Optimizing L&D through AI Analytics with Kayvon Touran

How effectively are you using AI and analytics when it comes to your Learning and Development priorities?  In today’s episode, my guest and I discuss the transformative potential of AI in assessing competencies and skills through advanced performance measurement methods, including AI-facilitated self-assessments and 360 interviews. 

My guest is Kayvon Touran. Kayvon is the CEO of Zal.ai, a startup he co-founded with Joseph Rousseau, and world-renowned learning scientist Dr. Stephen Kosslyn. Previously, he spent six years at Noodle, where he co-created a platform for universities to manage their lifelong learning courses. 

During this tenure the team identified a significant gap in the market concerning organizations’ ability to effectively contextualize, apply, and measure their L&D investments. This insight inspired the launch of Zal.ai, a SaaS product that combines cutting-edge learning science and generative AI to transform workforce learning and development.

In this episode of Leadership: Human-Style, you’ll discover:

✅ How AI can revolutionize L&D’s effectiveness 

✅ The role of HR in helping companies adapt to AI integration

✅ Inspiration around how to enhance performance measurement

🔗 Connect with Kayvon Touran on Linkedin: https://www.linkedin.com/in/kayvon-touran/ 

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Transcript

Lisa Mitchell [00:00:00]:

Isolate this one. Hello, and welcome back to Talent Management truths. I'm your host, Lisa Mitchell, and today I am joined by Kevan Turan. Kevan is the CEO and co founder of Zal AI. Welcome to the show.

Kayvon Touran [00:00:14]:

Thank you so much, Lisa.

Lisa Mitchell [00:00:16]:

So let's kick off, shall we? And I'd love for you to share with us a bit about your background.

Kayvon Touran [00:00:22]:

Yeah, absolutely. Well, I've worked in early stage technology startups my entire career, and I've had the pleasure of wearing pretty much every hat in the organization. And I spent the last about, let's say, six or seven years working in education technology at a company called Noodle in New York City. And at the time, it was really like an incubator. And so it had a bunch of different companies solving different problems from pre k all the way through post secondary. And I got sort of sprinkled around the portfolio, operating as a product manager, and along the way, just became more and more infatuated and enthralled by learning and development and its potential and what it can do for organizations and for individuals. And so I found myself, a few years later, venturing off of my own and starting my own company in the space, which I'm really excited by.

Lisa Mitchell [00:01:22]:

Excellent. Okay, so, just focusing in on Noodle, first of all. So, Noodle is a learning management system, and it. So it's. And it's in the education space, is that correct?

Kayvon Touran [00:01:33]:

So, that was one of the products that we launched. Essentially. I'll give a quick sort of brief in terms of what Noodle does is, essentially, they were like an online program manager for universities. And so what that means is they would partner with a school like Tufts, and they would go to Tufts and say, hey, tufts, let's take your data science program that's in person, and let's take it online. And so they would help Tufts translate the curriculum into effective online learning, host it on an LMS, do all the marketing, do all the student support, et cetera. While I was at Noodle, was working and reported into the CEO, and I was responsible for new product development and specifically a product that was effectively like an LMS and then some. And so that was the thing that I was focused on for a number of years.

Lisa Mitchell [00:02:28]:

I see. Okay. Now, something you shared with me when we first got acquainted was how it was kind of fascinating in that experience during that time that you would meet with lots of different organizations. So you saw how they were. They wanted training. They were always asking for it and wanted more, and yet didn't necessarily have clarity around you know, what were they going to get from that? And how would they measure if it had any kind of impact? I was hoping you could tell us a bit more about that, maybe.

Kayvon Touran [00:02:57]:

Yeah, I mean, it's really fascinating. So by way of what I was doing at Noodle, you know, we were looking to work with schools of continuing education and professional study, and I had the opportunity to speak to, I'd say, close to 100 of the top schools in that space and their administration. And the thing I kept hearing that I thought was profound was they'd say, you know, it's really interesting, again, without naming names, but someone will come to our school and let's say I'm thinking of some of the most prestigious universities in the world. They'll come to our school and they'll look for custom education for their company. And what's interesting is a lot of times they don't need our help just for the content, of course, but they also need our help in identifying who needs the intervention, the training, intervention itself. They need help in how post training they'll be able to apply the learning and development, and then, crucially, how they'll actually measure the efficacy of the investment so that they can go back to their executive team and say, hey, can we get more funding to do more training? And I just always thought that was really interesting and profound because the people who are selling you the content, these are higher at institutions. I believe strongly that their purpose is to help educate and that there's a nobility in that. But at the same time, I mean, you're asking the same person who's selling you something to tell you how effective it was.

Kayvon Touran [00:04:35]:

And it always felt strange to me that companies themselves didn't have those mechanisms at their disposal. And it's interesting because in my career I've learned so many times that when you start from a place of assumption and you start to really understand the situation and you apply some empathy, you realize actually, well, it's actually really hard to measure that. It's actually really hard to put those systems in place. And I sort of just became more and more infatuated by that. And yeah, it was the inspiration to start my own venture.

Lisa Mitchell [00:05:03]:

Yeah, it is so interesting because I'm smiling as you're saying that, because as a consultant who works with organizations on custom leadership development, group coaching, et cetera, right. I'm developing programs all the time, and it is a regular thing that organizations are like, we need some kind of program or help over here. But it's very vague in terms of how they might articulate often at the starting conversation what exactly? Like, why, why they feel that it might be because they've got a chorus of, you know, senior management saying, you need this. Right. We need to teach everybody about accountability. And then when you really start digging into it, you've got people with differing definitions of accountability or whatever the thing is, and different understanding of what will be different. If we actually do provide some education around this and it has an impact, what would we see if we looked at people if we peered in through that window six months, one year from now? And I, I, for me, I relish that part. That's the part where I shine.

Lisa Mitchell [00:06:08]:

I love it. But if you haven't really spent time learning and doing needs analysis for a long time. Right. And being able to see full cycle of implementing something like this and then measuring impact, it's not going to be part of your vocabulary. You're not going to know even what questions to ask. So I think it kind of makes a case for why you need somebody with good experience to help you with it. Right.

Kayvon Touran [00:06:36]:

So, well said.

Lisa Mitchell [00:06:37]:

Yeah, yeah. Fascinating. Okay, so this kind of wet your whistle. So you went away and you ended up co founding Zal AI. So tell me a little bit about what it is and kind of what's, what's, what are you trying to accomplish here?

Kayvon Touran [00:06:56]:

Yeah, well, I'd say that our ethos of what we're trying to do is help organizations contextualize, apply, and measure the efficacy of their learning and development. And it's really interesting. So I have two co founders, both of which have sort of an interesting background that I think complements my background in tech. One of them is sort of an entrepreneur in the space and a technologist and a really talented software engineer who sold his company at Noodle. And that's how we became acquainted. And the other is our co founder, who's really focused on the pedagogy behind sort of the assessment piece of what I just mentioned. Right. Sort of those three tenets.

Kayvon Touran [00:07:46]:

And so his background, I mean, he is one of the foremost cognitive neuroscientists in the world. And also his emphasis of his research had been primarily in learning science. So he was at Harvard for a number of years as faculty and then as dean. And essentially he's written a number of books now on the topic of using generative AI in application, of creating role playing simulations that can be used to promote and to really teach in an active manner, active learning. And there's so many things that I could say about that that get me really excited. But at the high level, what our company does is it's trying to solve the same problem that we were talking about at the beginning. How can organizations have a layer that they can implement in their learning and development where it becomes easier for them to contextualize their learning so that there's less far transfer and things feel more relatable for the end learner? How can they apply that learning? Right. And then how can they measure the efficacy of it? And I think that there's a great deal of value that we can create for companies and the people and their employees by doing that successfully.

Lisa Mitchell [00:09:04]:

Okay. Yes. So with the neuroscientist, what's his name, so that I can put it in the show notes if people want to look up his book.

Kayvon Touran [00:09:11]:

Sure. It's doctor Steven Koslan.

Lisa Mitchell [00:09:14]:

Stephen Koslan. Okay. Thank you very much. All right, so it's interesting, too, just even hearing about the business model and how the three of you each contribute different pieces. So what's your main part in the whole thing?

Kayvon Touran [00:09:32]:

Yeah, my main part is speaking to as many L and D leaders as possible and learning from them and learning, to my earlier point, being able to empathize with their situation, because I think my background as a product manager, I recognize if I'm going to move into a phase of managing a product where I'm trying to experiment and optimize it, which I think a lot of L and D leaders do with their learning initiatives, there are some tenants that are really foundational, that are simple but not easy. And so those are things like, well, how do I benchmark efficacy today? And how do I create a culture where my constituents, my learners, are telling me, hey, I think this could be a better way that we could do this, or here's some ideas for some things that we could train on. I think there's so many interesting proxies to L and D, and I know firsthand from being product manager how hard it is to really have a culture of analytics, experimentation, optimization. It's easy to say those things. It's really hard to do them in practice. And frankly, I think there's so much money being poured into that industry, for example, for something like product. But there isn't the same type of emphasis in L and D, in my opinion, tooling specifically dedicated to those types of questions. There's a lot of tooling dedicated to content, content delivery, but I don't think there's as much attention to measurement, efficacy, these types of things.

Kayvon Touran [00:11:04]:

I think there's a great opportunity to help L and D leaders with an emphasis towards that.

Lisa Mitchell [00:11:10]:

Yeah, you're absolutely correct. That tends to get lost in the shuffle, not without good intentions. It's partly because they're okay, wow. If you build it, they will come. So they deliver something. It's great, it's well received. And before they can, even if they wrote a level four or five evaluation strategy, they can't even execute upon it. Cause they're being pulled onto the next thing or there's no budget and headcount to put against it and all that.

Lisa Mitchell [00:11:35]:

Cause I've been there and I see clients there all the time. So, yeah, so anything that we can do to make it easier. Right? So that. Cause I do. I do think it's unfortunate because, you know, iterating, embracing the iteration. That's what this is all about. It's full cycle from needs assessment to design, development, delivery. And now we're gonna evaluate it, and that feeds back into the cycle.

Lisa Mitchell [00:11:59]:

So, yeah, we're missing a key element if we don't, if we don't get there. And I want to acknowledge the reality that people are operating under. Okay, so tell. Tell me a little bit about how does that actually do this.

Kayvon Touran [00:12:13]:

Yeah, absolutely. So this is where things get really interesting. So, essentially, we think of performance measurement as something that should be done, uh, often, and, uh, that will only lead to, uh, sort of richer data to it that inform sort of that cycle that you just mentioned. So the way we do it, essentially is we can either measure based on a role definition and the bundle of skills competencies that are relevant to the role. And so it can be applied as almost like a performance management type of application, or it can even be done at a particular skill level. At the high level, what we do is we have a user take a self assessment that's facilitated by an AI that we contextualize and we customize. So that, for example, if we use the example of a project manager at a company, a project manager would take a self assessment, let's say, around leadership or let's say, in for their role. And they would actually have a role playing exercise with an AI that would represent one of their colleagues or one of their stakeholders, and they would go through that exercise.

Kayvon Touran [00:13:31]:

And then in addition to that self assessment, which is creating sort of this objective score in terms of how well they performed in that scenario, that's realistic to their job, they would also have their colleagues and their manager take a 360 interview that's facilitated by an AI. And this is really interesting as well, because the 360 interview is a happy medium that we see between sort of the standard asynchronous surveys that are usually measured on some kind of self described Likert scale and like a one on one interview that happens with a real person. And then essentially what we do is we push together all that information that comes from the self assessment and the 360 review, and we create this comprehensive report. And, of course, if that's done, say, on, say, a temporal interval of quarterly or it's done on a project basis, we can start creating trend analysis over time to show how people are developing in the application of L and D. Of course, where this gets really powerful is, let's say I develop a training intervention. Let's say I hire a large custom EDda provider to produce some kind of learning. I can take a pre and post assessment, and I can have a really meaningful measure in terms of the impact. And that's something that gets me really, really excited but happy to answer more questions in terms of how it works, because there's obviously a lot more to it than that.

Lisa Mitchell [00:14:56]:

Yeah. So I'm a huge fan of the before and after snapshots for assessments to be able to gauge and demonstrate the shift that people may have experienced. So, okay, let's break this down. So, first off, some listeners may be maybe wondering, with the 360 interviews that are done by a bot, basically, are those related to the role play that was done? Like, are they like. Yeah. So are they watching this or something? There's a recording, and then they evaluate it.

Kayvon Touran [00:15:30]:

So I'll explain sort of the relationship. And this is really where Steven's sort of innovation comes into play and is in the topic of his book. So, essentially what we do is, well, sit down with a company that we're working with, and we'll define very clearly a rubric of proficiency. That rubric can be skill specific and be role specific. Oftentimes we find organizations are investing a lot in L and D, and they don't even have that piece. If they can't even measure or define what good looks like, that's usually, like, our starting point with them. But the sort of part of what we're doing is we're taking that rubric of proficiency, we're feeding it into our system, and then the AI is looking to fulfill the criteria of that rubric through the 360 interview as well as through the self assessment. And so essentially, what we're doing is we're measuring the same things on the 360 interview.

Kayvon Touran [00:16:31]:

We can measure how your colleagues perceive your proficiency in said skill and in the self assessment we can objectively measure how well you've demonstrated that skill because there's a transcript with the bot, and then we have another AI analyze that transcript using the rubric. And so I think where this gets really interesting then, is we're not relying on surveys where people are self describing their proficiency. We're actually creating objective measurement from the self assessment and then all of this amazing context that's sort of layered on top of it through this 360. And so you end up with a really powerful result.

Lisa Mitchell [00:17:10]:

Yes. So, again, I just want to be very, really clear, though. So the 360s, these interviews, are people. What's the frame of reference? Do people have access to that role play or the transcript? Or they're talking more generally about their perception and experience of this person's skill.

Kayvon Touran [00:17:28]:

Exactly. So, for example. So it's the latter. So, for example, it'll be in reference of the same thing that's being measured, but they won't have access to the self assessment results. Those are private.

Lisa Mitchell [00:17:39]:

Okay.

Kayvon Touran [00:17:40]:

And so essentially, it might say, okay, this 360 reviews to help lisa identify where she might have opportunities to improve as a leader. And then based on the temporal interval that we set, it might be based on a particular project, it might be based off in the last quarter. Tell me about this aspect. What's interesting and what we found in that approach is that we end up getting much richer data from the respondents. And things that we want to prove in the lab or in a research setting is that we're also collecting more. I want to be careful about saying that, but more truthful response, because that's sort of a classic problem with 360. Right. Those are the types of, I think, stretch goals that we really aspire to.

Kayvon Touran [00:18:32]:

But I think just in this immediate term, we're collecting more data, and we're collecting it faster than you would in traditional means.

Lisa Mitchell [00:18:41]:

Yeah, absolutely. How long does it take somebody to do one of these 360s? Like, if it was a manager? Like, are they really short or long or.

Kayvon Touran [00:18:49]:

I mean, so we can set them up for either, obviously. And it's funny, with 360, of course, it kind of, there's the whole range. Typically, what we recommend is something that takes about ten to 15 minutes to complete for a respondent, and we recommend testing often, again, say, at the end of a project or a more frequent temporal interval than is typical. Yeah.

Lisa Mitchell [00:19:15]:

Okay. Yeah. I asked because that's one of the biggest complaints by all people that get asked to do a lot of 360s. Right. For a lot of different people, it can be the biggest time suck and then they have no time to do their actual work. So that could be a tricky thing, depending on how often we're asking and how long each one is. So something to manage for sure. Okay, so very interesting.

Lisa Mitchell [00:19:38]:

So we've all heard, I think, by now, unless you're under a rock, you know, AI and the bots are coming and also garbage in, garbage out, they can hallucinate and give you all sorts of crazy things and make things up. And, you know, in this case, with these interviews, when they're assessed, or actually more on the role play, I guess, when they're assessing for skill against the rubric, how do you solve for the fact that, you know, there's a missing human element? Yes, it could be ostensibly more objective, and yet that subjectivity, that human element, when it comes to, um, you know, global, the global type of communication, what's not being said specifically in terms of word choice, but rather through tone, expression, how do you, like, how do you get at that stuff?

Kayvon Touran [00:20:27]:

Yeah. Okay, so there's. There's a few questions layered in there. Um, one of the ways that we're improving, uh, the. The, um, the AI's coherence, if you will, is through, uh, retrieval, augmented generation. And so that ties to contextualizing without getting too jargony, that that was a.

Lisa Mitchell [00:20:50]:

Jargony at all, that.

Kayvon Touran [00:20:52]:

What I basically mean is that we're having the AI respond to respond based off of context that we're feeding it. So in the case of feeding it things like a job description, feeding it things like a rubric, and telling it to focus on those resources for how it's generating its output. That's been shown in a number of research papers that it improves the coherence, the likelihood of hallucination drops. And so we're very. That's why our approach emphasizes that. So that's like one thing that in terms of sort of the hallucination factor, you're right, though, that, like, the nature of this technology is that it can hallucinate. And depending on the application, that can sort of be a feature or a bug for us. It's super important that we stress to anyone that we're working with that the output of the measurement, that result that I mentioned to you, that squashes together the self assessment and the 360 result is still something that's then reviewed by an individual team manager or by an L and D team.

Kayvon Touran [00:22:05]:

So I'm hesitant and wary of anyone who says, I can fully automate an end to end workflow without a human in the loop for a number of reasons, I don't think that's wise, but I do think that our approach is creating more, better data faster. And if someone's role flips from author to editor, I actually think that's a good thing. There's probably a higher impact.

Lisa Mitchell [00:22:31]:

It's an amplification is how I see it. And, you know, even just how I'm using, I'm going nuts with perplexity lately. I just love it.

Kayvon Touran [00:22:40]:

I love that one.

Lisa Mitchell [00:22:41]:

Oh, yeah. I asked perplexity AI. There's an app listeners, I got a free year or something with LinkedIn subscription that I already had. But anyways, it's great because it just, what I find is it elevates what I can be spending my time on. Right. So if I'm a. Okay, case in point, here's a personal example. Going to Italy this fall for our 20th anniversary, me and my husband.

Lisa Mitchell [00:23:05]:

And so, you know, I'm researching like, what's the best way to spend three days in Venice, you know, this kind of thing. Like, what do I absolutely want to see? And I don't want to see it all. So I need to prioritize and so I can, you know, ask it very specific questions to say, analyze based on some, you know, the top review, travel, blogs, like whatever. And you can apply this in the workplace, you know, with pick your content and just change it. But what it does then is then, okay, it's that much quicker. I'm not scanning through multiple Google pages trying to figure it out, and I've got somebody already comparing opinions and ideas and different itineraries. And it just makes it much more simpler for me to make a decision so I can get there in a quicker point of time. So that's really what I'm hearing from you.

Lisa Mitchell [00:23:52]:

Right. So this measurement output smushing together the results of the role playing, the 360 then is still reviewed internally by someone who knows the person, human to human. And what if they saw something that worried them or that they felt was inaccurate? What could they do about that?

Kayvon Touran [00:24:12]:

Well, they can edit the output of the report itself, and so that sort of updates everything in the system. I think the other really interesting thing is sort of what they could do with that report from there and how it could be used. And we have lots of ideas for that. The other thing that I just want to mention, because there was the last part to your question, that's an area that really gets me excited as well, is around sentiment analysis.

Lisa Mitchell [00:24:37]:

Yes.

Kayvon Touran [00:24:37]:

And I think there are two really interesting parts to that one, both in the self assessment phase of any type of role playing analysis, like you mentioned, sort of the how I'm communicating, which is so important, especially if we're measuring any kind of durable skill, of course. And for that, we've been experimenting with voice based as well as with even like, someone having their camera on and using facial recognition to see their body language, if, you know, because a lot of our work life today occurs on Zoom, so there's actually not that much of a difference. And it can be illuminating in terms of, wow, I didn't know, like, I kind of have a stinky face when I'm, you know, when I'm communicating about something like that. And it's helpful. The other piece of that that I think gets really, really interesting, too, is when we think of 360. I think this is another kind of like, holy grail that people talk about, which is I'm not just, when someone goes through the 360, instead of just analyzing who they're sort of reviewing, what if we were reviewing how the respondent was actually providing the feedback and the sentiment around that. And I've been speaking to a number of consultants who actually focus on 360, and that's a really interesting area that they're focused on as well. I think there's a lot to derive from there.

Kayvon Touran [00:26:00]:

There's two examples of, I think, sentiment analysis in that land, which I think gets really interesting.

Lisa Mitchell [00:26:05]:

Yeah, yeah, that's really cool. Thank you for giving that additional insight. All right, so we're nearing the end of our time together, so I did have another question that I wanted to dig in. So we're sort of going down the road and turning slightly left. So because you and I, what we share in common is we're talking to a lot of HR and learning leaders in the course of a week, a month, etcetera. And I'm just wondering what you're noticing out there when it comes to their attitude towards AI, what they're doing, what they're not doing.

Kayvon Touran [00:26:40]:

Yeah, I'll tell you, I think generally seeing generally, and I'm curious how you see this, too. I think there's a lot of interest, there's a lot of intrigue. I think what I'm hearing is that a lot of executive leadership, at least we speak to a number of firms and professional services, are taking a more conservative approach when it comes to AI. They might have hrtainous run what I think is sort of like a more defensive mandate where they'll have employees sign a waiver that says, I'm not supposed to be using Genai they might think that people are definitely still using it behind the scenes, but they don't want to be caught with a customer saying, well, I hired you to do this work, and then you just had AI spit it out kind of thing, which I think is just such a fascinating kind of time that we're living in.

Lisa Mitchell [00:27:30]:

Well, it's, you know, this is where the greatest policies are born, right? They come out of fear, you know, either management's fear or employees, you know, one person's stupidity. And so now we're going to do a policy that everyone must abide by. That's funny. So people are actually, wow.

Kayvon Touran [00:27:47]:

Well, and I think generally, like, I do think that there's an element, too, where like, you know, and it can be tough, I think, for people in our industry who are like, even on LinkedIn, because it's a bit of, it can be a bit of an echo chamber. It's like everyone's using AI, everyone's experimenting with prompts of. But a lot of people I speak to are not. And they're like, well, I haven't really, and I don't know what it is. I don't know if fear is the right word. I think there's just kind of, maybe there's an apprehension or I don't know where it derives from. And I've been trying to understand that more closely. I think any new technology, especially as one as powerful as sort of consumer grade generative AI, gives people pause, and they're like, wow.

Kayvon Touran [00:28:29]:

And so I feel like there's less adoption than I expected for a number of things. And I find that really interesting.

Lisa Mitchell [00:28:40]:

Yeah, you just wonder. So do I, by the way. I find it equally interesting, and I'm kind of wondering because I think it came out what, end of 2022 chat GPT so we're just coming up on two years, so I wonder if it will accelerate sort of adoption and more like, when will we get to the tipping point, right, with the sort of regular consumer audio audience? Anyways, we won't answer that today. One other sort of related question is, when it comes to who's leading the charge for AI in organizations, who is it typically? What do you see?

Kayvon Touran [00:29:17]:

Typically, what I see is it's very department specific, and it's a department leader that is kind of leading the charge, very contextualized to their specific flow of work. But I mean, I believe strongly that HR should be leading this charge. I mean, the data is out there in terms of sort of the bundle of skills and how pretty much anyone who works in sort of a knowledge worker profession. There's something like 60% of the skills, 70% of the skills is going to evolve because of Genaida. I mean, who better than HR to be leading that change effectively and partnering with the individual department leaders, helping identify what the gaps are, what the new workflows could be and how to train on those things? I think it's such an exciting time to be in HR and L and D, and I think that role is going to be so pivotal strategically for companies who are going to have this competitive advantage where they jump on this opportunity. And I'm not a crypto person. I'm not. I could say this, I don't think this is a trend.

Kayvon Touran [00:30:28]:

I think this is here to stay. I think this is equivalent of a mobile. I think it's equivalent of Internet. This is going to be a core technology that impacts how we work. And I think it's a really exciting time to be in HR because they have the opportunity to guide their company's, you know, teams through this sort of transformation.

Lisa Mitchell [00:30:50]:

So, yeah, well said. And music to my ears, because that I agree with you wholeheartedly. HR learning OD leaders that you that are listening today, I mean, this is really, if you haven't dug into it yet, it's an opportunity to shine where you shine best, which is, you know, being that strategic partner in the middle of the organization that has the broad perspective and understands both the people and the business side of things. Right. Like the operations piece and how they come together. And so being able to really apply that lens to this new frontier. Right. And how do we upskill and support our people is going to be critical, I think, for how we move forward.

Lisa Mitchell [00:31:33]:

Awesome. Well, thank you so much for coming on the show. It's really been a pleasure. And I wish you and Zal all sorts of success. It'll be great to follow you as we go forward.

Kayvon Touran [00:31:46]:

Thanks so much for having Lisa.

Lisa Mitchell [00:31:48]:

My pleasure.