In this video, John Sumser is interviewed at the HR Tech conference and asked about the evolution of HR Technology over the past decade.

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Important: Our transcripts at HRExaminer are AI-powered 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.



JS – John Sumser, Principal Analyst, HRExaminer

00:00:00:01 – 00:00:33:29
Great stuff. So John this is the 20th anniversary of this particular conference. I know we go to a lot of conferences out of the year but this is this is a big one you know and you’ve been involved in this space for quite a while. I remember you and I had met a long time ago when I worked for Monster Worldwide. Tell me a little bit about what are some of the really substantial shifts you’ve seen. Know not necessarily in last 20 years but certainly in the last five or 10. And what do you think some of the sort of hot topics are for the conference that’s going on today.

00:00:34:17 – 00:01:46:02
So I’ll take the last decade or so we’ve really been engaged in something that’s nowhere near as complicated as people have made it out to be. The move to the cloud, started with the move to floppy disks that moved to hard drives to move to super hard drives the move to CD’s and then the move to the cloud. Right. And so it’s a it’s a question of how software and data is stored. But what happened in the process of moving to the cloud, is the cost of processing and the cost of storage all fell almost to zero making it possible to do things that we believed for 20 years weren’t possible. So it used to be in the very beginning of software, the way that you found anything was by looking through the log. There was always a log of every keystroke entered into the software until that became such a great big pile of data that you took snapshots of the database instead of having everything in the database. Today you could again afford to have everything in the database and so that makes it possible to think about data.

00:01:46:20 – 00:01:54:00
In ways that are different than we’ve been thinking about it before. And that’s spawning a great deal of innovation.

00:01:54:17 – 00:02:16:18
So let’s talk a little bit about that from a standpoint of scale right. Because when it comes to H.R. you’re not just dealing with large global banks you’re also dealing with Betty’s bakery shop around the street or you know a small company here in medium size. So how does that, you know concept of data applications and storage. You know if everyone is trying to use data. How does that how does that occur at scale?

00:02:17:29 – 00:02:38:04
Well so. So I’m a little confused by the question. When I think about scale I think about big companies doing big stuff. And I guess the other way of thinking about scale is services that that have lots of little customers and how you handle how you handle data with lots of little customers I think. I think the the question is open.

00:02:41:09 – 00:03:10:10
What most vendors would like to do is generalize across their entire database. But the legislation particularly the GDPR are about what you can and can keep on hand and the employee records that information about employees are starting to seriously affect what people are thinking about. So so the scaling questions for companies that serve the SMB market are wide open.

And. as we think about how organizations really start to apply that data. You just talked about your 90 page paper coming out and you know artificial intelligence machine learning cognitive computing whatever term or definition may be out there is becoming something. It’s certainly analytics. Ah ah ah ah ah. Becoming increasingly important if not a requirement to organizations to really be able to run their businesses. So one of the things that you and I have coming up this fall is we’re going to be looking at doing a podcast series. I’d love to learn more about the paper but we’re really going to be talking about applications of AI in HR. And do you find is that. Where are we in sort of the adoption of those technologies. Is that part is that a transformation yet to come are we in full swing. How do you see that? So in order to do the research that I’ve just just wrapped up I went to meet the heads of departments at Stanford MIT the University of Toronto and Berkeley

00:04:07:08 – 00:04:25:02
And uniformly they described artificial intelligence as a conversation of intelligence that could engage in a complex changing conversation with you and innovate the topic by itself.

00:04:25:25 – 00:05:17:15
While there may be an example or two of that out in the wild there’s nothing like that in HR. Most of what’s happening in HR is very advanced statistics so you can run. Complex regression analysis a million times because processing is cheap data storage is cheap and you get some very interesting results out of that. But there’s nothing particularly intelligent about that. Well there’s it’s very artificial but it’s not anything that anybody in the academic world of artificial intelligence would call it anything like artificial intelligence. Mostly that label is used by vendors who aren’t clear about what they have. So they’re telling you how they built it. Right. What you really want to know is what’s the problem. How do we solve it?

00:05:18:08 – 00:05:19:28
Yeah. And I would think that you know the goal certainly is to try to alleviate some of those problems or at least help them be able to focus in on the goal they’re trying to achieve right. Hire people in less time find faster whatever it may be find those better candidates and get to that end result. But when you when you look at these technologies do you see that as. It really is beginning to solve those problems. Do you see it as it is helping uncover additional challenges or do you really see it as you know it’s a building block to trying to. You know a ways to the means right. Trying to get to a better end. So there’s there’s a really interesting thing going on small startups.

00:06:02:28 – 00:06:38:24
In this environment. There are a lot of small startups doing things here. But. Their investments come from these CEOs. Who want to see evidence of a sales trajectory associated with research and development. Which means all they’re doing is trying to go solve all problems. So so the first wave of technology and the small startups is being applied to all problems now. Big companies big established companies have a very interesting advantage in this particular technical shift

00:06:39:19 – 00:07:00:15
They come with data. And if you have data then you can do stuff. Small companies have to give away their services to get data so they can grow and start to understand what they do. If you’re sitting on a great big pile of data you could do all sorts of stuff. So for example. The kind of initiatives I’m seeing are like in scheduling if you have a piece of software

00:07:01:02 – 00:08:26:26
That as a lot of users and it does a lot of hospitals shift scheduling. You build a comprehensive schedule for the entire hospital. And then the machine as a matter of course will come back and go you know. You’ve got Sally in the respiration part of the ICU. But her scores are. Patient satisfaction with shots are really low. If you put Kathy over in that same slot her scores are higher. A better net patient satisfaction score for the entire shift. If you make this swap or Sam over and bedpan administration only comes about 80 percent of the time you might want to have Arnie who is always looking for extra shifts and always shows up on time on your shifts so you’re not. Dealing with too many bedpans being unclean. Right. And so so the system just makes that assessment of the schedule. As a matter of course. No. I don’t get it. But he knew you could do that. Right. So it’s not an old problem. But it is an old problem and it makes the user of the software more effective. While putting less time into solving the problem. So they spend less time in the software get greater value out the back end. And I think that’s what the first wave of this stuff is really going be about.

00:08:26:29 – 00:09:01:21
You make an interesting point about old problems right. How many old problems. Is technology in human resources or talent management still trying to solve. Are we still are we working on you know for lack of a better way of putting in a backlog of old problems or are we looking at really not necessarily solving new problems but creating new opportunities so weird. Weird is that like. Where does that balance start to tip from only solving old problems or primarily solving all problems to really creating new opportunities. Well so so in order to get to where we are

00:09:03:06 – 00:09:19:03
We built software and software has institutionalized these departments in HR. So there’s a recruiting department and onboarding function and a learning department and an O.D. Department. Bob Bob Bob. Bob. Well those are separate functions.

00:09:19:03 – 00:10:21:01
You know employment branding for example if employment branding isn’t the initial phase of onboarding I don’t know what it is. All right. And so. So if you tie employment branding to onboarding to get the net goal of the most productive employee in the shortest amount of time. You get very different answers to old problems. But if you go and you’re trying to solve the onboarding problem or the recruiting problem and don’t look at the bleed between the silos you get very different answers and so that but that kind of thinking. Employment branding is onboarding. That gives people headaches. Right. And generally you know personally I like to read things that make me angry because it makes me think and I like to. Look at problems that don’t fit conventional definitions because that’s where innovation happens. And so we’re going to see a lot. The idea that training and learning is somehow separate from recruiting.

00:10:21:06 – 00:10:33:28
That’s bizarre. That’s completely bizarre. You want to be able to train enough to tell if somebody is going to fit well. And so that’s the beginning of some other training

00:10:34:21 – 00:10:46:06
Requirement. We couldn’t do that before because our niches were our silos were so tight. And the new stuff is allowing us to see the correlations between things and trying new ideas.

00:10:46:06 – 00:11:06:18
Wow. John I know your time is valuable and we need to get you get you get get you back onto your schedule here. But I really appreciate stopping by today. I’m looking forward to continuing that debate and that discussion this fall. I will try to find ways to make you angry so we can talk more about innovation.

00:11:06:20 – 00:11:43:24
Good good. That will be fun.

It will be fun.

Nothing that clarifies technical questions more than rigorous debate.

Well, I’m sure my wife would tell you I’m very good at that.

Now if people want to me that you’re wrong and I’ll tell her she’s right.

Oh she’s going to love it. So people follow you at HR Examiner on Twitter and some share on Twitter at John Sumser @johnsumser.

OK. So there’s just the one not the both.

You can follow HRExaminer but the better flow is on @JohnSumser I’m sure.

So absolutely. That’s great. So follow John at John some sir on Twitter. And thanks for stopping by and enjoy the rest of the show.

It’s nice seeing you Greg.


Thank you.


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