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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: Ben Eubanks, Principal Analyst, Lighthouse Research & Advisory
Episode: 323
Air Date: April 19, 2019

 

Transcript

 

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

00:00:14:02 – 00:00:32:28
Good morning and welcome to HRExaminer’s Executive Conversations, I’m your host John Sumser and today we’re going to be talking with Ben Eubanks who is principal analysts at Lighthouse Research an advisory and most importantly the author of the book on artificial intelligence for HR.

00:00:32:29 – 00:00:33:27
So Ben, how are you?

00:00:34:29 – 00:00:39:01
Hey John, I’m doing very well. Looking forward this conversations it’s gonna be a lot of fun.

00:00:39:06 – 00:00:53:19
Yeah I think this will be fun too. So would you take a moment and introduce yourself you know, tell us how you got to where you are. You’re a funny story about. About when you were a little kid dreaming about being an analyst in HR Technology, like that.

00:00:54:10 – 00:01:01:06
So that’s actually very fitting for me when I was a kid I wanted to be in HR and I didn’t know what was called yet but somebody…

00:01:01:10 – 00:01:07:21
Holy moley I knew you were strange, what you wanted to be in HR when you were a little kid.

00:01:12:10 – 00:01:47:22
I was the older middle child out of four boys and I had known the position authority of being the oldest but I would have liked broker deals and get things done and so I felt like. You know there’s a there’s a job for me and that’s in the future right. And so. I got to college and found out there was a thing called HR and started studying that realized that’s what I’ve always wanted to do. And so I got my degree there worked in a jar for several years before I stepped out into the research side started to. Do the analyst thing and and love that because half because I’m a tech nerd. Let’s see what’s going on the technology. But also because I love researching understanding what the best companies are doing and helping anybody else can pick up someone’s ideas.

00:01:47:25 – 00:02:05:06
Awesome. I would do that. I think this is like the three-hundredth interview on these radio shows. That this is the first time somebody said they wanted to be in HR. Everybody else sort of fluked. So I’m delighted by that. So Light House research. What’s that?

00:02:06:08 – 00:02:41:02
So I spend my days at the White House research. This June actually makes the three years that we’ve been in operation and it has been a ton of fun. We spend our days researching the top trends in H.R. town oxygen learning development. Understanding what the best companies are doing and helping everybody else to get some insights into how they can mimic this practice. And we’ve always heard if you want to be great with a great group we’re doing. So we’re trying to get some insight into those things. We also talk about the technology side too that we work with. Sometimes you work with vendors in the space on thought leadership things like that but I love understanding those kinds of things again that natural curiosity you and I were chatting about that before the show started.

00:02:41:02 – 00:02:49:24
Like we have this natural curiosity we want to know. Everything there is to know and we can’t. So that means it’s going to find it prioritize which things we want to dig into it anything else and that’s what I get to do. Every

00:02:49:27 – 00:02:58:14
Day at my house. So what are you currently working on. That’s that’s that’s a beloved relatives button but what’s the current projects.

00:02:58:14 – 00:03:23:25
So we just finished a report some research on reskilling and upskilling employees and surveyed about a thousand learning development rationals and employees to understand the preferences what they’re seeing there. A lot of can’t go really deep into that one necessarily but there’s there’s a lot of focus on disruption driving a need for upskilling or reskilling employees. But eight in 10 of those learning professionals said this is driving a real need for this and our company we’re seeing it already today. And

00:03:23:25 – 00:03:55:20
And one of the other fun questions we asked was we asked both populations what is the number one skill someone needs to be successful at work and out of that. The number one skill for both groups which was kind of fun to see they matched was communication skills. But beyond that. A lot of the things that came out of it were around the soft skills piece. The things that. Transfer from job to job you can do this and different careers. This follow you for a long time. And again it wrapped into a conversation we have having a minute but I’d love that focus on the soft skills piece. We’re actually. So that was one of the things we just finished we’re actually about to launch a new report

00:03:56:01 – 00:04:16:07
On chat bots in H.R. A.A to understand what’s going on there because there’s so much commoditization maybe but there’s so much happening in that space. And employers are asking us hey who’s doing that. What’s new. Who’s going to talk to who’s you know what their capabilities. And we started looking and found that so far we found between 20 and 30 companies just in town our position

00:04:16:16 – 00:04:21:05
That have chat bots are offering. So we’re trying to dig into those understand what those capabilities are as well. So

00:04:21:15 – 00:04:27:10
Again just things that we’re curious about think you’re hearing about customer things like that and that’s some examples of some of the research we’re working on.

00:04:27:25 – 00:05:07:06
So I took a global survey that I’m sort of nursing you existence a global survey is about adoption of intelligent technologies in nature and we look at 20 intelligent technologies the curve to figure out who’s. Do you buy this stuff is who’s trying to. And then we look at will it replace jobs or do people believe the most interesting statistic has to do with checkbooks and what we’re finding is that for every project that’s out there there’s one that that is in the car Rick

00:05:08:28 – 00:05:42:03
the failure rate of Chatterbox projects this and this is not you know sort of screaming chat bots where you’ve got a decision tree that the live chat about steps a champ or benefits this through but the conversational knowledge mining tap of 50 percent failure rate and it’s going to dig in and understand why that failure rate is like that generally has to do with the fact that people are not told what it takes to actually it’s the the ownership and operation of the snakes.

00:05:42:06 – 00:05:58:12
So I’ll be really excited to see what you learn. I’d be really excited to see your research. Excellent. Yeah. So you. Well you wrote you wrote the book on A.I. and you wrote the book. What was it like to write it and how’s it doing.

00:05:59:18 – 00:06:29:22
So the book has been going going very well. All the feedback I’m getting is very positive so far good reviews and things on Amazon to go ahead. That’s not the only signal I have but I don’t know that I’ve read it right. But for example I last week I was in DC at an event and someone reached out said hey you know I know you’re in DC I saw you were sharing a social or their stalker ish a little bit. She said Hey I’m just cheers could you come talk to our team because I bought the book for everybody our team because we’re actually looking at this as the next iteration of how we’re going to implement our technology.

00:06:29:22 – 00:07:00:28
I’d love to hear your take on it. And so I show up and I’m hearing from people that have said all read the book her whole team of 20 H.R. professionals serving a global organization has read this and so. Those kinds of insights those kind of pieces of feedback are what I have so far I’m curious to hear. How it’s going with the publisher but they’ll let me know in a few months I think what the what the actual numbers are. But it’s been an interesting process to see how it all goes to anybody when I was writing the book. It was a labor of love and more labor than love. The longer that project went on just because it was tough to put 70000 words to paper on something.

00:07:00:28 – 00:07:31:11
But it was at the same time something I’ve always wanted to do. I don’t know if it was as early as my desire to get in an H.R. that I want to write a book. At some point but it’s been really interesting for me to see the process work with the publisher and everything else I’ve learned. I have definitely learned a lot and I want to explore that and and have I plumb the depths of what I what I can learn from them. And that process and I don’t tell my wife that I’ve already started thinking about the next one. So she and she might kill me if you heard that but I’m already thinking about the next one and what’s going to be about when it can come out things like that.

00:07:31:12 – 00:07:38:02
There was just so much fun for me to have a book as a big reader. I love having a physical book in hand and being able to share that

00:07:38:25 – 00:08:11:18
Interesting. So I’m more interested you would know than a person when you started thinking about that you had me to to write the book and that’s something the people in each are have to do. What I know about you and I spend the vast majority of my time on the topic and my experience is working in that area means you’re constantly faced with the fact that you don’t know what you’re talking about and I’m curious about how you navigate that.

00:08:11:21 – 00:08:44:19
I think it’s the call that imposter syndrome the sense that you can understand it all and you are therefore incompetent. It’s a characteristic of dealing with the techno. We will use fast as videos so what was it like for you to try and try to get concrete about how. Wow. Being in need of technologies and having to deal with this thing that everybody in nature is going to do. Which is the sense that you had with it.

00:08:44:29 – 00:09:15:07
So it’s funny because when I was writing the book the The approach is very much to be a practical guide for each our leaders business leaders who were curious about how these technologies are going to affect hiring and training and talent management and all these other types of processes that I am very familiar with. And so I want to write from that perspective because I wanted the average age our leader to like you said not very competent with the technology. That’s not an indictment. It’s just they don’t spend all day digging into this stuff it’s not what you know.

00:09:15:12 – 00:09:48:24
There is one chapter in the book that is solely about how the technology works the ins and outs. A lot of the history of it. Things like that so they can really wrap their minds around it but the very first thing is not chapter is this is the shortest chapter in the book because you are not a technologist you are a business leader and you’ve got to learn just enough to be able to ask questions about this. I know you talked a lot. Being able to ask questions of vendors and being able to dig into how these decisions are made and sort of signals as this algorithm using to make this prediction. Begin to ask questions like that in a competent way but also being able to type back to the really practical impacts of adding the day.

00:09:48:25 – 00:10:23:08
I’m not on the hook as an H.R. leader for you know the coding language my my algorithms written in or things like that. I want to look for how we’re hiring who we’re hiring how quickly it’s happening who we’re training how developing them. So those. I did my very best to give them that good foundation and it’s the same exact foundation that I needed when I was starting this this process of researching the book and writing about it because I want to understand everything I could about that. But also giving them the the understanding that you were never gonna be fully immersed in this because this is not the world you work and live in but you should know enough to be able to ask some questions and to push back if you get

00:10:23:22 – 00:10:55:23
You get that answer you’re walking through H.R. TAC and 98 percent of vendors have a I listed up there as their their core capability right. Again you and I have seen that many times being able to push back a little bit and ask them some questions about how it’s working with what tools they’re using things like that. So they feel comfortable with it. That was my end goal. And hope is that once they finish this that chapter of that book. They’ll be able to feel confident enough when their CEO came to them and say hey. Herbert’s staff what does it mean for us. They to answer that question in the context of how it’s going to affect their people.

00:10:55:29 – 00:10:58:27
So you’re still we’re going to talk to the leaders

00:11:00:19 – 00:11:30:17
who are trying to wrestle with this stuff. They all have this nagging sense of they’re screw you because they don’t understand it well. And so so I would just double dome of course to if it’s just how did you deal with that. Because you had to experience it in the process of writing the book. You have to you have to have the same feeling very good which is that you believe holds true power you’re really good so right.

00:11:31:03 – 00:12:05:04
Yeah. So say I sometimes measure how many things you catch find by how many tabs I have open in my my web browser and I have 46 open right now of things that I want to go back and read through that I’ve opened in the last month on things all related to my algorithm biased decision making like all those things that wrap around the conversation. Because I knew when that. Turned that manuscript into the publisher the very next day it was. Outdated to some degree. Right. One of the companies I mentioned. Could do something new when it comes on mentioned wasn’t there anymore. So that was that’s part of it writing a book about technology is knowing that you’re going to struggle with that a little bit.

00:12:05:12 – 00:12:37:12
So you’ll match your question though the way that I approached it is I. Approached I looked at the history of it. I looked at some of the core capabilities the technologies. I read things that are outside of our space so not listening to a we were talking about it but listening to technology leaders listening to what some of the leaders in the space are doing how Google talk about it. How does Adobe talk about it how does something other companies that are leading the ad space. I can never pronounce the name of the video chip company in a video or whatever it is. Like how they talk about it. Those are some really great resources and they’re pioneering some amazing developments in that space

00:12:37:24 – 00:13:08:12
That apply to everything right. Just as I’m reading it I’m translating in my head oh this does this well let’s apply that to H.R. how would this how this plan of magic a candle do a job or how would this apply to. Using sentiment analysis to understand how your employees are thinking and feeling. And so as I’m reading it I’m translating it and I’d like to think that the book is just a pure translation of that kind of thing. I’m looking at all these applications of different. Intelligent algorithms throughout the world and every single part of our personal professional lives. And translating those into some specific H.R. applications. So

00:13:08:12 – 00:13:39:15
So it feels very consumable for the person reading that and they can then take that and apply it in some way. Like you’re saying I’m over they’re rolling out chat about imitation maybe think about some other sort of tool they can feel confident enough to be able to do that with a little bit of a little bit of grit I guess versus feeling like they’re just floundering and not sure what what it means and giving up. So where I am H.R. is going when I talk about the topic presented a topic a lot and I talk about it. One

00:13:39:15 – 00:14:12:23
One of the things I mentioned always is we always think of automation as the value point right. But automation is been around for a long time. If I had back in my life all the hours of time that everybody promised me with all these cool apps and tools and productivity everything I’d be sitting on a beach somewhere I would never do any work but instead were busier than ever we’re doing more things than ever because we can scale what we can do with these tools. And so I talk about automation as let’s automate these low value tasks. Right. You mentioned the chap on a minute ago using a decision tree to move someone through the candidate process or to answer questions from an employee self-service sort of perspective.

00:14:12:24 – 00:14:46:06
Let’s just automate those low value things. That H.R. spends a lot of time on. But don’t add value. To the business to the bottom line and instead look at those higher value tasks that were that would need to be doing. And using A.I. tools and insights to actually augment our human abilities those in those tools can help us to see trends that we can’t see with the naked eye. They’ll help us to see insights and things like that and if they can surface those at the right time contextually when we need them or we’re trying to make a decision it’s not making a decision for us. It’s not giving that insights that when it’s time to make the decision we have the most relevant and up to date information possible.

00:14:46:07 – 00:14:53:26
I think those are the real value points in and how A.I. is rolling out how it’s being adopted and how it’s going to impact organizations for the better.

00:14:54:12 – 00:15:21:12
So I think so many ready for work so it’s like you just said if each is like cleaning your house that’s the result of having a guy in H.R. is going to be a cleaner house. And so I wonder I wonder that that’s that’s actually very interesting to me. If there are some things that are more like magic that you imagined happening like like you start cleaning the house and you discover the secret room

00:15:23:10 – 00:15:25:14
you know you know I don’t.

00:15:25:15 – 00:15:28:21
I’ve always dreamed 30 a secret room in my house.

00:15:29:11 – 00:15:34:29
What were the results of its offer offices. Go go go. You got to see there is and it is.

00:15:35:00 – 00:16:10:08
It is very cool. I’ve been there with the team. I love that. So in the book again I’m trying to be very practical. Not. Not a ton of its forward looking like here’s me guessing about the future because I want it to be immediately applicable but one of the things I did talk about and there is the idea of a self developing organization. Let’s say that you have an A.I. tool it’s kind of overlaying and looking at the kind of jobs you’re hiring for the kind of skills your employees are training on things like that. And it sees a wait. Look John you’ve been you’ve had these four or five jobs open for for two months now. You still don’t have enough qualified candidates in them or you’ve turned all turned down all the Kennedys because they’re not they don’t have these key qualifications.

00:16:10:08 – 00:16:50:07
But we look internally we have a dozen employees that are 80 percent of the way there. There’s just a 20 percent skills gap match. And so it will go in there start training those employees without having been told will start prescribing some training for those people so they can close that skills gap. And it’s not recommending them pushing them into the candidate. Q As a potential candidate if you do reach out to and source them just like they’re an external candidate and so I talked about some of those kinds of things in the book right. I think a I can help us to be smarter realize things that aren’t even there right. Maybe that’s our secret room that secret candidate internally that’s a great fit for the job you just didn’t know they were there because you didn’t have the insight on their skills or you didn’t have the insight on how close that Delta was between what they can do today and what they need to do to accomplish that job.

00:16:50:07 – 00:17:25:02
And it’s interesting because I wrote about that then and this week at the skill SOF conference I heard from some of their their team they’re talking about doing things very similar to that some of their new tools and the new training and things. And so it was validating and a little scary to think that some some some prediction that an analyst actually made may actually come true. That doesn’t doesn’t usually happen. And now if we if we always take fun make fun of the transfer thing else in the space just because it’s funny how it all works. But. You know that’s one of the one of the examples that I wrote about that I also think is actually something that could come true and is actually closer than I thought it was gonna be when I wrote about it.

00:17:25:20 – 00:17:39:09
So how says it it of Rumi fragmented space. How fast is the future arrive. Owns take for us to be all right worldviews or goodness all A.I..

00:17:39:13 – 00:18:12:00
That’s a big question and I don’t know for sure I will say that the larger the organizations are the more likely they are to be men in these tools adopting some of these tools because they stand to gain more. It logically makes sense if you have three hundred thousand employees you’re gonna get more out of something that’s automating and giving you intelligence not just because you have more data for the system to consume and get smarter as an algorithm. But also just because there’s more manual stuff be done with an organization that big. The smaller the organization goes that’s where it gets the less likely you are to benefit as much from one of those the less likely you are to be able to afford one of those things.

00:18:12:02 – 00:18:42:26
So I would actually believe that at some point in the future I don’t know how how close it’s gonna be. Some of the tools at least on the lower end are going to be become commodities. We’ll see you know maybe the chat bots for example some of those tools. They’re doing basic functionality. It’s gonna become very inexpensive to grab one of those and to implement it and even if it doesn’t. Revolutionize how your H.R. practice or how your town access your practice is running I see those sorts of things being being able to feed into that more quickly and more easily.

00:18:42:26 – 00:19:13:07
Again I don’t have a really quick. And simple. Pat answer for that one but it’s a good one I love talking about I love talking to. The. H.R. leaders about how far how far into the process they are where they’re going and again you talk about a minute ago the research you’re doing. You’re getting some insight into that because that was one of the struggles I had I started the book. I went back and forth on doing. An actual survey. To examine this. And the problem was I went through when I was reading all the surveys that I found on the market and at the time you know two years ago and

00:19:13:24 – 00:19:45:25
They were all over the place you know we did we did one asking T.A. leaders about. A.I. as a priority it was dead last in their terms of priorities. But then you look at a separate survey and. 80 per cent of. T.A. leaders say they’re going to have A.I. in the next five years. And the problem was when I found out after digging into this interviewing a ton of. Executives is that they didn’t exactly know what that meant. They weren’t sure if some vendor told them what it was. They kind of took them at their word. But they didn’t know what I was and they still are not clear on it.

00:19:46:06 – 00:20:20:00
So that’s again why why you and I are doing the good work and going out teaching what it is giving some insight on that so that they can understand. Oh yeah. You know what. We’re not even doing that stuff. We’re not interested in doing stuff or we’ve already started that and realize we started that we’re using some machine learning in the process already. And so. That’s the biggest problem I found was that people in the audience weren’t clear on that. And so a lot of the survey data that I saw coming in. A couple of years ago. Was very muddy and I didn’t trust it very much so I didn’t even do a survey myself on the topic I just used a lot of use cases and stories and examples of what was happening

00:20:20:09 – 00:20:26:00
To illustrate it. Because. Those comments were getting a benefit even if I didn’t know. Whether was a lie or not.

00:20:27:04 – 00:20:51:04
So I have this I have a different a different view. The view is something like. It took the iPhone to go from nowhere to everywhere. And it is roughly a steroid enhanced iPhone doesn’t have a physical form and so it comes into the organization. Ivy outside of your house that goes into the house.

00:20:51:19 – 00:20:54:26
It’s domain not in Alabama. But that would be kudzu probably right.

00:20:55:18 – 00:21:32:05
I somebody had a little shop of horrors close to the plant takes with the inside of the space. I think it works like that. So when I sit down I was I was doing some research about resume development recently. So I decided to make a resumé with Microsoft Word as I started. So Ford said oh it looks great would you like some help. And so. So that’s something every desktop in the world. So there’s a I already in every little tiny organization everywhere like some plant growing or so iPhone that doesn’t have a seat.

00:21:32:06 – 00:21:56:21
It just goes in and it’s the new thing. So sorry as it happens pretty quickly anyhow using our practice should be concerned about keeping their jobs. One of the books one of the jokes in the book is if you if you think you’re really great at data analysis is not the only thing that you have to offer then you probably need to get other skill pretty quick because the algorithms and other tools are much better than us at certain things like data analysis

00:21:57:18 – 00:22:32:29
For example. And what I think is there are going to be some jobs for sure that are going to be minimized or eliminated totally. For example if you have a big recruiting team and you have someone that’s dedicated to scheduling and their entire job the boss can do those things right now already they can handle that scheduling set up everything else and they can handle it more quickly than you can they can respond to keynotes more quickly it’s more consistent. And so I think some of those types of things are going to absolutely go away. That does mean though that for those jobs that are left behind you know going through the furnace and being refined like steel

00:22:33:28 – 00:22:53:19
Or iron I guess when you’re the thing that comes out the other end is going to focus more on those human skills of work. The service oriented things the creativity of curiosity the things that we can’t program a computer to do. Those things are going to come more important once we come come out of this other side of the automation wave. Let’s call it.

00:22:54:07 – 00:23:26:07
That’s interesting. That’s interesting I I hear a lot of sort of excluding you you know half the time what I do is just take this inquiry view and so I wonder if there aren’t some pretty important hard skills out there that are difficult to understand. What are the actual things that you have as opposed to a universe of. It’s it’s weird to think that the future of nature is continuing to be stowed when everything else is giving business or home. So it’s a weird it’s a weird

00:23:26:28 – 00:23:32:02
Forecast. So it’s pretty broadly except it goes.

00:23:32:27 – 00:24:04:02
Yeah I would say there is there is some research I believe from McKinsey that says if you want to look at more hard skills the things they look at are non routine manual work things that require more discretion. Those types of things that the job that are less routine in nature will absolutely be somewhat shielded from the automation the A.I. those things that are creeping in. The Ivy example you gave. The more or the less routine it is the more likely it will be shielded from that. But even if you’re doing something. Again relatively high level I’m analyzing

00:24:04:11 – 00:24:34:18
Data looking for trends and patterns trying to identify and understand these hypotheses. Like that is something that you’d have a data scientist doing. But you and I have helped companies that have tools that will automate most of those that analysis already. And it just requires instead of. A team of people to do that analysis. It requires one person to step in and say OK. We’re on translate this and give you some actual practical takeaways for how you need to take action on this next. And even that might be automated at some point. So. It comes to mind.

00:24:34:19 – 00:25:04:26
We always think of robotics taking over like an assembly line or blue collar type work. But JP Morgan Chase automated three hundred sixty thousand hours that their attorneys and finance even were doing on contract management contract reviews. Things like that. And replaced a team of dozens of attorneys with a handful of attorneys and an A.I. enabled database. So it’s not just the jobs that are. We always picture in our head like automation. Yeah those guys are out of luck. You know someone building a widget on an assembly line. That guy’s in trouble. Now

00:25:05:07 – 00:25:10:00
It’s other jobs as well if they are very routine in nature. So you see job loss going

00:25:11:17 – 00:25:47:05
I think that there will be job losses for sure. But I also think that we knew once there are gonna be ones that we can’t even forecast. And actually I was talking to a group of team leaders last week in D.C. and was one of them said hey you know do that do that analyst thing prognosticate a little bit. What do you think is going to come in the future in terms of new types of jobs. And so I’m talking about. Examples like having someone that their sole job is to design an employee candid experience for example on the T side. Right. We have people unlearning their learning experience designers they develop learning experience journeys with people and try to craft this journey that engages the learner throughout the process.

00:25:47:05 – 00:26:19:01
Why would we have that on the Kansas side as well. It’s one that’s their whole job is thinking about ways to connect someone into that make them stay in that make them excited about it. Just like we have you know developers at Facebook trying to figure out how they keep us on the platform for 10 more seconds a day 30 more seconds to see one more ad for them. That’s not a that’s not viable for us but from a candid experience journey like let’s keep them connected into this journey let’s keep them excited about coming here. Let’s make this a process they want to go through. So they want to join us. So I think there will be other jobs that change and shift and are created that we can’t even forecast yet.

00:26:20:03 – 00:26:35:11
Fantastic. So we have blown through our half hour and haven’t even gotten close to all the things we might have talked about. We should do this again. Absolutely. Thanks Rick. Thanks for taking the time sir. Any single thing you want the listener to take away from all right by the book. What’s

00:26:35:11 – 00:27:11:06
What’s it called. The book is artificial intelligence for a jar the hard titled remember. It is on Amazon. 12 million everywhere fine books are sold. I would love to have someone read it and also just reach out. Let me know what you think of it. Again getting good feedback on people enjoying the book and looking for ways to apply it. And so I’ve absolutely love for someone to check it out but just as a take away guys coming right it’s already here in some cases. Don’t wait. Don’t wait to see what this means. Wait so it means for your organization it’s ours. Ideally suited of all the people in your organization to have conversations about this because we know the skills that we have in the organization.

00:27:11:06 – 00:27:16:16
We know who is at risk for automation. And we can lead that conversation versus waiting for someone else to come to us.

00:27:17:00 – 00:27:20:22
Fantastic. So please reintroduce yourself and tell people how they might get a hold of you.

00:27:22:10 – 00:27:33:24
I am Ben Eubanks, principal analyst at Lighthouse Research. So glad to have been here today with you John and if someone wants to reach me you can get me at Ben Eubanks on LinkedIn @ Ben Eubanks on Twitter, website is IHRA.IO

00:27:34:08 – 00:27:53:21
Thanks Ben. It’s been a real treat. I appreciate you taking the time to do this. And thanks everybody for listening in. You’ve been listening to HR Examiner’s Executive Conversations and we’ve been talking with Ben Eubanks who is the principal analyst at Lighthouse Research. Thanks, and we will see you back here next week same time.

00:27:53:21 – 00:27:59:12
Bye bye now.