Ep 150: Navigating AI’s Tsunami – Strategies for Recruitment, Retention + Growth


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As the business landscape continues to evolve at an unprecedented pace, organizations of all sizes are increasingly turning to Artificial Intelligence (AI) to gain a competitive edge. However, successfully harnessing the power of AI requires careful navigation through a rapidly changing technological landscape, ensuring fairness, transparency, and accountability remain at the forefront of every AI application.

In today's episode, we dove into the challenges and strategies for organizations to effectively recruit, retain, and grow in the AI era. With thought-provoking insights from industry leader Usha Jagannathan, the episode provided valuable guidance for business owners and decision makers looking to leverage AI in their operations.

Recruitment strategies were a central focus of the discussion. Jordan Wilson, the podcast host, acknowledged the unique challenges faced by smaller companies when it comes to AI adoption. In response, Jagannathan emphasized the need for organizations to study prospective candidates and understand company-specific skill requirements. Applying this understanding early in the recruitment process can help identify the right individuals who possess the necessary expertise, igniting a successful AI journey.

With the ever-changing job market and evolving technology, upskilling and reskilling have become crucial aspects of organizational growth. Jagannathan highlighted the decreasing half-life of skills and the pressing need for a reskilling revolution. As AI continues to play a transformative role, it is essential for businesses to invest in training programs that empower their workforce with the latest AI-related skills. Collaboration with educational institutions can also help in developing tailored courses that align with specific industry needs.

Usha Jagannathan stressed the importance of keeping up with technological advancements to avoid becoming technologically obsolete. The emergence of low-code or no-code AI solutions has made AI more accessible to a broader range of individuals, regardless of their coding expertise. By embracing such tools and leveraging cloud services, organizations can develop ethically responsible AI products and foster innovation within their teams.

Explainability in AI applications was another vital aspect explored in the episode. Providing customers with transparent and fair explanations for AI-driven decisions, such as insurance claims approvals or denials, enhances trust and accountability. Techniques like lime or SHAP can shed light on the decision-making process, offering both local and global interpretations. Business owners and decision makers should actively seek explainability and view it as an opportunity to improve internal processes and better serve their customers.

The episode also underlined the significance of diversity in AI leadership. By embracing diversity, businesses can ensure that AI applications are designed with a wide range of perspectives, minimizing bias and maximizing the potential for innovation. A diverse team can challenge assumptions and produce more comprehensive and ethical AI solutions. As the episode came to a close, Jordan Wilson expressed his gratitude for the valuable insights shared by Usha Jagannathan. The focus on continuous learning and adaptability was particularly impactful. The fast-paced nature of the AI landscape demands that organizations remain agile and open to exploring new opportunities.

In conclusion, successfully navigating the AI tsunami requires a combination of strategic recruitment, ongoing reskilling efforts, transparent and explainable AI applications, and a commitment to diversity in leadership. By embracing these strategies, businesses can position themselves as AI innovators and gain a competitive edge in today's rapidly evolving world.

Topics Covered in This Episode

1. Strategies for recruitment in AI
2. Upskilling and reskilling with AI
3. Navigating the ever-evolving AI landscape
4. Identifying skills in candidates or employees using AI

Podcast Transcript

Jordan Wilson [00:00:18]:

Using AI and even just keeping up with it can seem like a tsunami at times. Right? Especially what we've seen with OpenAI the past couple of days, but it Seems like there's always new tools and and tips and technologies. And how do we make use of all of that? How can we build strategies to, recruit better, retain better talent, and to grow, right, to grow our careers and to grow our companies. That's what we're gonna be talking about today on everyday AI. Welcome if you're new here. My name's Jordan. I'm your host, and Everyday AI is a daily livestream podcast and free daily newsletter where we help everyday people, like you and me, Not just understand AI, but how we can all actually use it to grow our companies, grow our careers. I'm extremely excited today to talk about those things.

Daily AI news

Jordan Wilson [00:01:04]:

But before we do, As always, let's first take a quick dive into the AI news. And, actually, you know what? If you're joining live, thank you. But let me know what you think of all this OpenAI, drama, everything that's been going on. We're gonna be talking about that here in a second. But, also, if you're listening on the podcast, thank you for that as well. Always make sure to check your show notes. You can always come back in and join the live conversation after the fact. So, let's talk about what's going on in the world of AI news.

Jordan Wilson [00:01:30]:

1st, Yeah. There's other companies out there in the AI news right now aside from OpenAI, but, Anthropic yesterday announced Claude 2.1. So, Claude is their, large language model from Anthropic. Kind of the the big piece of news here is a new extended context window of 200,000 tokens. What that means, if you're not a dork or care about tokens or do token tests like I do. Essentially, that gives you the ability to relay over 500 pages of information to Anthropics Cloud If you are on their prepaid plan. So the company, Anthropic, also said that this new update in 2.1, Cuts down, or gives a 2 x decrease in hallucinations. So, apparently, fewer hallucinations.

Jordan Wilson [00:02:19]:

We'll be testing that out As well as improvements in their API, which could finally bring web access to Claude. That's why I tell people don't use it right now. Well, maybe That will change in the near future, especially bringing web access could be huge. Also, they announced a new workbench, which is similar to OpenAI's Playground. And This is very recent. Right? Because, Google, just a couple of weeks ago, announced a $2,000,000,000 investment. Couple of months ago, Amazon, announced a $4,000,000,000 investment. So if you wanna know who's competing with OpenAI, it is anthropic cloud for now.

Jordan Wilson [00:02:52]:

Speaking of OpenAI, yeah, we're gonna talk about it, y'all. We're gonna talk about it. So OpenAI has officially officially officially. Right? Because Could you could you even keep up with this? It was it was a tennis match, you know, back and forth watching this, but OpenAI has officially brought back Sam Altman as their CEO. So original founder was fired on Friday. So much drama back and forth, you know, nonstop. But, it is official official now after much negotiation to bring him back. So OpenAI in a statement says we have reached an agreement in principle For Sam Altman's return to OpenAI as CEO with a new initial board as of Brett Taylor, who's the chair, Larry Summers, and keeping on Adam DeAngelo.

Jordan Wilson [00:03:33]:

Interesting. And then saying we are collaborating to figure out the details. Thank you so much for your patience through this. Some responses, Sam Altman put out a statement on Twitter saying, I love OpenAI, And everything I've done over the past few days has been in service of keeping this team and its mission together. Also, important to know because originally, Microsoft CEO, announced that Sam Altman was going to be joining the Microsoft team. So about that, he said when I decided to join Microsoft on Sunday evening, It was clear that was the best path for me and the team. With the new board and with Sadia's support, I'm looking forward to returning to OpenAI and building our strong partnership with Microsoft. So, Microsoft CEO, Sadhi Nadella, also put out a statement saying that he supports, Sam Altman's move away from Microsoft and back OpenAI.

Jordan Wilson [00:04:23]:

A lot going on. And then also, apparently, it looks like, Greg Brockman, who's the president, is back as he shared, a selfie in the wee hours of the morning looking like it's we're back with the OpenAI team. So so much going on In the world of AI news, we're gonna be dissecting this probably a little bit more, next week. And, also, Emmett Sheer, interim CEO, who lasted 72 hours. Presumably, he's done OpenAI really didn't say that. He put out a statement essentially saying, you know, he's glad to have been a part of the solution. So, presumably, his time as interim CEO is is up. So, yes, that's a lot.

About Usha Jagannathan

Jordan Wilson [00:05:01]:

We're gonna have more news as we always do. So make sure if you haven't already, Go to your everyday AI .com. Sign up for the free daily newsletter. But that was a lot. Thank you for your patience y'all because I'm excited. I'm excited, for today's guest. So please help me welcome to the show. Let's bring her on.

Jordan Wilson [00:05:22]:

There we go. We have Usha Jagannathan who is a responsible AI leader at McKinsey. Usha, thank you so much for joining the show.

Usha Jagannathan [00:05:33]:

Thank you. Thank you for having me, Jordan. You know, it's a pleasure to be here and especially right before Thanksgiving. And I'm hoping many people would be unwinding If they are on the show, wonderful to see all of them here.

Jordan Wilson [00:05:46]:

Yes. Yes. Always always great. You know, if you're a podcast listener, you gotta join the live stream. There's so many everyday people, even some, AI professionals, people from big companies. But, hey, shout out to Doctor Harvey Castro saying great to be here. Brian saying good morning from Minnesota. Woozi joining us from Kansas City.

Jordan Wilson [00:06:03]:

Thank you all. What do you wanna know? What would you like to know, from Usha when we talk about navigating this AI tsunami? But let's start there, Usha. The the last couple of days have been insane. What's what's your take on everything that has unfolded? Because it's literally been a tsunami the past 5 days.

Usha Jagannathan [00:06:22]:

It is. It is. So, you know, we are overwhelmed by this AI news and what since last Friday. Right? So we are struggling to understand the, benefits, risks, and, and also, you know, how a board can, take us forward, because it started all with the executive board leadership. So what you see here is the takeaway for all of us is, like, The diversity matters and how the leadership sets you know, it speaks volumes, like how the leadership sets the strategy and how they create the vision. So those are all the things that I look at as, the takeaways, and I'm hoping, like, it's, dust is settling down where, you know, Sam Altman is going to be, expected to return to OpenAI as a CEO, and we did see the news on Microsoft, CEO, talking about it. Like, wherever, he is, you know, we are going to support. So those are the things so the takeaways.

Usha Jagannathan [00:07:20]:

You know? How it was a win win situation at one point, what Microsoft was suggesting, and then, later on, right now, we know Sam Malcolm being expected to return back to see open open AI. Yeah. So it it has been a whirlwind, and, the takeaways for us is, Like, how are we going to because we are it's, AI is disrupting the world every minute, every second of, the day today, and, How we are going to navigate this and how we can build responsible AI products and also make sure, like, we can be able to, you know, Stick to the mission, what they are talking about as benefit, all of humanity. Right? So the diversity then definitely matters.

Jordan Wilson [00:08:04]:

Yeah. Absolutely. And and and real quick, you know, let's let's maybe even, Osha, just just hit rewind. So maybe could you just explain a little bit, even about your, kind of personal and and professional experience in AI because, I believe at, at at McKinsey, you were helping, to develop and deploy some AI applications there. So maybe just give us a very brief overview of kind of your professional background as it comes to AI.

Usha Jagannathan [00:08:30]:

Yeah. Sure. Yeah. So, I started off as, in the software engineering background, and then I pivoted When I was starting to build lot of, faculty student AI projects at, Arizona State University, and that's when I said, like, okay. I would like to come out of the zone, Comfort zone and move into the corporate side. And, I went into corporate working at Marsh McLennan. Earlier, I worked for Washington Post as a consult, And then I moved into, McKinsey. And, the recent experience with McKinsey where was, Like, trying to, build and deploy applications, and also making sure that, you know, how we can deploy it in the cloud, like, a lot of cloud migration that's happening across all organizations and making sure that I'm I'm a passionate, more responsible advocate to make sure we are able to build, with fairness, transparency, accountability, because all these things matter, especially if it's a management consulting.

Usha Jagannathan [00:09:36]:

You look for, you know, the accountability, everything, in every organization because every you know, the regulatory standards, how they are scrutinized, the auditing for, all of this, the AI models, what The responses it spits out, you know, what it gives, we want to make sure that it is ethical and fair manner.

Using AI in recruitment

Jordan Wilson [00:09:57]:

Mhmm. Yeah. You know what? I love that you had a background of the Washington Post. You know, I that that always hits home. I love having people who used to work and Newspapers. I used to work at newspapers way back in the day. But but let's let's talk a little bit, Usha, just about present day, because it seems like like companies are always struggling, especially smaller companies, you know, not the size of of your McKinsey's in the world. But How can you know, maybe what are some, gas, like, high level strategies, you know, for companies on how they can start to use AI and to start to navigate this AI Tsunami.

Jordan Wilson [00:10:32]:

Maybe specifically, let's let's talk recruitment. So maybe what are some strategies, for companies and how they can best use AI for better recruitment?

Usha Jagannathan [00:10:41]:

Okay. Sure. Sure. Yeah. So it's, basically, like, starting with the recruitment if you talk Talk about, today, what I see is we have seen, the tech layoffs have become the new normal. Right? In the entire, Past, like, last 9 to 10 months, lie right from since December 2022, November or December 2022, right when Twitter dismantled the and Media communications team, the responsibility team. Since then up until now, you have seen, like, very recently we saw Meta dismantling the responsibility team. So Not necessarily I'm talking about responsible AI, but many teams that got collapsed.

Usha Jagannathan [00:11:20]:

What you see today is, like, the recruitment has started, But the recruitment, what they are doing is they are putting the feeler out, I feel, personally, that, so they are taking their own sweet time in order to hire peep. And there could be, but at the same time, today, it comes with, it's a need of the above where many companies, what I notice is, like, They want to close their budget for the end of the year, so there might be people who would be hiring. And so we need to watch out What that they can close out before the 15th December, when everyone go for holidays so that you could be able to, you know, apply and Get the job what you are looking for because you want to land well in the next role, whether you're transitioning or you're, basically coming out of as a graduate I'm trying to seek, for new freshers. For all of them, I would suggest that, you know, look into the, media and look into What do they are looking for? Study the, you know, do your homework with the company and see whether you could be able to, reach out and, apply. And it's not easy said than done. So, it's like how we make it work, that, that takes a lot of time and, you know, proper planning and organizing.

Upskilling with AI

Jordan Wilson [00:12:40]:

So, yeah, that was great. You kind of tackled it from both sides, but maybe even from, you know, people who are trying to grow their careers, With with AI, which I think is just as important because sometimes from what I've seen and from what we've talked about on this show, sometimes it's it's the individual employees, You know, in these, you know, small and medium sized organizations that are actually taking the bottom up approach. Right? And they're saying, hey. You know, the company hasn't said anything. Let's try this. So maybe how can, you know, those out there listening, how can they maybe upskill or or reskill? You know? Let's say they're they're mid career and maybe they they they don't have, you know, AI governance in place or AI initiatives in place. How can they upskill and and also help to to bring that to their organizations?

Usha Jagannathan [00:13:25]:

Absolutely. Yeah. So there was a recent article that came in, Harvard Business Review where they say, like I've noted it down here. So the average half life of skills is now less than 5 years. And in some tech fields, it's as low as two and a half years, which is quite true. Right? So, The companies, what we see, all the organizations, upskilling is, alone won't be enough. But in order to upscale, they are like they all are in this where they want to see, how they can upscale their employees because they don't want to, want to lose their talent because once again, using the resources to bring back new talent, it's going to cost them. So the need for a reskilling, revolution is quite apparent.

Usha Jagannathan [00:14:12]:

Right? So one thing is, reskilling, it's, it's you can see in the Harvard Business Review what's the return is. It's a strategic imperative. Because In this, labor market, what we see today, we want to make sure how we can reskill, based on what the company, it's based on, like you need to develop skills that are company specific. So let's say it's a supply chain or it's a A beauty brand company. Based on that, what that they are looking for, if it's specific to AI, data and AI, then What did they are really what tools that they are using? What did they are looking for? And you could be able to see in YouTube and many medias, that where they would talk about, oh, we are leveraging, AWS, and we are leveraging LLM integrating, this particular, tool, Jene tool. So that gives us a hint. Okay. This is the cloud technology that they are using, So let's upscale on that if you're applying for an architect position.

Usha Jagannathan [00:15:13]:

Okay. Something that they might need in order to Sure. It doesn't have to be a certificate culture, but having that credential companies look for.

Jordan Wilson [00:15:22]:

And, you know, we will be, sharing this in the newsletter, this, you know, kind of article from the Harvard Business Review that Usha just, that just mentioned. But I'm curious because I'm looking at it now, and it says a generation ago, the half life of the value of a skill was approximately 26 years. Now the half life is often less than 5 years. Right? So, I believe half life is is kind of your ability to, you know, retain, the skill set that that you, you know you know, kind of learned or have acquired. Usha, maybe I I mean, do you know why it's, You know why it's down like that? Like, is AI changing how employees are able to to learn and properly Leverage skills, or is it just we need to learn and leverage way too many skills that we need to upskill now maybe monthly instead of yearly? I I mean, why why do you think the shift?

Usha Jagannathan [00:16:16]:

Why why do we think the shift see, it's, see, earlier, we used to call it as a digital era. Right? But today, we call it as everything. It's an ever evolving AI landscape. Everyday, some tool is coming. We cannot keep up rest with each and every tool, And that's why I mentioned, you know, as, the HBR review is talking about, we need to be company specific. Let's say that you are working for a management consulting, but They might have a technology on behind it, and if they are working on certain tools, then make sure if you're not, you might be working on 1 particular tool, Just don't get hooked to that particular For instance, you might be very good in Python and Java, but if there are some skills that it is better to learn, if if I'm, like, back in engineer, having that holistic experience, something else that you would like to learn, to have that experience, that that might help that might, that might help you to, what I always tell whether it's students, community, or, the, junior colleagues that I mentor or my peers that I tell, like, if we don't keep up with the technology, we will become technologically obsolete. That is so so true today.

Jordan Wilson [00:17:27]:


Usha Jagannathan [00:17:28]:

so hard. Sure you you agree. Yeah. It is hard. It is hard. It is hard. So generally tell them, you know, like, for instance, you know, doctor, Andrew, Angie, or, like, Or, like, all these, data all the scientists, the experts that you know, if you're following them, They have the, for instance, deep learning courses, some of the courses that they have. Make sure what what it is you can take it.

Usha Jagannathan [00:17:56]:

Like, for instance, they have a wonderful course that's generated there for Everyone, you do not need to have coding skills in order to have the a, learning. So right now, what you see, especially today, You see is, like, everyone is going towards the no low code, no code. So how we can take, for instance, Party Rocker, One tool that they have, introduced, service that AWS has introduced recently. So it doesn't need coding. So how we can learn that and, you know, leverage So that we could be able to, apply. If your company using AWS, how we can apply that. So That that's that's how I look at.

AI has an easy learning curve

Jordan Wilson [00:18:33]:

That that is such a good point, and it's something that we probably, Usha, don't talk enough about because I think there's, You know, maybe rightfully so, but there's this stigma around artificial intelligence because it's not new. Right? Artificial intelligence has been used in different sectors for decades. But before, it's like, yeah, you might had to have a a master's degree or a PhD in in computer science or something else To to take advantage of of deep learning and neural networks and and building these models, but it's not like that anymore. You you like you mentioned. Right? No code or extremely low code. Right? So we've seen some recent offerings from even, you know, Microsoft 365 Copilot, Their new studio that allows you to build some really robust solutions with with drag and drop. So even with that in mind, maybe let's talk about that a little bit more, you know, because maybe the average everyday person is kinda scared when they hear, oh, AI or generative AI. Sounds difficult.

Jordan Wilson [00:19:29]:

Sounds like I need, You know, Java or Python or or whatever, but it's not that's not necessarily the case. Right?

Usha Jagannathan [00:19:35]:

It is not. It is not. So, for instance, currently, I'm, Like, I'm I'm a, associate consultant for, Arizona State University for the W. P. Carey School of Business. So what we do there is, like, we, we work on the industry, academia partnerships. And what I do is, It's like we are building technology training programs in communicating with the industry. So let's say it's, ServiceNow, for instance.

Usha Jagannathan [00:20:04]:

Then the the, in ServiceNow, you notice, like, you do not need to know the entire ServiceNow, tools, but they help you to What we do is we build a course, and and that course can be able to be leveraged with the people. If the if you want to become an IT assistant, Then you can be able to take that course, and that course can help you at the end, create a build an exam, which were you know, Like, you can take a certified system admin exam. And once that exam is done, they might take you For an IT, associate technician, level course. But what I'm trying to, say here, they also have introduced AI in it, And you all you need to know is how you navigate the ServiceNow platform and understand it, and then how you can be able to leverage that AI application. Everything you can do it with less code, but you are able to understand how to navigate the platform, you know, how you can be able to manage their instances. Very beautifully, the course is structured, and, so I'm going to facilitate the course. Just I'm giving an example. So people are the companies are trying to, see how they can partner with with universities and bring these programs so they can get the talent also, talent pipeline.

Usha Jagannathan [00:21:23]:

And at the same time, They can show, like it it's open to everyone, not like, not like only people who are, certain degree, like an undergrad or a grad degree, But anyone can be able to use that space. So that is something like, you know, I feel like there is no necessary for degree, in order to because you see, like, how they are going paperless, which I want to bring this up, you know, a minute I would like to take on this, where, there are many organizations, have apprenticeship programs, work study programs where they do not look for, I've spearheaded these programs at McKinsey, Marsh, McLennan, where you do not need to have a certificate, showing, like, you have a degree, But we bring through vendor partners, and then they they might have a background. And there are some people who had background in carpentry, in something, you know, a janitor supplies. And it's totally different, but they come with the passion of developing software. And that's where they start, and you don't expect that they need to know in and out of, AI skills or in and out of software development skills. But we train them, groom them. That was one of part of my role and mentor them. And after 6 months to 18 months, we assess, Reassist them.

Usha Jagannathan [00:22:41]:

If they are good, then we you know, good in their tech talent, then we hire them. So I I don't think that Everyone needs to come with a certain degree requirement as long as you have the passion.

Jordan Wilson [00:22:55]:

I love that. Yeah. Especially in a field that is Growing so quickly, and this is a conversation for another day. Right? Because half of the, you know, universities out there are are banning generative AI yet, You know, so many companies are needing to hire people with that experience. So, Usha, great point that sometimes all it takes is if you work at a company, Have the passion, upskill, reskill. Such a great point. Gonna gonna take a question. Great one for Mike here.

Example of an AI solution

Jordan Wilson [00:23:23]:

So just Pretty pretty simple here, but just saying, Usha, please tell us about a solution you have built, presumably, you know, in AI. But, yeah, maybe let's talk, You know, specifically, what's what's an example of a of a solution that you've helped built around, AI, and and maybe what was the the impact that it had?

Usha Jagannathan [00:23:41]:

Oh, sure. Yeah. Definitely. Yeah. So, I would like to step back, like, at Marsh, like, Oh, one company before, like, that I worked for for Marsh McLennan. So, the products we build is for customer facing, or it could be internal facing. So thank you, Mike, for asking that question. So, it was like for, Let's say for client, claims approval for insurance quote.

Usha Jagannathan [00:24:07]:

You, the, the application is like, You want to make sure that AA, what it builds, the a, like sorry. What the product that, that has been built, You wanna make sure, like let's say you are applying for, claims. You have submitted a claim, and I have submitted a claim, but US got, accepted and mine got rejected, then I need to know why it disapproved, the why the model, the, you know, rejected my claim. Then it would say, like, probably it rejected. It gives you the counterfactual. So I created an explainability with my team To add an explainability component where using lime or SHAP, where it provides you a local or a global interpretation, but at the same time, it provides the counterfactual Saying like, okay. If it's a claim or an insurance, both. We did both claim approvals, model and also insurance, cross selling.

Usha Jagannathan [00:25:01]:

Okay. If This, the the insurance quote. So if, for instance, like, the last 3 months of Usha's utility bill was not paid on time. And for these reasons, this, you know, the insurance quote was rejected. And if she were she had paid it on time And if she can come back after 3 months and does everything on time, probably, it would, you know, the higher insurance quote can be approved. So it gives you all alone can be approved. These are, like, 3 different sets of products I'm talking about. I'm not trying to go in tangents.

Usha Jagannathan [00:25:35]:

What what I'm trying to say is Giving that counterfactuals that that helps to understand the customer. Oh, okay. This is the reason mine was rejected or mine was disapproved, And it is not something of gender bias or anything. So that is that explainability component as extensively I've worked with Mike, And where we try to create the add the counterfactuals and say, this is the reason that this was denied or this was disapproved. And if this can be changed in the course of action, when you reapply, yours will get reapproved. And that is, that model that I built, I felt like I've that was felt like an accomplishment. The reason is you are trying to build something, not only creating a Seamless digital experience for the customers, but you are also making sure that the customers are able to understand what exactly the model is, providing the response because there are so many innumerable chatbots out there today, and you want to make sure the response that you get It's not only today human like content because of the that we leverage, but we also want to make sure that it is fair, and it provides you the right information. Right? And it, you know, you need to feel comfortable about, okay, this is what that I expected.

Usha Jagannathan [00:26:52]:

Oh, and is this the reason that they need that explanation? Is this the reason that my claim was denied? And they want to know the they have every right to know that answer.

Identifying skills with AI

Jordan Wilson [00:27:02]:

Yeah. The the the explainability pieces is always huge. Right? Because both internally, you have to kind of be able to Interpret the the whole black box, but the explainability also helps on the back end as well. You know? Maybe in this example, why a customer's Claim maybe was accepted or why it was denied. That's that's a great point. So, this is another another great question here from Cecilia. Cecilia, thanks for joining us because this one hits on Kind of strategies, retention, and growth. So, Cecilia asking, have you seen how AI can help identify capacity for skills and candidates when specific skills are not identified.

Jordan Wilson [00:27:43]:

I love that because we just, you know, kind of had the, you know, the janitor, kind of example, you you know, kind of transitioning or upskilling, but how can maybe organizations use AI to help identify Skills in, you know, candidates or maybe even employees that aren't easily identifiable. Is is is there a way to do that?

Usha Jagannathan [00:28:04]:

Yes. Yes. I believe, Yeah. I'm sure, like, a couple of years ago, you know, the a hiring algorithm that, You know, every organizations uses this. So, even, McKinsey uses the hiring algorithm. So making sure that there is, because we did, face, like, quite a few years ago, like, where, you know, we all saw in the news where Amazon had the hiring algorithm, issue, and then they fixed it. So the same way here, what they try to do is, like, Better what kind of skills? Is it matching, matching what that they are looking for? For instance, like, let's say I'm coming with no background at all, with some, some background in history, but I am passionate and curious about. But what, Cecilia, that we look for is, that ATS will be able to track, Like, certain keywords that how it is fed and making sure like, have they done some projects even if they have not built any applications? Have they worked on some projects, some software development projects? And and what we look at is, like, if they have developed some boot camp projects through that vendor and what kind of, projects that they have showcased.

Usha Jagannathan [00:29:21]:

And those are the things that, that gets picked up. And then even, you know, we just, it's always a human behind human in the loop, they say. There is a human behind it and not just discarding the, resumes, like, Making sure, like, okay. If there is something, like, that they have built the products, we take that and we look into it. Okay. Why don't we bring this candidate and have a look at it And talk to them and why they are interested to come come in here as an apprentice. So, you know, starting with an apprentice in a junior level role, I'm trying to understand. So what I would say is, like, the specific skill set, if it is not identified, it's gets identified in, based on how you feed, you know, certain stop words that you give, how you feed in the algorithm, And making sure that you don't look for whether they have bachelor's, whether they have master's, but also look for what type of project they have done.

Usha Jagannathan [00:30:14]:

And I think, like, even if you're bringing people with graduate level, people, what I look for is I have recruited so many people, and, what I see is You we look for what type of projects that they have done, how it is going to be relevant when they come here to, get into the transition into this room. Because that matters, because that hands on skills is what every company looks for. Even when you're coming into a leadership role, There is, like, the technical skill is what that they look for, so you can be able to steer and mentor the people, that you are going to train in your team.

Usha's final takeaway

Jordan Wilson [00:30:50]:

You know, Usha, I feel I feel we have a little a little bit better idea now of of how to to kind of navigate this AI, tsunami and everything that's happening. But, You know, we because we did talk a little bit about, you know, strategies for recruitment, for retention, and for growth, and and a lot more. But maybe what is what is one takeaway as as As we wrap up today's show, what's maybe one takeaway that you really want people whether they're trying to grow their companies or grow their careers? What is the best way for them to navigate? You know, this AI tsunami, what's that 1 piece of advice that you have for people?

Usha Jagannathan [00:31:23]:

Okay. Sure. Sure. So, make sure, like, be curious. Whether, you know, people, if you have worked completely in, like, let's say you are a student watching this, podcast. Make sure that You, you know, you might have done all projects in the academia setting. Do not, worry about you know, come in and test the skills. Do you know, try to explore if you can take internship.

Usha Jagannathan [00:31:54]:

And even if it's not a paid internship, if you're in the middle of your junior year or soft, before getting into your senior year, make sure that you are able to get some unpaid in intern job and volunteer and Learn that skills so that you get that industry knowledge, and you could be able to, easily transition into, Going for a paid internship, and then probably the company will acquire you as an employee. So, that's for the students, I'm saying, but for career seekers or transitioning, I would say, still, you know, be curious and, try to explore. Like, even if you have never tried low code, low code, It's not like it's completely no code. If you are going to be a tech if you have a technical background, then I would suggest, like, We would be customizing that code a little bit. It's not going to be a completely zero code. Right? So in that way, Because low code, no code is getting so much popular, how you can leverage that in your, in your work? Because if you are using AWS or Google or Azure, definitely, you would be making sure that not only cloud agnostic tools you would be playing with, what kind of cloud native tools With do their services that you can leverage. Because already, the licenses are paid by your company. So how you can be able to leverage those services And bring that in your applications that you are developing, and make sure that we develop ethically responsible AI products Because so that, you know, it serves, for the social good, for the company, for the customers, and for whomever that you build.

Usha Jagannathan [00:33:32]:

That is something that I would say. Stay curious and, come out of your comfort zone to try and explore.

Jordan Wilson [00:33:40]:

That's perfect because that's what we do every day here, Usha. We always stay curious. We're always trying to learn. So thank you, so much for joining us on the Everyday AI Show. We very much appreciate your time.

Usha Jagannathan [00:33:52]:

Thank you so much for having me. I really appreciate it, Jordan. Alright. Have a wonderful Thanksgiving.

Jordan Wilson [00:33:58]:

Thank you. Yes. Yes. To those out there in the US, we hope you have a great Thanksgiving. Yeah. We will be off the show tomorrow, but we'll be back Friday. Don't you worry. So thank you so much for tuning in.

Jordan Wilson [00:34:06]:

Make sure if you haven't already, go to your everyday AI.com. Sign up for that free daily newsletter. We're actually gonna be making a couple tweaks and a couple changes, but we're gonna be asking you about them first. So if you're not already signed up, make sure you go sign up, and make sure to join us back again to not Tomorrow, but Friday for more everyday AI. Thanks y'all.

Usha Jagannathan [00:34:25]:

Thank you.

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