There is a story Adarsh Lathika returns to when he wants to explain what he means by foundational skills. It comes from the early days of COVID lockdowns. His wife, working in Perth, told him about a young developer on her team: one of the most talented coders she had encountered, technically brilliant, professionally promising. When the lockdown hit and his flatmates left for their hometowns, he was left alone in the apartment.
He had ready-to-eat meals in the house. He did not know how to cook. He had not eaten for many days when he died. Some pre-existing health conditions made things worse. But the proximate cause was a gap nobody had thought to name: extraordinary technical depth, almost no supporting competency outside of it.
“Even if I am the best at what I do,” Lathika says, “if I don’t have the supporting competencies, am I going to be productive on a long-term basis?”
The story is extreme. The principle it illustrates is not.
Lathika is a strategy and learning systems practitioner who has spent over fifteen years across higher education, executive learning, and consulting, watching the gap between what institutions teach and what actually makes people effective grow steadily wider. He calls his framework the Anatomy of Work. Its central argument is that the misalignment between work and human cognition is becoming the defining challenge of the AI era, and that most organizations are not yet taking it seriously.
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The Shadow Curriculum Nobody Officially Designed
When Lathika talks about the shadow learning system, he is describing something most learning professionals have sensed but not fully named. Employees and students are using AI tools to build their own learning experiences, in parallel to whatever the institution or employer is offering, because the official curriculum is not keeping up with what is actually needed.
For students, the degree still matters. You need the credential to get the first job, at least in most contexts. But alongside the formal program, students are building custom GPTs, running feedback loops on their own work, designing self-directed skill development paths that have nothing to do with the syllabus.
For employees, the calculation is starker. With mass layoffs happening every few weeks, with average job tenure sitting around three years, with the implicit contract between employer and employee fraying visibly: why would someone commit their learning energy to what the company wants them to learn?
“The company may want me to learn Python,” Lathika says. “But I as an individual may feel: coding is as easy as picking two systems and generating code. Let me learn storytelling instead.”
The result is a growing divergence between the organization’s learning investment and the employee’s actual learning direction. Both sides are losing.
Cognitive Debt and the Junior Employee Problem
The most practically urgent issue Lathika raises is one that learning designers need to sit with. AI is compressing the timeline through which junior employees have historically built domain understanding. Where it used to take twelve to fifteen months of doing repetitive work to understand why that work mattered and what it contributed to the business, AI can shortcut that process to a few prompts.
The problem is that the shortcut skips the learning, not just the repetition.
“He or she is not really developing intuition,” Lathika says, “and therefore not building the ability to judge AI output on a long-term basis.”
This is what he calls cognitive debt: the accumulated deficit that builds when people spend their early careers copy-pasting AI responses rather than developing the foundational understanding that makes expert judgment possible. When those same people are eventually in roles where they need to evaluate, critique, and direct AI output, they lack the substrate from which that judgment grows.
The experienced professional verifying AI output is fine. They have twenty years of accumulated pattern recognition that lets them catch the errors, the context gaps, the places where the model does not know what it does not know. The junior employee who has never developed that foundation is not a reviewer. They are a relay.
What Speed Optimizes Away
Lathika asks a question that deserves to be used in every L&D strategy conversation: what gets lost when speed and output become the primary measure of success?
His answer is specific. You lose the people who are foundationally strong. You lose depth. You build skills at a superficial level that cannot handle the variance and edge cases that real work generates.
He connects this to something deeper than learning design: the performance metrics and reward systems that sit around the learning culture. Redesigning an L&D approach without redesigning how people are evaluated will not hold. The incentive to move fast, close the ticket, clear the assessment, will always override the incentive to sit with complexity if the measurement system rewards the former.
“You are not only redesigning your L&D plan,” he says. “You are also relooking your performance metrics and your reward system.”
This is, frankly, a harder conversation than most L&D teams are equipped to lead alone. It requires allies in finance, in HR leadership, in the business units whose operational definitions of success drive behavior at every level.
Three Shifts That Are Already Happening
Lathika identifies three fundamental transitions underway, and is direct that they are not coming: they are here.
The first is from institutions to individuals. As the employment contract becomes more transactional and more temporary, learning increasingly follows personal career logic rather than organizational logic. The institution that wants to remain relevant has to become a place where people build the cognitive and behavioral skills that keep them employable across contexts, not just the technical skills that serve the current employer’s roadmap.
The second is from courses to systems. The transactional model of learning design, a project with a start and an end, a completion rate, an assessment score, does not produce the kind of continuous capability development that an AI-first work environment demands. The shift is toward building self-servicing systems: tools, workflows, communities, accountability structures that make learning ongoing rather than episodic.
The third is from knowledge to judgment. Knowledge is now abundant and instantaneous. The question is not whether you can access the right information. The question is whether you can evaluate it, contextualize it, critique it, and make a decision from it in real time. That ability is not built by learning what AI can tell you. It is built through the accumulated experience of working through problems, making mistakes, and developing the intuition that recognizes when something is off.
The Transparency Prescription
Underneath all three shifts, Lathika keeps returning to one variable: trust. The shadow learning system grows in the absence of transparent communication. Employees who do not know what the organization plans to do with them, what skills will matter in six months, which roles are at risk, will default to self-preservation. That is rational. The response is not to stop that from happening, but to give people enough honest information that their self-preservation aligns with what the organization actually needs.
“Employees today are very smart,” he says. “They read things even before they’re made public. If you create a culture of trust and transparency, perhaps you’ll be able to manage that shift.”
He is not naive about the difficulty. In a world where predicting the next quarter is hard, committing to a five-year talent development story is harder. But he is clear that the alternative, managing by opacity, optimizing for EBITDA at the expense of learning investment, treating people as expenses, is making a bet that AI will continue to deliver returns without a human workforce capable of directing it.
That bet, he suggests, is not as safe as the balance sheet makes it look.
Listen to the full conversation with Adarsh Lathika on The LX Ops Show or wherever you listen to podcasts.
Podcast Transcript– Jason Gorman with Adarsh Lathika
Jason Gorman (00:00)
Welcome to the Learning Experience Ops Show, where we talk about making learning better that’s future ready. I’m Jason Gorman. I’m the founder and CEO of Jackrabbit Learning Experience. We spend a lot of time as learning professionals designing courses, building programs, and refining content, which we should. That’s the work. But I keep running into a question out in the world and in my own head that I can’t quite shake, which is,
As we are doing the work of building courses, are people waiting for us to actually finish the work that we’re doing? Because I think increasingly they may not be. I think learners, employees, and students are more and more going out and just grabbing AI tools that they need and building their own solutions to help them learn. They are figuring out how to learn what they need when they need it, maybe without us more increasingly. And I don’t know that we have
fully reckoned with what that means for the systems we build and the roles that we have played as learning professionals and will play into the future. And my guest today has been studying exactly this. He calls it the shadow curriculum, and his research is surfacing patterns that I think all of us, every learning professional needs to understand. He’s asking about what happens to human cognition when AI
there’s more of the thinking for us and what institutions need to become if they want to stay relevant.
Adarsh Lathika is a strategy and learning systems practitioner working at the intersection of AI human behavior and the future of work. He has spent over 15 years across higher education, executive learning and consulting, and is currently building frameworks like Anatomy of Work to understand how learning work and performance are being reshaped in an AI first world. His work focuses on helping institutions and organizations rethink
not just what people learn, but how, why, and whether learning translates into real-world capability. Welcome so much to the show, Adarsh.
Adarsh Lathika (02:12)
Thank so much, Jason. Quite excited to be part of this podcast.
Jason Gorman (02:16)
Awesome, so glad to have you here. All right, let’s jump in with some lightning round questions. What is just a 30 second elevator pitch about what you do in your
Adarsh Lathika (02:25)
So I try to study the misalignment or the growing misalignment between work and human cognition. That’s what my focus is. So the eventual objective is how do you drive performance and productivity.
Jason Gorman (02:37)
What do love the most about your work?
Adarsh Lathika (02:38)
think the more I delve deep into it, the more I realize that I don’t know much. It’s a very open canvas. In fact, when I started on this journey, I went back to my 10th standard biology text to understand brain and really go back to the basic concepts to get a sense of what the neurotransmitters does, what each part of segments of brain does to human behavior and how they are related.
So it’s been therefore an exciting part to really relearn a lot of things that I left behind some 20 years ago and then repurpose to what we are experiencing today so that we are a bit more doing better for ourselves in coming future.
Jason Gorman (03:22)
And just to get you know a little bit better just in your day-to-day work, if you are really in the flow state and you’re just kind of time is passing quickly and you’re totally immersed, what are you most likely to be doing?
Adarsh Lathika (03:33)
building something. So I do have some background of ⁓ programming. So I leverage my coding a bit to build. So I started building productivity tools for myself so that I can do things ⁓ much faster. I don’t really need a team to do it for me. From that I started then once I started getting hang of that I started building slightly more complicated tools as well.
Jason Gorman (03:35)
Hmm.
Adarsh Lathika (04:00)
something that helps me both personally and professionally. So that’s what I do most. That what I do enjoy doing most.
Jason Gorman (04:07)
That’s wonderful. And I just described a little bit about what you do. You just gave your 30 second elevator pitch. I’m curious, what was it that inspired you to do what you do?
Adarsh Lathika (04:19)
So, I mean, a few things that are elicited over a period of time is that, one, humans are important. I think as a species in this planet, we have lot of responsibility to what we do, why we do, and equally important why we don’t do something. So a couple of years ago, I decided to take a career break and
That was the time where AI was coming, this whole charge GPT moment happened. And the first thing that came to my mind was the 1984 from George Orwell. And the more I was understanding what charge GPT was promising, more I was fearing maybe to some extent that that obedient world may happen in my lifetime. And I was not very happy about it.
So question was that, how do you then give the power back to the humans? So I started thinking about it. I had the time to do it. I started listening to lot of podcasts around the world. I think that’s something that internet just allows you to do. So I started reading a lot as well. And then once I started reflecting back on that or injecting that on top of my professional experience,
Jason Gorman (05:07)
No.
Adarsh Lathika (05:32)
I felt that maybe ⁓ we need to go back. So what could be the gaps that can develop over the next three, four years? So my understanding was that knowledge is becoming abundant and instantaneous. So when that is happening, what is the role that as a human we will play at workplace? So then that’s when the whole idea about judgment and context came to my mind. since then, I’ve been focusing on
Jason Gorman (05:56)
You kind of touched on this a little bit already, but what are you the most anxious about for the future?
Adarsh Lathika (06:02)
Personally, I don’t like somebody else taking decisions for me. I’m not a big fan of that. So, and I think when it is AI to make that happen at a large population scale, then I think we, especially as a, at the context of India, we take a lot of pride of being the youngest nation or youngest nation for the next say 20 years.
But imagine a case where in which we have a huge population of youthful, high energetic population, but nothing to do. And I think that can become very negative, very fast that we can’t really control. So that is what I’m a bit worried about because there’s a journey that as a country we are going through since independence and we had the time.
to course correct because time allowed us to do it. But now AI is making everything redundant. I’m not so sure whether we are responding correctly to what AI is today and what AI promises to be tomorrow. So that is something that makes me anxious. But at the same time, it also is a great opportunity that can unlock if you’re using it the
right way. So I am trying to focus more on the latter.
Jason Gorman (07:18)
Good. And last lightning round question. What is a skill? Can be related to your work, not related to your work, but what’s a skill you’re trying to learn right now?
Adarsh Lathika (07:27)
So, one thing which I am trying to learn is how do I sell better because I am a solopreneur. So, I love building and I could afford to be a builder maybe 10-15 years ago. Not so much in AI first world. I can’t afford to do it. So, I believe that everything and anything related to selling as a skill, right from public speaking to story, narrating a story.
Jason Gorman (07:41)
Yeah. Yeah.
Adarsh Lathika (07:52)
to being in front of a crowd and speaking those, these are the skills that I’m working on and I use AI for that as well.
Jason Gorman (08:00)
Good, yeah, wherever we can get that advantage, let’s get that advantage. All right, well, we’ll move to the kind of longer form questions here. I mentioned that you came into this field of learning through operations, things like building and scaling systems across university campuses, online initiative, executive education, corporate settings. And you have said elsewhere that the pattern you have seen
Adarsh Lathika (08:04)
Yes, yes.
Jason Gorman (08:29)
is that we typically optimize for delivering learning, but not translating it into human capability. And so I’m curious if you can give me just a specific moment or project that you were on where you really saw that gap clearly, just to illustrate.
Adarsh Lathika (08:47)
So the background to that is that I have a habit of going through P &S. And something that was very stark and became very strongly apparent to me was that human resources are always on the expense side of the balance sheet. And it’s just OK. Maybe in early.
2010s it was okay, but with automation data science now AI coming to picture I think We need to start valuing human for the Contextual understanding Yoshi has and that is with the current gap that AI has because Like if you had to come compare US and India and I want to start
I want to open a restaurant say in San Francisco and I want to start a restaurant in Bangalore. Now I will give the same prompt for both cases. I am more likely to rely on the response that AI gives me for opening a restaurant in San Francisco because India as a country we contribute to less than 5 % of internet data. So that means AI doesn’t have the context in which a city like Bangalore operates.
Of course, there might be some aspects that we can definitely apply, but the finer things will still be amiss. So that can only be brought in by a human.
So therefore, human is valuable. So where I realized this was when data science started becoming very popular. We started a data science school way back in 2014-15. Now, so I think at that time, we were probably the second most prominent education provider in the country to launch a data science course.
And what I felt was that I asked the best question, why is a learner who’s done a bachelor or a master’s going to again come to my campus to sit through a 9 month course, a diploma course, what is that he or she is going to achieve or what is the aspiring to achieve? We might think that it is the skill of neuroscience, but the fact is that he or she wants a job. So what I did at that point of time is that even when we had
Jason Gorman (11:02)
Mm-hmm.
Adarsh Lathika (11:05)
Launching the pilot batch I created a system. So we partnered with IBM big data at that point of time We partnered with a couple of there is this company so that they can not only bring in their own faculty for doing guest lectures, but also Give them internship and then final placement jobs as well. So because I was able to create the ecosystem that program
became successful and this is time when I was monitoring it, it was one of the largest, ⁓ most successful program within the company. I think in a three year period, it graduated close to 670 people. So it was, so that really worked for me. So what I’m trying to say is that I put the learner as the focus and I put all the business metrics around it. Because I was able to do that,
we were able to create a business which was sustainable. Sustainable because learners wanted it. We were not trying to push a product. It was actually pulling learners to our product. So if you have to now put in the context of what AI is doing. As I said earlier, I’m trying to learn public speaking, improve my public speaking skills. How am I doing it? I record one. I raise my hand for every opportunity that I can get.
Jason Gorman (12:06)
Mm-hmm.
Adarsh Lathika (12:21)
Second, when I do land up that opportunity, I record the conversation. Then I have a custom GPT created. I feed that according to the custom GPT. Then there is a rubric that I created based on my secondary research that I’ve done on how a public speaking should be done. The intonation, the vocabulary, the fillers that you use. There are eight, nine parameters that are there. So I keep, I have created a feedback structure wherein I get a report saying, you are done.
good in these five things, you are okay in other two things and these are definitely two things that you need to improve upon. that becomes very important because as I said earlier, the employees are learning and everything is still an expensive item for an organization. They are seeing a world where in which more and more people are getting laid off. So employees today are not seeing that commitment from the organization for their career path.
Jason Gorman (13:13)
Mm-hmm.
Adarsh Lathika (13:18)
So the question comes back from an employee perspective is that if the employer is not promising me a five years or 10 years with them, why should I then commit another five years? So my company may want to learn, say for example, Python. But I as an individual may feel, okay, I don’t want to learn Python because now coding is as easy as just picking two system and generating code. Then why should I bother about it? Let me learn storytelling. So the company may be investing a lot on
getting the person to learn coding. But I may use in ChatGP to your Gemini or some other coding instance to improve my public skills or storyboarding skills. So that is going to be dichotomy, which will the gap is going to continue to widen, widen and widen.
Jason Gorman (14:03)
Mm-hmm. You have described what you call a shadow learning system. And you’ve kind of just went into that a little bit just now. But the idea, basically, as I understand it, is that learners are using AI to build their own tools in parallel to any kind of formal education that they’re getting.
And they’re doing this in part because institutions, employers or universities aren’t actually keeping up with what’s possible. And so I’m curious, can you walk me through how you think this is happening now, how this shadow learning is happening? Just what does that look like for a student or an employee?
Adarsh Lathika (14:46)
So the scenarios are different for both parties. For the student, institution is still important because you still need a degree to get a job. At least from where I come from, that’s a basic qualification that it does not matter what degree it is, but you need to have a graduate degree to get your first job. Therefore, institution is still important. That scenario is slightly different for somebody who is a working professional.
Jason Gorman (14:56)
Mm-hmm.
Mm-hmm.
Adarsh Lathika (15:14)
largely because one, you keep hearing this news about people getting laid off almost on a every third day you get a news assembly or there’s a mass layoff happening in a globally repeated company and you don’t know whether it’s going to be you next or when is your turn and historically what I experienced is that the while we talk about it in the vision mission or the
company statements, we are not really very transparent when it comes to employee communication. So that gap also adds to this anxiety that the employees can have. So that means it’s a dogged out world. I feel that the employer is not committed to my career growth, my professional growth. Therefore, I do what I believe is the right thing. So if somebody comes to you today and tell me you learn
Google AI Studio because that is the most important tool today, I go and learn that. Tomorrow, Anthropic might launch a better coding agent than that. So I learned that tomorrow. what internally it indirectly does is that it just, gives me as an employee, it gives me a sense that I’m actually learning and doing the right thing. But what it internally does is it’s creating a lot of anxiety inside my subconscious mind.
Jason Gorman (16:13)
Yeah. Yeah.
Adarsh Lathika (16:32)
that I am always hyper which means that I not focused at work I am not giving 100 % at work so the company starts suffering and over a period of time I also start suffering so it’s a loss loss game that both of us are playing so in that way what I feel is that even if it hurts be transparent with the employees say be very clear that these are the five things which is going to happen in the next one month
because it’s difficult to predict what’s going to happen after that. therefore, explain your decision very detailed to the employees. I think employees today are very smart to understand when somebody’s been genuine and being honest in their communication. And I’m sure that people will rally behind that. And even if they end up being laid off, they have a very better understanding of why that happened and why they don’t really fit into that plan. I think that becomes very important.
Jason Gorman (17:21)
It seems like there’s this kind of necessary degree of trust here between the employee and the institution. And it seems like with this shadow learning system, if an employee or a student is using tools to…
to do their own learning, why do they need the institution for that?
Yeah, why would they depend on the institution at all?
Adarsh Lathika (17:45)
So if you look at student as a cert, you still need a degree, therefore institution becomes important. looking ahead, I think institution can actually transform into a place where students build their cognitive and behavioral skills, which I believe are two important segments of skills which are going to keep them employable in the next 5, 10, 15 years. So that transition needs to happen.
But from an employee perspective, that’s a very pertinent question and it’s not very easy one to answer. Largely because I’ll again go back to the fact that ⁓ all the employees and employer related investments are still expense in the balance sheet and not really a detailed investment. So historically, what has happened is learning and development teams tried to optimize the training intervention. So as long as you are the
either the in-house team or the external vendors are able to meet a minimum level of quality they always go for the cheaper option because at some point of time the EBITDA or the PAT is going to come into picture and they try to always protect that so when you start doing it, it becomes very difficult to make the transition to something because you are trying to meet a short-term objective but not really building a company for
next 5-10 years. So that becomes very challenging for the L &D team. And that means that they need to work very closely with finance and the purchase for most part to ensure that to convince them that why this investment at this rate is important. Because even when for example, I have seen cases where in which you are trying to deliver an AI training to a set of people. So the CHRO may approve it, the L &D head may approve it.
Jason Gorman (19:05)
Mm-hmm.
Adarsh Lathika (19:31)
but then again it goes to the purchase and the finance what they do is that they just look at the proposal as excel items they just do a comparison with what is available in the market and they look at high level keywords say you know I can get you 10 % cheaper, 20 % cheaper and then the question becomes what is cheaper to be delivered and I don’t think HR or L &D fights beyond a point and then you always end up extending or giving a substandard training program to the employees
which means that employees are not happy. Again, the loop just continues. So we need a very confident and assertive NND team to actually make this work.
Jason Gorman (20:08)
You’ve also written about this notion of cognitive debt, which is that AI is reducing engagement and pushing people away from thinking and more towards just verifying the output that the AI produces. And so if you’re a person who is responsible for making courses,
a learning designer, instructional designer, how do we address this? How do we address this pattern to help people engage more deeply, not just verify, to stay kind of in the moment with the things they’re engaged with?
Adarsh Lathika (20:47)
So I’m not an ID person, so I don’t know how the courses eventually get built. But I will explain my thought process here. If you are somebody who has been working in a specific field with significant experience, then it is OK that you get AI to do the work and you play the role of a verifier. That means that you are doing your work much more faster and much more efficient manner. You are not sure you are playing
Jason Gorman (20:54)
Yeah.
Adarsh Lathika (21:13)
the role of a team lead to say 15 team members or 15 AI agents, which is alright. But if the objective of the training department is also to create a funnel of leaders who are going to take up the mantle say in next 2-3 years and currently you are given a team of say junior resources or people with say less than 2 years of work experience, how do they build the domain understanding or enough of
domain understanding that they are building their expertise, they are building that level of intuition so that they can then therefore review the output given by AI. That is the challenge.
so what historically used to happen is that you join a company as a junior resource and you had the sufficient amount of time where in which you start by doing the repetitive task for say six months, one year, and then you start doing more of a repetitive task.
And at the end of a 12 month, 15 month period, you get to understand why that repetitive task makes sense. And therefore, what that task contributes to the business objective of the company or the segment that you are part of. But what AI does today is that because AI can do or shortchange that 12 month of learning into a few seconds or a few prompts, right?
Junior resource then becomes just copy pasting the AI response and feeding into the next step or next stage. So he or she is not really developing an intuition and therefore not building on the ability to judge AI output on a long-term basis. So if as an L &D professional or as a CXO who is looking to build a culture, we build a long-term business which is going to sustain for the next 10, 15, 20 years.
You are not really doing good by having the same old approach what you followed say 10-15 years ago for learners who are coming in system today. What is the right solution towards it? Now I can only hypothesize, like for example you can probably give more projects to them so that they understand the why behind what they are doing. But I think
You need to experiment a lot at this point of time to figure it out for you. Because I think the answer depends on a lot of things, including what part of the world you’re coming from, what industry you are in, what is the business objective of the organization. So probably you can factor in all those things and then find the answer that works for you at that particular stage of the company or the face of the company.
Jason Gorman (23:47)
That’s great. you have asked the question, what gets lost for us when speed and output become the primary measure of success?
So I’m going ask you that question. What’s your answer to that question? What does get lost when speed and output become the primary measure of success?
Adarsh Lathika (24:05)
So when speed and output becomes primary measure, I think you lose out on people who are foundationally strong. And that foundational skills are important on a long-term basis, especially because unless your foundational knowledge, foundational understanding of your business, your industry, your segment that you’re part of, you are never able to…
catch the problems in the air response or the variations in air response. But habitually, over a period of last many years, we are used to rewarding people for speed and agility in the way they work. But what we should also realize is that, is it really creating a learning beyond what is supervision? And I believe in most of the cases,
It might be just building skills at a superficial level and not allowing for the skills to have the depth that it requires. So I think that is going to be the fundamental challenge going forward. it also becomes that you are not only redesigning and relooking your L &D plan, but you are also relooking your performance metrics and your reward system so that you are enabling the right learning culture and therefore the overall
Jason Gorman (25:13)
you
Adarsh Lathika (25:14)
the public culture.
Jason Gorman (25:15)
This takes us a little bit back to what you something you said before about human biology, that you went back, you studied some things that maybe you had looked at years ago, but you looped back to it because you felt it was so relevant to where we are right now. ⁓ This intersection of, yeah. ⁓
Adarsh Lathika (25:32)
So the.
So the trigger point
was COVID, the first phase of COVID. So at that point of time, I was in Bangalore, my wife was in Perth in UK. And she was telling us that there was this young chap in her team who was probably one of the best developer that she has seen. And he had one of the first casualty in the first phase of COVID.
Now one interesting point that she told us while she said he was brilliant in coding, he did not actually pass away because of COVID. He actually passed away because he had not eaten for many days. This is despite the fact that there were couple of ready to eat meals packets available in his house. So what happened was that he was staying in apartment with his colleagues. So before the COVID lockdown happened, his colleagues moved to the hometown and he was left alone.
He did not know how to.
because he did not know how to cook, he had the raw materials but he starved himself to death. I think he also had some health conditions which complicated things for him. Also that actually got me thinking. even if I am the best at what I do, if I don’t have the supporting competencies, am I going to be productive at a long-term basis? And I think we can see good enough analogies in sports. Now I don’t know enough personalities in…
basketball or soccer that you may relate to but from a cricket there are characters, individuals who have come shown so much of promise but they just fizzled out because they did not have the right character, they did not show the right attitude, they did not show the right discipline for them to be selected after the first few matches or first few series to represent the country. So now if you have to
consider that in an employment scenario. Earlier you are probably the best salesperson in your company but you were rude with your colleagues, you would not cooperate with anybody else, were more like a bully to your team members. And the company probably would close its to because you were bringing in the revenue. But now what AI has done is that AI can do a lot of things that you are doing.
The question is that what is that you need to be doing to ensure that you are still relevant in the overall scope of things. That means that you are not only a good person or at least good working professional but the collateral aspects of who you are. That means you need to have a good health. We are picking up, you need to have good discipline. We are picking up lot of the bad habits of
workplace which have impact on workplace. For example, the amount of screen time that you all have. It’s very stupid to expect that just because I’m not using mobile at workplace means that I’m more attentive at workplace. No. You are using a lot of screen time when you’re doing commute at home which is already impact your ability to focus which extends to your performance at work as well. Or the
there’s a mobile app feature, right? When you make a payment, the message comes to you, the OTP get generated and the app is automatically reads. Now from a user point of view, you may think it’s faster, it’s convenient, but what it does is that it is impact your short-term memory. Over a period of say two, three years, your ability to retain facts for a short-term is get compromised and that also access to your workplace. So when AI is doing all these things,
Jason Gorman (29:01)
Mm.
Adarsh Lathika (29:11)
What remains with you as the biggest competitive advantage is your ability to review and judge, judgment, critical thinking. And that, if you are in a meeting and you need to respond fast, you cannot always then Google or use AI for the facts. Then you take your sweet time to apply your mind, your logic to come to a response. You should be able to give that response at an instant level. So that…
ability to get again compromised because you are doing so much of other things outside of work which is impacting your performance at work. that’s the so all these things when I start realizing for myself I of course there were enough research out in the the public space about this but in some cases I started doing it experimenting on myself as well. Okay if I do these things I’m able to see some visible
qualitative in my own way of judging it, I may improving it there. So once I start seeing some slight positives, I started doing more and more research. That’s how the entire narration of work and life outside work came to picture. your human biology becomes important, human thinking becomes important, our ability to, our cognitive thinking becomes important. So I started to see all this together so that as humans we are more productive.
Jason Gorman (30:29)
This actually dovetails perfectly with my last question here, which is you’ve identified three big shifts that we’re in right now. I’m kind of curious to hear your take on all three of these. One is from institutions to individuals. The second is from courses to systems. And the third is from knowledge to judgment, which you’ve just talked about here for a second.
⁓ Some of these feel difficult for us to redesign our organizations for, to teach people how to think in terms of systems or to apply judgment over just accessing knowledge. And from this dependency on institutions to more of a self-reliance, self-reliance as an individual.
But I’m curious if you could kind of unpack the three of those a little bit more, because those shifts are interesting.
Adarsh Lathika (31:25)
Okay, I think before I start, I think it’s important to understand or question the definition of organization or company to start with.
Jason Gorman (31:33)
Mm.
Adarsh Lathika (31:35)
The moment you are asked a question of defined organization, it’s most likely that at some point of the definition, we will write that it’s a huge company with employing hundreds of thousands of people, making billion dollars of revenue, working in say, 10 different large markets of the world. But that reality is true only for few people. As organization, I might still be a billion dollar company, but employ only two people. Now the question of
what is the role of learning and development or skill in that such a company, verse-versus a traditional company which is running for last 50-60 years, is already a billion dollar but is it going to be there for the next 5 years, 10 years, 50 years, I don’t know. So we need to ask those questions and contextualize the definition of organization for yourself and then look at these shifts. So when I’m looking at moving
from institutions to individuals, the learning. With this amount of layoffs happening and also the biggest impact I feel that AI is having on workforce is not the layoffs that is created.
Adarsh Lathika (32:46)
I think that is a simpler evil that it is doing. I think the biggest impact is the fact that it is limiting the creation of new jobs. For example, when the Infosys or Wipro or IBM came into being in India, it created a workforce, it became a billion dollar business, but it also created a workforce of 200,000, 400,000. So the income got distributed to a larger population.
But now companies which are already talking so 5 million dollars, 10 million dollars that wealth is getting consummated in one or two individuals because that’s just a one-man company or two-man company. They don’t have a hundred-member team or two-member team, they don’t have it. So in that because this change is happening, how much, we should also understand when we study the average tenure of
working closely today in a company. I think it’s around three years, three, three and a half years. So considering all these factors, companies also will start questioning. If AI is going to do everything, if I already know that my employee today is not going to be there after three years, they might always question. And we are always treating learning and development initiatives as an expense and not as an investment. They’re used to do that. Then companies will start thinking,
how much am I going to spend on this learning interventions? At the same time, from an employee, they will also start thinking, if I know that, irrespective of whether I do 70 hours a week or 100 hours a week, I sacrifice my personal life for my company, but the company may actually let go of me just over an email or maybe even a tweet at some point of time. So how much of that commitment is there? So I need to take care of myself. Therefore, I learn what I believe is good for me
in my understanding today. So I shift, I do whatever I I think that is a shift which is happening. So that shift cannot be managed unless you are very transparent of in your communication because I do believe employees are smart. They are very vocal. They may not always show it out, but they are smart. They read things even before things are made public. So I think if you create that culture of trust and transparency, perhaps you’ll be able to manage that shift. But shift is already happening.
The second part is that if a learning requirement comes, the challenge for the learning and development team or the initial act is to list a possible set of mentors who can deliver that research objective and what are the courses that they will do. So it is always a transactional, it’s always a project. There’s a start and the end. But what we need is to move away from that mindset to create a system which is self-servicing.
So you are moving away from the role of just curating a set of courses to your team, to creating a system where there are tools, there are workflows, there are communities through which you can work. And also, you are creating accountability for yourself. In the old way, you are supposed to learn, say, Python or Excel. So how are you measured on the efficacy of that training program?
how many people did complete that course? How many people cleared the assessment with 80 % score? Which doesn’t really work in real life because everybody wants to know what’s the impact on my top line or my bottom line. So that transition needs to happen. And I think when you move from a course to a system, creating or curating a system, I think we’ll be able to see how much of the learning interventions can be
lifelong continuous and therefore create impactive business and therefore become more sustainable. I think that becomes very important. The third thing as I said earlier, everybody has knowledge today, it is available on the click of a button now. So then what is your role? Your role is to therefore critique that response, your role is to judge whether the response is relevant to you.
And how do you build that ability is very much have actually.
done that in the old way for say five, 10 years. Now somebody who’s entering the workforce, how do they do it? They never got an opportunity to do that. So there probably if you you like to spend weekends to do that extra projects, do that volunteer work so that you can build those domain expertise and therefore build that intuition which will lead to judgment. So that’s the shifts that I’ll stop.
Jason Gorman (37:20)
These shifts are profound. They’re going to be difficult for us to navigate, I think. and I don’t think that we have a choice. I mean, I I I appreciate how you’ve unpacked these and the things that we’re gonna have to think about and re-reorient ourselves to. And what is so striking to me is that
These things will happen to us, right? The I don’t know, I don’t know what the phrase, like the the horse is already out of the stable. Like there is no way to stop this from happening. This is going it is it is happening, it will happen. And I think the more that we can understand where we are right now and what the future has in store for us, the the better the more time we can give ourselves to prepare for the future. So I appreciate your framework here. I free I appreciate how you’ve broken all of this down.
Adarsh and it has been such a pleasure to have you on the show. I really appreciate you coming on.
Adarsh Lathika (38:13)
Thank you so much, Jason. I really enjoyed ⁓ taking it through the thought process as well. Thank you.