Making Ideas Accessible| Keith Goode | 545

How do you make ideas accessible to your audience? Thought leadership is, well, by definition, a complex idea, a counterintuitive idea, or even one that's at the cutting edge. And those sorts of ideas aren't easy to put into simple 6-second sound bites for for your target audience. So today I'm speaking with Keith Goode. He's the VP of products and services at Zeroed In Technologies, a firm that focuses on workforce intelligence. In today's conversation, we'll explore how to make ideas approachable versus going too deep too quickly. We also talk about experimenting with different sources of data, find new insights, And finally, we talk about helping people find answers to good questions. I'm Bill Sherman, and you're listening to Leveraging Thought Leadership. Ready? Let's begin.

Welcome to the show, Pete.

Hey, Bill. It's great to be here. Thanks for having me.

So I want to dive in from a perspective. There's a lot of conversation about AI, but there's not a lot of conversation around how do you explain AI, right? And how do you make an idea accessible, right? Because there's some tension there. You can get someone who's technically proficient and they can layer you with stats, or you could sort of gloss over it. So how do you do it? How do you get someone familiar with a new idea like AI, which sprung on pretty much the consumer and the business user at the end of 2022. How do you explain an idea not too deep, not too narrow, but just right? Sort of the Goldilocks approach.

Right, right. It is. And there is a lot of things out there to help in that process. But I would say through my experience, the first is obviously identifying a business issue, a concern, an idea that's going to be— that AI can help use to answer those questions. So first, understanding the problem, understanding an issue is the first step. And then second is looking for data that's gonna help answer that. And that could be a very broad subject, a very broad topic. So we do a lot around using AI to help predict who might be at a flight risk within an organization. As you know, Flight risk can be very expensive. You hire someone, you train someone. Those are the primary costs. And then the opportunity cost is where do you think that person's going to go? They could go to your competitor. So there's that opportunity cost. So looking at— working with organizations to help identify, well, what are some key elements that might help us better understand where flight risk comes from and look at the data that can help support that? What we do is we then quantify that information. We look at historical data, aggregate that together, and generate AI model. An AI model is unique in the fact that it looks at every instance of a, a prediction, and it looks at those features that you establish as going to be able to help predict the outcome, and it determines for each occurrence what features might be most important. So each prediction is, is fairly unique, and that's what AI brings to the table. But we take that a step further and use what's called explainable AI. It's being able to reverse a given prediction back through the model to say, well, why was Bill's prediction different than Keith's if we used the same features? Of course, the values might be different. So then it analyzes the values and says, well, for Bill, we found these features more important, and for Keith, we found these features more important. And that helps people then to take that prediction and do something about it. Right.

Because if you were to give a warning to say this person is likely to leave your organization, but you don't know why, then if I'm a manager or leader and go, I don't want to lose this person, what do I do? If it's not explainable, it's not actionable. In a lot of ways. You can't just walk up and say, hey, are you thinking of leaving? Don't do that. Right? Right.

Right. Yes, exactly right. And you get a prediction. And a lot of systems, it's kind of a black box. You just see this value. This person is predicted to be a flight risk, say, and then you scratch your head and say, why? And in a lot of solutions, that prediction may not come from that client's data, but may have been aggregated with different companies' data that is very dissimilar than yours. So having a, you know, not knowing where that, that prediction is coming from and what was used to drive that prediction, it kind of reduces the value of it. It does.

And you mentioned the concept of black box on data, right? So you mentioned models and I want to make this a little bit more visible. So If you and I were to come up with factors and a list of factors that promote flight risk, you know, we could sit down, you and I, and come up with a list of a couple dozen probably within an hour, right? If we were to turn to specialists on that, the organizational psychologists of the world, they would add another layer of expertise. But when you get to AI, the number of dimensions that a model could be looking at is so much more complex, right?

Oh, it really is. And it reminds me of kind of a unique story that we have. We've worked with a large retail company and we had a wide range of HR and business data for those factors, for those features. And you think retail, you think high turnover, right? But that's on the store level. Behind the store in the corporate office, you've got very tenured people. So having a— in the old days, what they would do, the statisticians would create multiple different models for those different categories of people. But using AI, it was able to determine quickly what type of factors, what type of features were going to be important to do a prediction based off of where the person was working, how they've traversed through the organization. So that's really exciting to see these models be able to learn from their data to come up with a better prediction. And it saves time in building a whole bunch of different models that then have to be maintained on a regular basis.

Well, and it shifts in some ways the burden of the expert, right? Because those experts are still highly useful for the interpretation, but even more useful for asking the right questions because that's the part that the black box, the model can't do, right?

In AI.

Right, exactly. And we've taken that a step further and we've done the explainable AI, but we also came up with this very unique concept where basically we use a waterfall graph. And if you're not familiar with that, basically it says, okay, you start at this point and then there are certain ones of those features that may have raised your prediction of leaving and then some may have lowered it. So you have a bar for each one of those factors and then you can kind of see each factor how much based off the bar width was important for one prediction on determining if that person is going to stay or leave. And then on top of that, because we now can take that one person's prediction and run it back through the model, we can then actually kind of do some tweaking. We'll say, well, let's suppose that, you know, if we gave this person a pay raise, how would that impact their flight risk? Or if we, you know, gave them a promotion, or if they were demoted, or if they were to change to a different store, how would that impact their flight risk? And It's amazing to see how something like that, putting it back through the model, can instantly generate an idea or a better decision-making on working with that person or a given person's ability to stay within the organization. Right.

Because again, if you're the manager or the leader, the last thing you want to do is something that either has a low chance of impact or a high chance of negative impact if you're trying to keep the person, right? Good intentions gone wrong.

Yes, exactly. Exactly. So it's that what-if modeling that you can also get once that model's been baked and you have the appropriate capability to send a prediction back through the model.

So let's talk about sources of data, right? And some of this goes back to the classic correlations, you know, in a Stats 1 class, you know, that the, as the sale of ice cream goes up, so does the murder rate, right? And it's the classic lesson that correlation is not causation. No, people don't go on killing sprees because they have ice cream, right? It's the heat. There's an underlying factor. So as you're building something like explainable AI, is it a set of data sources that are already— you're pulling from, I could imagine, and then ones on the general economy and things like that, and then you layer in the company's data, or how does that work?

Yeah, that's a great question. And typically how we work with it is through a template. So we've kind of templamit— have a template of some key factors that we've used in the past, key features. But when we work with our, our clients, our model is extendable, it's configurable. So we will work with them to identify different data elements and then test that through generating the model. So you, you generate the model, you then run the model against that same test data to determine how effective that model was. And based off those features, maybe the model is not that, that effective. Maybe it's, it's above that 95% confidence ratio that we're looking for. So we may have to drop one feature and add a couple more. And of course, depending on the client's data and how much historical data that you have could play an impact to that. We've worked with different clients to find very unique elements. Everything from obviously your, your well-known metrics like tenure, promotions, and salary, but also very unique things like how far of a commute a person has to work if they're still coming in, or how many friends that they have, you know, based off of their, their engagement scores. We could look at, you know, how we could use some of those features off the engagement score to help generate that model. So there's a lot of different things that we could use to fit a model for a given client, and we'd work with them And they have ideas too that are unique to their organization.

So I wanna ask a few questions about explainable AI and I wanna ask it from a thought leadership perspective. Who are you trying to explain explainable AI to? Who's the audience, if you will?

Yeah, that's the important thing 'cause again, Tech for the purpose of tech is just not solving a business problem. So it's got to be a business problem. And in our cases, what we found, for example, at the retail organization that we worked with is we were targeting store managers and regional managers so that they could look at an individual person and identify what could be done to reduce turnover in those organizations. That a lot of times that also equated to potential just training opportunities. So that's where we were, we were looking at, you know, the actual workers outside of HR, the partners of HR, if you will, that could use that information to make better decisions. Right.

And so there's several layers that I want to unpack. You've got probably the individual who makes the decision to bring your firm in. So you've got a buyer as a target audience, you've got the folks within HR and IT who are charged with implementation and making it work, but you've identified the end user, right? And that person is the one who's going to make either this work on a very simple level or have it struggle, right, to create value.

Yeah.

And it is a struggle to think that just implementing it will provide it and, and they'll come. It's not the Field of Dreams. Uh, so it, it does take effort. It does take resources to drive behavior. This is something new. It's unique. And we've seen amazing technology come recently being out into the marketplace. However, the adoption and satisfaction of the technology, it's just not there. If you look at some recent studies around the adoption and satisfaction of HR tech over the past year, unfortunately, it's not what it should be. So everybody needs to do better. But I think it's also a level of adoption, a level of changing people's behavior to utilize this. It means making it easy for someone to, to find the technology and maybe someone that's not technically savvy to be able to figure it out. So we're talking about a generational gap that also has to be, be considered.

And I'm also thinking that when you get to that end user level, that retail manager, for example, you could have someone who says, look, I spent a lot of time getting to know each of my people. I don't need software. I don't need artificial intelligence. I manage by walking around. I know the store, right? Why are you telling me you know better with this data, right? Yeah, yeah.

And, and that's, that's really interesting. And, and I think great that you have those people, but think of it as just an augmentation, just an, you know, I've seen people too where they, they walk around, they think they had that idea, then all of a sudden they see some pieces of data that was put together in front of them and they'll say, oh, I didn't know that, right? And then it kind of— the light bulb kind of goes off and it's just, it's an additional augmentation that those people can utilize now. So it is a little bit of a training, a little bit of a curve, a little bit of an effort to get them there. But, and it doesn't happen overnight. It is not, oh, well, we've implemented this new technology. Yesterday and everybody's going to start using it tomorrow. Not quite there. Yeah.

And I think there— it's the difference between telling someone, hey, here's a tool and a better idea versus, okay, you don't have to do this anymore. Trust the system. Because until there's some proof and you can show me, hey, this works, both in predicting and also helping me preventing people leave, I'm going to be skeptical. Right. Yeah.

Yeah. And think about the, you know, you have a prediction, there was something done, and now that prediction didn't come true. So, right. You know, where's that trust? And that's why we've always got to periodically retrain those models and always check for the relevance and the effectiveness of a model to ensure that the confidence is there. And that does— it's not a one-time. We typically work with our clients and retrain models either semi-annually, annually. And of course, the new data is always changing. So we're always running the new data against the model. But retraining that model, you should be considering it on a periodic basis depending on the type of model.

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So I want to ask two questions, and I'm gonna ask them together. We're going to focus on one, then we'll turn to the other. How did you get into thought leadership, and how did your organization get into thought leadership? I want to start with you. I want to hear your journey. How did you get to being an advocate and a voice for these ideas?

Well, I've been in, in this space— my background has been in IT and information systems, and, and every day I wake up and I'm thinking, how can we add value based off of this data? There's nothing more exciting to me than being able to look at some data that's been sitting on the shelf for the last 5 years that we can aggregate somehow and make better use of it. So that's what really jazzes me and that geek in me really enjoys. I think, however, the secret is really being able to take those unique solutions and make them effective and sustainable on a regular basis. So my background has been in information systems and HR, primarily actually in the learning space. And Zero In, the company I'm with now, is my colleague Chris Moore. We've been in the learning space for, I hate to say, over almost 30 years now. So in the learning space was really interesting too. We find that there it's data-rich but information-poor. And so we've developed, zeroed in, and we're not a transactional system. We're not an HR system. We're not a learning system. We're not a benefits administration system. But we collect the data from those different systems to be able to bring it together and then provide value and information across the HR spectrum and being able to then configure in business data, whether it's finance, whether it's productivity, whether it's sales data. In order to gain that value of HR data. And the challenge has really been, you know, HR is highly transactional. Everything's been transactional, but being able to take that now and roll it up and aggregate it into information where it's been missing. And when you can satisfy that type of gap, it's very rewarding.

And so we're zeroed in. How did Zeroed In decide, and what has it been doing from a thought leadership perspective to get these ideas out there? I mean, obviously you don't have a budget of a Towers, for example, right?

Zeroed In is a really unique organization, privately held, but we've been profitable. We actually saw the dot-com error and we saw that, you know, expansion and then the more or less collapse of that. So we've always been, been very physically conservative in our growth, which has been good and bad because you see, you know, there's other organizations kind of come and go. They get all that VC money and, you know, all of a sudden they're huge. We've been really trying to build it organically and organically with, with our clients, organically identifying what the needs are. And then having— I've been very proud of our technology group, our developers that understand AI, that understands generative AI, that understands the technology to be able to build that platform, but yet make it configurable for each of our clients' uniqueness. And that's really what we've tried to do is we've really tried to focus on the value adding value as opposed to what's the next shiny object.

Well, and I think there's a couple layers there that you mentioned that I want to call out. In making the technology explainable, you make it accessible to individuals. And if you're trying to do organic growth, it's important for people in the organization to be talking about these ideas. But it's even more important for people within the organization talking to their peers and being able to explain what it is, as well as to colleagues in other organizations and saying, hey, I found this new tool, you should check it out too. And so by making an idea approachable, explainable, and clear, that's how you get the word of mouth. And people starting owning ideas beyond you just having to talk about them again and again.

Yeah, yeah. And I see that a little bit as a struggle. I think people in today's world—

Oh, it's not easy. It's noisy work, right?

Right, right. They want that quick fix. They want the, you know, this is the answer, this is a one-click answer. And I think what we're trying to do is also provide that information in a unique way that can help them get to that. But by all means, it's still people-driven. It's still, you know, people looking at things in a different way that can drive better decision-making. But there isn't a button to click and say, here's your decision. That's not there. And I don't think it should be.

No, I don't think it should be either. I mean, we're not talking about like going on to Amazon and doing a one-click buy it now sort of thing, right? We're talking about people in the workplace in leading and responding to their needs and helping to understand patterns of behavior. And that's not something that you— you still need that human touch and level and go, yeah, here's what the data is missing. I know that that employee has had a daughter who's been sick and she's been out and hasn't been in and her attention's been split, but it's not because she's losing interest in the organization. She's got sick child and has had a sick child for a few months, right?

Yes, yes. We work with a city municipality and our representative there, such a great guy, and we'll— he'll give us a data challenge and we'll go through the data and, you know, part of the analyst analysis processes, maybe we'll hone in on a given person in a given department. And he's so funny, you know, sure enough, he knows that person, you know. And we're talking about a, you know, almost a 10,000-person organization, and we'll find kind of an anomaly on, on something, and we'll start talking about it. And it's, it's been great. We, we've worked with them, we've done things like, for example, in municipalities, they do a lot of budgeting, and the budgeting is typically done in Excel spreadsheets outside of the HR system. And then of course you have the HR transactional system. So we've been able to, to look at the, the budgeted positions and correlate them to actual positions. And of course, people changing every month— the budget is a budget for the year, but people, right, all the time So we've been looking at— we came up with a metric we call a turnover savings metrics, which I'm sure it's been out there for a long time, but it more is probably more valuable in the municipalities as perhaps the private organizations. But, you know, we've looked at the churn inside of an organization to say, hey, you've got a gap where this position was supposed to be filled for whatever reason. And it wasn't. It could have been to movement. What maybe a person went to another department, another division, turnover, whatever it might be. So there's a lot of, a lot of factors to consider in looking at and determining that turnover metric. But at the end of the day, here, here's the amount of budgeted money that, you know, perhaps wasn't utilized. And he's like looking at ways to put it back to make service bonuses or merit bonuses. And to make people more engaged in the workforce and rewarded for what they're doing based off of something that was budgeted, to me, that, that is really exciting and, and is changing the way people feel about working there.

That's a very cool point, right? Is how do you reapply those dollars more effectively once you know you have those insights?

Yeah.

So as we begin to wrap up here, Keith, I want to ask you a question. I want you to think back of you earlier in your career as you started to get into the world of thought leadership. What advice would you give yourself then that you wish you knew, that you'd learned?

I would say look for the business problems. Look for the problems. Don't take a solution and look for a problem. Start with a problem and look for a solution. That is where I wish I would have known in the past, maybe where I am today is it's driving value based off of solving problems. And I'd like to say that the technology is has that in mind, but unfortunately I don't think it does. I think people are taking technology and trying to find a home for it, and sometimes those homes don't match up. So I would say, you know, look, look for the, the business problems and then the technology to help solve those problems. Does that resonate with you, Bill?

Yeah, that does. I mean, because at the end of the day, in Organizational thought leadership. It's an idea that creates impact for your organization, for your end users, for your employees. If you've got an idea but you can't apply it to the business, if you can't make sense of it, well, it may be an interesting idea, but it's unlikely to be an applied idea, and it's more likely to be sort of an academic idea that sits on the shelf that people look and go, yeah, that's a good thing to know, but what do we do with it? You've got to cross that last mile, right, from an idea to action. I mean, that's the language of business.

Yes. And something that I was thinking about too is if you have— you're trying to solve that problem and it's not being solved by, say, the tech or the idea, don't continue to spin that wheel, right?

Right.

It's a hard thing to do. You want something to work, you want it to work, you want it to work. And then sometimes, you know, knowing when you need to find a different way, that's sometimes a challenge.

There's a phrase in teaching of writing called kill your darlings, right? So the ideas and the turns of phrase and language that you have as you're doing creative writing, maybe a short story or a novel or something, You have to be willing to let those go to create a better reading experience for your reader. And I think what you're talking about is very much a kill your darlings experience. You've got a pet idea or something that you love. It's not working. You can't make it work. Let it go, right?

Right.

Not all ideas are ones that you have to bring across the finish line.

That's right. That's right. But you can always use it in another story, right?

Yeah, exactly. So I want to thank you, Keith, for joining me today. This has been a fantastic conversation.

Thank you, and I hope your audience is finding it as valuable as, as I have. And if they'd like to continue the conversation, I'd be more than happy to do so. I can be reached at, uh, zeroedin.com or my LinkedIn account, which is Keith A. Goode. And again, I'd always be happy to continue the conversation.

If you're interested in organizational thought leadership, then I invite you to subscribe to the OrgTL newsletter. Each month we talk about the people who create, curate, and deploy thought leadership on behalf of their organizations. Go to the website orgtl.com and choose join our newsletter. I'll leave a link to the website as well as my LinkedIn profile in the show notes. Thanks for listening, and I look forward to hearing what you thought of the show.