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HRx Radio – Executive Conversations: On Friday mornings, John Sumser interviews key executives from around the industry. The conversation covers what makes the executive tick and what makes their company great.

HRx Radio – Executive Conversations

Guest: Carol Leaman, President & CEO, Axonify
Episode: 328
Air Date: June 21, 2019




Important: Our transcripts at HRExaminer are AI-powered (and fairly accurate) but there are still instances where the robots get confused and make errors. Please expect some inaccuracies as you read through the text of this conversation. Thank you for your understanding.

Full Transcript with timecode

00:00:13:25 – 00:00:33:24
Good morning and welcome to HR Examiner’s Executive Conversations. I’m your host John Sumser and this morning we’re going to be talking with Carol Leaman who is the CEO of Axonify. I can’t begin to tell you how excited I am to talk to her because she is changing the way learning and development works, so good morning Carol. How are you?

00:00:34:12 – 00:00:36:06
I’m great John. Thanks for having me.

00:00:36:26 – 00:00:46:07
Yeah. So so much to cover but would you start by introducing yourself and you’ve got this tremendous history. And so talk a little bit about the tremendous history.

00:00:47:03 – 00:00:48:07

00:00:48:08 – 00:01:11:04
So I am what is sometimes referred to as a serial entrepreneur. I have been running technology companies for gosh more than 20 years now. And all of those companies serving what I would call a unique need of particular customer segment. And Axonify is the last of one of those.

00:01:11:21 – 00:01:48:00
We identified a really clear need about seven years ago in the learning space. And so we embarked on this journey to build technology that would solve that problem for our what we believed to be our customer base. So I’ve had a long storied history of running technology companies super fun. Some great successes and and this is I would say of of the various ones that I’ve had this one by far and away is the most impactful and interesting and truly I think driving value for our target customers.

00:01:48:12 – 00:02:10:13
So a couple of years ago you were you were given an award as Canada’s leading female entrepreneur. That’s quite a mantel. And so be interesting to understand how you feel about that but also if you have a couple tidbits that you might want to dispense to the aspiring women entrepreneurs who listen to this show, that would probably be pretty helpful.

00:02:12:00 – 00:02:29:28
Yeah. Well thank you for that. I continue to be surprised that anybody notices to be honest. I’m just you know working hard like everybody else and trying to do my thing. And when you get recognition it is for me anyway always a surprise and a delight and

00:02:30:16 – 00:03:06:17
Really makes me feel quite humble in fact it’s not me it’s the teams of people that I work with and have worked with over years that really drive our success. And so I appreciate it tremendously. But you know it certainly doesn’t go to my head. It is truly a team effort. I would say to your your point at the end about being a woman and tidbits for other women it is the case that I am unusual in the sense that certainly when I was coming up over the last 25 years and building my career there were very very few women in technology that I could point to

00:03:07:01 – 00:03:39:27
As examples or mentors for me. And so I just took the approach of why not me. Why why can’t I achieve what up to that point had. Been. Success in a largely male dominated industry. And I had a moment of realization probably 15 or 20 years ago. Where I realized I’m as smart. As anybody else. I make good decisions I sometimes make bad decisions and I’m completely okay with owning that.

00:03:39:28 – 00:03:41:04
And I think women

00:03:41:17 – 00:04:15:02
Tend to stress about and worry about not measuring and and being an impostor and not being as smart as others or men in particular and that’s just not true. And so I just think having confidence believing in yourself and asking yourself the question over and over again if you look around at everybody else why don’t you why why not you. Because you are as capable as anybody else. So for all the women out there go with the why not me message.

00:04:15:02 – 00:04:24:26
That’s great. Thank you so much for the show excellent work on its service. It’s a micro learning or what the world is. Hmm.

00:04:25:02 – 00:04:59:05
So we are as you said a micro learning platform that is focused on companies that have largely front line workforce that are often widely geographically dispersed there they tend to be hourly paid sometimes very high turnover and it’s really really difficult to train those individuals with consistency and quickly so that for the period of time that you have them they are as competent as quickly as possible and they mastered the material.

00:04:59:06 – 00:05:35:11
So we came up with this really unique way that involves a three to five minute a day highly targeted personalized experience. That adapts to what every single individual knows day in and day out and measures what they know and ties it to the business objectives that whatever they’re working on is designed to achieve. So if they’re selling product we train them on ways to be more effective at selling if they happen to be working in an unsafe environment or a risky environment.

00:05:35:11 – 00:05:58:15
We can keep them safer and we do it in that three to five minute a day fast game a side experience that is irresistible for those employees. They want to have it so they do it voluntarily and love it and they learn much more quickly and then get the business results that much doctor.

00:05:58:18 – 00:06:06:02
So so so depicted so too are the specific things. Sure.

00:06:06:28 – 00:06:37:14
Yes for sure. So we have many customers for example who use it for sales application. So I’ll I’ll give you maybe a couple of examples. One being sales and one being a loss reduction application. So in the case of sales if you have retail associates that are front line in your stores and those associates are there to sell more product for you as we’ve had some customers say to us.

00:06:37:15 – 00:07:40:08
My objective is that when a customer walks in the store the retailers though see it because they lack confidence or don’t have knowledge. Don’t run in the other direction and hide behind an aisle and that in fact is a common occurrence in retail because the associate is afraid to have to address the question. So what we do is on any device whether the point of sale terminal the individual’s mobile device or some kiosk in a break room as an example we allow that retail associate whenever they have three to five minutes on a shift to have very granular learning in topic areas that relate to products that they for example need to push because they’re on promotion or how to appropriately address the customer so that they feel confident in answering a customer’s question and potentially increasing the basket size and all of the things that that customer purchases.

00:07:40:18 – 00:08:37:12
So we grow confidence we grow knowledge very quickly which changes that retail associates behavior to drive sales in those stores much more effectively. So that that sort of revenue growth example on the expense reduction side we have many customers who use it to train on topics where they are already incurring and measuring loss. So in warehouses where these employees are doing very risky things like driving forklifts and climbing tall ladders we train them on topics where that organization is looking to reduce incident rate and we close those gaps in knowledge person by person in those topic areas and do it very surgically measure what they know and don’t know work on an individual basis day in and day out.

00:08:37:21 – 00:09:08:16
And we tie then what that knowledge looks like to the behaviors exhibited and the actual incident rate and what we know is if you can drive people to get on to that three to five minutes a day in those targeted areas you can drive down the expenses associated with those incidents and losses and in a very very measurable way. So anywhere anytime fun the highly targeted.

00:09:08:19 – 00:09:16:03
And it really doesn’t matter what the content. So it can drive revenue reduce expenses train on H.R.

00:09:17:18 – 00:09:31:10
regulatory type thing. The whole point is we drive knowledge and use actually brain science to ensure that knowledge sticks and then gets applied on the job. So

00:09:31:10 – 00:09:49:11
So so one last person would sort of come to a room and then which begin to see how it works. The the as I listen to you describe it sounds like you need some sort of a minimum number of people doing the same job we’re used to big search.

00:09:49:12 – 00:10:30:23
So yeah it definitely work better at scale. So you can train one person doing one job absolutely. But from a machine learning in the A.I. aspect the larger the population the more effective it is. And so from the point of view of being able to apply the the the processes that give you sort of predictive analytics around for example sales growth or expense reduction and also from a content creation perspective the larger the population the better.

00:10:30:23 – 00:10:46:26
But we have customers who have you know 100 people all the way to more than a million people. And everything in between. So it really just depends on what you’re looking for people to change behavior around based on knowledge.

00:10:47:00 – 00:11:24:09
So so you just accidentally blew my mind you said matter of factly just so that you to produce predictive analytics about Rover Yeah. Just be absolutely sure that I said that. Yes. So this is not what I get it marks but research is recording and will always be useful. So what. Let’s say let’s say you two actually do them over the reason to dispute it but it’s quite a claim.

00:11:24:09 – 00:11:28:17
No one is the champion of the world who actually believes you deserve it.

00:11:29:27 – 00:12:16:20
I would agree with you there are there are people who believe us and fortunately but we do get a lot of disbelief when we let people know that it is possible to predict business outcomes that results from behavior change that result from what people know and you can do with it. Gail and we’ve had several customers now who were in that land of disbelief like there’s no way that you can tell us we are going to sell more of Product X if we get people using exotic fi three times a week and they’ve actually applied their own data scientists to the data to replicate what we’re doing because of that disbelief.

00:12:16:23 – 00:12:48:21
And I’m smiling because in every case that that’s happened we have been able that they validated absolutely everything that we’ve claimed so it is possible. We think we’re the only ones doing it and it is highly valuable for our customers who then can adjust what they’re doing as a business to be much more agile and double down on the things that work to make them more successful businesses

00:12:51:09 – 00:12:53:00
that’s really that’s really interesting.

00:12:53:01 – 00:13:25:15
So I wonder if there is a is there sort of a practical limit to this capability. One of the things that you see L.A. departments doing in in more white color more knowledge worker environments is encouraging everybody to take design thinking classes or that sort of touch with we’d imagine that you can draw a line from design thinking you know maybe because there isn’t.

00:13:25:19 – 00:13:25:24

00:13:28:00 – 00:13:37:23
I imagine that that that as you you think about where this this operation goes in the future you you think about heading towards those

00:13:39:12 – 00:14:03:25
less direct on the job training in the more ethereal things. So how do you think about the relate between business outcomes and this kind of sad thinking that that is is what it will do in larger organizations that don’t have the direct worker to revenue line.

00:14:04:02 – 00:14:39:09
You’re absolutely right. When you start to get into what I would call more leadership related topic things like design thinking where it innovative type thinking that you’re looking for people to really you know get their heads around what ends up happening is that you really do have to think very very hard about what is the business outcome that results from what people know and are doing on the job.

00:14:39:11 – 00:15:37:28
And sometimes it’s difficult with things like leadership training design thinking training to see that direct tie to to the outcome. So it is you know just state of the technology today. It’s much more applicable and provable where you have direct business outcomes which is why we tend to focus on organizations with that frontline workforce. But I can tell you that we also have many customers who do use it for things that are more difficult to measure and because of the brain science that is involved in the actual learning process what we know is people understand and perceive even as individuals themselves that they are learning and retaining and thinking about operationalizing the knowledge that they’ve gained more effectively.

00:15:37:28 – 00:15:59:14
So how that translates in design thinking sort of environment is definitely more difficult to prove. But we’ve got anecdotal evidence that from the learners perspective they perceive the way it’s done to result in their ability to apply those principles more effectively.

00:16:00:00 – 00:16:02:12
So did you ever see that movie Clockwork Orange

00:16:04:12 – 00:16:30:12
years ago many many years ago. Yes that was quite a record for the head of the guy sitting in the chair of his eyes or pride. And so the shoe print. Yes. I think I think a lot of employees and just thinking about trading like that sort of way. OK hold on. I would sit here and get my big dose of

00:16:34:09 – 00:16:54:12
I think you’re saying a lot we haven’t talked about directly. You’re saying that the struggle for a liberal is a different kind of experience or employees the the come into the room and we’re going to fill you up with knowledge and you better pass the test Warragul.

00:16:55:01 – 00:17:25:24
That is 100 percent the reason in fact why it on a fly exist today. We had a nine initial customer who said classroom training ELA math training with long heavy modules is not working to change the behavior in my employees that I need to see and I need just a better way to get their attention hold their attention and turn that into action on the job to help me improve the business.

00:17:26:10 – 00:17:59:01
So we conceived of this idea to completely change that fire hose experience on its head and move away from one and done long form not measurable sorts of experience. And so for that customer which is now you know was the initial reason we ended up building the company. We created a three to five minute experience that was highly appealing to the individual so we game a fight.

00:17:59:03 – 00:18:35:24
The experience using about 20 different game mechanics and we used cognitive principles that are actually designed to create memory and retention in the brain faster than anything else because a firehose experience as it turns out is about the least effective way to get an employee to remember anything. In fact as the person is sitting in the classroom or even watching a video that’s an hour long they end up having memory degradation start almost instantly.

00:18:35:24 – 00:19:17:15
So by the time they’re finished they don’t remember much of what they just turn. And in fact what we know is that 30 days later the average human being will only recall about 7 to 10 percent of what they learn 30 days earlier. So we decided that’s not the way to train people. How do you get people to remember long term and sustain that knowledge forever. And so we incorporated three very core cognitive concepts working with a brain science researcher that we now know create memory and retention of knowledge faster than anything.

00:19:17:20 – 00:19:58:19
And it’s an experience the employees love because it is fun and fast and personalized to them so that they don’t feel like they’re just getting one size fits all they get something that is really closing their knowledge gaps and teaching them something new and they recognize that so they don’t get bored with it. And and it’s all of those things wrapped together that make it a very very different experience than simply sitting in a room and having somebody talk at you and have that employer hope you’re going to remember anything and use it on the job.

00:19:58:19 – 00:20:18:27
That’s really interesting so so to Greg is the records the the work that I’m doing. How how are the various intelligence technologies part of the work that you do. Sure there’s probably be some machine where you’ve talked about Gabe McDermott the juices of hotels together.

00:20:20:13 – 00:20:56:26
Yes. So we do employ a whole bunch of techniques though cognitive concepts that have been well proven over the last number of decades to work to solidify knowledge in the brain. And so those are very very key to the algorithm that personalizes the experience to every individual every day. So absolutely critical to the extent of my platform is the algorithm that incorporates that personalized adaptive brain science based experience.

00:20:56:27 – 00:21:26:29
We then collect as a result of that three to five minutes a day all kinds of data around things like what was the job type that the individual is engaged in. What’s the knowledge that they were delivered today. Did they know or not know that knowledge. What game did they choose to play. How well did they do in that game. How long did it take them to go through the learning session. How long have they been on the platform.

00:21:26:29 – 00:22:03:00
We collect in any single session somewhere between 15 and 20 secrete data points across all of those factors that then get aggregated many many ways. And so we take that data and we apply the machine learning to the data to be able to extract the relevant themes and the statistical correlations with all of those factors together but also tied to the business outcome.

00:22:03:12 – 00:22:10:11
So we have many customers who allow us to know on an ongoing basis what their sales are.

00:22:10:11 – 00:22:47:27
For example what their losses are and the machine learning and A.I. takes the learning data we collect with every session and the business data marries those together and then extract the key themes so we can say you know if you for example get your employees playing games more often than not as part of the learning experience because they don’t have to. But if you do get them to play a game then they will participate two times more week on average.

00:22:48:00 – 00:23:30:09
And if they participate two times more a week their knowledge and these key topic areas that directly correlate to your revenue generation will grow more quickly and it will drive your sales by an extra 4 percent next month. So it’s a very very robust end to end closed loop type of experience with lots of different tools and technologies applied to all of that data to be able to extract and then elevate and expose Statistical Themes and actions that result from learning.

00:23:30:12 – 00:23:34:05
So what are the ethical issues you worry about in this process.

00:23:34:05 – 00:23:54:03
You’re known in the world where where you are generating machine manipulation of human emotion cognition. So there must be some places where you think there are to be bad rules or you’re worried about rules.

00:23:54:13 – 00:23:55:27
Where do you see the role.

00:23:56:14 – 00:24:34:25
Well one of the questions that we’ve had customers ask and you know I would say in a very small number of cases there are learners or employees ask is how is this data being used you know from a performance evaluation perspective. So for example if I’m a truck driver and I keep not getting questions right in you know various topics that relate to truck driving safety. Am I going to be fired. How does that you know get exposed to my supervisors and then used against me in those sorts of situations.

00:24:34:27 – 00:25:13:17
We’ve been able to mitigate that objection quite effectively. We we haven’t had any customers. Use the data punitively against their employees. It’s really there from the perspective of trying to make the programs. Better and more effective to grow the skills and knowledge of your employees sets. So that’s one sort of worry we had in the early days would how would that data be used. Because you you know at you know somebody sitting in a classroom and they’re getting trained and they leave the room you have no idea what they know and don’t know.

00:25:13:18 – 00:25:58:18
Were they daydreaming for an hour. Were they thinking about dinner last night or what they’re doing tonight. You just simply don’t know. And until something negative usually happens on the job and you understand oh my gosh they don’t understand something. And now we have to address this. Those other traditional ways we train people and kind of understood their levels of knowledge with exhaustive fi you know specifically what every single person knows and doesn’t know in all the topics that matter. So the use of that data we did worry initially would potentially be used but we To date seven years later are happy to say our customers use it in very positive ways to lift up and enable their workforces.

00:25:58:18 – 00:26:35:08
And given the skills shortage the labor shortage. People can’t afford to not be skilling up there people in the most effective way possible. So that was that was kind of I would say the biggest ethical issue that we had. You know beyond that. We don’t collect what I would call a ton of personally identifiable information. Many times our customers give us just employee numbers you know so we don’t get things like birth dates so that we know the age of the individual and can expose like if you’re 70 years old in the work places many people are today.

00:26:35:13 – 00:26:53:20
That somehow data is being used against you as an older employee versus a 30 year old in the workplace for example we don’t tend to collect that level of data. We don’t need it. And so we don’t get into some of those more granular ethical situations.

00:26:54:25 – 00:27:16:24
That’s interesting. I have I have a million more questions for you but we are nearing the end of our time together so we’ll have to get you back and go a little deeper on some things. It’s been a great conversation. Thank you so much. Which would take time to reintroduce yourself and tell people how they could find out more?

00:27:16:24 – 00:27:46:07
Absolutely. So this Carol Leaman. I’m the CEO of Axonify. We are the world’s leading micro-learning platform that gets your business outcome based on knowledge. And if you’d like to learn more please visit our Web site at w w w dot Axonify dot com a week if you’d just type in Axonify in a search engine you will very easily be able to find us. And we’ve got a team of people waiting to answer any of your questions.

00:27:46:07 – 00:28:10:00
Thanks again for doing this Carol. I really appreciate the time. You’ve been listening to HR Examiner’s Executive Conversations and we’ve been talking with Carol Leaman and you spell her last name, L-E-A-M-A-N, who is the CEO of farAxonify and it is a company worth checking out. Thanks for tuning in. And we will see you here next week. Thanks very much. Bye bye now and thanks again Carol.


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