AI-First Approach Delivers Million-Dollar Savings

Introduction

Nolan: Welcome, everyone, to the Learning and Development Podcast. As always, I’m your host, Nolan Hout. This is sponsored by Infopro Learning. Joining me today is an incredibly special guest, Sriraj, who is the CEO of Infopro Learning. For over 30 years, Sriraj has been a catalyst for change in the organizations he has been a part of. Early on, whether he was advising Microsoft on how he was using products in innovative ways that they didn’t even know about, or most recently, where he’s making some pretty big waves in learning and development, which we’re going to unpack here in just a minute.

Today, we’re going to discuss a range of topics, primarily focusing on the concept of AI. However, instead of discussing the application of AI, such as how we’re utilizing it, we’re actually going to explore the real-world results of AI and how these investments in AI have been paying off for organizations, not just Infopro, but for many of our clients. I’m excited for this podcast to begin, and I’m sure you are too. Sriraj, welcome to the podcast.

Sriraj: Thank you, Nolan. Thanks for having me.

Sriraj Mallick’s Journey from a Technologist to CEO of Infopro Learning

Nolan: Before we begin, we always like to start with a little bit of an origin story, wouldn’t you, Sriraj? So, if you don’t mind, I mean, you’re the CEO of Infopro Learning. You obviously didn’t start your career there. Where did you start, and how did you get to becoming the CEO?

Sriraj: I started my career out of college. I was an entrepreneur and founded a startup, primarily in the technology sector. I was a technologist, developing software for large organizations in the area where I was operating. Back then, it was in India.

And soon I grew out from that area and joined a large organization which was again in a role of setting up their e-governance practice in India and slowly moved up the corporate ladder and had different opportunities to lead different functions that gave me the spread of understanding businesses not just from an engineering standpoint but also through different functions like sales and marketing, finance.

And so, I was spearheading an acquisition we made at some point, which provided me with valuable lessons on establishing new businesses. This experience came into being because of my past experiences. So, I have been a builder since day one, and in every role I’ve had, every opportunity I’ve had, I’ve utilized my builder mindset to grow that business. And then some eight years back,

I happened to be in this conversation where the parent organization, the major investors in Infopro Learning, were looking for a new CEO, and I felt like there was a time for the HR and talent development space to get disrupted. I really wanted to have this experience and apply some of my expertise, as one of the things that drew me to this space was my conviction and purpose of unlocking human potential.

And so, I built my business, I built my team members and myself applying the same purpose. This company gave me that, and it fired up my passion. And so that’s how I became the CEO of Infopro Learning.

The Leap from Tech to L&D

Nolan: So, did you find yourself before Infopro Learning? You mentioned your engineering and technical backgrounds. How did you feel going from possibly one end of the spectrum to the other end of the spectrum from like a people, you know, like solution you’re solving or me, or maybe the intersection is a lot tighter and there wasn’t much difference, but what was like that leap for you to focus more on the learning development training and versus the heavy tech side?

Sriraj: It was a big change for me in terms of being in the digital or the tech space and then moving to the HR and talent development space. It was a big change. However, the role was also a step up from my previous one, and that was a challenging requirement. So, I felt it was time for me to challenge myself and utilize all the capabilities I had developed. This role would help unlock my further potential as a business leader. So, I took on this role, but most importantly, my calling was driven by the fact that this company was built on a foundation of unlocking human potential.

Therefore, everything we do, both then and now, with our clients and employees, is based on this foundational philosophy of helping unlock the potential of the people we work with. The services and product line we have at Infopro clearly address that purpose.

Unlocking Potential Through Shared Vision

Nolan: Yeah, and when you look at unlocking that potential, is there like a core behind that, right? Many people believe there are various ways to achieve that. They go about it in different ways. Is there a core belief that you or Infopro Learning may have that aims to unlock the potential of people? Here’s that trend line; here’s what we focus on as an organization to ensure we are doing that.

Sriraj: Yes, I mean, if you look at it from a systems perspective in an organization, the HR team, specifically the talent development or the learning development team, has a specific mandate to develop their people, right?

All the services that this organization provides, from leadership development to coaching and skills development, Infopro Learning offers these services on a global scale. This is the connection between our purpose and the services we offer.

AI’s Impact on Learning and Development Costs

Nolan: So, one of the reasons that I wanted to have you on was this major press release that just came out, that you penned and this, so August 6th, for those that, that, when this comes out, where you are promising a 30 % reduction in learning and development costs while simultaneously expanding learner a reach. I mean, that’s a pretty lofty mission or promise, I guess, if you would perform, you know, guarantee.

Could you tell me a little bit about how that came about? For instance, we’ll unpack how we’re living it, this, that, and the other. But how did you come up with the idea of putting this into the world? That’s a big, big thing to put out.

Sriraj: That’s right. Actually, two years ago, when we began experimenting with AI, machine learning, and similar technologies, we realized that they would have a profound and disruptive impact on our industry, particularly on our business. So, it came across as both a threat and an opportunity.

And so, what we did, Nolan, was we said, if we go in this direction, we have to almost think about cannibalizing our own services, right? And we’d better do that ourselves and start learning how to really utilize AI in our workflows, reaching the same level of quality and, in fact, higher capability, and pass on any savings that we gain as a result of that.

Intelligent Design Framework (IDF)

Nolan: So, for those that don’t know, Infopro Learning is a professional services consulting organization. So, they work with large organizations to develop custom learning and training solutions. Think, you know, kind of bigger, larger transformation projects. So, I think when Sriraj is saying, you know, cannibalize our business, he’s saying, well, AI is set to take a lot of the work that we used to do. I want to add some context.

Sriraj: Yes. Thank you. And so that’s what we started forming this task force, and we started having this task. We understand the trajectory of impact we can expect to see in two to three years. And now I reflect, I think we were still not as ambitious in our vision as we should have been, despite making significant progress.

And simply because just the strides AI has taken in the last six months or every month, you see the capability of the models and what they’re able to do is increasing dramatically. So, going back, we set up some task forces that were tasked to look at our workflows, the major workflows in our organization, when we produce a service like training content and think about how we can use AI in this workflow so that we can,

Really amp up the quality, create an immersive learning experience, design a better product, and do it at a much lower cost. So, that was the remit given to the task force, right? And then, we had a delivery model, and the result of what this task force did and the innovations they introduced through our delivery process is what we call the whole new framework, the Intelligent Design Framework. We are now moving on to the second iteration. We are at IDF 2.1 now. We recently launched version 2.1, which is a set of processes connected to the AI tools we have developed over time. These tools help us, from demand planning for learning to the intake process, where we act as a performance consultant. We design new learning programs and learning journeys.

And all the way to structuring a storyboard. Essentially, we delivered high-quality work on the design side. Then came production, where we had to do a lot of media uplift. And so, as you already know, there are hundreds of tools out there that create synthetic media, right? And so, we were able to utilize some tools and work very closely with those product teams, right? To share our understanding of what we needed from a learning standpoint. These were very early days. We also help them build new features in those tools, which we are now using for our clients, thereby significantly reducing our effort to deliver high-quality learning programs.

Nolan: Right. And I think one of those tools that you’re a partner with is Colossyan. Is that like an example of one of those tools that you’ve been able to leverage to reduce the time costs associated with an asset that used to take a lot longer to make?

Sriraj: They have done a fantastic job. For example, they developed the avatar concept that we frequently use in the learning world. They also released their video-based branching scenarios. And so, we create many branching scenarios. It really helped us elevate an average learning experience to something more immersive and interactive at a significantly lower cost.

Leveraging AI for Enhanced Learning Experiences

Nolan: So, those are like front-end, right? As you mentioned, you’re taking script writing just as a really simple thing. But the whole storyboard process sounds like. You’re building a lot of AI automation. Additionally, there is the production of the asset, which we have already discussed. What about the non-fun thing? Like the not-pretty pictures, things like that.

Have you been able to leverage much AI, or maybe it’s more automation on the process side of things? I’m assuming Infopro has around 500 instructional designers, a large number of instructional designers. How are you leveraging AI to keep the gears spinning behind the scenes and driving efficiency?

Sriraj: So, when we came together, the good thing about AI is that adoption has been really high. Typically, with technology, adoption takes some time. We didn’t limit ourselves just to a design or synthetic media. We examined every step of our value stream for each service. We examined the roles of deterministic and generative systems. Therefore, wherever we utilize AI, such as for generating ideas or synthesizing our frameworks with client content, we ensure that we have robust guardrails in place to prevent the use of content that we should not be using. We hold the privacy of client content.

In a very strong way. So, we had to secure everything that we are doing. Even when we do so, we still have many of our current workflows that operate within traditional models, as some of our clients are in highly regulated industries and have not permitted us to use AI at all. So, it’s a bit of that transition, but we have been utilizing AI use cases across the board in every element of the learning and talent development life cycle.

That comes to my mind, Nolan, many companies are looking for personalized and adaptive learning experiences to find a learner where they are and start from there. We are helping them build and utilize AIs in the tools they use, allowing them to craft journeys that are highly adaptive and personalized.

Outcomes of AI Adoption at Infopro Learning

Nolan: So, from what I understand, at least I’m capturing, with Infopro, by the nature of the work being done, so many different, we probably have more instructional designers, a bigger learning team than the clients that we work with, by the nature of that being our core focus. How, to the extent that you’re comfortable, what if, the results of this, right?

I think you mentioned earlier that our strategy is to really understand it before helping our clients better understand it. What kinds of benefits has Infopro Learning seen by adopting these initiatives, such as the IDF 2.1 that you mentioned, and the partnerships with Colossyan? What are you seeing as a result of this?

Sriraj: I think one of the challenges that most organizations face when they are large teams like we have is that we struggle to find a consistent approach to a quality deliverable. Every person is at a different stage in their life cycle, right? And so, it happens over time. What we did with AI was, regardless of where the person was, we were able to lift everyone through certain standard outputs that we created. For example, when designing a learning journey, we created a model that enables it to act like a performance consultant. As an instructional designer crafts a learning journey, it becomes a sort of thinking partner and a nudge.

Did you think about this, or, huh, if this is the use case, how about these three things? So, it becomes like an ideation partner. Ultimately, our employees, whether instructional designers or learning experience architects, must make the final decision. We’ll never allow an AI-produced work to go out anywhere. What we did differently was create AI.

Almost like a co-partner who is an assistant that is helping us reach higher levels of quality. Actually, I could also use the phrase ‘unlocking the human’s capability faster and better.’

Nolan: Yeah, I heard somebody say recently that they think of themselves as the CEO and encourage their team to think of themselves as the CEO and every AI tool they use as their employee. And so, I have an employee who does my script writing. I have an employee who does my media production. I have my employee that does this. Because when you start thinking that as a thing, you know,

assistance, co-pilots, co-agents, whatever it is, it really, you start to realize there’s a little bit of ownership there, okay, I’m giving my colleague this task, but before I submit it, I have to do it. As a result, you did it at Infopro Learning. I’m assuming the natural extension from that is working with these other leaders of learning companies.

Has Infopro Learning been partnering with other agencies? Because you mentioned cannibalizing. It’s an interesting thing. Are you afraid to go to the market and share the secrets with other L&D leaders? Or are you realizing that if I don’t teach them, somebody else will? Could you tell me a little bit about how that maturity has been, and perhaps some lessons you’ve learned or some things that similar leaders have shared with you?

Collaboration Over Cannibalization

Sriraj: Sure. One of the best ways of unlocking the potential of others is to give and share freely. And that creates a loop where you learn more effectively and efficiently. That has been the philosophy. We share everything we learn and create with our clients and other learning leaders. And we learn from them on a day-to-day basis. We learn about their challenges and what would not work for them, as well as what would work, to provide an example.

The area where this really started coming together is learning leaders, specifically in large global organizations. They are also facing this challenge, three major challenges. The half-life of skills has reduced from what was once five years to two and a half years, and it is even shorter now. And so, there is this continuous churn of skills development, and skills in organizations are their competitive advantage.

So, the leaders are facing that as a challenge. And then they face the challenge of managing a large learning team, which handles learning administration and oversees hundreds of other tasks that are non-core to their business, thereby consuming their energy, effort, and many resources.

Now add to all of this the disruption that AI is causing, right? So, when you think about this, this is a time of perfect disruption that a training or a learning leader would face. Additionally, due to the economic uncertainties that organizations face, there’s a significant emphasis on optimizing efficiency and reducing costs within these organizations.

Where all of this started coming together is that we began solving this problem for our own company and business, having built these tools fairly early on and given them time to mature. What happened is that with that maturity and our capability in designing new operating models.

The ability to govern an end-to-end global infrastructure for running learning programs enabled us to integrate all these new tools and technologies into the entire life cycle of what we now call a managed learning service. Right from your demand planning to designing programs to managing hundreds of trainers and, in fact, thousands of trainers and vendors across a library of content and different kinds of training programs,

It is online or in person, and so on. Now that we had done all of this and it had matured, everything came together. What we found was that in every program we implemented for these organizations, we were able to save at least 35 to 40%, and sometimes even more. In fact, we have examples of much more than that, but at least that, right? And that was phenomenal.

Holding Strong to 30% Savings

Nolan: So, that the 30 % is like the, you know, if we’re able to spend, save 40%, I’m willing to stick my neck out there and say 30 % because I am confident that if the company does invest at this type of scale, 30 % actually is the benchmark that I’d want them even to hold me accountable for.

Sriraj: In fact, a week back, I was in this business review meeting with a major energy organization, and we are running their entire managed learning services program. Six months in, we have already saved over $3 million.

We have a projected trajectory of saving at least $7.5 million by the end of the first year. One of the comments I received from our customer was that they expected any other partner to have taken 18 to 24 months to reach this level of value unlock, which Infopro Learning’s team achieved on this project. So, it was a very proud moment for all of us.

From Cost Cuts to Capability Building

Nolan: So, how are you advising, and one of the things that I talk quite a bit about when I’m consulting organizations on AI is this idea of you save this money, or you, I should say, you save the production costs, but you want to reinvest that, or I guess the question, I usually say don’t look at it as just saying, hey, I no longer need this money to run my business, I more say, use that money to expand your reach so you’re able to do more with less or do more with the same.

How, you know, and this client or another client, how are you saying, take this cost savings that I give you and reinvest that into a way to expand your reach or to do something else? Or are most companies being forced to cut that cost, and in doing so, do the same with the reduced cost?

Sriraj: I think the latter is what most companies are going through right now. There’s a mandate from the CFO for the organization to reduce costs. However, what we have seen is that they still want to ensure the internal talent is well utilized in other parts of the business where they are scaling, right? So, we have been able to see a lot of that. We have also ensured continuity, which includes the knowledge that organizations gain by rehiring many employees. We have a model that allows us to rebadge some of them and then train them on our tools, providing the same level of Infopro services that they would offer. That has also worked out nicely for us.

The 30% Savings Blueprint

Nolan: So, if a company is, if somebody is listening to this and they’re thinking, well, gosh, that sounds great. What is the path to saving 30%? How does somebody get from this idea, this concept, to reality? Like, okay, Infopro, let me take you up on this guarantee. How does that work?

Sriraj: That’s a great question and before I answer that Nolan, I want to add couple of other use cases so everyone, all the listeners understand it’s not just one-use case in terms of training design or development but in a training organization, there are hundreds of trainers and you will see almost, there’s a lot of inefficiency in training the delivery component of it. Sometimes the classes are not optimized. The number of participants in the classes is not that high; it is much lower than that.

Yeah, exactly. The trainer’s schedule is not optimized. So, when you look at it, we have something; we’ve built something called training efficiency forensics. It has 15 parameters to intake the data points of your training, regardless of the training you’re doing within an organization. We utilize an AI tool to run analytics and identify gaps in 15 key areas. We refer to it as the forensics approach to create optimized efficiencies, or optimization, which becomes one of the strategic drivers for reducing costs.

And examples like this are used in every aspect of running a training function. So, in effect, really making them inefficient, but then using that, not just leaving it there, because we come from the talent development learning background, sort of lifting the learning experience, making it personalized and adaptive, right? And ensuring that we focus not just on completion rates, which should increase dramatically, but also on the outcomes that this training program will help businesses achieve. We are laser-focused on those areas and provide our customers with the entire analytics infrastructure to achieve ROI in those areas as well. Coming back to the question you asked about how they do it, what are the steps they should be taking to do this?

Usually, these programs are large-scale initiatives that require long-term commitments, and the first step is typically a business case workshop. So, what we do, Nolan, is we have a team that goes into this discovery process. It’s a one-week process, which takes one week, but usually involves two or three interviews of a couple of hours each, where we capture, at a very high level, where this organization stands in our maturity curve, in our maturity model, from a learning design and development standpoint.

Based on that, we can create a business case document with a hypothesis about what we understood and what the future state could be, as informed by these interviews. And then we create a roadmap and provide a financial commitment that, given your current spend and the infrastructure you’re running, the deliverables you have committed to your business, and the promises you have made, if we were to undertake it.

Here is the impact we will make, and this is the new cost. That gives them an idea of how we start the process. We have a relatively straightforward framework for adoption, starting with the pilot. Typically, customers undergo a contracting process, and then we initiate the transition plan, which, in these organizations, is one of the most important exercises. So, we’ve built some blueprints that allow us. Do a risk feed transition. And so, from there, we conduct some shadowing, and then we proceed along a path that leads to a steady state in this process.

Understanding AI Risks and Data Security

Nolan: So, you mentioned risk, and I think that’s a big takeaway that I hear a lot from people. It’s changing, it’s interesting. I remember talking to a gentleman two years ago who had been on the podcast a couple of times, and he said, ‘Listen, I’ve just been told I can’t touch AI.’ Nobody in the organization can. It was a big financial institution. He just said, listen, it’s a complete no-go. But then I talked with him six months ago, and he’s like, yeah, you know, they’re, they’re now starting to drive actually, um, that, but I think that’s one big area. I think.

I think some people, you know, synonymize AI with risk and kind of view that as a, well, if that’s where this is coming from, if all of my cost savings are coming from AI, then maybe we’re not the right fit. What I’d love to hear from you is one thing: how risky do you think AI is, particularly for your clients? And just in general, right? How big a risk is it, and where do those risks live? I think that’d be like my first question, and I’ve got a follow-up on that as well.

Sriraj: I think data security and privacy are very important elements. One of the concerns when using AI is that we are very cautious about not using clients’ data in a large language model that exposes them to privacy issues. Very strict. We have gone through multiple audits. Our clients have approved wherever we use it.

But it’s not just the use of AI that creates these efficiencies. AI is one big enabler. For instance, another use case I mentioned is training efficiency. Well, we’re using AI more like an Excel sheet to crunch numbers for some analytics, right? That does not create privacy, as there’s no content being sent to the LLM.

What I believe customers really fear is that their organizational data will be leaked. We must be very clear that that’s a no-go area for us. We work with the top five pharmaceutical organizations, banks, and manufacturing companies, which have high compliance and data privacy standards and risk tolerance.

So, we recognize this as a significant risk, and we ensure that we validate every step of the way, obtaining approvals. When there are absolutely no-go areas, we will not use AI in those areas.

Nolan: Yeah, one of the nice things, at least, was that I was doing a webinar yesterday, and I was demonstrating Notebook LM and some of the power that it can do, which is a great tool. And someone had mentioned in the chat, ‘Is this a secure tool?’ And somebody immediately was like, No, no, absolutely not. And I was like, well, it is. And so, I was thinking, reflecting a little bit more back on.

Okay, this person was like, ‘No, you can’t; it’s highly insecure.’ But then Google has been fairly big on, even if you use the free model of Notebook, we’re not using any of that. We will only store the data you provide so that you can reuse it. We’re not training our models on it. We’re not doing so, I do think there’s also some misconception of it.

But I think there’s also the idea of working with a client on the difference between, well, this is protecting your data, but if it’s not a part of your ecosystem, then it’s as unprotected as you storing, you know, I don’t know, credit card details on your laptop, right? There’s an inherent risk to it, in the sense that someone could crack your Gmail password if you sent yourself an email with your customer list. So, there’s obviously inherent risk, but one of the things you said, which I think is apt, is.

Advancements in AI and Learning Technologies

Nolan: I did this, like a fireside chat with a couple of other people. And was on the idea of how we work with vendors to improve efficiencies? And it wasn’t really on AI. It was really more about the things that you’re talking about. And I remember a conversation with a lady who was the CLO of a smaller company very vividly. She had a PhD in adult education principles.

And she’s like, ‘I spend five hours a week managing my LMS as the LMS admin.’ She’s like, I must be the world’s most expensive LMS admin. And so, I see that I didn’t guess she was on PayPal. She’s like, just doing the same thing. She’s like, I spend so much time just doing these things, and I feel so bad doing it because, and I think for me, when I look at my adoption of AI personally,

I’ve actually realized that’s where the big gap lies: my ability to think strategically and use the tool to drive the business has been magnified with AI. And it’s actually also helped me understand that there are things I should no longer do, because I can be a magnifier at the top, and everything I do at the top has much bigger results down at the bottom. And so, yeah, I do think.

I think organizations are becoming a little more mature in their approach to where they spend and what they want their talent to be. Do they want to continue building roles in their organization that don’t have a high growth pattern? And it’s like a colleague of mine, a gentleman I met at a tech company. He said, ‘ I’m outsourcing this role to you because I don’t have a growth path for this person. ‘

And I was like, that’s a phenomenal way to look at it, you know, I don’t have, this is such a small department, I’m not going to be able to take that person and bring them into here. And so, if it’s not strategic, and if I can’t provide an incentive for this person, then it doesn’t work for me. So, I’m sure that there are tons of efficiencies gained outside of this AI realm, you know, as well. Where do you think so?

Nolan: You know, your Infopro is kind of at the edge of AI. You’ve mentioned that you’ve invested in a kind of transition for the company over the past two and a half years. So, you have a bit of a leg up on some of the others. What would you say the biggest advancements have been so far for you? Maybe you personally, or perhaps as an organization, that’s really surprised you and been like, ‘Whoa, I didn’t think that.’ What do you think will be coming in the near future that you’re excited about?

Sriraj: There is a lot to unpack. As we began developing our capabilities, we recognized the dramatic shift in the future of humans and AI as they started working together. Today, we are considering it in the context of being a human being and an agent. Soon enough, you will see multiple agents working with a human being.

The result of that work is dependent on the quality of context you provide. So, we at Infopro have realized it’s not just your AI infrastructure, the LLM, the chat, the agents, or the prompts. It is about the infrastructure that we need to build to provide context to AI agents, ensuring they don’t hallucinate and deliver precise results for various functions and roles that employees perform in organizations.

If that were not to happen, then the adoption of AI would be limited to tasks that a human being cannot perform. It’s still a very good place, but people who are looking for precision, correctness, and high quality that aligns with their organization’s goals consistently.

They will lack trust for those kinds of workloads. So, for workloads to be truly successful, organizations must start thinking about context engineering and framing their vision on how they will provide the right context to agents. We are currently doing cutting-edge work in this area at Infopro, as we are the enablers of how human beings and agents will work together.

Another area we believe will be very disruptive is learning, as we know it today, whether through online classes or in-person training, facilitated training, all of which require content to be created, case studies to be developed, and experiences to be prepared beforehand, right? And then consumption occurs either through a learning management platform or by attending a classroom session or a Zoom call to access the content.

One of the problems in this design, which we have traditionally been solving for, is that once you make it, it’s not personalized for each individual, right? However, upon closer examination, it becomes clear that AI can create training on the fly when an employee needs it customized or personalized to their proficiency level for a particular skill.

So, that’s a big advancement we are seeing; we are working on creating some of these learning programs on the fly. We are also seeing that it’s not just the chat interface that is limited; it’s also the interface itself. Over the next few years, we can expect the user experience for consuming learning to expand beyond chat. In fact, the new user experiences will also be generative in nature. Now, employees and learners can engage with more interactive, engaging, and scenario-based learning.

On the fly, we created user experiences like those we built on a rise, articulated a storyline, or synthesized, etc., etc. You know these tools. So, we see that coming in the future. I see a lot of work we are doing on coaching on the job, coaching, and performance support. We see a lot of precision in that area, especially in use cases such as onboarding or leadership, including conflict resolution.

Or giving performance feedback to someone. If you’re going into a meeting, you need instant coaching, and there you go. And so, for all these to happen, Nolan, we have realized that context is the oxygen that will allow this infrastructure to really thrive. And that’s an area that we’re all trying to.

The Future of Human-AI Collaboration

Nolan: I was reflecting on a product that we helped launch, which was the leadership coach. And I was talking to somebody about the error rate of that. Right. And I said, listen, take anybody who’s been trained on anything, right. Blanchard trained consultant.

They pay whatever money they have to do. They can now do the Sandler sales training. You hire somebody from Sandler, and they become your sales coach. How many mistakes do you think that person makes in their life? How many times do you think they quote the wrong thing or give the wrong thing? How many times are they out of the office? How many times do they have their own agenda, or do they show up wrong? And what does that lead to? And I was like, you know.

Hyper-Personalized Coaching Without Bias

Nolan: With ours, you mentioned context; we’ve put the equivalent of that in. Now, let’s put our own content into this coach. Now, yeah, I am sure that there are times when the coach might get something wrong. Still, I would guess that it gets it right more often than a human would, given its ability to avoid bringing in other conversations, not to engage in all these other things, and to be hyper-personalized for me.

Because I’m now engaging with that, what do you think about, like you think that’s right, or think that’s wrong?

Sriraj: Yeah, in some cases, error rates are like a no-go. If there is an error in some functions and some industries, you can’t live with that. So, even if a user or employee generates an error, it’s easy to deal with; you have systems and processes in place to address it.

What we have not built is the new design and the process to deal with an agentive error. So, you are seeing this whole movement where the time you were investing in design comes down, but the time you’re investing in validation goes up. So that’s the whole area of trying to create new processes around it.

Nolan: Yeah, absolutely. And I think that’s where I was talking. I think a lot of us are in this mode where we see. You know, it’s like me, I have a very old truck. It’s got 200,000 miles on it. I want to buy a new one. However, the cost of buying a new one is significantly more than what I can trade my truck in for. And so, the gap is so wide that it actually prevents me from taking any action. It’s going to take something catastrophic. I like my truck to break down, and before I get off my butt and spend whatever I have to do for a truck, and I think a lot of us in the learning space are like, know that’s where we have a vision of where it’s headed in two or three years. It’s not quite there, I’m not quite ready to be there yet. So, what do I do in the intro, right?

Do I keep creating rice corn? And what I tell people is, ‘Listen, you’re already going to create rice courses for the next two years.’ It’s, you’re going to, that’s what’s going to happen. You’re your storyline; that’s going to be the case for the majority of us. That still has to happen. Now you have two choices. You can either spend $100 on it or $20 on it. Would you rather spend $20 over the next year, save yourself some money, and then invest in the new truck? You know, I tell people not to get so caught up in whatever move I make now that it has to be my move in the future.

There’s nothing wrong with making a quick decision and saving some money upfront to help fund that investment down the road. And I wanted to answer my own question of where I think the big leap in AI is: being able to leverage AI as a thinking partner.

I think it was a watershed moment for a lot of people, as they, when they’re at their various stages of adoption, it’s when you go, and I’ve seen it actually in the eyes of my team, when I opened up Claude and I said, I don’t know how to do this. Please give me a recommendation.

In their minds, they were thinking, ‘I just thought we did that to create a picture or to create an image or create something.’ So, for me, and I think for all of us as we start to use it, once the model was sharp enough, actually, to help me think and get me to my destination, that was kind of a watershed moment for me.

Vision and Commitment Moving Forward

Nolan: You know, before we end the podcast today, thank you, Sriraj, for investing your time with us. Is there anything you’d like to leave with the audience? You know, we started by talking about this commitment to the industry: if you work with Infopro Learning, we’re going to save you 30% of your costs. We’ve covered the journeys of not just AI, but also those in general, such as L&D operations. Do you have any final words of advice for our listeners and the other L&D leaders on the show?

Sriraj: Yeah, I think like all of us, the talent development space, who are day in and day out thinking about skills development and talent development, they are facing tremendous challenges, disruptions, and opportunities. I think that just being out there, experimenting, trying, and trying again will lead us to some amazing solutions, and then sharing them with others who have worked with us.

What we can learn from you is an amazing opportunity for all of us to level up. I think that if you look at the last few decades, you can characterize them as huge technological advancements for enterprises, from data and analytics to cloud, and all those positive developments have occurred.

I believe we are at a stage where the biggest step change for enterprises and their competitive advantage lies in solving the equation of human plus AI. And if we can solve that, and work towards that goal, helping to unlock the true potential of our employees and learners, that would be a great future to participate in.

Closing Thoughts

Nolan: Well said, and I’d be remiss if I didn’t end now that we’ve covered all of this serious stuff. On a personal note, those who don’t know, Sriraj, you have unlocked my potential. This podcast was very much your idea, for saying, know, Nolan, this is something that you’re good at. I think you should see what you can do. I think you should start this channel and see who you can get on and have these conversations.

And so, I want to thank you for unlocking my potential with this. We would have never had this podcast with thousands of listeners and followers, everything like that. I feel like this is a real full-circle moment for me; you’re pushing me to unlock this potential within myself. So, I want to end by saying thank you for that. You’ve been a great mentor for me along the way. All right, we’ll talk soon. Thank you.

Sriraj: Thank you, Nolan. That’s a nice thank you. Thank you, Nolan. Bye.