Matthew DeMello 00:00
Sachit, welcome to the program. It’s a pleasure having you. I think the hiring conversation overall is extremely necessary. I feel like I’ve recorded at least like three podcasts this week where, even if that wasn’t the central topic, that that topic took over the rest of the podcast, and I think a big part of the reason why is when thinking of talent as an evolving system of skills, you know, potential and decisions, rather than people filling roles or the biggest challenge, then becomes not just finding bodies, but it becomes figuring out who should do, what, when and how, across 1000s of employees and countless projects in traditional approaches, at least from what we’ve heard on the show previously, simply can’t really keep the pace with the technology. And I think that really creates a unique moment we haven’t seen in the industrial revolution or really elsewhere, with AI now positioned as both a competitive differentiator and a workforce enabler. We’re talking today about how is AI changing the way enterprises approach talent management and workforce planning. But before we get to that, I think it’s really the agentic systems, systems that can jump between programs, you know, be assigned very specific tasks really changes the conversation about skills automation, and should human beings be be in control of this workflow, rather than maybe a more automated system, but just starting from that, that that starting place, how are you seeing agentic AI change the way that enterprises are thinking about talent, hiring and Workforce Strategy.

Speaker 1 01:45
I think it’s first important to just define what an agent actually means. And in my definition, it is a piece of software that is able to take a workflow, automate it, and effectively be able to operate without the human actually being present during the workflow. So, for example, I could be asleep. I wake up the next morning, get to my machine and the agent has a set of work already done, ready for my review. That’s my definition of an agent. Now, when you start to apply that to the HR space, the hiring space, you start to see like real value that can be created, which in the past couldn’t be done, because human beings have limitations, right? We all can only do one thing at a time, right? Right? Most, most of the time. And it’s a situation where there’s all these complexities of, you know, having to do multiple tasks simultaneously to actually be productive at our jobs. So agents can come in and actually simplify that world, you know, substantially for us. So take, for example, an agent that can potentially interview a candidate on your behalf, right, the ability to bring back data, not just from one candidate, but from 100 candidates, and then organize that data so that you know, you can pick the top five candidates that you want to talk to further in the interview process. That’s an amazing revolution that agent AI can actually bring to the hiring space that hasn’t been possible before. And so you basically flip the script towards creating the types of experiences that effectively were not possible at human scale, and move to an agentic experience where it is not possible at machine scale. And so, you know, one of the things that a lot of candidates complain about in the hiring process is that they never get to speak to somebody about, you know, potentially putting their hat in the ring for a job. Once they apply, they just get that thank you email, and that is the only interaction 99% of candidates have most of the time. We now have the ability, with the advances in technology, to completely flip that script where every candidate that qualifies for the job can potentially get a conversation with an agent, and that is now possible based on the advances we’re making in the technology, right? So that’s a way to think about it. Now, of course, if I apply that logic to all the other parts of the HR process, all the way from, you know, finding the right talent, connecting with that talent effectively. You know, moving talent internal to the organization, all of these processes can be reinvented thinking about the automation that this agentic AI can bring to the table. And so that is what we’ve been doing here at eight fold, is working with our customers and exploring the far reaches of the technology and pushing the envelope in terms of what’s possible with the tech, right?

Matthew DeMello 04:23
And this is, this is going to be a trend, a trope that we see throughout AI deployment, which is, we can deploy AI within our department, or genetic within our department, you know, for efficiencies, maybe we start there versus bringing it through the rest of the business. I know HR is a really great place to do that, especially before you’re bringing these systems before customer facing systems, but also that agentic is going to be in the other departments as well, if not off the bat, then eventually, and just for those different opportunity spaces, then if we can even differentiate between industries. A little bit I know, I know, especially legacy industries and regulated industries, your financial services, your life sciences, organizations are going to be particularly interested in this technology, because especially compliance, is such a strong use case, not just for deterministic, then generative and especially agentic AI, but also they have, you know, the same workflows everybody else does where AI, where agentic especially is applicable. But tell us a little bit about the opportunity space in regulated industries for agentic.

Speaker 1 05:35
The first thing to remember is that employment itself is a regulated industry in most markets. Effectively, if you look at the US, there is a lot of different laws that govern employment right across all the different states that make up the country. So when you look at regulated industries, obviously they have to comply with not just the laws within their individual industries, but also employment law horizontally across all the industries, right? So, and that is the important thing to just remember. Now, the benefits that you have with using agentic AI is that agents can be trained to operate within certain guardrails, and they will adhere to those guardrails because those are built into the system, right? The same way as an human recruiter has to go through a training process in terms of remembering the types of protected characteristics that you cannot ask in an interview process, an agent can be trained to operate within those guardrails. So in a lot of ways, what agent AI actually does is that it helps drive compliance in all of those industries. And you know, employment law being as complex as it is, sometimes can be so local. As many of you know, there are laws that even govern certain cities. Now, New York City, for example, had its own regulation around AI that came out a few years ago, and we have all these other laws in different parts of the world. So the complexity for a highly regulated organization to operate in a world where there’s all these changing laws, etc, you know, AI is able to keep up with some of these laws that are happening and then effectively adapt these much more faster than humans would otherwise. So it’s one of those situations where, yeah, AI can be trained to operate within guardrails. And I think that’s a that’s a huge benefit.

Matthew DeMello 07:17
First and foremost, with most HR departments, regardless of industry, regardless of whether in a they’re in a more regulated, regulated space, a less regulated space might not matter much, because, as you’re saying, employment itself is an extremely regulated space. But HR teams, at this moment of AI adoption, are tied intrinsically to change management, which is kind of the word we give for AI adoption, where it involves directly, where it’s not involving the technology, but it’s really about what what change in how humans relate to each other? Do we need to drive through the organization to really prepare give us? I’m hoping we can get both sides of the coin here, as I was saying, in that double layer of you know, we’re using agentic AI in HR to help people prepare for change management. But also, how does agentic change the conversation? No pun intended, but in change management about how to adopt these systems.

Speaker 1 08:13
So it’s important to just remember that the modern HR organization and most corporations was created as a function of all the specializations needed to basically comply with local laws as well as federal laws, and at the same time, make sure that it can be done in a cost effective fashion, right? So if you look at hiring workflows, or if you look at onboarding, or if you look at some of these other services that HR organizations provide, it was all as a result of, how do you organize humans to perform these tasks in as efficient a manner as possible, right? But they were primarily built for human scale. Now, when you start to think about agents being able to perform these same tasks and be able to perform these tasks side by side with humans, you have to, in some ways reimagine what the workflows might look like, and just to make that very concrete, right, take, for example, the process of interviewing candidates using AI, right, which is a service that eight fold offers, and we’ve taken it to the industry over the last couple of quarters. Some of the things that we have learned as we’ve deployed some of these agent taking capabilities in the market is that it actually fundamentally changes the hiring workflow. For example, the traditional interaction would be a recruiter reaches out to a human being on the other side, tries to figure out a time that they can actually connect, then has a conversation, collects data, and then talks to the next human, one at a time, with Agent AI, it can Simon simultaneously talk to 20 100 1000 humans simultaneously, and then you have the ability to collect that data in a way where no scheduling was required, right? Because AI is always on. I mean, you can press a button and start a conversation. So it fundamentally changes the workflow of how hiring. Actually happens, and that is an example of where you have to fundamentally rethink, how should you lay out your processes to live in this world where AI is now available? And that’s just one example, but you could effectively rethink every single process within HR, thinking about it from the perspective of what’s possible, using agentic AI, rather than humans being the only option which has been the case in the past, right? So, yeah, in a lot of ways, change management is about rethinking how you should optimize for these different types of workflows, and optimizing for the things that AI is good at versus the things that humans are good at and still constructing these while considering the human element of what makes organizations great, right? So putting the right checkpoints and the right decision points in place for humans so that they can participate as well. So that’s what we believe in, and what we have seen in practice as working well is a situation where you rethink processes from the ground up,  right and right now.

Matthew DeMello 11:02
And just alluding to my intro to today’s show that that we’ve had so many conversations on the show, whether or not it was the designated topic or not, it wanders off into this space of folks not really being sure that the skills gap widening under the force of what you’re talking about, where the technology is moving very, very quickly, and that definition of what these systems are good at versus what humans are good at changes just as quickly. This is a really tough challenge. The skills gap also is not new. The skills gap was, was there before AI, it’ll be there. There is no after AI at this point, but it probably would still be there if there was an after AI. But just in terms of what that skills gap, where the state of play on that is across industries, specifically with regard to AI, in this new wave of agentic systems coming off, coming across the horizon, it strikes me that there are kind of two approaches that that everybody’s trying to feel more confident about, and that I describe it as kind of a binary spectrum between hyper specialization. Let’s hire directly for the use case, the talent, the experience, and using a tool as closely to our organizations and say, Well, if this person use ChatGPT to get all of these metrics, they say on their resume for this specific workflow, let’s throw throw them into the exact same position at our company, have them give them the exact same tool and expect the same results. And on the other side, there’s a school of thought saying, Well, this technology is going to change all the time. You know, even if you’re good at prompt engineering right now, we’ve had guests on the show saying, live it up now, because prompt engineering probably won’t be that important in the next couple of years. So what you really need is this anomalous thing that’s really, really hard to find on a resume, which is a mindset change. Sometimes it gets called, oh, we’re looking for curiosity, people who people who are self starters. And then it’s really, it really gets much more high level idea ideological. It’s really hard to imagine or even espouse concrete advice for business leaders of well, if you want that mindset change, if you want this thing called Curiosity, here’s what to look at on a resume. I’m just just want to tee up the question of, you know, how agentic AI can play a role in closing those skills gaps. But I’m also interested in your thoughts on what’s the most appropriate between these mindsets. Are these, the only two choices we have are, is there? Is there a third or other way? Or does this? Does where you land on this kind of spectrum I’m putting out there really depend on your industry, your organization? Yeah, just your your thoughts there, especially as we go into this moment of agentic, AI.

Speaker 1 13:57
The skills gap conversation has been around for quite a while, right? Like the last time I remember is during Covid, every company had to shift their strategies dramatically, right? I actually happened to be at a company that was massively affected by this. At the time, I was at Uber, and it had to shift from the rights business to the eats business. And, you know, had to upskill and rescale a whole bunch of people as it moved, you know, sort of like business was dramatically impacted by covid. I think what AI has done is it’s created a similar shift right, which is all the companies that are impacted by AI have to, you know, adopt new technology and quickly move in a new direction. And so it’s causing this constant battle of trying to figure out who are the right people? How do you upskill them? How do you reskill them? How do you bring in the right people from the outside? How do you optimize talent in this mode where the world is changing around you, right? So some version of that same kind of situation we saw five years ago is playing out again. Now, in my opinion, that is going to be a constant, right, which is. The only constant is change in this world. So you know you as an organization, are going to have to figure out, how do you basically move your talent towards acquiring the new skill sets that they need so that your business continues to thrive and survive in this ever changing world. So I happen to subscribe to the theory that you need to build fluid processes that allow your talent to be upskilled now with Agent AI specifically, right? One of the things that you can get, which you previously didn’t get, is, instead of theoretical conversations about what are the skills on somebody’s resume, you could actually deploy an agent to go and have a conversation with an employee, validate their skill sets, understand what are the transferable skills they may have, and so you may actually rethink the way you actually go about driving change within the organization, right? So a voice agent that can have a human like conversation with an employee comes back with data that the HR organization can then use to determine how to mobilize that talent in the right direction. That’s the kind of change that is possible. And then I think it’s a question of, you know, specific industry or a specific vertical, where maybe there’s a, you know, a generalist type skill set may be more valuable, right? So that you can retrain people and make them much more fluid in driving towards a particular area of the business, right? So, and I’ve seen these kinds of transitions play out at various businesses. There is no one size fit all answer. But generally speaking, the more trainable and the more malleable your people are, the more you’re going to be able to survive some of the major shifts that happen, and particularly as these ships get shorter and shorter, as we’ve seen in the last summer, right?

Matthew DeMello 16:34
That malleability ends up kind of in that category of the amorphous. You know, hard to define quality of curiosity, mindset change, self start, any, any kind of idea, on, on, on, maybe what leaders should be looking for, either in resume bullet points or even their experience within a company that that shows that flexibility, agility, malady, malleability, curiosity that’s going to help them really identify employees that are going to thrive in this agentic AI era.

Speaker 1 17:09
Typically, when I hire, what I’m looking for is basically folks that have shown the ability to be athletes, right? Which is, they have shown the past track record of being able to adapt to change so environments in which maybe they have been exposed to the kind of dramatic change that I just described in my own experiences. And then the second thing you also want to look for is, you know, how they’ve been able to create, in my case, when I have product people, right, create new things in this changing type of environment. So that’s one of the ways that I would look for, is past experiences that index well against the future types of experiences that we are likely to see in the environment. And the second thing is just the high IQ ability to, you know, be athletes and adjust to those sorts of changes, right? So that those are the come to mind.

Matthew DeMello 18:00
And it sounds like from that answer. And I’m digging into this. I know, I know you’re we’re talking a lot about a lot of stuff today, but I’m digging into this because I’ve had so many podcasts kind of land on a non answer, you know. And I know the audience is really asking just based on my email alone, but it sounds like you want to use the resume to identify, oh, that seems like that person went through some, some change and then, but it’s really going to matter about getting them in the interview and saying, How did you go about that change? How out of the box were you? What did that really so it really is in tandem which, which might sound like we’re saying the stating the obvious, but really, maybe the subtext to take away from here is you’re not going to see that as the bullet point on the resume. You’re going to see that in the story of the resume that they went through the change, and then you want to hone in on that in the interview. Yeah. And I think that’s, that’s some practical I see you nodding right now, just for the folks at home who can’t hear you, anything to add there. It just in term, am I getting that right? Yeah.

Speaker 1 19:02
So, I mean, like people that have gone through that sort of transformation and the change and have survived it, and can, you know, talk through it in a behavioral interview, where you you hone in on the specifics of what they experienced and how they navigated that change, that’s a great way to get to the answer in terms of, are they the right person to navigate the next change at your company?

Matthew DeMello 19:20
And I think really, even as we get to the later stages of this technology deployment, what we’re understanding in a very simple one sentence answer of, hey, what are human beings good at? That these systems aren’t really good at human beings are really good at change. Human beings are really good at agility. Whereas these systems, you put them in stone, they’re really, really hard to move in another direction, to turn that aircraft carrier around once you once it’s especially been cooking on business goals, in good data for a little while now. Sachit really, really appreciate you being with us this show, helping us get some answers that I think we’ve been dancing around on past episodes. And. Really giving the folks at home some advice they can take back to their organizations. Thanks so much for being with us this week. Thanks for having me on.