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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: Joe Hanna, Chief Strategy Officer, Workforce Logiq and Managing Director, ENGAGE Talent
Episode: 348
Air Date: December 13, 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

John Sumser 0:13
Good morning and welcome to HR Examiner’s Executive Conversations. I’m your host, John Sumser. Today we’re going to be talking with Joe Hanna, who it is now the Chief Strategy Officer at Workforce Logiq. He managed to sell Engage Talent into Workforce Logiq and the pair of highly expanded highly interesting company is where he is resting his bones these days. Joe, how are you?

Joe Hanna 0:40
Fantastic. Happy almost holidays.

John Sumser 0:42
Yeah, happy almost holidays indeed. So, why don’t you tell me a little bit about how you ended up here. This is a pretty remarkable story for my money. So let’s have the Joe Hanna story.

Joe Hanna 0:54
I appreciate that John, and you know I’m going to focus on the last five, six years here because that’s what’s most relevant to your audience and people who have as much attention span as I do. But we started working on what became the Engage project back in late 2013, early 2014. The idea back then was focused on skills gap, helping companies match people with the right roles, identify where there may be gaps, maybe do something about those gaps using technology and identifying opportunities for people to be better qualified for jobs and generally approach matching people with jobs by looking at their skills, the functions they held in the past, instead of titles and synonyms and keyword the way it was done, historically done and still being done by by many organizations and technologies today. And that evolved and pivoted a few times until the into the scalable and what became widely adopted Engage platform and with Engage we help companies with three areas.

Number one, with market intelligence that covers labor, market intelligence as well as competitive intelligence, understanding where they sit versus their competitors where the market is going. What does the supply of different skills look like? What are the most valuable skills? What are the new skills? How are different titles are evolving in the market and what the current future of work may be telling us on what skills are needed and what what people are working on today. The second pillar is taking that and make it more I don’t want to say tactical, but execution oriented recruiting and sourcing, whether that’s pipeline building, I can find people who are more likely to be interested in your company and more likely to be open to engagement with the recruiter today more likely to change jobs and the next 920 days client why that is and translate that to messaging and campaigns to increase the likelihood that you’re going to engage with with those candidates. And then the third bucket which is kind of a split coin to all of that is utilizing all that intelligence whether it’s about you as a company competitors, what’s happening market as well as the algorithms and machine learning that identify people are likely to change jobs, use all of that internally and leverage it for better engagement for better communication programs. For for identifying new to pipeline, you may, you may need succession plan for example. So that’s, that’s what engaged ended up being as a platform. And we had about 150 customers or so split between enterprises as well as staffing and scarps. So that’s where we engage was earlier this year. And as we go through our journey, the company was not for sale or not in a process, but through mutual connections and knowledge of each other in the market. The discussion started with Workforce Logiq, and I can tell you, we know a lot of services companies will work with them every day. MSP’s RPO’s. I don’t think that there is any other company out there that took very deterministic, very specific steps in recruiting and executive team as well as focusing on innovation as well as investments and innovations as Workforce Logiq did in the past two years. So this was a company and generally, you know, an industry that hasn’t innovated much MSP RPO has been somewhat stagnant in industry for a while they went out there and brought in an executive team that understood the value of data the value of innovation, the value of automation and predicting, instead of reacting to market realities, and how all that can transform business, whether it’s HR business or otherwise. And as there isn’t that many services companies you go out there and look at especially the big boys they are whatever baker’s dozen that list how many patents they have in the second slide when you’re talking to them or how many innovations they are working on. So it was love from first sight, if you will, innovation is our DNA and we found The discussion was going to be fluid. We’re finishing each other sentences and what technology can do. And then most importantly, how can services on top of technology can really change how solutions are delivered to the customer? So we went through five, six months process. We officially became one company six weeks ago, seven weeks ago now, and we are out executing and the early implication is that was maybe one of the better acquisitions mergers that happened in our market and the past few years.

John Sumser 5:34
Well, I hope it works well. Let me just try to summarize a little bit what you said. So Engaged Talent is / was a company that’s focused on predicting a number of things about the labor market in particular, it is good at predicting the likelihood that a particular person will leave their job in some specific time frame or As a part of that, that they would be interested in hearing about new opportunities. So it’s sort of predicting the likelihood of success of a recruiters outreach to a person. And then that data becomes very interesting in the aggregate as competitive intelligence about competitors, and also interesting intelligence about the evolution of the market as a whole. So you can start to see movement and predict movements inside of the labor force. And these are the sort of core predictive capabilities, engage talent and get that right,

Joe Hanna 6:38
hundred percent. You know, we like to say that we do it in four pillars, if you will, talking from a personal level, understanding the likelihood that they’re going to change jobs or be open to recruiter outreach, why and how to be effective with this outreach. Second, aggregate at a company level understand how the company is going to fare better its competitors and attracting or churning people and the next 90 time to 20 days and why and then aggregate that at their industrial level and all the way to macro labor market level where we’re able to do things like forecast unemployment rates and a lot of what the BLS produces, and the results report with high level of accuracy weeks before it’s

John Sumser 7:24
okay, and then the integration with workforce logic. So workforce logic is an RPO. That means that the company at its heart is an outsourcing solution for companies with big needs in the recruiting marketplace. So they do recruiting for their clients. And the object of fitting engage in I would guess is that by being able to accurately target likely successes in the recruiting process, they’re able to come to the market with higher productivity rates over this course, the basic idea.

Joe Hanna 8:02
So workforce logic is both an MSP focusing on contingent workers as well as a rising star and RPO market. But traditionally, they were an MSP. And that’s they’re really focused on or workforce logic seen a market and what they are attempting to deliver to the market is what we’re calling universal sourcing that covers full time staff. It covers contingent staff, as well as gig and finance workers. And we’re taking very specific steps in each one of them is to be the partners choice for customers in all three areas to full time RPO type relationships that’s powered by engage power by other services and other technologies that we’re building other alliances and partnerships that we’re putting together. That’s a no brainer, but MSP and contingent workers it’s an interesting one that the company’s heritage and we have a lot of data we manage about $3 billion warmth and respect in that area on the marquee customer, key companies fortune 500 and the world. And we’re using some of this data in addition to engage capabilities to change how we target and identify and optimize the contingent targeting as well as so Debbie us and putting them together for the customer to the Zephyr project. And I want to talk more about some of the work we’re doing there because it really is pretty cool. And then the third is the gig and freelance economy. And that has been so far somewhat of a wild west, if you would, how companies are engaging with gig workers or freelancers. And we are bringing in partnerships with the likes of our pork and compliance programs and program management that’s sitting on top of that to be able to streamline it for our customers as well. So universal sourcing is the strategy and we’re filling in each one of those spaces by like second, right, so

John Sumser 9:59
check this out. staff with contingent workers and the gig economy that’s new to me, that’s gonna be fascinating. If you can predict movement inside of that world, that’s a different set of rhythms than a standard career thing that you’ve been focused on all these years. He must be pretty excited down. That’s another reason

Joe Hanna 10:18
we are on this. You can see the big smile on my face right now. Maybe you can hear it. You know what one of the things that make these people really excited is proprietary data and data that no one else has access to, that they can build models and problems that becomes very differentiated. Now are being part of workforce logic, we have access to huge amounts of property Terry data that no one else has. And one of the first things that we started working on together and we actually put out a press release about a couple weeks ago is a way to predict if someone would be open to contingent engagement or not. And traditionally, if that person is Wasn’t hasn’t worked for you in the past or hasn’t raised their hands and applied to a job that pays attention to nature, no one knew whether someone will be open to that idea or not. We are about to release this in a couple of weeks here by the holidays and it will be very in the hands of our teams and customers but we are predicting is on all the millions of people who now know where contingent in the past what patterns from the two person becoming contingent continuing to be contingent or moving from a traditional full time role to contingency and think about it that is, you know, depending on who you ask, between contingent and gig that is going to be bigger than full time in the next five years. And there isn’t actually there is very few solutions out there are companies that are attempting to do anything creative and innovative in that area. So yes, very exciting. Definitely kind of leading into market same way. We came at the full time market with a different approach.

John Sumser 11:59
I’m going to be actually Just to see what the results are as you go through that. So you’ve been at this a while you are pioneering in the idea that you can take a I don’t know, I think one time you told me that you had 40,000 data services that flow into your models for how people move in the labor market. And so you’ve learned the ins and outs of some things, what’s changed in the way you think about how the intelligent technologies work?

Joe Hanna 12:25
Let me stop by this. I will say that customers do not care about the technology as much as they care about a solution. So over the past four or five years, as we worked on this project, Grant talked about 30,000 companies about the topic of recruiting about using predictive analytics and in talent acquisition and HR in general. And that common theme that I would say I learned out of that is companies like to hear about technology, but they don’t have not all of them. necessarily have the capabilities to understand how to about the integrate it turn it into a consistent source of solving a certain solution. So we end up with with what we see in a lot of HR departments today, where we have 15 to 20 different tools that are thrown around. And it’s left up to the teams to where they use them or not. And then by the end of the year, they look what worked, what didn’t work. And here is a new shiny object, something new that’s got about that that’s implemented, let’s buy some licenses and see whether it works or not, I think where what we’ve matured to especially over the past two years, so is a team that focuses a lot more on whether the customer is the right customer for our tech or not, and then how they going to adopt it implemented, manage the change management and behavior change that needs to go with adopting new technology, how it’s going to integrate in their workflow, how they going to change the workflow to make the technology Corporate so whether it’s AI or any other technology, I think we’re coming to the conclusion as a, as an ecosystem here that AI is not going to solve major problems by itself, it still needs to be put in the hands of the right people, and it needs to be implemented correctly and to be adopted and integrated for to deliver the solutions. I would say that that maybe is the biggest change and how I’m thinking about AI and packed on delivering actual results over the over the past few years.

John Sumser 14:35
So I think what you just said is that there are two factors limiting the sort of pace at which technology enters the market. thing. One is customers don’t really buy technology, they buy solutions to problems. And thing too, is what it’s possible to do with machine learning and artificial intelligence. Main not the As expansive as it looked five years ago, you can do more interesting complex things. But this sort of implication that we had five years ago that there would be some sort of super intelligence is the thing sitting on your desk is being replaced by the idea that there are these smart tools that require human beings integrate that in consumer what you said.

Joe Hanna 15:28
Yeah, that’s very well put, I would summarize it and maybe one sentence I’d say they are AI coaching tools, ai assistance, a I supporting platforms, but they are not here to completely replace anything that a human is needs to do at the end of the day to make their job successful. They will take pieces of the job and pieces that will make them a lot more efficient. quantou lot more data than we can crunch to an hour around, deliver results and pop up in front of us that we can leverage but the key is I need to leverage them. And I need to they are not going to replace me. I need to go actually do something with the results

John Sumser 16:10
that you can show you how just have a curiosity, how do you teach your clients to use the results, right? Because you give a prediction and a prediction is like you go to Las Vegas and the understand the odds. But no matter how you understand the odds, you could lose 40 times in a row, and the odds are still the odds, right? So the thing that we’re talking about is teaching people to use probabilistic information to make decisions in a world where that was all done by gut hunch. How do you teach people to not blame the machine and take responsibility for the decision while you queue up a good softball for them to hit?

Joe Hanna 16:50
It is about changing workflows. So if your workflow today was to take a very simplistic workflow, I have a wrap And one of the avenues I decided to take is to find a built in passive candidate outreach campaign for that wreck one of the funnels that I’m using to create candidates for the Shrek something that we all in a lot of our teams do every day traditional way, I’m going to look at my database, find people who are likely it fit for this role, I will go look on social media and identify people who are likely fit to that role. And then I will use some sort of my own assessment of looking at someone’s profile to identify whether they may be interested in this or not. And that’s how bring them down thousand people to maybe a manageable 200. And then I’m going to build likely a very templatized campaign that I’m copying and pasting or just pushing a button and sending the same message to everybody changing lady the name of who it’s going to and wait for somebody to respond. Traditionally, that’s how we approach that part of the process for able to do something with with engage, for example, and other technologies that are coming in the space is, number one, identify people who may have not been identified with traditional matching technologies. They may be our part of companies that have that have this technology stack but not necessarily have they don’t necessarily have that and their profiles or and the resumes or in your database. Refresh that data that you have about them. So you have the most up to date, contact information and ways to go after expand your pool by targeting people who may be by now qualified for this role, but they are not holding the same exact title they may be one step below or can make can use a natural tip number one, identify people that you otherwise would not have identified or would have taken you off along cartitles defy using human human boots. Sources brute force if you are, if you add a second, now you have that pool thousand people prioritizing code to go after becomes a very mundane task and becomes somewhat random and so on based on everyone’s experience and every recruiters experience is different. We’re automating that for you giving you better than both. Third, what this data that we’re using Pamela, let’s not use templates, let’s use what machines suggesting that this person is likely to respond to, by looking at thousands of points about our company calendar peers about what’s happening in the market today, or maybe even what the weather is can the convocation today. So you’re changing a workflow by informing the human who the end of the day is the one who would have to establish the connection with that person by informing them in ways that were not possible before and if they do not buy into that actually do not help them understand the value of how changing the workflow is going to really change Engine their day and make it a lot more efficient. They didn’t fail. So adoption training, change management, planning success stories, team implementations, not just one personally by all those become just as important to this as they were and implementing DRP systems 20 years ago.

John Sumser 20:19
Okay, so the ethical problems in the business or what?

Joe Hanna 20:25
How much time do we have?

John Sumser 20:29
We don’t have we don’t have much, but it’s an interesting arena. And so you probably have enough time to up the tiniest nutshell. Yes, sir.

Joe Hanna 20:39
Yep. You know, there was a survey that the the Institute of the future of life, really cool produced by the institution, by the way that I suggest that anyone who’s interested in and the impact of machines and AI on what our future would look like, I really recommend that you follow you follow these guys founded by some very Smart people in the world and being led by professors and researchers in the area. So they did a research survey of practitioners and they I say a year ago or so ask them what are the top ethical issues that you see in an AI, they came up with a few, but the top three that may be impact us the most in our area, one replacing to mind where they I am intelligent, that will take away our jobs or things that we do every day. So we need to think through that second, carrying human bias into these models. We are all biased in a certain way and spoken situation and different various and different degrees to that every day in our life, whether we are conscious of it or not. And the risk there is that we will tweak these models to follow our bias in ways that we cannot undo. And again, but you know that the fact that we’re training them using historical data is where the biggest risk is Because you’re taking decisions that you remain, that may have been bias and telling them learn from that. So that’s a big one. The third one, which is, which is quite interesting, and then we’ll actually maybe have a clearer different discussion of how AI may increase the gap between wealthy nations and Otherwise, we’ll see individuals and otherwise Nah, fixed. So those are three things that came out of that study that I think are worthy of discussion, but definitely something that we think about everything.

John Sumser 22:34
If you’ll send me a pointer to that study, I’ll publish it in the notes for the radio show. That would be very helpful. I’m sure there are lots of people who are listening who would love to see it. Sure.

Joe Hanna 22:45
That’d be great.

John Sumser 22:46
Okay, so we have exhausted our time together. As always, it’s a deep dive into the mystical realms of real data science. Thanks for taking the time to do it. Would you take a moment and reintroduce yourself?

Joe Hanna 23:01
Yep, thank you, John and the time flies when we’re talking as usual, Joseph Hanna, I’m the Chief Strategy Officer of Workforce Logiq, Managing Director of Engage Talent, which is now part of the Workforce Logiq family. You can find me on our website at, feel free to drop me a note directly at Joe Hanna at

John Sumser 23:24
Thanks, Joe. It’s been a great conversation. We’ve been talking to Joe Hanna, who is the Chief Strategy Officer at Workforce Logiq. Thanks again for taking the time, Joe and thanks for tuning in today. We will talk to you same time next week. Bye Bye now.

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