As organizations race to become future-fit, there is a growing emphasis on redesigning talent practices, focusing on a digital and transformation mindset to achieve success.
This episode of Hireside chats features an interesting conversation about building a truly digital and skills-powered workforce in the age of AI and automation. SatJ and Satish Rajarathnam, HR and Global Head of Talent Transformation at Mphasis, unravel the essential ingredients that contribute to a dynamic, adaptable, and skills-empowered workforce. We’ll explore innovative strategies, share real-world insights, and uncover the keys to shaping a workforce ready to conquer the challenges and opportunities of tomorrow.
Read the blog version of this engaging conversation on the topic that has taken the world by storm, with plenty of practical takeaways to be better prepared for the future of work.
Sat J: As organizations race to become future fit, there’s a growing emphasis on redesigning talent practices. Focusing on a digital and transformation mindset to achieve success. According to the World Economic Forum, skills gaps will remain high as job requirements continue to change due to accelerated technology adoption. 50% of employees will need to re-skill to take up new jobs internally, leaving HR managers faced with the challenge of building a workforce that is fit for new and emerging job roles and requirements.
A company is only as innovative as its employees, and creating a future-fit workforce that can adapt itself to the world as it changes can be a company’s greatest differentiator. And with the right kind of talent, a company can quickly adapt to disruptions and advancements.
How has the concept of talent transformation evolved over the years? And why has it become a critical focus for organizations today? Also, what are some of the large transformation programs that you are aware of that are shaping the industry today?
Satish Rajarathnam: Talent transformation has evolved over a period, right from the industrial age to the information age. It’s all about the task-oriented approach and action-oriented approach, about knowledge-based skills, knowledge-based roles, and so on. So, it’s a significant paradigm shift from a talent perspective. So that in itself has transformed significantly over a period of time, that’s the first part. Adding to this is the entire digitization and digital transformation. Technology transformation has helped enable talent to transform significantly.
So, looking at the 20th century, the 21st century is going to be another big movement. Rather, I would say, from a talent perspective, employees will have to continuously adapt to newer models, newer skill taxonomies, newer tools, platforms, and whatnot. But what this has done, in my view, from a talent transformation perspective, is it’s gotten the global talent together. It’s what I call globalization. Business is expanding, and geographies are expanding, which gets us to a state where you ought to have cross-cultural understanding and sensitivity, which is very critical – cross-cultural collaboration. So these are two key elements because of digitalization that’s happening has got the world together.
So, talent transformation has not begun to include elements of cultural nuances. And how do you work with different groups, different communities across geographies and so on. It will call us – a distributed agile working model, so if you bring in the plethora of opportunities that we have from a talent transformation space. Because of this technology shift, globalization is huge. What is also done is not just from a tech space, there’s a big shift in terms of the entire skills portfolio. Most of them focus on the IQ part, and the EQ has changed significantly, too, in my view. The emotional quotient – focus has shifted to skills that are unique to humans. Not the AI or bots or robotics or automation that I’m even talking about. People have not started focusing on “Hey, how do we inculcate critical thinking? How do we inculcate empathy and sympathy? How do you bring in emotional intelligence or EQ, as well as the SQ, which is a social quotient, back into the H in HR?
How do you accentuate humans in Human Resources? I think that’s the transformation that we are seeking. Now, having said that all of this is definitely going to transform the way that you’re working. When I say that the agility of an individual, of an organization, and talent transformation plays a significant role in transforming the agility of an organization. It talks about health, wellness, IQ, EQ, all that I’ve spoken about through various platforms and technology again. I just want to bring another cliche of hyper-personalized enablement quotient as well. So that’s the change that I’ve seen. That could be more as well from a talent transformation evolving from where it used to be to where it is right now from personalization to hyper-personalization – that’s something that I want to put out right from a criticality perspective.
Why is it critical? Well, like I said, technology is changing almost every week, every fortnight, right? Whilst we are talking, there are new tools coming in, new platforms coming in, new skills coming in and so on. So, how do we keep pace with the changes that are happening technologically? The metaverse for example and everything is now moved into the metaverse. Your avatars are coming in; it could be learning, it could be AR, VR. Everything is on the metaverse right now. Your employee engagement, all of these are happening in the metaverse right now. Your hyperscalers, what we call the cloud, AWS, Azure, Google and whatnot, all of these guys are getting into cloud technology in a very big way. It’s not new, but right now it’s caught up significantly, and that’s why it’s very critical. The latest phenomena of open AI, whether it’s chatGPT or Bard or any of the other open AI platforms that we want the next goal. It’s already more than the goal right now, in my view. So data, data science, data analytics all of these are very critical in today’s context. Why? Most of these, if you link back the macro, and microeconomic changes that are happening and how these are impacting business in today’s world. And all of these are computed, all of these are worked, prescriptive, and predictive. For all the technology that I spoke about brings in much higher value order into business nuances. And that’s why I’m saying this is very critical. And this is the outcome of all of these, this is in fact the war of talent out there.
Someone has to stay on top of this tech advancement, keeping all of these elements together and keep innovating something new, or else we’re all going to be redundant. At some point in time, the processes and platforms that we have will again be obsolete in a very short time. The way that we look at it from a pre-pandemic to post-pandemic perspective, the digitization part of it, or digital, digital transformation perspective, has grown leaps and bounds.
What has this impacted? It has impacted the way we operate business. That means operational efficiency. And for us to get operational efficiency, we’ll have to transform talent that could help enable us to do this as well. That’s another critical part. So, if you look at it, there are many such critical areas that talent transformation has been looked up to, more so when adopting all of these.
To me, the most important part is how we create resilience or, rather, I would say, an organization’s resilience. These are very critical, in my view, from a talent transformation standpoint.
Sat J: You spoke about how the job profiles are changing with the times, and workplaces are continuing to get disrupted. You highlighted a lot of in-demand skills that are emerging. So tell me, Satish, how can HR practitioners create this agility in the workforce, and how are HR practitioners responding to this future disruption on demand?
Satish Rajarathnam: HR plays a pivotal role in business. But HR is a custodian of talent, the way that I look at it, or an enabler of talent.
Are we enabled enough to look at how our baseline skill taxonomy is in the first pass? Do we know what we have as talent within our organization? Do we have a taxonomy? Do we have a baselining? So when we have to do a baselining, we’ll have to go ahead then and say, “Hey, how do I assess my internal talent from a skill capability standpoint of the organization.” That’s very critical in my view. So that’s one way of looking at it. Now, having said that how do we create agility and resilience? Maybe it could be through bringing people together and talking them through what is needed to foster a growth mindset and how we do this collectively as an organization. So that’s the agility that we need to look at considering the micro-macroeconomics that’s been playing into the last year or so and maybe the next few months or so.
HR plays a very critical role in creating an agile organization, a very nimble organization to quickly learn, unlearn, and relearn. How do you enable employees and the firm to kind of shape up to become much more agile? How do you foster a growth mindset? That’s another very critical part that HR has put into perspective. And for this, HR has to be on top of data. They need to be able to read data and gather nuances like how the employee experience is moving, and how the learning quotient is evolving. How is the emotional quotient transforming? How do you look at the ENPS? Pick up all the data, put them together, and the performance culture of the organization. So, as HR, we’re custodians of most of these factors, and I think we are in a situation where we can have a Hawkeye view of which way to traverse to help enable the organization to thrive in an ever-changing economy.
DEI, too, is critical. How do you foster a diverse workforce? How do you be much more inclusive as an organization? How do you lead innovation and make sure that large organizational problems are resolved to solution orientation and so forth? So, DEI is another piece outside of the tech piece and the talent piece that I just spoke about. Most importantly, you know, with all the abbreviations coming in, EX is the employee experience.
There are various vehicles, various channels that the HR function can use – a dip six star way, you can do an FGD, you can do a one-on-one performance threshold, to know how things are transforming and moving, you can look at the skills, whether it’s moving upward or not, a lot of indicators out there for HR to look at where collectively we are moving as a firm, as individuals and so on.
That, to me is, is, is how HR can bring in a transformation in itself and make sure, not just the employees but also the firm to be attuned and and stay updated as far as the industry or the market conditions go.
Sat J: Now, Let’s just shift focus to talent acquisition. Can you highlight to me how organizations are leveraging AI and automation, especially the talent acquisition function?
And, if you can give us some examples from your own experience in recent times, how have you and your team adopted AI and automation in enhancing candidate experience, improving throughput, and so on?
Satish Rajarathnam: We’ve gone to an era where conversational AI has picked up a lot of tactical work that a recruiter was doing or has been doing. Now, we’ve put together an algorithm heuristics around to screen a candidate for a position. The traditional way of doing it would be we’d look at a keyword search and say, of the 100 profiles, 25 are relevant, and so on.
Now, we’ve taken this to the next level of paradigm. The algorithm that we have, the heuristics that we have, not only pick up the keyword search, but what it also does is a conversational AI with the candidate. In a much more intuitive way, when the candidate logs in, the bot out there, the AI also picks up and starts saying, “hey, you applied for this specific job in a company, let’s talk about what you’ve done? I see this in your profile. Why don’t you throw some color around it?” etc.? Then, the candidate starts typing in, narrating what he or she has done. The best piece of this is the heuristics – its audio and video biometric enabled. So, you can also negate the versions of fake impersonation.
So there are three different aspects – one, you’re assessing a candidate for best fitment to the job description we have. Two, we’re looking at a candidate not to be fake or, impersonating or fraudulent. All of these are done on the same platform in just one go. Then we move on to look at the candidate experience, the bot calls the candidate, posts an offer, and keeps him or her engaged, saying, “hey, thank you for accepting, is there anything else that we could do for you? I also see that you’ve updated about 30, 40% of your documentation. Do let us know if you need any help to upload the remaining” and so on and so forth. Right. So there are, there are tools, there are technologies right now that could kind of make all of these task-oriented actions very seamless using AI.
As a platform technology, thanks to conversational AI, the bot, and what we call large language models, the NLPs, across languages across platforms, you get things done much quicker and faster. What this does is increase the efficiency. Look at the productivity that we’re getting from recruiters. They can focus on their core of reaching out to the right candidates and not getting swayed into candidates who do not meet the criteria. We call this co-bot a co-aided bot. So, the recruiters use this significantly because more than half of the task is done on this journey. And the rest is picked up from a decision-making point done by the recruiters.
Do we use this technology to identify skill gap analysis? Again, your algorithms, the machine learning algorithms bring in – if a candidate has applied with a specific skill cluster and been assessed before recruitment, can that be picked up into the learning path of an organization? So, that is also done using heuristics. That’s also done using an algorithm that matches and says this individual has come in with this specific skill competency and proficiency. And for this individual to grow from point A to point B, these are learning paths that are personalized to him or her. He or she can pick this up vertically, or if he or she wants to move horizontally, that’s also possible. And that’s an LXP coming into place and matching the entire learning experience for that individual as well. So that’s from a learning perspective to a large extent.
Next, we look at real-time feedback. The ENPS and all that you run through from a performance feedback perspective as well is also almost live. You have bots that have come in to say, “Hey, I see that this month you’ve met so many KPIs, but this just hasn’t been adding onto your goals or the OKRs of the organization or for your team.” These are avenues that you can look at. So you have a co-assisted bot that’s letting you know as a coach, as an advisor, as a mentor, at every point in time, saying these are the learning gaps that you can fulfill.
These are your performance gap that you can consult, these are your career gaps that you can look at. So it’s making life much easier. It’s all aided, if I were to put it, for someone to take decisions much faster, that’s what we call predictive analytics to a large extent, or if I were to move to the next level, prescriptive analytics as well, it’s not just about predicting, but also coming back in the tool saying, hey, looking at your data in the last six months to one year, this is how you’re progressing, but for you to progress to this goal, you need to speed up here, you need to slow down here.
So it also starts prescribing to a large extent, from an algo perspective, to make life much simpler. And basis which the candidate or the employee or the learner can fine-tune his or her speed of learning certifications, what he or she should be doing should he or she take a, you know, ARVR course, CBT, virtual learning, instructor-led, or getting certified on ABCD over a period of time. All of these recommendations are right up there in our face. You just have to start clicking and saying, Look, I am signing up for these courses and moving on to a large extent. So that’s the way that we look at it across the plethora of HR lifecycle processes to a large extent. The most critical employee experience that I spoke about.
How can you move your employee experience into the metaverse? Can we have avatars to take an individual towards his or her well-being health consultation? The employee engagement quotient that we will talk about. All of these are also driven through AI to a large extent. That’s the way that I look at it. Holistically, in every aspect of hiring to retire, we have tools, we have platforms, and we have engines that are trained to kind of help aid candidates, employees, and retirement as well. All aspects of the life cycle that we were looking at starting.
Sat J: It just sounded like a Sci-Fi movie, Satish, and how organizations are leveraging all these technologies, not just to nurture and identify the right talent for them, but also going beyond recruitment, ensuring that it plays a role in developing employees, upskilling them and contributing to a highly skilled workforce. And I’m sure you know the technology changes have, as you rightly described, transformed talent practices and enabled HR organizations to go beyond conventional ways of working and truly provide employees and candidates with a wonderful experience.
One last question on the AI automation side: Are you seeing candidates being open to this change? It’s very easy for organizations to adopt these practices and then launch them on the candidates, but do you get a sense that candidates and people who are being subject to these methodologies are embracing this, or is there resistance?
Satish Rajarathnam: Yes and no. To a large extent, thus far, I see this as candidates are much more open because of the ability of an avatar or an AI to connect to the incumbent much faster. That’s one. The ease of traversing the entire process chain, I’m keeping away from the interview process for now, barring that, if you look at the process, the policy perspective, it’s much easier, whether it’s a candidate from a TA perspective or an employee from an organization perspective. It’s speed, agility, and efficiency. It gets done much faster, quicker the way that it is. The heuristics pick up if it’s a policy decision, you need not go into any specific tool, then read out yourself and then pick things out and say, yes, I can and I cannot, but it’s all in the bot out there, you start typing, and by the time you finish typing, you get the answer out there as well.
So it’s a speed at which you get the knowledge within your environment, whether it’s a hosted AI for that matter, or if you’re going to go through an open AI perspective, either way out there, it’s a speed at which you get information, knowledge right up front for you to take a decision that’s from an employee perspective, from a candidate standpoint, it’s much easier, he or she can look at, let’s say, for example, a characterization of a interview for example. A ticker goes out. The candidate gets the option of picking and choosing. Hey, I got to see my interview is available tomorrow at 8 p.m. IST or 2 p.m. EST. He or she gets the option of picking and choosing the time slots based on their availability. Otherwise, if you look at it, a sourcing or a recruiter or a scheduler will have to keep making 5-10 calls to keep changing the timeline every now and then. So that eradicates the task. So there again, if you look at it, it’s speed and efficiency. Right? And what we’re also doing is personalization to the candidate by saying, look, you pick and choose what time you are available, and we will get the interview scheduled out for you.
So, candidates are very open right now. That’s from a scheduling perspective. Today, we’re looking at a lot of nano assessments coming in. No more looking at a large platform without naming them out here. Okay. Going the entire nine-yard assessment for about an hour, 2 hours sitting, and taking your entire kindergarten to high school to engineering that you’ve studied, but today, technology has brought in nano assessments. Let’s say you’re hiring for a UI developer or a UX engineer, for example. Your assessments are attuned only to those specific areas, right? You want to debug only on Angular. You want to debug only React and get deeper into it. So your nano assessments, which are mostly gamified and most of the time right now, make it much simpler for the incumbent to also to kind of take the assessments because today assessment is a misnomer. People do not want to get into assessments; they think it’s a one-hour, two-hour long assessment. So technology has brought in change to a large extent, saying I will kind of probe into a particular tenant of a full stack because that’s what I’m hiring for. You run an assessment towards that. So that’s making life much simpler. From a candidate’s perspective. Take the same example of candidate experience or engagement or post-offer follow-up from a recruitment perspective every time you need to reach out to a recruiter to find out, “hey, I’m trying to upload this document; I have this error, what do I do?” With AI, you don’t have to have a bot out there to assist.
Everything is there on your platform. So candidates a, from a pre-hire perspective, and employees from a post-hire perspective, and policy and knowledge repository and using a bot. It’s just kind of helping them a lot from efficiency, speed, ability to take decisions, and keeping them engaged to a large extent.
Sat J: What advice would you offer to CHRO talent leaders out there who are kind of very excited about what they are hearing, what they are reading about the power of AI and automation and technology but are unsure about how to initiate such a transformative change?
A lot of us want to do so many things but are hesitant to take the first step forward. So, what would be your recommendation? And if there is some sort of a step-by-step approach to starting small and then, and then scaling up, is there an approach that you can suggest to people who want to adopt this journey?
Satish Rajarathnam: Well, from the way that I look at it, I don’t know if that’s a secret sauce or a silver bullet that I have. But the way we should look at it, the way that I kind of looked at it as well, is about picking up areas where we need efficiencies to be built out. Pick up areas where we need productivity to be advanced pick up areas where we need much faster data to make decisions. So, we need to pick and choose our battle. Let’s not try to wage the entire war in the name of AI and say; I’m gonna sweep this entire talent supply chain ecosystem and talent management. Just by procuring an AI platform and getting things done, which will never happen.
There are organizations that have procured large platforms and have used only about 25-30% of their efficiency. So, the way that I would look at it is to break it into chunks and look at your hire-to-retire process. Look at what it is that you want to left shift and how much of those can be left shifted using digitization. That could be part A. How much of those can be done using digital transformation in the sense that can be put in an AI? Can I put in a bot? Like the examples that I quoted, a candidate’s experience could be done by a conversational AI bot. For an employee, a policy question, or even today, a leave application is all done through a bot; it says here you go, you have 20 personal leave, 10 this leave, and 20 that leave. You pick and say, I want to apply this, it’s all getting done.
So it is about picking and choosing what we want to leave shift in the first place. Second, put in a 2×2 and look at if I were to invest here; what is my minimalistic effort that will maximize my output from an impact perspective? Yeah, a simple 2×2 box put the entire life cycle I had to retire from a business perspective. Look at these pain areas and within that stack rank and say, if I want to tweak, let’s say sourcing, right? If I were to bring in an intelligent system that would do my sourcing instead of parsing, right? And if that’s going to save time for my recruiters and they focus on their core of recruiting, I would want to get there as quickly as possible.
Very small moves can make a huge impact. Candidate experience, post-op candidate engagement, learning, skill gap analysis for your current role, and gap analysis for the futuristic role that you’re aspiring for.
Much more vivid in terms of their learning performance consulting. Can I bring in a tool that will come back and say, this is every stand-up meeting. For every scrub that you deliver your speed, your velocity is X. If you want to move to X plus two or you want to make it to X or the velocity of your delivery.
These are areas that we figure out. This is what we’ve seen in your system, right? Performance consulting is another key as well. So, yeah. It depends on the organization to prioritize where and how they want to kind of bring in transformation using technologies specifically around any of these AI or board or automation and digitization and so on and so forth.
So, again, just to quickly sum it up, the way that I would look at it is to split your larger OKRs into multiple goals into specific KPIs. Pick up those KPIs and find out, do we have aid, do we have tools to kind of help enable our employees to perform better? And if I were to do that, can I bring in a technology intervention that will fasten their learning, that will, that will kind of fasten their performance, career, skill, and other stuff? Then, bring it accordingly.
Sat J: Thanks a lot, Satish. And before we sign off, we usually have a customary rapid-fire round. There are a few questions that I have for you. And what we are looking for is very candid, short, and quick responses. So, let me begin by asking you the first question.
A personal experience where AI-powered insights led to a game-changing hire or a game-changing situation?
Satish Rajarathnam: Well, a recent one, we had been through multiple profiles, but then we thought we would run the heuristics around it and figure it out. Not just from the profile we also picked up information from the social portal as well. The AI tool helped us aid in specific areas and a specific dimension, narrowing down our search to a specific candidate. It was brilliant!
So, looking ahead, what future AI advancement are you most eager to incorporate into your talent acquisition playbook?
Satish Rajarathnam: I wish I could look at the Star Trek kind of stuff! You widen your fingers and get to see the candidates in your pipeline right from how many applied, how many are sourced, shortlisted, and so on and so forth. To make hiring managers’ lives simple, faster time-to-hire, with lowered hiring costs – basically bring in much more efficiencies – that’s something that I want to look at from a futuristic state standpoint.
Technology transformation that will help us decide on talent much faster? Making the decision to build, buy, and borrow much faster than where we are today, I think that’s what I’m looking at from any of these technology interventions.
And the last question for you is fast forward, maybe a year from now. And if you had to pick between a human recruiter versus an AI bot, whom would you pick?
Satish Rajarathnam: Tough question. I would put my money on a recruiter who’s well-versed in running an AI or a bot engine. Because at the end of the day, humans are the ones who would take decisions – the bot can only help aid a decision.
SatJ: And there you have it. The secret sauce to building a truly digital and skills-powered workforce with AI and automation at the helm of talent acquisition.
This episode was filled with plenty of takeaways for HR professionals to build a truly digital and skills-powered workforce, and the interactions between generative AI and HR that has the potential to transform the whole talent landscape as we know it.
If you enjoyed this episode, please leave a review or share this on your social. For more details, listen to the Podcast available on all your favourite podcast platforms.
Subscribe to Hireside Chats today for interesting insights from the frontlines of recruiting.
Also don’t miss the next Episode of Hireside Chats available to stream on all your favourite podcast platforms.