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Hosts Stacey Harris and John Sumser discuss important news and topics in recruiting and HR technology. Listen live every Thursday or catch up on full episodes with transcriptions here.

HR Tech Weekly

Episode: 221
Air Date: June 5, 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.

John Sumser
Stacey Harris


00:00:14:20 – 00:00:26:01
Good morning and welcome to HR Tech Weekly One Step Closer with Stacey Harris and John Sumser. Good morning Stacey. I think this must be the international edition.

00:00:26:17 – 00:00:46:06
I was just going to say you must be playing Double-o-seven music for me here. Yes it’s a great way to start off the call this morning. I’m located in Barcelona Spain this morning looking at a beautiful blue sky. So I can’t complain at all. And you’re you’re in California this week aren’t you.

00:00:46:29 – 00:01:05:06
I’m beautiful. Well it’s actually beautiful it’s kind of gloomy San Diego where I am spending time with Cornerstone at their users conference and it’s been interesting. I love San Diego.

00:01:05:16 – 00:01:07:24
But tell me about Barcelona president Robin

00:01:09:18 – 00:01:10:06

00:01:10:06 – 00:01:43:24
Well I haven’t got a chance to get out and experience that too much this way but I have been experiencing beautiful weather. I love the fact that here in Barcelona but they love color so all the women and the men both very very colorful business casual environments. Generally when you go to Europe or you go to other places oftentimes everybody’s in black suits and you know maybe a little bit of color in their shirts or their stock for their time. But here wonderfully colorful dress jackets and you know skirts and it’s this is the beautiful place to be at and have a conference.

00:01:43:24 – 00:02:03:20
But I’m here for the talent conference when I get out a little bit hopefully tonight I will let you know how the rest of Barcelona look. My understanding is that it just got amazing architecture and I’m so I’ll be seeing a little bit of time downtown tonight. But the rest of this week I’ve been here at the conference center which is at the Fairmont Hotel in Barcelona which is a very nice event location.

00:02:03:23 – 00:02:06:21
That sounds great to Kobe Bryant Township.

00:02:07:16 – 00:02:43:06
Well for those who don’t know talent but they are able. They’ve been primarily a talent management solution in the European markets are down in France. They’ve got over 2000 customers and the talent management based last year they launched what they call talent hub which is sort of a. Stripped down version but it got a version of a core a term with effective dating and all the things that need to go along with that and document management and employees don’t service them and yourself the risk that they are quickly building out a portfolio of applications that run from recruiting and learning and performance management all the way through.

00:02:43:06 – 00:03:15:20
Now it would be sort of the core rate our application and they also I think have a very strong standing in this space around sort of regional and localized requirements where the European environment which is always a bit of a struggle for anybody who’s worked in the European environment. So they have customers in 130 different countries in this space. And it’s been very exciting to see you know we generally see a lot of these large global age technology vendors primarily being U.S.

00:03:15:20 – 00:03:36:12
centric. This is definitely an organization that is not U.S. centric. And there they have most big plans to stay within the European market for their base but they’re getting a large set of customers working with them. So really excited to be getting a chance to sort of see where they’re out this week and understand a bit more about their clients and their customers and the work that they’re doing. So it’s been a good week.

00:03:36:12 – 00:03:39:01
So what’s the big news though in China.

00:03:39:26 – 00:04:24:16
Well I think the biggest news this week from them is their partnership with Microsoft. So for those of Microsoft partners with everybody and you know that’s kind of one of the big things you know when all the analysts were sitting in the room and they were sort of talk about what they’re doing and of course there’s always the question Is it exclusive with Microsoft it’s never explosive it can’t be listed right there. Their home model is about partnering with everybody even in a competitive environment. But they have it seems been one of the first. The market they were mentioned in you know Microsoft CEO’s recent presentation of the new tool around Microsoft Kraft and Microsoft Graph is sort of the connective tissue between Office 365 all of the cloud service offerings that Microsoft has.

00:04:25:04 – 00:05:01:13
And the idea of a profile that you know you have around which would be Microsoft customer but work organizations that would be their employees. And this Microsoft Graph interface environment that of tool that they’re calling is being utilized by the talent soft product to basically connect the two world which is Microsoft application and Office 365 and all the things that go along with it including teens in the Word and Excel and the SharePoint all those type of things with what’s happening in your H.R.

00:05:01:13 – 00:05:24:21
environment to go along with social requirements and scheduling and communication. And so those two worlds they basically announced a partnership where talent staff will be leveraging that graph interface in those graph tools to bring data in and pull data from their environment back and forth between Microsoft Office and count them.

00:05:25:02 – 00:05:55:08
And they had a pretty high up representative there from Microsoft to announce this and they walked through several examples of how their tools that would be leveraging the Microsoft data to to help their companies you know do H.R. processes and gather data on their employees in ways that that made sense from an H.R. perspective such as scheduling performance reviews task management within their environment.

00:05:55:08 – 00:06:27:03
Those type of things. But it was very very interesting and they focused on this idea of work. You know the idea of H.R. and technology in the flow of work that everything you’re doing now is going to be in the flow of work very nicely mentioned to you John is one of the people that they 40 years back a major tech conference session who had mentioned that early on as the idea that that H.R. technology would be more about the application layer versus the platform. And that has definitely informs their thinking in this area.

00:06:27:03 – 00:06:31:09
So that was a big big base that they’ve been talking about today.

00:06:31:09 – 00:06:38:01
Do you remember that presentation where you mentioned that somebody that I would never use for language

00:06:40:11 – 00:06:41:21
in the flow of work.

00:06:43:19 – 00:07:13:22
Because because because that’s not is there is a lot of additional intellectual baggage on the idea that people don’t want to learn multiple interfaces. So the best the best place to do your H.R. stuff isn’t slack or some other communications platform. And I’ve had I’ve been for for a month trying to figure out what in the flow of work means.

00:07:13:22 – 00:07:16:29
And it just means that you put your

00:07:18:15 – 00:07:29:28
processes closer to people by being in the tools that they spend their time doing rather than intuition and learn how to use your average Joe.

00:07:30:01 – 00:07:30:18
So yeah.

00:07:30:19 – 00:07:39:21
And you know that but I didn’t worry about for a long time in the flow of workers and Asians was was I was a health spa treatment. Was going to go

00:07:41:11 – 00:07:46:16
get a massage and Jim will work there.

00:07:48:00 – 00:08:29:02
It does not roll off the tongue as easily as some other thing. But I think the thing which you mentioned which is the idea that you know the H.R. system may not be the primary point. Right. You know the place where you’re going to be doing all the work that but it is more of a layer that you’re pulling data into and pushing data out from right and doing a lot of the analysis algorithm. Right. That was one of the other things they mentioned they are starting to roll out some algorithms that will help rank and rate you know applicants for a position which we know brings with it a lot of concern about data and privacy and ethics around that.

00:08:29:07 – 00:09:07:24
They were very careful to show that they were going to show where their algorithm you know what type of things they were using to create those algorithms. They did share that in their demonstration. But the conversation around data privacy here was a little different. I thought that would probably intrigue you as well their data privacy conversation was much more focused on specifically the idea of making sure that people were aware of what you were capturing and why you’re capturing it but not as much emphasis on some of the things we’ve been talking about in the state which I think is about how that data could be used in a biased way.

00:09:07:25 – 00:09:14:02
There was not as much conversation here about a concern around data bias as I’ve seen in this case as much.

00:09:14:20 – 00:09:39:02
Well you know the the the conversation about bias in the stage it’s really it’s really a serious conversation because you can scare people in this stage by pointing to regulatory problems associated with real heavy pressure issues and in general people in the states are terrified about a

00:09:41:17 – 00:10:06:24
legal framework that shows it’s against the law to be biased in these very specific ways. But it’s real. It really is a compliance conversation. Bias is a sexy way to talk about approach and that kind of compliance is secondary to privacy which is a human right in Europe.

00:10:07:05 – 00:10:10:20
And so you’d expect it to be different do this for you are different. And

00:10:10:20 – 00:10:11:29
And the question of whether or not

00:10:13:24 – 00:10:23:11
there is bias comes after the question of whether or not you can prevent the system from working against you in the first place.

00:10:23:29 – 00:10:37:01
That’s a very much better way of saying that I did but it was it was an interesting perspective because we we talk about data privacy in the States but it is definitely not at the same level as they have here.

00:10:37:03 – 00:11:06:09
And yes I think you know there was just you know there’s their talents office like I see with many vendors here particularly there’s not a fear that it’s just a matter of practicality about well you just have to make sure that you’re you know just doing all the things to make sure it’s transparent very much a feeling of you know sort of this is just how we do you know artificial intelligence there’s that transparent conversation versus sort of having it in the background doing the work thing.

00:11:07:08 – 00:11:36:28
Well I think you know this is this is this is muddled. I’m sure we we were you in the bush for somebody in the audience was in the stage when there is a law business approach towards us from the world can we get away with breaking immersion economical moral issues that goes much the cost of abiding by the law vs. what’s supposed to break.

00:11:37:05 – 00:12:06:26
Well that may not be a universal global way of thinking about what laws wrote. So places like big parts of Europe. The idea is that if there is a law then you walk around work with that. That’s what gets the story out. That’s not a congenital American way of thinking about what the law which

00:12:09:05 – 00:12:11:08
we might very well get some comments on that.

00:12:11:09 – 00:12:46:18
I don’t know is probably the suspect but I will just say it definitely. I don’t feel here as much fear as I think we see in the States when we’re talking about this topic even from the practitioners the practitioners where we’re really interesting and in their comments and questions in this space. They were here you know listening about what the tool was doing but very excited about the opportunity to use some of these tools and just really trying to figure out when they could put them in place and and that again I think sometimes you hear in the state much more pushback though on that turf will be allowed to use it.

00:12:47:01 – 00:13:32:11
I didn’t hear much of that from the customers here either. They were very interested in how quickly can we get this in place and how quickly can this be. The other thing I think that was interesting is that you know they are definitely using the language here of ill management and competency management. Again another term that we see used in the states definitely with learning technology. But you know here this idea of field mapping goal and competency management was central to a lot of the artificial intelligence they were talking about and the tools that they were sort of bundling together as part of this office 360 connection and conversation as well. So

00:13:32:11 – 00:14:04:21
So that was nice to say that those are terms that were front and center for a H.R. tool that start from the talent management perspective. Right. It’s much what you would expect if an elemental system came out with an H.R. capability. They definitely show that that they are learning and in performance. That was very good as well. You’re at another talent management vendors event. You’ve worked cornerstone this week. How about their update. Anything there that you know is similar to what we’re hearing here on the European side.

00:14:04:21 – 00:14:11:00
Well sure cornerstone is is there is doing two things that are pretty interesting.

00:14:11:01 – 00:14:17:10
The first one is they’re rounding out their entire operation right now has a major eye.

00:14:19:13 – 00:14:27:23
And that makes some of a relatively comprehensive suite provider. And what they’re doing in the places where they do

00:14:30:13 – 00:14:41:03
functionality is they are a we build digging out the availability of quality over a patient you have the interfaces very large.

00:14:41:16 – 00:14:46:17
And so those are the two. Those are the two primary announcement this week.

00:14:46:26 – 00:14:52:01
And that puts cornerstone on the track to be

00:14:54:27 – 00:14:59:05
a key competitor in this space.

00:14:59:23 – 00:15:04:15
A tool to our system right. The API is for payroll.

00:15:04:15 – 00:15:07:05
But how much of the rescue function on

00:15:09:28 – 00:15:11:23
so that’s that’s our forest.

00:15:11:25 – 00:15:16:20
Now now has a passing. Some people will say no.

00:15:16:22 – 00:15:33:26
Is my wife and I ran a very long very wonderful workshop for cornerstones Executive Advisory Committee. It was a room full of very senior H.R. folks.

00:15:34:24 – 00:15:50:01
And we took them through what I think is is probably something that Heather and I are going to do a lot of the coming years in a workshop that starts with how do you make sense out of all of the new technology that’s coming along.

00:15:50:02 – 00:16:04:09
And then we split into two workshops after I ran an experiential section that gave you a sense of what it’s like to be inside of a model.

00:16:04:09 – 00:16:25:00
What do you do about the fact that the way the machine sees the world in the way that you see the world are different and Heather’s other segment of the workshop was a disciplined run through over methodology for understanding how to process a decision.

00:16:25:03 – 00:16:57:17
Once you had an input problem machine right. And so and so we’re looking at some of the very practical parts of the integration of machine intelligence into working and what and what that means. There was a there was a tremendous success and I think Heather and I learned as much as everybody else in the room. Because there are all sorts of new ways of understanding what’s happening in organizations.

00:17:00:20 – 00:17:21:00
Very neat and and were they big and small organizations or was it a wide mix of organizations or for that. That’s a pretty big mix of customers who they have. Not sure who’s on their advisory board but then always make the big difference and where they have locally or other over does it.

00:17:21:09 – 00:17:29:23
Note these were all dance music. These were the big bills the executive advisory board of their

00:17:31:29 – 00:17:33:10
largest clients.

00:17:33:21 – 00:17:39:02
So so it was it was 15 or 20 sizable.

00:17:39:03 – 00:17:51:16
There was somebody from Amazon was there and somebody from U.P.S. was there some major health care providers were there and they all had very similar problems such

00:17:53:08 – 00:17:58:09
and they all were somewhere down the road to

00:18:00:01 – 00:18:18:10
where David is ever a crack heart ached or in a way that has them lookouts and they all were there weren’t all about about 60 percent of people in the room held the responsibility for their companies to protect the BAIER

00:18:20:03 – 00:18:27:01
So it was it was. It was an interesting group of large sophisticated players

00:18:29:06 – 00:18:29:23

00:18:30:00 – 00:18:36:25
A question of their participation was amazing. Not the same that I did in my work in my workshop.

00:18:37:00 – 00:18:53:27
It’s a it’s a well established simulation where you have different teams something that looks like the same set of rules but there are one or two tiny little differences

00:18:55:12 – 00:19:08:12
and you’ll get them good at playing a game with that set of rules and then you start to mix people from different teams. So you end up with different teams trying to get something done.

00:19:08:13 – 00:19:10:05
When the rule definitions are

00:19:11:23 – 00:19:49:21
which is what happens when you have a machine offering a recommendation versus the ways of the human what are you direct this there’s often a dissonance and part of what you have to figure out how to do that to function well in a in a in an environment where the workforce is composed of and machine intelligence is is make sense of those rules differences and learn how to voice dissent when you don’t agree with the decision with the recommendation on the machine.

00:19:51:28 – 00:19:55:10
So that’s that’s part of what we’re trying to to get through.

00:19:57:09 – 00:20:39:27
That’s actually a really you know it’s interesting because you know here at the tone of the event I was particularly impressed when the the there there head of technologies. But the guy who oversees the infrastructure and all that here and then put up a couple of slides about of his role in the work that he was doing. And one of the things he definitely focused on in his role and the company’s role was is in the area of digital ethics as they called it. And I think it was exactly the right thing his his wife particularly said that their approach to digital ethics is to be more open and transparent about usages be able to blame the recommendations which is what you just talked about.

00:20:40:00 – 00:20:57:01
And I think this is what you’re talking about except social responsibility over personal data protection. Is that what you what you think you’re talking a little bit about there’s this idea that you have to disagree in some cases and that might be in a case of social responsibility or ethic decisions right.

00:20:58:13 – 00:21:11:02
But it could also be just to Germany show show show we’re moving into a time where there’s going to be probabilistic information about everything.

00:21:11:06 – 00:21:12:26
Imagine that there.

00:21:13:22 – 00:21:42:24
I don’t know what real numbers but imagine that there are 10000 possible decisions that people could make in the process of winning a major drug for every one of those 10000 decisions five years from now there’ll be a statistical prediction about what the right thing to do is. And that can be from as simple as do we make an exception to the pay advance policy.

00:21:44:06 – 00:22:12:24
Who do we promote or how do we intervene in this particular environment. And as a consumer of that information the machines are never really going to get better. That 80 percent 85 percent and so the question of what do you do with the prediction that says there’s an 80 percent likelihood that it’s this way versus a 20 percent likelihood of accelerating

00:22:15:00 – 00:22:55:25
boils down to how do you trust the machine and what do you trust machines to do. And and so so that that makes it a little weird that the process of getting to use these tools will be one of developing trust machines output and the machine’s output is not is going to be imperfect because the current way of thinking about how you build a model of a decision is that you use the least number of variables possible to predict the history right.

00:22:55:25 – 00:22:55:29

00:22:55:29 – 00:23:03:00
And so there’s always going to be a gap when you when you build the model that way.

00:23:03:00 – 00:23:39:06
And that gap is where all of the errors now are in our organizations. Today we really don’t cultivate dissent as a part of management. So so I’ve never I’ve never heard of a manager whose idea is I want everybody in the Thomas team to disagree with me. That’s not the that’s not the industrial management model. But what you need going forward is lots of people who are willing to disagree and stick to it.

00:23:39:28 – 00:23:56:28
What you saw in the exercise that we did you’ll recognize the behaviors instantaneously. When when when when it became clear that there were two sets of rules in operation some people who tended to be white men

00:23:58:22 – 00:24:16:26
asserted that they knew what the rules were they we were going to do it their way. Right. And some people who tended to be women rolled their eyes and went oh here we go. I didn’t see

00:24:18:24 – 00:24:55:24
the right. And there weren’t. There were there were tons of other reactions which consolidated in those in those two places. Both of those approaches are the wrong way to deal with with something that has to do with the rules being off. So being assertive and self-confident isn’t the answer and being passive aggressive listening to that. The right answer is to go wow something’s wrong here I’m really sure the rule is X but you have to build.

00:24:55:28 – 00:24:56:05

00:24:58:14 – 00:24:59:09

00:24:59:10 – 00:25:08:08
I think that has more to do with your work in school avoid doing anything with cheating.

00:25:08:27 – 00:26:04:18
This is sort of an interesting take. I think what you’re getting at might actually know if you look at it on the flip side possibly right. The chief product officer here at town says Aleksander told me he did a presentation on the better presentations I’ve seen in the last couple of years on this topic right now. We always get the. Are you going to be in the West world environment or are you going to be you know in the in the Star Trek or whatever else you know is a positive environment for you know artificial intelligence. But his approach to this was to flip the question a little bit and to ask about what he called you know I don’t think this is a correct term but this human singularity instead of a computer singularity right which is the idea that if the technology is you know sort of has all these capabilities we as humans have to decide what we want technology to do versus what we want to hold on to.

00:26:04:25 – 00:26:34:26
And when we make a decision about what we want to hold on to then how we use the technology becomes more interesting and thought to that is the conversation about decision making should have a better human being who can sort of make a more compassionate decision that than a computer can make right. But that means you have to understand that you can push back right to be compassionate. You have to know that there that the computer doesn’t have the capacity to be compassionate. Right.

00:26:35:09 – 00:26:40:12
I don’t know do you think that fits together with what you’re talking about John or is that going in a different direction.

00:26:40:12 – 00:26:44:22
No no. It fits together really well.

00:26:46:27 – 00:26:55:12
Colleagues who are digital and we are going to have you know I don’t know to look like a balance out but I’ve been seeing more and more stuff from vendors

00:26:57:17 – 00:27:02:06
that looks like the slide I’ve used in these days is in Ivory Coast.

00:27:02:06 – 00:27:25:23
I recovered house. It looks like intelligence is Ivy and it’s covering the house. And so. So there are going to be you know I see I’ve seen full spectrum H.R. technology providers with 50 or 60 different points where they have an intelligent recommendation engine above the door

00:27:27:15 – 00:27:53:21
each one of those things is a model. Each one of those things is somebody who’s opinions of how things work and you have to vet those both before you set them free in the organization. And while they’re operating to make sure that they’re OK and I don’t I don’t think anybody that I would cover is prepared for that level of management of the technology

00:27:55:09 – 00:27:56:12

00:27:57:18 – 00:28:20:19
And that’s the exact word management of this technology because we just assume that I think the artificial intelligence shooting can be used everywhere. Right. But I think the better question is what do we want to hold onto. I really like that question. I think it’s something we probably should think a little bit more about. Which means you have to understand exactly where that line is.

00:28:20:21 – 00:28:54:24
But I know this is this is a fascinating conversation on the front that you know the technologies in the age or space today now that are moving in H.R. are the technologies that we’re more focused on the softer side of H.R. right the talent management systems both of the organizations we’re talking about today John Cornerstone and town thought were primarily those systems that were thinking about learning and performance and succession planning all the things that we generally would align with the softer side of H.R.

00:28:55:11 – 00:29:12:06
and they’re getting into the more sort of critical talent profile and core H.R. math in place those first major self-service areas. It’ll be interesting to see you know can these two worlds fit together in a lot of I think it has to do with exactly what you’re talking about right.

00:29:12:07 – 00:29:17:14
They have to understand where the artificial intelligence system and what they’re covering up to what it’s like covering up.

00:29:17:27 – 00:29:28:06
Yep yep there’s going to be a really amazing time to be really raising time because things are different now.

00:29:28:27 – 00:29:48:26
Now we didn’t have because both of us are traveling and we’re sort of at events we didn’t get to any of the news and topics for this week a major tech based. But next week we’ll be back on talking a little bit more about news and topic but I think this has been a great conversation this week about the state of a guy getting into probably some of things we’ll be covering in your your upcoming book. I

00:29:48:26 – 00:30:10:29
I would assume that we’ll get ready aim at domain of 83 three children wouldn’t have a big 120 by the time I read to start writing books one our brain will be moist at that point. Yes. Yes. Even as you can see brain cells weeping as we speak.

00:30:12:19 – 00:30:16:18
So enjoy the rest of your trip up your back in of truth.

00:30:17:12 – 00:30:27:17
Well I’ll still be still be here in Europe but I’ll be vacationing nothing but I just couldn’t do the radio show that was we should be fine. But I’ll be back the following week in town. Yes.

00:30:27:17 – 00:30:55:26
Wish me good purpose and I’ll be in New York. So we’ll be figuring out titles at three yeah. OK. You guys another great another great conversation. Thanks so much for taking the time out of your globe trotting to do this. And we’ll be back next week. Thanks everybody for listening in and listening to HR Tech Weekly, One Step Closer with Stacey Harris and John Sumser. See you soon. Bye

00:30:55:26 – 00:31:07:00
Bye bye now.


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