right? Like, that you took it upon yourself a few years ago once when Chat GPT really came out, you know, and, educated yourself on on how it like, the inner workings of it and and all that. And, and that’s real so that’s number one that’s really amazing. And and number two, it’s almost surprising to hear that, like right. Like, my my mind goes to, like, what like, where is the gap? Like, why is this gap happening when these resources are like, ChatGPT is easy to access. Right? Like, assuming you have a computer, which, of course, not everyone does, but assuming you do, like, you can access even the free version and play around with it a little bit. So are you seeing like, where are you seeing this these these issues happen? Maybe from, like, the perspective of a workplace. Yes.

Malvika Jethmalani: So first of all, I think it’s important to understand that it’s not just a usage gap. So I actually became aware of this problem, the first time a few years ago when I read a book called Invisible Women. And this was even before the Chat GPT Mhmm. You know, blow up and and craze and when AI started to feature regularly in the news. And, essentially, the gist of this book is that our entire world, whether it’s business, education, the design of our cities, medicine, science, everything is designed based on data. Right? We make most of our decisions based on data, but the problem is that a lot of the data that’s used to make these decisions and design products and services and societies in general does not contain female data. It’s mostly male data. So if you whether it’s, you know, drug development or, how cities are designed or how the protective personal protective equipment is designed or how, car crash studies are done. It’s all these on mail data. And so then fast forward to now where AI is a regular feature in business conversations, the the gap occurs in a couple of ways. So first, we already know that there’s not enough women in AI. Only about a third of AI professionals are women and only, 18% of AI researchers are women, but it’s beyond this sort of participation crisis. Women are not enrolling in AI training programs. So women make up only 28% of enrollments in worldwide AI training programs. And that’s really alarming because employers now say that 66% of employers say that they will not hire someone without AI skills, And 71% of employers say that they would rather hire someone junior with AI skills than hire someone senior without AI skills. And women also consistently lag in AI usage in the workplace compared to men in similar roles. And if you look at the roles that are most vulnerable to displacement by AI, like the administrative or clerical type jobs, women make up something like 94% Right. Of that workforce. Right? Some people have called us kind of the pink jobs. And then it’s even beyond the workplace. So, you know, AI image and video generators, hypersexualized women, deep fakes are much more likely to target women than men. So those are some of the ways in which, this gap materializes not only in the workplace and beyond. Yeah. It’s interesting because it really is multifaceted.

Rachel Cossar: Right? Like It is.

Malvika Jethmalani: There was actually a Norwegian study done that also showed that, that there is a problem with AI usage in educational institutions as well. Yeah. So when, students are told not to use AI, women comply and men don’t. Right. That’s interesting. If you remember the the famous kind of TED Talk by Reshma Sojani, the founder of Girls Who Code, she talks about how we raise girls to be perfect and boys to be brave, and that, you know, that is how it manifests itself. Right.

Rachel Cossar: There’s so much trial and erroring that is, required within, you know, even just picking up an AI tool. Like, it can be so daunting when you have limited knowledge in in that tool or space or whatever. And then, like, to to take that first step to actually, like, try something out, right, can be, really daunting in and of itself. So I remember you had said that, like, the step that had really stood out to me was that, like, clerical kind of admin roles are typically or or predicted to be one some of the first to, like, really be replaced by AI or people will be using these AI assistants, you know, for better or for worse. And then right? And so then these women will be losing their jobs, and that’s that’s something that I, I thought that that was particularly troubling. It is.

Malvika Jethmalani: And and those populations are the ones where, you know, those individuals typically are not in a position to get their employer to sponsor. Right. Them to go to a training program at MIT and, you know, which costs thousands of dollars, or to even go and invest in their own upskilling. Right. Yeah. Yeah. Fascinating.

Rachel Cossar: So, like, what are your hopes for women shaping AI as a community?

Malvika Jethmalani: The so my mission for the community is, you know, we we exist to provide women with the knowledge, the influence, and the networks to not just be passive users of AI, but to be active architects of AI’s future. So what do I mean by that? You know, many of us are using AI tools, including myself, but, I talk to a lot of women who are not aware of the gaps that you and I just talked about. Mhmm. And they are not aware of, what they can do in their organizations or the communities or institutions where they live, work, and play to, ask the sort of second order questions Right. And to push for ethical AI and responsible AI and inclusive AI and AI for good. What how do I even do that as a woman in my workplace and in my community and in my, educational institution? What questions do I ask? What policies and programs should I push for? When I’m evaluating a vendor, what do I look for? And so my goal is to equip women with the knowledge to be able to do that so that when they are invited to the decision making table, they can speak intelligently, and appear knowledgeable about these topics, because that is, I think, 80% of the battle. Right. Absolutely.

Rachel Cossar: We’re gonna come back to something you just said in in one of our next questions. But I wanted to change gears here a little bit and think about, you know, what are some of the biggest opportunities that you’re seeing with AI from the perspective of a of a people leader? And this could be in relation to everything you’ve just shared. Right? Like, maybe there are some ways that AI can actually be leveraged to encourage some of that inclusive conversation and really make sure everyone’s getting their hands hands dirty. I was curious what you know, in terms of opportunities from that people leaders perspective, what comes to mind? There’s so many. I’ll give you a couple.

Malvika Jethmalani: So, you know, one of the one of the areas where this comes to mind is leadership development. You know, leadership development is, we don’t always see the greatest outcomes with leadership development investment. Sometimes because the programs are generic, they’re not tailored to individual skill levels or learning styles. Other times because it’s hard to pull leaders out of their day job to train them or to coach them to be better leaders. Mhmm. And now we have tools that, allow for that type of coaching to happen in a just in time manner, in a personalized manner. You’re very familiar with this given what you’re building. Yeah. At at Virtual Sapiens. So I I think that that there are tools that are are beginning to solve for that, which I’m very excited about. I think that’s a huge opportunity. I serve on the advisory board of a company that does, AI powered leadership coaching as well. So I think that’s great. I also think that you just talked about this. Right? We we talked about bias, AI bias a lot, but there are AI tools that can help reduce bias. So in the HR world, the the primary example that comes to mind is Textio. I recently read a case study about, text one of Textio’s clients called Upwork, and Upwork used Textio Lift. They want their goal was they wanted to reduce bias and performance reviews, but importantly, they wanted to provide actionable high quality feedback to Mhmm. Yeah.

Rachel Cossar: And this is a problem that almost every company is trying to solve. Right? No. No.

Malvika Jethmalani: Nobody 98% of HR leaders say that their performance management process is not working as expected. And so Upwork implemented Textio Lift, which is essentially designed to give writers, uses AI to give writers recommendations on making their feedback fairer and more actionable. And what they found was managers, who use the tool wrote 20% better quality feedback, and they spent less time writing their reviews. Yeah. And which manager doesn’t want that? Right. So I think those are some opportunities, but I also think there are some gaps in terms of how leaders are implementing AI. Right.

Rachel Cossar: And and are I mean, are those gaps kind of related to the ones you just mentioned? Or Somewhat.

Malvika Jethmalani: I I think that so what I’m seeing in my advisory work, I talk to a lot of CEOs and founders and and often, some board members as well because I specialize in working with private equity backed companies. So I think two types of leaders are emerging. The first one is how can we cut headcount as quickly as possible and take cost out of the p and l? And the second one is how it’s saying how do we augment and enhance the work of our people and how do we serve our customers better? And so I’ll give you two examples of sort of what not to do and and what to do. Yeah. Right. I recently read a case study of a casino in Vegas. Interesting. That’s where you and I met. Yeah. Not at a casino at a time. I know. It sounds like It sounds a lot more fun than, you know, what we were actually doing. And, so this is a case study with Vegas Casino that wanted to automate the drink ordering, for, customers who are playing the swap machines. And at first, it appeared to be a great idea to management, but, this is a classic case of management sort of sitting in their ivory towers making decisions without going, to the place where the work actually happens to see what is taking place. So if you know anything about slot players, first or second try, they tend to switch machines. Yeah. And so if you’ve ordered a drink through your slot machine and now you switch, suddenly you have chaos. Right? So management fails to understand two things. They failed to understand customer behavior. Mhmm. The other thing they failed to understand is the shape of the job, the roles and responsibilities of the job. So the server’s job to create connection and camaraderie and make you feel warm and welcome. The server’s job is also often, especially the Vegas casino, to flag when someone has been overserved and should no longer be served. Or if they’re acting inappropriately and and to maintain kind of order and safety for everyone. Right? And so to reduce that job to someone who just brings you a drink is dehumanizing the role. Yep. And so they didn’t understand that the server, once they see you, they they know that you’re the one who ordered this drink and they can bring it to you even if you switch machine, but they also failed to understand the other aspects of the job. And so, of course, they had to reverse. Now the the example of a company that has done, AI that is deploying AI really well, I think, is IKEA. So IKEA, like many other companies, brought in AI chatbot to automate customer inquiries. And so their AI powered chatbot took over 47% of the customer inquiries. And that freed up a lot of customer service agents. Right. 8,500 people to be specific. So what IKEA did is they took those 8,500 people and reskilled them Mhmm. Into virtual interior design consultant roles. Right. And by doing so, not only did they protect those jobs, they created a brand new incremental revenue stream of $1,400,000,000 from virtual interior design services. So I think IKEA is a great example of a company that understands that profit and people are not at odds with each other. In fact, one drives the other. Right. Yeah. And that’s a great those are great examples, you know.

Rachel Cossar: And it is, some of these decisions can seem like simple fixes or no brainers, right, until you actually play the consequences out and then realize that it’s actually not as simple. And there are many things, you know, some something can be a lot more complicated or a lot more complex than it might seem at face value, right, to your point with the waiters. So something that you said to me when we, connected recently that I’d heard also at a conference we met at was about this, you know, the way we build skills is over time and oftentimes having to go through some of the, you know, less flashy parts of a job. Right? And, AI can take away a lot of that, but then what does that do to different pathways for development and, like, the development and movement of people through organizations? Right? So how do you build talent without having them go through some of the, I think it was your word, the drudgery? Yes.

Malvika Jethmalani: This is something I’ve been thinking about a lot. Mhmm. In fact, I, posted about it on LinkedIn to sort of tap into the collective wisdom Yeah. Of my LinkedIn network recently because I have not been able to come up with a good answer. So, you know, when when I was early in my career, I learned the business by doing the grunt work, if you will. I was in meetings, taking notes Mhmm. And, you know, getting exposure to these very senior people and learning just by observing how scheduling interviews, drafting policies, and job descriptions, and reviewing contracts. And now AI can take over a lot or if not all of that work. And so precisely to your question, how do we develop early career talent if that boring but important entry level work disappears. And it’s not just in HR. Right? Junior lawyers draft NDAs or or basic, you know, contracts. Entry level, recruiters will, you know, schedule back to back screening calls. And now you have HR tech that can completely automate the screening. So the the hypotheses that I have come up with are we need to radically redesign early career roles, giving junior people, I think, more responsibility sooner, pairing it with structured coaching. Also, we need to bring back apprenticeship learning. You know, we used a lot of our jobs used to be apprentice type jobs. I think apprenticeship could make a comeback with AI sort of serving as the tutor, but not a substitute for human mentorship. I think job shadowing, simulations, you know, whether it’s using virtual reality or augmented reality could be really helpful and pairing that with sort of real time feedback loops. And and I think that might replace, you know, task based learning. And I think organizations need to invest more deliberately in kind of workplace academies or internal boot camps. But, also, I don’t think that this is a problem that the corporate world can solve alone. I think we will need governments and educational institutions to completely shift the paradigm of how they prepare young people to for the future and to enter the workplace or to enter the world of entrepreneurship, whatever that that path is. And I think that traditional path of go to college, get a degree, get a job, spend the first couple years doing the grunt work, and then get promoted. I think that path is going to we we will need to rethink that path, and we will need to start teaching young people earlier, for example, how to work with AI and how to work with agents, how to think about using AI in a responsible and ethical manner, but also doing all of that while preventing cognitive atrophy. I think we are at huge risk of cognitive atrophy as a student, and I think that our governments and educational institutions need to think about how they will continue to invest in helping young people learn those critical thinking skills and logical reasoning skills that you and I had the luxury to learn on the job in the first couple years, and these people won’t have that luxury. Yeah.

Rachel Cossar: I’m so glad you brought that up because I do think that like, when I think about the most efficient ways and the most most powerful ways of working with AI is is when you’re asking the right questions and you’re being thoughtful about the information you’re being fed and you’re questioning that and you’re going to direct sources, and, like, really having this rigorous, back and forth with the AI versus, you know, taking everything that you’re given at face value. Right? But we know that, like, we then we see the way people digest the news. Right? And myself included. You read something and you’re like, oh my god. And then you find out that it’s very miss misrepresented or whatever. You know? And so I totally agree, and I do also worry about that. Like like you I think you put it perfectly, cognitive atrophy. Right? Like, how do we make sure because because the skills around asking good questions, like, that is an incredible skill from an interpersonal perspective and then also, like, AI to human perspective. Right? So, we try to encourage that in the way that our clients use virtual sapiens because, you know, we don’t all we we don’t want people to take everything our AI says at face value. We want people to take what our AI shows them about their behaviors and then, like, wonder why and talk to their coach about it or their peer, and then they themselves are the ones who decide how they would like to move forward. Right? Like, we could have the AI tell our our users, like, this is how you should act. But I was like, I don’t think we wanna do that for a number of reasons. I think we want the humans to decide for themselves which behaviors, you know, are most in in line with their intention. Right? Yes. And so much of it is gonna be context dependent. Exactly. And business dependent. Yeah. So, yeah. But I also think you’re right to go to go all the way back to, like, these academic and and educational institutions. Right? And and it’s interesting because some of them are like, well, we’re just not gonna allow AI. And and you’re like, well, definitely, I don’t think that’s the way. You know? Like, you have to figure out a way to incorporate it intentionally and in a way that is going to empower your students. Yes.

Malvika Jethmalani: If you are in the business of preparing your students for the future Yeah. World of work, I don’t think banning AI is the answer. Yep. No. Great.

Rachel Cossar: So, you know, the the last question for today, is around, like, yes, you have this incredible community, again, which which I’m so glad to be a part of, women women shaping AI. But, like, what would you beyond that, or using that as a basis, like, what are some of your favorite ways to invite people, women and but also everyone. Right? Because it’s a scary space for a lot of people, to get involved with the AI strategy within their company.

Malvika Jethmalani: My favorite way to encourage people is to just to encourage them to to learn and experiment. Whenever I discover a new AI tool that is that works for me or is it’s making my life easier, I try to share it, and and spread the knowledge. So if you’re within your organization, I would encourage you to understand what are the second order questions that I need to ask when I’m at the decision making table. But in order to do that, you need to first understand your business in and out. Right? Sometimes I feel like the more things change, the more they stay the same. And the first principles have not changed. Understand your business in and out. Understand your p and l in and out. Who are your customers? What is your company doing to serve your customers better? What should your company be doing? What does your competitive landscape look like? What are the strategic priorities? And then from there, that knowledge should then help you shape your AI transformation, whether it’s you’re leading it at the org level or within your team. So maybe start by documenting your key workflows, document the repetitive work in your within your little team or department that you’re leading. Review, you know, potential tools that your team could use. Maybe consider a week long hackathon or two, you know, two day hackathon or something, to allow each department to experiment. Give them a small budget, right, to do that. And, create some meeting free time and some focus time, some deep work time for them to do that. And then do some pilots and, you know, scope the work, to use those tools appropriately. Obviously, involve your, you know, legal department to make sure that you’re taking data privacy and and security into consideration, and then you’re compliant with things like the, you know, EU AI act or the California privacy act or GDPR or what have you, and review tools, you know, across the board. And I I guarantee you will find areas within your team or your broader organization where you can augment the work of your people and and kind of make it easier. And by doing that, you’re also, investing in your people’s education, helping them reskill and upskill. And that is the number one thing that people are looking for from their lawyers, by the way. So if you read the latest report from McKinsey called super agency in the workplace, the number one thing it highlights that employees are looking for is they’re looking for more formal training and support from their employer on AI related matters. Yeah. That makes a ton of sense.

Rachel Cossar: Awesome. That’s great. Thank you. So, in closing, how can people I mean, you’ve even in the past, you know, twenty five minutes cited a number of really fascinating studies. So how can people follow more of your work, you know, what you’re reading, what you’re posting about? Follow me on LinkedIn.

Malvika Jethmalani: Follow Women Shaping AI on LinkedIn. I regularly post, what I’m learning about, what I’m reading about, questions that are on my mind, like the question about, you know, how do we bring early career talent up to speed? And I like to tap into everyone’s collective wisdom, or you can visit my website at @thisgroup.com. Or if you wanna learn more about the community, it’s womenshapingAI.com. Or just send me a message on LinkedIn if you wanna chat any or all things people and AI. Awesome.

Rachel Cossar: Malpika, thank you so much for your time and for sharing so deeply with our audience. And, of course, thanks to our audience for joining. As always, we’ll see you next time. Thank you.