Episode Transcript
[00:00:10] Speaker A: Hello and welcome to Hacking Kaizen. I'm Graeme Newman. Today we're with Tom Heath, former global head of talent acquisition at Yieldapp and director of business development at Adeco Thailand. We delve into the exciting and rapidly evolving world of AI in talent acquisition, which is transforming the recruitment landscape, albeit with some growing pains.
The recruitment industry acknowledges the frenetic pace of technological change, which can be daunting for many. While organizations and individuals are playing catch up, there is a palpable fog of uncertainty about the full potential of these new technologies. So how might we be critical and question AI's role in candidate screening and selection, noting the inherent biases in many AI driven screening tools? While these tools can handle basic tasks, they often fail to enhance diversity and inclusivity, areas where human judgment is still crucial. As the AI recruitment industry matures, it is expected these tools to become more sophisticated and better aligned with organizational goals. Discover how a blend of advanced technology and human insight is set to reshape the future of talent acquisition and management in the recruitment industry. Stay tuned for an enriching conversation.
[00:01:36] Speaker B: I think it's a really exciting time at the moment. I think, you know, people are right to be a little bit scared and a little bit apprehensive.
You know, the pace of change is frenetic at the moment and, you know, I don't think that's necessarily going to slow down. I think most organizations and most individuals out there are playing catch up. You know, I think at the moment there's probably a bit of a fog in terms of really people understanding what these new technologies can do. And there's also that fear factor of, well, I just don't want to understand it. I don't want to get near it just yet. But I think there's a lot more exciting things to come. I think we're in the very nascent stages of the industry. I'd love to come back and do this again six months down the line or every quarter and really look at what is developing out there, because I think we're going to see very, very exciting things and wholesale change sweeping across pretty much every industry in the next six to twelve months.
[00:02:28] Speaker C: So in terms of acquiring talent, how is Gen AI being used to enhance the recruitment process?
[00:02:34] Speaker B: I think it's a really interesting question and it's probably quite pertinent at the moment because I've just stepped out of my full time role as internal talent acquisition within a blockchain crypto company that I've been supporting for the past three years, really to focus on that element, focus on the use of AI within the recruitment and talent sphere, but also in the broader sort of startup and emerging technology markets. I think my immediate response is it's not really being utilized as well as it should be or as well as it could be.
[00:03:07] Speaker C: Why?
[00:03:08] Speaker B: Because I don't think anybody has really sort of grasped what the full potential is yet. If you look at what AI is doing at the moment within the whole sort of recruitment, talent acquisition, talent, sort of pipeline space, it's potentially something that's been around for five, six years. When we look at applications being screened by chatbots or large language models and things like this, it's not necessarily anything that's new. And I don't think really yet, and not just across talent and recruitment, but across most industries, we haven't really sort of grasped what the full potential is. So people are using it to create job descriptions, or to create job advertisements, or to look at social media posts and drive content.
I mean, I was using it for lots of different things around the creation of employment contracts, company policies and things like this, which is great. It is an extremely useful tool, saves a lot of time, a lot of resources. But I think the potential is far more than we've grasped and far more than that people are really exploiting at the moment. And it's a thing because the level of talent hasn't developed their knowledge of AI yet. So people are using AI at a grassroots level, whether it's chat DPT, whether it's copilot, whether it's Gemini, whether it's Claude AI, whatever agent or whatever platform we're talking about, these are sort of input output platforms. Whereas the true benefit, I think, from working with AI comes when you can start to program your own agents, when you can start to build your own applications, when you can really start to understand the LLMs behind these generative AI tools.
[00:04:46] Speaker C: So in terms of the talent acquisition space, Tom, what benefits have you observed so far with GenaI?
[00:04:54] Speaker B: The main benefit is resource saving and time and cost benefit saving, really.
I don't think this is something that's really been truly exploited yet. A lot of companies are still trying to understand how best to make use of AI. I think there's a certain degree of fear.
What do you roll out? How do you roll it out? There are hundreds and hundreds of new applications surfacing every day. Trying to work our way through that minefield is really, really difficult. But from a personal perspective, the work of three or four people I was doing single handedly on a lot of the more mundane tasks around, whether it's policy, policies, employment contracts, job descriptions, you know, a lot of this work is streamlined very, very effectively by the use of AI tools and even, you know, to go so far as performance management, a lot of the, you know, annual review processes and things like this can also be, you know, very effectively streamlined. So it's effectively like having, you know, a team of four people, you know, whilst a full time member of staff is one. So that at the moment is sort of where the main benefits are. But again, I don't think it's really sort of scratching the surface in terms of the potential.
[00:06:06] Speaker C: What role does AI play in terms of improving specifically the candidate screening and selection process?
[00:06:14] Speaker B: I would say it doesn't, if I'm brutally honest. I think there's inherent bias built into a lot of these screening tools and again, they've been around for a long, long time. This is not a new conversation. It's not a new issue that we're having. I think there is a large degree of danger when you're having an agent or a bot or whatever you want to call it, that you set certain parameters on that is pre screening a lot of the candidates that apply to a position because the process is only as good as the data that you put in. And unfortunately, a lot of these times there's inherent bias that's built into these tools. Whereas at the moment what a lot of companies are trying to do is diversify. They're trying to expand the talent pool. They're trying to diversify the talent pool with different levels of education, with different Personas, different backgrounds and things like this. And again, what these tools are doing is not necessarily assist that process. And again, you know, I'm talking very, very generally here, very generically, I'm sure there are some really good tools out there, but my understanding, my knowledge, and again, you know, I'm not an expert by any stretch of the imagination, but, you know, they're not really performing the task that's needed.
[00:07:27] Speaker C: You touched on diversity, and in terms of the whole kind of inclusion conversation at the moment, there is not necessarily any AI value to ensure that that happens.
[00:07:38] Speaker B: Yeah, yeah, I think that's true. And again, it's because we're scratching the surface with this stuff. Using the capabilities of an AI model to screen applications based on a narrow set of parameters is not making best use of the tools we have available to us. I think as the knowledge and the industry matures, these platforms, these tools, these applications will become a lot more sophisticated, programmed around a lot more data points and really a lot better at providing a company the kind of talent pipeline that they need.
[00:08:10] Speaker C: So, yeah, so what measures would you like to see in place to prevent this bias?
[00:08:16] Speaker B: It's a difficult question. I'm not necessarily one that I've got an answer for, to be honest.
I think the danger with AI again, is not just around lack of diversity in terms of talent acquisition and recruitment. There's a lot of issues around these programmable models where they are running on a particular bias, necessarily. Know what the answer is. I think as the industry matures, as I say, these platforms will become a lot more sophisticated and again, there is a high degree of catch up to play. Again, the shift in industry's use of AI has been seismic, although we're still not anywhere where we need to be. And when we look at the development of other new technologies, be it the Internet, be it different platforms that are out there, it's taken a while for people to truly understand the capabilities and to really be able to build around that. And I think at the moment a lot of the tools that are coming out are really sort of basic entry level stuff.
[00:09:19] Speaker C: This also leads onto the candidate experience. There must be an impact on them as well, not only receiving information from recruitment companies, but perhaps how they position themselves.
[00:09:30] Speaker B: Yeah, and again, I have to keep sort of reiterating, I'm looking at this from a very sort of singular point and I'm making very broad, sweeping generalisations. Organizations. I've been out of the recruitment consultancy world for about three years now, having been embedded within a startup company. So some of my points, some of my comments may be wide of the mark, but the feeling that I get getting back into the recruitment world and looking to exploring these different tools that are available to us is they're just not necessarily being used in the right way or they're not being used, as I say, as an augmentation tool for the individual. I think if anything, and I'd be interested to know what feedback there is out there, but I think if anything, the candidate experience is worse because of these tools, because of the lack of human interaction, how AI impacts the individual, the candidate, in terms of their search, in terms of how they apply to roles and things like this. Again, I think there's a lot of base level information out there about how to tell your cv to get through these pre screening bots and things like this, which again I just think is very basic stuff. So yeah, I think there's a long way to go on that aspect from a candidate experience. I think anybody who is able to understand the AI landscape and can bring those tools and those experiences and those skill sets to the table, you know, obviously that's where there's a lot of opportunity at the moment because again, there's not many people out there who've really sort of started to explore these different capabilities in a huge amount of detail.
[00:11:05] Speaker C: Would you like to see the recruitment industry having some sort of governance regarding the usage of AIH?
[00:11:11] Speaker B: I think there needs to be governance overall. And obviously that's one of the hot topics around AI. And again, unfortunately, when we look at social media and the various different regulatory bodies still aren't able to govern meta or Facebook or TikTok or whatever platform you want to use. I don't even know how old Facebook is, 20 years down the line, and they still aren't able to really regulate or monitor these different platforms in a way that makes sense. So governance and regulation of AI overall is a huge, important topic. And unfortunately, I just don't think that there's necessarily going to be anything particularly useful or positive that's going to happen, that the tech itself is developing far too quickly for the regulatory bodies. I think really to be able to keep up. Whether that's a good thing or a bad thing, I think depends on the individual. I am, I guess at heart, a bit of an anarchist, and if things develop quickly, then let's see where that goes. But I do think there's an inherent danger. Again, we're talking about the personal information. If you're using AI in your candidate screening process or however you're using it, we are pouring a huge amount, and not just in recruitment, but we're pouring a huge amount of data into these LLMs. And we don't necessarily know how that's going to end up as a candidate. If you send your application over to a company, whether it's GDPR, whether it's the data protection regulations here in Thailand, okay, the company can give you that data back, can remove you from their database, but once your details are in an LLM or an AI platform, how do you get that information back? So again, just sort of very basic level things, but maybe there's an answer to that one. I don't necessarily know, but I think governance is important. I just don't see how the regulatory bodies are going to be able to move quickly enough to be able to regulate AI in any really true sense of the word.
[00:13:05] Speaker C: Let's move on and talk about motivating and retaining talented. How is Genai being used to understand and enhance employee motivation and engagement.
[00:13:17] Speaker B: Again, a really interesting question, and I'm sure there are people out there doing some really cool stuff around this. I think that's the next step of where AI takes us. I know personally that we've rolled out a performance management tool. We've rolled out employee engagement tools in the previous role that I was in. Not necessarily AI based, but they are important aspects. You know, I think that is where the next level of these AI tools and platforms are going to come from. I don't necessarily think we're there yet. I think it could play a huge part. But again, as with anything AI, it has to be taken with a pinch of salt, you know, again, you have to apply that human element to it to really sort of be able to implement effectively. I think, again, like you pointed out, Graham, AI is great. You know, for the data points, it's great to get a lot of, you know, information from the system, but then you have to understand how to implement that and use it and how that's going to affect the individuals within your company. And we are talking about individual. What works for one person isn't necessarily going to work for another. So I think there's a huge opportunity there. I'm just not necessarily sure that it's being, I'm sure there are people working on it. I haven't seen anything out there quite yet that makes full use of AI to look at those engagement tools and things.
[00:14:35] Speaker C: Looking ahead, how might these AI driven initiatives successfully boost employee motivation and engagement? How might that work?
[00:14:44] Speaker B: I think at a very basic level, people want to learn. People want exposure to these new technologies. It is going to be career defining. There will, without a shadow of a doubt, be skill sets that are left behind because of the development of AI. And people are nervous about that. People are scared. People are worried for their careers. There's a lot of data out there already about loss of jobs through use of AI and things like this.
For me, the basic level with that is that companies look at their learning and development plans. They allocate sufficient funding, sufficient budget, or even just time. It doesn't necessarily need to be a cost. If you can allow your teams a couple of hours a day, a couple of hours a week, whatever it may be, to be able to embed themselves in these new tools, I think the benefits for the company are immeasurable. So I think at a basic level, that's probably why companies need to start, is just allowing their people to explore these different products.
[00:15:42] Speaker C: Yeah, I'm thinking specifically in family owned companies and listed companies. If we look from an HR perspective, certainly you can predict employee turnover and address retention issues, right?
[00:15:54] Speaker B: Absolutely. I mean, again, I think they're potentially. I don't think there's necessarily a silver bullet for that stuff. And yes, there will be tools and products out there and they're already out there. And obviously these companies are now starting to integrate AI within those products. But there's no silver bullet for retention. There's no silver bullet for. For maintaining your talent pipeline.
It's an amalgamation of different processes, different individuals, different products.
[00:16:23] Speaker C: You've worked client side and in house in terms of recruitment. Tell us about how this technology could potentially impact the HR side inside the business.
[00:16:34] Speaker B: I think it's got potential to be incredibly impactful. I mean, again, I'm conscious that I'm sort of reducing everything to quite a sort of generic basic level. But every single function in every single company in the world is going to be impacted by AI and it's going to start, I think, relatively slowly. The technology is developing quickly, but we're not necessarily seeing the implementation and uptake of the functionalities of AI being embedded within companies quite as quickly. So HR, I think, will be fundamentally changed by AI. I can't necessarily speak to particular sort of products or particular processes are going to change, but again, bringing it back to that sort of basic level, you know, just being able to do the basic administrative support functions, being able to conduct, you know, performance management, you know, whatever it may be, in a much more resource effective manner. And what might have taken a week before now takes an hour. Just from a cost and resource perspective, that's significant. The problem on the back of that is that, you know, if you have a team of 15 people in your HR, your internal talent acquisition team, you're probably not going to need 15 people in the next six months. And again, if you are focused on just bums on seats, you're not really making best use of the tools that are out there. And again, there then becomes that human question of that, which is, yes, you're reducing cost, yes, you're reducing resource, but you're also making people redundant. You're making that environment harder for those individuals, but it's really on those individuals then to keep up skilling themselves and stay ahead of the curve. So significant impact on companies from a cost benefit perspective, but significant impact on individuals as well.
[00:18:13] Speaker C: Yes, good news for shareholders and the board, perhaps less so for people working in HR, and sometimes they seem to be at loggerheads with the rest of the culture. Of the company, and I'm not quite sure why, but I think potentially there is an element of aligning that more through AI, through the culture of the company.
[00:18:33] Speaker B: Hrs at loggerheads with the board, is that what you mean?
[00:18:37] Speaker C: HR at loggerheads with the other functions in the organization?
[00:18:41] Speaker B: I think I have to be a little bit careful what I say here. I think that's quite true of a lot of more traditional organizations in Thailand. I think a lot more of the more forward thinking companies realized, and have realized quite a long time ago that HR is a strategic function. It's not just an administrative support role. It's not just about running payroll. It's not just about shuffling cv's or arranging for pizzas on a Friday. Whatever it is, the advent of the CHRo role has come on leaps and bounds in the past three, four years at least. I think a lot of the more forward thinking organizations, organizations understand that HR is a truly strategic function. You know, the chief of staff role, although not directly associated to HR, is certainly something that we're seeing a lot more prevalence, you know, particularly within the startup environment where, you know, a CEO as a right hand individual that is able to understand all of the different departments within the company, is able to facilitate and roll out a lot of the messaging from the CEO. And that, you know, is, you know, has that HR element. But again, I think overarching all of this is, yes, these tools and products are out there to improve process, reduce cost and things like this. But any company that truly believes in its culture is not going to leave that culture to an AI product.
Culture is led from the top down, but it's built by every single individual in that company.
I've had the privilege of working with companies that have fantastic culture. I've had experiences of working with companies that don't have such a great culture. And it's difficult, it is really challenging to get right, but it's not something that's intrinsically going to be fixed by AI. That AI can feed into that process, it can give you data points, it can give you an understanding that you might not necessarily have had before, but it's down to the individuals within that organization to create and drive that culture. I think a lot of the interesting things around AI have been more. But when it comes to culture and development of culture have probably been, as we've seen, the advent of remote working, which again, that's a, you know, I think everybody at the moment, I don't really want to go into remote working because it will probably cause uproar. Everybody seems to have an issue, a take on remote working at the moment, whether it's right, whether people should be returning to the offices or not, that's not for me to comment. But you know, I think some of the interesting things I've seen around, and it's not just AI, but certainly employee interaction, employee engagement, are much more focused on that truly sort of globally remote workforce.
[00:21:10] Speaker C: Well, that's certainly a trend. I think that's here to stay remote working. Let's look at some others. In terms of the future outlook and trend, what emerging AI technologies do you believe have the most significant impact on the future of recruitment and talent management?
[00:21:26] Speaker B: That is a tough question. My answer is probably broader than just specific to recruitment, if I'm brutally honest.
Where I see, I think a lot of really interesting stuff happening is the interface between AI and what some will call web three technology, but certainly the integration of these different technologies to be able to benefit not just human resources and recruitment, but certainly the wider industry as a whole. When we're talking about decentralization, when we're talking about how that works alongside blockchain technology, how that also works when we're throwing AI into the equation as well, I think there's some really interesting areas there that are going to alter, again, not just recruitment and talent acquisition, but a lot of different things as well, specifically to recruitment and talent acquisition. There's some people doing some really cool stuff out there. I don't really necessarily think it's gained the traction or whether it will gain the traction, but we've been through the whole NFT boom. We look at individuals who are tokenizing their cv's, people who are signing up to web three platforms, whether it's from a freelance work perspective, that are able to tokenize their skills, tokenize the roles that they pick up. Cool stuff happening out there. Again, I don't think we've really sort of hit that point where it's going to get universal uptake. I think a lot of these things are still far too niche for the wider population to really take up on them. Some of the areas I would like to see developed are. That was my next question, beating you to the punch.
I'd like to see large language models used to be able to take a lot of data points that happens across the HR, the employee, the recruitment process, and really produce meaningful insights.
Let's take, who can we use as a big thai company here? Let's take macro. Macro. Let's take macro. Macro must have hundreds of thousands or thousands of applications every year. They will have thousands and thousands of interviews every year. What happens to all of that data? That's a huge amount of very, very important data that really just falls by the wayside.
Companies will try and do things around, you know, how they can use that data to improve their processes, but it's not really, you know, I think these, it's aspects like this that I think large language models are purpose built for. If you could take all of that data that you have from your incoming applications, from all of the interviews that you have in an organization, whether it's with your HR team or department heads, but not just that. If you then use all of the data in terms of how that onboarding process goes, who, who passes the onboarding process? Who do you release within the first three months? Because they've just nothing been able to fit into the company well enough. Who stays with the company for five years? Who leaves the company after two years? What are their performance review processes? What's the data that's coming out of these employee engagement surveys, the performance review surveys, if we could develop all of that data, and that starts to really inform HR, the board, of what do our best people look like? Alternatively, what do the people who do not survive here look like? And again, to inform decision, not to make a decision. Again, the danger is there that you become too reliant on, on that data and you allow it to make the decisions for you. You always have to factor in the outliers. But that, for me, I think, is an area that really hasn't been explored or exploited yet.
[00:25:02] Speaker C: Lastly, Tom, can you advise on how an organization should prepare and adapt to these emerging trends and what steps they should take ahead of the curve in leveraging Genai for recruitment and talent management?
[00:25:15] Speaker B: Well, I think if you're not already looking quite closely at these technologies, you're already already behind the curve. And I think most companies need to sort of wake up and realize that if you don't start to at least get an understanding of the impact of these technologies, you will be left behind. We're not yet seeing the mass adoption of AI at a sort of corporate level. We're seeing a lot of the large corporates, whether it's the big four, whether it's the large technology companies, obviously they have massive budgets, they have the knowledge, the skill set and the technology base, to be able to, to really sort of look at the impacts and actually implement different sort of AI models. But not every company is like that. And I think depending on the organization, you just at least have to be aware of it. You really need to be focused on how can I make use of these tools to improve my department, to improve my processes, to develop the wider organization as a whole. I think, yes, that has to be at a company level, but it's also an individual level. If you as an individual are worried about your skillset becoming irrelevant because of AI, then you really need to sort of get focused on how you can improve that skillset. And again, that doesn't necessarily take huge budgets. I've spent three months scouring the Internet for free courses, and there's a huge amount of free courses out there that people can sit. It's a couple of hours a day. And really every individual needs to take that ownership and that responsibility to improve themselves, company wise. If you've got the budget for it, roll it out. Make sure that you're implementing a learning development budget specific for AI. I mean, look at the key departments within your organization, whether it's the software development process, whether it's product design, product development, whether it's HR and recruitment, whether whatever department it is. If you have that L and D budget, start to apply it to look at these tools. But if you don't, if you don't have a significant lnd budget, you can still allocate people time. You can still give people a couple of hours a week to really start jenning up and understanding these technologies.
[00:27:23] Speaker A: Many thanks to our guest, Tom Heath. There on our website you can find the program notes and a reading list for this episode. Hacking Kaizen is produced by DSA. Nikki edited the show. We'll be back at the same time next week, but until then, from me, Graeme Newman. Many thanks for listening.
