Episode Transcript
[00:00:06] Speaker A: Hello and welcome to the Axiom Insights Learning and Development Podcast. I'm Scott Rutherford. In this podcast series, we focus on driving organizational performance through learning. In this episode, we are looking at the intersection interaction of artificial intelligence and human intelligence. How the technology and human factors can work together to support business growth, productivity and innovation. All those good things. My guest is Dr. Guido Manaya. He is the CEO Chief Learning Officer at Manaya Learning Global Solutions, and he has many years experience solving learning challenges. Guido, it's great to have you here.
[00:00:42] Speaker B: Oh, thank you so much for having me, Scott. I look forward to our conversation.
[00:00:46] Speaker A: So there's no ignoring. I think everyone understands the impact of AI. You simply can't ignore it. And it has, I think, universally understood. There's a lot of potential to change how we interact with information, how people work. So looking at, in workforce development training, what have you seen the impact of AI as we stand here today?
[00:01:10] Speaker B: Sure, and I think that's a great question. And when we, when I think about this, you know, I look at it both from my own lens as well as, you know, the research that's available.
[00:01:21] Speaker A: Right.
[00:01:22] Speaker B: So from my own lens, we moved down this path as an L and D services provider over a year ago, and we looked at it in, in terms of how could AI reframe some key roles within our organization like instructional design, how could it reframe that? So we looked at what tools were available, what might be possible, and our instructional designers went down that path. Then we did the same thing with our facilitators. What could our facilitators learn if they had a better grasp of AI, what could they learn that could help them really make them more productive? And so all of that was most helpful to us as we went down this journey. And most recently, I began a program with UC Berkeley on AI business strategy and implementation. So looking at it from a leader's lens, how do you identify those initiatives where AI could greatly benefit your organization? And then how do you develop a strategy and then implement? So we've got 300 people in my cohort from all over the world, and it's just exciting to see whether they're. We have one that's a surgeon, we have folks that are in every industry and how it's transforming their organizations. Really, I'd say that ChatGPT really propelled us into adopting AI as this transformational technology, as an LLM. ChatGPT and others really assisted us in that, in that conversion into this new world and their role in content generation and automating training from an L and D perspective is incredible. But I think there's much larger opportunities that will impact L and D as these technologies become more familiar to us and as we begin to, to, to. To leverage them. Right. So as an example, AI powered learning platforms, LinkedIn is using it, LinkedIn Learning is using it, Coursera is using it to personalize course recommendations again based on past learning behaviors and AI chatbots. So a24,7 learner support services are being utilized by Duolingo and others. And so you're beginning to see not just how the large language model can assist us, but now how can these other AI powered solutions help in the corporate learning and development higher ed as well as K12.
[00:04:31] Speaker A: Right. Not that recommendation engine view of AI I think makes a lot of sense. Just understanding that if AI's strength is being able to find patterns and to predict what comes next in a series based on a very large data set, then recommending what might be a logical next step in a learning course or learning sequence that plays into its strengths, doesn't it?
[00:04:59] Speaker B: Oh, absolutely, absolutely. At your ability to now personalize, automate, enhance the learning experience in ways that we're just really beginning to explore. So that, and so I mean even like PwC's virtual coaching with AI is leveraging a large language model, but in that case it gives employees real time feedback on communications, leadership, negotiation skills, helping them improve soft skills in a structured way. So and of those that participated in the program when the measurement was taken, 92% felt that it significantly improved their capability set. So you're seeing very, very innovative applications here that may be a little bit on the cutting edge for those large companies. And, and now how is that going to cascade down to all size companies? And I think that's the exciting part.
[00:06:03] Speaker A: Yeah, and you mentioned coaching, which I think which leads me into. I wanted to ask you more about the work you're doing at the intersection of human intelligence and artificial intelligence. Because coaching for the last many decades has been an intensely human process. It's involved, you need an expert listener, facilitator, asking the right questions, trying to counsel and support the growth of another person in a one on one coaching environment.
Putting an algorithmically driven coach into that I think a makes people a little bit nervous. But also the foundational question is can AI be as good or better than a human?
[00:06:56] Speaker B: Well, let's see. I think it depends on the context and I think a good analogy it would be, if you recall one of my favorite movies, Good Will Hunting and there's a scene in there where, again, Matt Damon and Robin Williams are having a conversation. And Matt Damon is just providing so much context around things that he's, that he knows. And Robin Williams initially was upset, but then he comes back and says, you know, you've read all these books, you can tell me exactly when this happened, you can tell me the location, but you didn't live it.
Now, I don't have any concern with you because you didn't live it. You didn't feel it, you, you didn't live it. You didn't. You didn't really understand it. You know the facts. But that's it. And that's really what AI is, right?
Can read over a billion books and, and be able to, to summarize anything. But in terms of the empathy, the creativity, the innovation, that can't be done. And that, that's probably, you know, 50, if it could ever be done. So we are nowhere near where we're going to have something replace a human because it doesn't have that additional context that only we bring to the table, if that helps.
[00:08:22] Speaker A: It does. I think it does build on. So earlier in the podcast series, several episodes back, which feels like an age ago in the evolution of AI, we did an episode with Dr. David Wynn and Judy Pennington of the University of Minnesota and Judy Pennington's advice in that episod. And I'll put a link in the related resources if you're listening to this and want to go listen to that one as well. But what she described was using the AI tool as a brainstorming resource or as a foil for asking questions and building on what you were just saying.
The humanity and creativity comes in the person.
You maintain that control over the creative process, even if you're using the technology as a sounding board, using it for what it's good at, for surfacing fact.
[00:09:20] Speaker B: Absolutely right. It can assist us as we develop a perspective on a question, on a challenge, on a problem.
Great research that we can get from AI. It can help us in formulating some of our conclusions, it could help refine our conclusions. But at the end of the day, we have to select what's really going to be the course of action that we take. And as you're training AI, the AI's ability to respond as a coach, as we were talking about a little earlier, is based on all the information we fed it that will train it. Let's say in Coaching for Leadership, we will feed it documentation, we'll feed it books specifically about leadership, we can feed it conversations that have been recorded that Role play, different kinds of skill sets. All of that it can take in. So that when you ask it, you know, how should I manage my employees when we're going to have a change in salaries, it's going to lower their salaries. It will take all of that context and information and say, here's a way that you can answer that. Right? But it's in essence using all that to predict what it should say and then give you its answer. It may be 80% right based on the information you fed it. And we have to then take that and say, okay, I get that part. And then we add our secret sauce, which is our perspectives as real managers and real leaders.
[00:11:01] Speaker A: So if you're counseling an L and D leader, as I said, I know you do, about how to approach solving skills development in an organization, Part of that is human skills, part of that is technical skills.
How do you advise them to find the right combination of tools and approaches? Using the technology where it works, using people where they work best?
How do you help folks navigate that balance?
[00:11:34] Speaker B: Surely I think that goes back to the experiences that we had in reframing the instructional design role, the facilitator role and others.
Basically we opted to look at it from let's take a real world challenge, a real business challenge that my company's facing, that any company's facing, and build a project based learning kind of curriculum. And as we are going through defining what the problem is, what information do we know about the problem, what information do we lack? So a design thinking approach and, and integrate. Now how could AI assist us in the solution so that that's a more effective way of teaching. You're not teaching an AI tool, you're deep into the problem now. Now what AI tool could assist me in solving this problem? And so the. You're much more invested in that kind of skill development, but you're also collaborating project based learning with other teammates. You're communicating, you are creating and innovating as a team. And those are some of those critical human skills that, that the human intelligence component that also are added to the equation. So while you're developing AI skills, you're also reinforcing or learning those other skills. And then you actually have a deliverable that's based on that kind of collaboration. And there's much more, how could I put it, much more of integration into your own learning through that process than if you just read a quick description on an AI tool. So that's how we're approaching it, through that immersion, that project based approach to developing our Teams and our clients workforces as well.
[00:13:48] Speaker A: Not to put words in your mouth for sure, but it sounds to me what you're talking about is really elevating the qualitative experience of the individual contributors in the organization, the people in the organization helping them perform better to be more valuable people. So they're not necessarily doing things more cheaply or doing things faster. Which are, which are the first go to reasons a lot of people look at AI and technology, well, it's going to be cheaper and faster.
But I'm sort of hearing you say that may not be the point.
Better may be the point. Not cheaper and faster, better, better.
[00:14:25] Speaker B: And how could I put it? You're in a different dimension based on the tools that are available to us, but they have to be applied in a realistic way. So we had one client and manufacturer, probably 60, 70% of their team is less English proficient. And we brought them in with those that are on the front line of manufacturing as well as the leaders, as well as the C suite. And we broke them out into teams and we looked at what is the customer experience all the way from the initial interest to the completed order that's been installed. What is that experience? And the challenge was how can that be improved? So some teams took a look at the sales process, other teams took a look at a process step within manufacturing. Other teams looked at the billing side of it and it was exciting. None of them had a strong, other than the CEO had a strong background in AI at all.
And together they collaborated on their part of the solution.
And it was when we heard the final presentations at the end of five weeks, it was just amazing. And they've actually implemented one of the solutions so far and they're looking to implement others.
And these are from teams that had no clue about what AI was.
It is possible that you can go in, look at it from the lens of a problem that you know very, very, very well. But now you're opening your eyes to see how could AI really help us solve for this. And the solutions, ideally to your point of productivity, might be 5x 10x that kind of employee, but really what you're doing is you're improving to the earlier point of that, that example, that customer experience. Right. And that's, that's what we all strive for with, with our clients and customers.
[00:16:31] Speaker A: I'd like to maybe delve even a little bit more into that example because if you're engaging members of the workforce, sounds like you had sort of everyone from the line to management involved in that discussion. Did that help perhaps mitigate fear about AI being a competitor? The AI is going to take my job. Well, maybe that's the wrong dynamic. Did you, did you surface that concern at all in that project?
[00:17:01] Speaker B: Right. Yes, I think, I think as you dissect the problem. So we'll take one of the issues that was dissected. One issue was that during the slower season of that manufacturer, they had to project. Okay, what kind of materials do we need?
All right, so that projection takes place, then high season comes in and they may be wrong. Right. So they have all this built up inventory of stuff they don't need as much and a need for all this other inventory that they haven't ordered yet.
So this, what they were able to find is if we can get it right, if our predictability is closer to target, we will lessen that. That, that issue. Right. Another issue was that from a billing perspective, yeah, it's okay during slow season but busy season, they were making mistakes. And some of those mistakes took more time for cash flow to come in and they were able to find AI solutions that solved for that.
And again, for many of them, it became okay. I can see how my job is going to get easier because of this. I'll have more time to focus on the stuff that I should be focused on rather than to solve these issues that, that, that ideally take too much time and it isn't really where my, my expertise is best suited. Right. So that's what they found. And, and once they found that, to your point, they felt much more comfortable with how AI can assist versus replace.
[00:18:43] Speaker A: Right, right, right. Yeah. It reminds me a lot of the business process transformation that we used to talk about a couple decades ago with just in time inventory management, when that was a new thing. So we're going back a few decades here, but it was transformative because it reduced carry costs and allowed the organization to operate in a more skinny, efficient fashion.
But within that just in time model of ye olden days was a lot of business analytics work that went into making the forecasts and the projections so they could try to do that. So someone was somewhere using Lotus123 or whatever it was at the time to get the numbers right. What you're saying is we're just leapfrogging ahead now to say, well, okay, we know we don't want to spend more on supplies than we need. We don't want to have inventory, carry costs and all those things that for a business are going to be an anchor on your ability to perform. But saying that, okay, well, there's now a better way to get to that result with using the tool for that forecast.
[00:19:56] Speaker B: Absolutely. And the tools are much more user friendly, so to speak, easier for us that aren't deep into algorithms to leverage and use. And I think that's the beauty of it, that as we really define our problems and then get to know how AI can assist, I think we're going to be amazed at how many problems we can truly solve that gets us to the next level of a company's innovation and capability.
[00:20:30] Speaker A: So that next level of integration, I think is really what. And again, you can correct me if I'm wrong, but I know that within your business you have an AI plus hi, artificial intelligence, human intelligence, sort of focus area, practice area, which if I'm understanding it right, is about elevating that integration of both human and artificial intelligence driven skills pervasively throughout the organization, not just as a point fix. So am I understanding that right? And how do you scale that into a company?
[00:21:14] Speaker B: Correct, correct. Well, as you're leveraging AI more in a effective, complimentary, partnering way, you do have new opportunities. So as an example, many established companies have so much data available to them from their years and years and years of being in business that they, they don't have time to go back and look, they don't have time to see, you know, is there any real important trends here that could help us, you know, predict what we might want to do next.
Now we're able to spend a little bit more time with what's already within our archives, repositories, and how do we now make use of that information? Right. It could be potentially helping you innovate into new services, new products that your data is telling you because your customers have told you about it. You just didn't have time to put it all together and say, wow, now it's crystal clear. So, so one of AI's great benefits, right, is it can review all of that information, synthesize that information and give us insight as to what we may want to do next.
[00:22:38] Speaker A: Right. Just putting maybe a finer point on that. From my own understanding, it sounds like what we're doing is not because there's clean data and messy data, to use very broad terms, but there's quantitative. The numbers are easy within certain bounds, but numbers tend to be easier. But business feedback inputs are not always neat and easy. I think you alluded to customer feedback. Well, if you have reams of customer comments collected over time from customers in various territories, the old way would have been to go through that, and have some analyst go through and code that feedback and put it in data entry so you could analyze it.
AI does all of that.
[00:23:26] Speaker B: Exactly, exactly. AI does, does all that. Now, now it's all based on the, if you're, let's say you're using chat GPT, it's based on the information that's made available to it through the, the large language model that, you know, the billions of books that's available to it through the Internet and everything else. And it, it's, it's, it's arriving at its conclusions with that. Or you can focus its attention and say, all right, I'm going to create a more customized chat. And you're actually telling it you are an expert in manufacturing customer service. Pull all the information just on that. And now I'm going to ask you questions. Not only am I going to ask you questions, but I'm going to feed you 20 years of data from my customers and I'm going to ask you to show me the trends, tell me what, what is trending that I've missed. Right. And it'll take all of that information and from that lens provides you its outputs. Right. And that's when it becomes more exciting. We have to become more and more proficient as to how to ask the questions, how to train the models that we want to use for specific things. And, and, but once we do that, to your point, it does that level of work for us in an instant. Right.
How can you not benefit from that kind of additional help for your insights as a business? As an executive?
[00:25:01] Speaker A: Right. As a business, of course. What's there that I don't see? Tell me what I'm missing. That's a really, really powerful question, but also applies within the confines air quotes of learning and development. You know, we have, we know, we know the training that we're making available to our workforce, to our learner audiences, however we define them.
What are we missing? Oh my gosh, we can ask the same question. We should be asking the same question.
[00:25:28] Speaker B: Absolutely. So like my doctorate was in who Moved My Classroom? And it was based on helping traditional instructors move into being more comfortable in a virtual training environment. This was back in 2005. And even back then we looked at four different corporate universities from large companies and the data that was available from level one evaluations, level two pre post testing, level three behavior change, level four return on investment, all that information sits somewhere.
And someone like, when I was doing my doctorate, I had two or three team members that were helping me go through that data to arrive at some conclusion and were we being effective with the program that we put in place?
Now what's going to be exciting is that level of information that's already coming to companies that they don't know what to easily do with. It can synthesize that. And from an L and D perspective, we had so many recommendations on improvements to courses, nobody really had time to read any of it. All we were, all we had time to do is, okay, that, that, that, that session's done. Let's move to the next one. Let's move to the next one. What's the next program?
Now we can actually go back and really refine and improve training programs like, like we've never been able to before.
[00:26:57] Speaker A: And one last thought I wanted to get your perspective on because, you know, sometimes I feel like AI has been with us forever. It's only been a few years. It feels like forever.
It's been very rapidly. Continues to evolve really, really quickly.
For folks who are not living and breathing it every day, it can feel a little daunting to keep up with it, frankly, and let alone figuring out how you can sort of harness it and use it. So from what you're seeing, is there an approach that you'd recommend in terms of getting your arms around AI in a, in a manageable way so you don't feel overwhelmed in keeping up with all of the, all of the latest and greatest bells and whistles?
Or is chasing the shiny object like that missing the point?
[00:27:49] Speaker B: I think it's, it's, yeah, to your point, it's, it's missing the point. I mean, the program that I'm in at Berkeley, I mean, we're learning about large language models, small language models, natural language processing. We're learning about computer vision. We're going to be moving into robotics.
But really, I would look at it just going back to the discussion about what challenges are we facing? When you look at. And we, you know, with Accenture, we would look at, like a slice of life. What does my day look like? What am I really spending my time on? So if you look at your, Your, your, your 12 hours, 8 hours, or 16 hours of work that you're doing a day, what are you really spending your time? Okay, there's a bucket of three hours here doing this kind of review that I could really be doing something else. Okay, so how else could you tackle those three hours every single day? Is there an AI tool that might be able to help you? And then you've personalized it, so your learning should be based on what helps you as an individual, what helps you as a team member, and, and what may help your company, because then it's personalized and you're learning those AI skills, you're staying abreast of what's the best tool that's come out in that topic. And that's when it becomes so much fun. Because now you got those three hours back. You can do something fun or you can do more research, whatever you want to do, but you've just solved a personal issue that's made you more productive and given you back time that you decide what to do with.
[00:29:31] Speaker A: Right, right.
To maybe extend that. If you teach folks in your organization how to approach that, how to use the tool, how to assess their own time investment, productivity, you could go beyond saving three hours of your own time a day and save 30% of the organization's hours in a day, perhaps.
[00:29:55] Speaker B: Exactly. Exactly. And that's when it becomes fun for, you know, the CFO and everybody else. Right?
[00:30:01] Speaker A: Yeah. And you could tell the CFO's ears would perk up at that sort of metric. Yeah, absolutely.
[00:30:05] Speaker B: Yeah. So it's, it's an exciting time. I. I am just so thrilled to be able to learn as much as I can, as during my three hours that I saved, you know, six months ago. And it is, it's a lot of fun. So. So just jump in if you haven't already, and just see how it'll transform your role and potentially your company as well.
[00:30:31] Speaker A: Dr. Gita Manaya of Manaya Learning Global Solutions. Always a pleasure to chat with you. Thanks for coming on the podcast.
[00:30:36] Speaker B: Thank you so much, Scott. It's been a pleasure.
[00:30:38] Speaker A: This has been the Axiom Insights Learning and Development Podcast. This podcast is a production of Axiom Learning Solutions. Axiom is a learning and development services firm with a network of learning professionals in the US and worldwide, supporting L and D teams with learning staff augmentation and project support for instructional design, content management, content creation, and more, including training, delivery and facilitation, both in person and virtually. To learn more about how Axiom can help you and your team achieve your learning goals, visit axiomlearningsolutions.com and thanks again for listening to the Axiom Insights podcast.
