Recruiting Future with Matt Alder - What's Next For Talent Acquisition, HR & Hiring?

Ep 638: Reskilling For AI

It’s been a tough couple of years for Talent Acquisition. There was a time during the post-pandemic hiring wave when companies were struggling to recruit enough recruiters. Now, the situation is the polar opposite, with TA teams shrinking and many recruiters being laid off. In such economically uncertain times, it’s difficult to determine whether AI is starting to be a factor that impacts jobs in TA. However, based on current adoption rates, it’s probably fair to say that AI is still only a minor contributor to the overall situation. But this is quickly going to change. As employers start to look at transforming TA over the longer term, AI and automation will inevitably play a significant role in shaping the TA teams of the future. This raises important questions about job displacement and reskilling. These are not just questions that AI raises for TA; they are questions that span the entire organization, with a strong likelihood that all jobs will be impacted in some way. So, how can employers prepare their workforce for these impending changes, and what is the likely split between job replacement, job enhancement, and job creation? My guest this week is Kamal Ahluwalia, President of Ikigai Labs and former President of Eightfold AI. Kamal has extremely well-informed insights on AI’s impact on jobs and skills and valuable advice for employers and employees. In the interview, we discuss: The impact of AI on work and jobs Jobs eliminated, jobs changed, jobs created Building scenarios for the future The critical importance of data How should employers be responding What is the role of HR and TA? Evolving processes to meet new objectives Reskilling and upskilling How do employers encourage people to learn new skills? What will work be like in five years? Follow this podcast on Apple Podcasts.

Duration:: 28m
Broadcast on:: 30 Aug 2024
Audio Format:: mp3

But this is quickly going to change. As employers start to look at transforming TA over the longer term, AI and automation will inevitably play a significant role in shaping the TA teams of the future.

This raises important questions about job displacement and reskilling. These are not just questions that AI raises for TA; they are questions that span the entire organization, with a strong likelihood that all jobs will be impacted in some way.

So, how can employers prepare their workforce for these impending changes, and what is the likely split between job replacement, job enhancement, and job creation?

My guest this week is Kamal Ahluwalia, President of Ikigai Labs and former President of Eightfold AI. Kamal has extremely well-informed insights on AI’s impact on jobs and skills and valuable advice for employers and employees.

In the interview, we discuss:

The impact of AI on work and jobs

Jobs eliminated, jobs changed, jobs created

Building scenarios for the future

The critical importance of data

How should employers be responding

What is the role of HR and TA?

Evolving processes to meet new objectives

Reskilling and upskilling

How do employers encourage people to learn new skills?

What will work be like in five years?

Follow this podcast on Apple Podcasts.

Just before we start the show, I want to tell you about Reckfest USA. Now, I've been going to Reckfest events since they started, and it's fantastic to see that Reckfest USA is back for a second year, bigger and even better than before. Reckfest isn't like other recruiting conferences, it really is a unique experience. So, Reckfest USA is taking place in Nashville on the 12th and 13th of September. If you go, you can expect a network with over 2,000 like-minded professionals, hear some great speakers addressing TA's biggest challenges, and get product demos from over 80 tech suppliers and solution providers. All of this delivered in a unique outdoor festival-style setting that's nothing like any conference you've been to before. The good news is, I've got a discount code, so if you go to Reckfest.com/USA, you can get tickets for just 99 dollars, if you use the code MATLDER-99, that's MATLDER-99. There's been more of scientific discovery, more of technical advancement and material progress in your lifetime than mine, at all the ages of history. Hi there, welcome to episode 638 of Recruiting Future with me, MATLDER. It's been a tough couple of years for talent acquisition, there was a time during the post-pandemic hiring wave when companies were struggling to recruit enough recruiters. Now, the situation is the polar opposite, with TA team shrinking and many recruiters continuing to be laid off. In such economically uncertain times, it's difficult to determine whether AI is starting to be a factor that impacts jobs in TA. However, based on current adoption rates, it's probably fair to say that AI is still only a minor contributor to the overall situation, but this is quickly going to change. As employers start to look at transforming TA over the longer term, AI and automation will inevitably play a significant role in shaping the TA teams of the future. This raises important questions about job displacement and re-skilling. These are not just questions that AI raises for TA, they're questions that span the entire organisation. With a strong likelihood that all jobs will be impacted in some way, so how can employers prepare their workforce for these impending changes? What is the likely split between job replacement, job enhancement and job creation? My guest this week is Kamal Alwalia, President of Ickicai Labs, and the former president of Eightfold AI. Kamal has extremely well-informed insights on AI's impact on jobs and skills, and valuable advice for both employers and employees. Hi Kamal, and welcome to the podcast. I'm Matt, good to be here. Thank you very much, an absolute pleasure to have you on the show. Please could you introduce yourself and tell everyone what you do. So I'm Kamal Alwalia, the president here at Ickicai Labs, and prior to this, I was president at Eightfold AI, which actually was a pioneer in bringing AI to HR use cases. So I think I'm looking forward to having our conversation. Actually you're the perfect person to talk about this topic too. Before we go into kind of any more depth, give us a bit of an overview about what Ickicai actually does. Excellent, thank you. So over the last 18 months or so, all the rage around large language models, et cetera, has been fantastic, especially for anyone who was in the AI field already like we were. So the awareness, the interest, and everybody's leaning in to what AI can do for their businesses, for themselves, I think it's fantastic. Now large language models are fantastic, largely for text or unstructured data, and we're seeing all those use cases proliferate around us. Ickicai has different set of technology. It's called large graphical model. A lot of the work was done by a professor there, Rich Shah out of MIT, he's been teaching there for about 18, 19 years. And that allows us to be really, really good at use cases around forecasting, planning, decision intelligence, things that rely on numbers and structured data, which is bulk of the meaty content that all enterprises have. So we are now solving a wide variety of issues, whether it's around sales forecasting, demand forecasting, consumption forecasting, as well as scenario planning, and being able to work with sort of the uncertainty that we find in the business world. So to some extent, if large language models are great with text and content creation, we are excellent with numbers. It's such an interesting time at the moment, because for quite a few years now, people have been predicting that AI is coming for people's jobs, it's going to fundamentally change the world of work. And I think that obviously now we're seeing what's possible and how quickly things are developing. What impact do you think AI is having on the world of work and how's that likely to kind of play out as we move forward? Great question Matt, so I'll give you my perspective and I frame it in this context. Think about this job displacement in three buckets, a third, a third, a third. The first third, this represents the jobs that will get eliminated and will not be needed once some of these AI technologies are installed, working, reliable, accurate, all that. And it will happen. The second third are jobs that will change dramatically. So what we do on a daily basis, for example in marketing, content creation, calling your prospects, a lot of the stuff that we are seeing with the tools that are emerging. So what we do on a daily basis will be very different going forward. And the last third will be net new jobs that will get created and a lot of these we are not even aware of that what will be these new roles that will emerge. And one good example is prompt engineering. This was nothing two years ago. None of us even in the tech world weren't aware of that there's a need for prompt engineers. But now it's a thing. And just like that, there'll be additional roles that will emerge, whether it's around programming, whether it's around testing, stuff like that. So the issue with this thing is that in the past, investors and others do chase the net new stuff with a lot of regular investment. So that part is fine. It'll emerge. It'll grow. But the first third, no one actually spends too much time worrying about it or making sure that the people who are impacted in that first bucket actually allowed the opportunity to transition to either a different role or move forward because that will impact our workforce. So that's how I would frame it. And also the timing is three to five years because although it looks like the whole thing started 18 months ago, it's actually been coming slowly for a while is here now. And the changes are closer than we realize they are. Yeah, I think I was going to ask you about timescales. So three to five years, that's not a very long time for people to adjust, for businesses to adjust to people think differently about their careers, is it? No, it isn't. And that's why I don't mince words around it, right? That's not just act that it's come whether it's do we want to let it come for our jobs or not? It's not coming for our job. It's just technology that's evolving and it will impact how we do our work. And how do you think employers should be responding to this right now? What should they be doing? What should they be thinking? I think there are a couple of ways to look at it. Number one is everybody whether it's a small company or big company. Everybody needs to internalize and realize that each of their jobs will change. Not just individual contributors, but executives, managers, everybody's job will change, right? So let's not sit on the fence and assume that whatever changes are coming, somebody else has to deal with it, I'm above all that. That will bring a different mind shit that, okay, what do I need to know differently? What are the new skills that I need? And how will I navigate these new roles going forward? So I think it's that kind of mindset, just like it's an opportunity to reinvent both the organization and our personal careers. And that's the first step. Now once you actually internalize it, yeah, it's coming and I would rather write this wave versus drown in it, then you want to actually give yourselves as much time as possible. Now the second thing that will be most important is the change management. So I think for every role and you can talk about it in broad categories around sales and marketing and engineering and HR, ID, et cetera, that what does the future look like? What will we do differently if we have these things working as if you want them to work? And then rethink on whether we have the right people or do we need to enable them to learn some of the new skills that are needed? Now to double click further, right now I think AI is more technical down the road, it'll become easier to consume and adopt. But right now doing that double click and understanding it a little more is needed to be more specific, to be an AI company, you need to be a data company. So first part is to get your arms around the data. And the second part there is that any company or any team or vendor, et cetera, that says okay, give me six months, 12 months, 18 months to clean up data so I can make sense out of it, that day will never come. Because you have to learn with incomplete, noisy data that's around you and there's plenty around us, right? So all that adjustment is needed and I think we should give ourselves time to adjust to that. And I think overall, as I'm sharing all this right, I'll be as specific as possible, whenever major technology shifts have come, the job creation has always exceeded what was taken away. So net net, it'll be a big positive but the transition is never that comfortable or easy or obvious. So that's why I'm actually being very specific that we should assume that all of our jobs will be disrupted. Absolutely. And are you seeing employers who are already responding to this in the right way? Because to me, there's sorts of discussions going on, lots of wondering what to do, but I'm not seeing much in the way of action for employers actually dealing with this issue, particularly as it's got such a short sort of time span horizon. That's a great point. Here's what I'm seeing. Clearly last year when Chad Geevity took off a lot of interest, everybody's leaning in and everybody wants to do something, right? So it did lead to a lot of experiments. A lot of things stayed in the experimental mode and didn't transition into full production mode. So that's okay. Also, the cost is high. There are a lot of compliance issues, data privacy issues. So there's a lot to work through. So that you end up in a good place and you know how to bring it into your environment and not jeopardize what's working. So that's one part. So and there's a lot of improvements across the board up and down the stack, whether it's going from large language models to now small language models, et cetera. Cost of using it, cost of inference. I'm also seeing VCs call about cognition as a service, inference as a service, things like that. Business goodness. Second thing that's encouraging is Gartner, for example, has started to write a lot already that large language models are not the silver bullet for all things AI, right? So there are certain use cases that large language models are great for. But then there are the things that they were not built for and mostly they're around numbers and structure data. And there are other things also, right? So there's more to AI than gen AI and there's more to gen AI than large language models. So that kind of awareness and the willingness to get, do the double click and triple click, I think that's very exciting. The third element is a lot of times organizations become risk-averse. What I'm seeing is most of the companies, big and small, are being shaken into being more forward-leaning and saying, okay, I do need to figure it out. And also they're being discerning that just having a chat bot type interface over everything is not enough because everybody will have it. So it's not a comparative differentiator. So what can I do with the technology available today to make it a differentiator? And so I know that leads to a lot of what you talk about, right? The changing skills, because this is a very different mindset. It's not like a steady state for a while, it will be very different and all of us need to sort of become comfortable with being uncomfortable for a period of time. And so all that is, I think, I think there are more people who are shedding their fear of failure to figure out how to get their arms around this thing called AI and how to benefit from it. We'll get back to the show in just a moment, but I wanted to take a minute or so to talk about something that I know is critical for you all right now. Tower Acquisition is going through an unprecedented transformation, and many of you are likely in the middle of planning your strategies as we move through 2024 towards 2025. We all know that operating models, change management and aligning TA with corporate objectives are essential parts of any transformation strategy. But with the market and AI technologies in particular evolving so rapidly, there's a real risk that your strategy could quickly become outdated. That's where strategic foresight comes in. It's a proven methodology that helps you build credible future scenarios, create agile strategies and most importantly have a proactive influence on what the future of talent acquisition looks like both within your organisation and across the industry. I know you're busy, so I've created a concise online course that breaks down strategic foresight into easy to learn tools specifically designed for TA transformation. It's quick to implement and will keep you ahead of the curve. You can learn more by visiting matalder.me/course, that's matalder.me/course. There's really never been a better time to shape the future of talent acquisition, so don't miss this opportunity to make a lasting impact. Obviously, most people who are listening to this are either working in talent acquisition or they're working in HR. What role does HR, you know, sort of harking back to the work that you've done in AI and HR in the past? What is HR and talents role in all of this? What can people who work in that sector? What should they be doing to really be part of that change and help drive things forward? I think they have a critical role to play because I firmly believe that the technology part will get sorted out and there is more rigor and scrutiny and regulations coming around AI-powered tools in HR and that also will be fine, they'll get to a good place. The thing that HR needs to understand and rethink is how to think about each of their existing processes and I'll give you an example, when 8/4 came out in 2018, HR was largely working in silos. Talent acquisition didn't really talk to talent management team that was working with the employees inside and the contractors were dealt with different systems. So you weren't really aware of what was happening in other stovepipes, so to speak and that lack of visibility and ability to leverage the data that's already there across all these different parts of the organization or the workforce that needed to be taken down. So the main thing was companies that actually leaned forward 2018, 2019, 2020, etc, a lot of them are very large organizations. They were all thinking about the outcomes that they want and then working backwards to change their existing processes and orthodoxies. So example being as remote work came in, all of us woke up to the fact that sky doesn't fall if your team is remote or parts of your team are remote. So that basically means that if you could hire from the best talent available at anywhere in the world, how would you do it? And how would you evaluate the skills? How would you once they are on board work to assimilate them into your team? How does the culture work in a remote environment? And what kind of different skills are needed to actually be adept at it? The role of communication, everybody adjusting to zoom or teams to actually work effectively with clients as well as internal employees. All of things were really orchestrated by HR teams because everybody else was busy with their functional teams. So I think that this need to get into the data part, understand what's available, what can be marshaled, what decisions would you make differently? If all of that data could be available to you and then you start to think back and say, "Oh, there aren't actually more talented people already in my company and don't always need to spend a lot of money and time going outside." And it is easier, cheaper, faster because we know how well we work together to actually invest more in the employees. So all this notion about re-skilling, up-skilling, I think is very legit. I think we should be investing more in it because reality is there simply aren't enough knowledge workers in the world who have the skills that everybody's looking for. Picking up on that skills bit, that kind of re-skilling and that up-skilling, there's obviously, as you say, everyone's job is going to change and there is a kind of big sort of personal responsibility on people when it comes to up-skilling and re-skilling. How do employers encourage people to learn new things when everything is so uncertain about exactly what's going to be needed? So I'll give you a politically incorrect answer. I think that change management will be more effective when we are thoughtful about the specific demographics that we talk about. And I'll talk about broad terms. Some people who come into the workforce, let's say 21 to 35, they'll adjust to the AI era a lot easier because they're already closer to the technology. There are a lot of digital things that they already used to, right? So making changes there, it'll be a lot different. Incentives need to be a lot different. And I'll give you an example. Some of us, uncertain about bitcoins or cryptocurrency. And I do know from a friend who actually runs one of the exchanges that the younger generation actually would love to have cryptocurrency as an option for their 401K, right? So there's a level of comfort that is not across the entire workforce. Now you get to 35 and 45. This is a critical part of the workforce because most of the work and a lot of the orchestration happens at that level. These are people who are good in what they do. They've already had enough experience. They are first line managers, mid managers, directed level people. So bulk of the actual productivity is in their hands and more in when incentives need to be given to actually incent them to actually adapt and learn and do all of that because their teams will follow suit. Then comes the 45 to 55 and this is where you're getting to the senior management in your organization and they have to drive the change. They need to be the change agents. They need to lead these transformation efforts and personally go through it and learn their skills differently so that the organizations and the functional teams that they manage, they see their leader leaning into this versus just sitting back and letting others deal with it. And then comes of course the CC suite and all that but that I think is at that level and the boards, they are all leaning into it. So I think the techniques and incentive structure and the motivation, it's not always about money. It is about being candid and supportive and I think really taking the fear out of the equation that the company is there to support the transition may take six months, it may take 18 months but the company will come out dramatically better. And that building that confidence and trust I think is the key and then you will start to see the snowball effect inside the organization as people say, okay, this was not that hard and I see others who are investing, it is a dusting time, right? So it's not like it's easy easy. But that change mindset needs to be embraced. Take out the fear and I think you will see and we are seeing that, right? Uber when that came out, everybody was worried. What's happening to the taxi? Now it's a transportation. Now you're already looking at the next one, not only does the whole world have an equivalent of right sharing, now there's housing sharing, now there's variety of use cases around that. So change does happen, it does impact people. We just need to be leaning in. And as a final question, you know, we're talking about this kind of three to five years, everything's going to change. Give me a bit of a vision. How do you imagine that things will be, what will work be like in five years time? I think there are few elements. One is, and I'll connect some of the things that I've said, right, about the prep on focus on the data and focus what you can do differently, et cetera. I believe the enterprise software business will be redone completely. All the applications that you look around in organizations and these large companies who have ERPs and CLM and everything in between, all of these are largely process applications. Click, click, click, workflow, whether it's onboarding, looking at my supply chain, this ad, these are all process applications and they're all old, 20, 30, 40 year old architectures and thinking, all of them will be redone and redone to the point where how do you bring in data and insights, not just from the siloed pieces inside your organization, but all the external signals that are available so that as a worker, whether you're in sales or marketing or engineering, you are actually able to see what's happening with your business. And their recommendation, you can double click on it and you can see, okay, why? And you will make decisions very differently. I'll give you another example on that. What I'm actually excited because I'm an engineer by training is I didn't anticipate programming to move so fast with agents that you can now write very easily with letter lamps, right? They're really good at writing code and that thing is taking off. So to some extent, it's not like the software engineering jaws will be protected. They may be changing faster than any other function in our workspace. Now the thing is, if it's can write the code that easily and be largely okay, maybe 70% 80% or 90% whatever it is, then more work needs to go into that last 10, 20% to verify, yes, properly do all those things that typically would be done by someone else, right? So the changes will be, I think adjacent that whatever you were doing, you'll be doing something either before or a little after. And that's what I see that if you go through everything and we already are, we're a small company, but we are not looking to build a large sales development organization because they're already tools out there like regi, jasperi, et cetera, that can do all that cold calling. Same thing with the hiring piece, right? If you had all the resumes available and the insights, how easy and we've already seen that, that how to actually cultivate an ecosystem that's interested in working with you. So whenever the jobs are available, you have a ready pipeline and you don't lose the ones that you didn't hire, you start with everything. So everything is incrementally building because it's not limited to our individual laptops or brain power to maintain all that combination of skills and people and what happened there. The system is there, right? So the ability to interface and we've seen that, right? All of us got iPads and then we suddenly saw kids working so easily with iPads, our grandparents working so easily with iPads because it was a much more intuitive experience. That is what AI will bring to us, that's all these complex things will actually become a lot easier to deal with. Kamal, thank you very much, talking to me. Enjoyed it. My thanks to Kamal. You can follow this podcast on Apple Podcasts on Spotify or via your podcasting app of choice. You can also search all the past episodes at recruitingfuture.com. On that site, you can also subscribe to our weekly newsletter, Recruiting Future Feast and get the inside track about everything that's coming up on the show. Thanks very much for listening, I'll be back next time and I hope you'll join me. Bye. Bye. (upbeat music) [BLANK_AUDIO]