Transcript
Mark:
And tell me about Reejig. I mean, what do you do?
Siobhan:
Reejig is a workforce intelligence platform. So we essentially help organizations have full visibility of all of the skills and work required within their organization, and then we help them find, move, mobilize, and re-skill their workforce. So we’re like that central nervous system that’s really driving decision making support for organizations to make really good and fair decisions when it comes to their people.
Mark:
And where are you getting the data from?
Siobhan:
So Reejig is an aggregator of information across many different systems. So organizations are sitting with a gold mine of data about their people. And what Reejig is doing is that bringing all that data together to give you sort of a 3D profile of every single individual. We’ll tell you what the person has done before you, what they’ve done well with you, but more importantly using predictive intelligence, what they could do next. And we take that data from, imagine when someone applies for an opportunity within your company, their CV, imagine all of the different projects and learnings and gigs that they’ve done within your company. We then pull that information in together and then folks are able to add information to that, they’re LinkedIn and any sort of information that tells them about career aspirations and passions. So we pull that all together to give one beautiful story about the individual sort of backpack of skills that they’ve collected their whole career and what they want to do next in that career as well.
Mark:
Could you give me a little bit of history about the company and what gave you the idea for it for one thing.
Siobhan:
My background, so I’m not a traditional CEO of a technology company. My previous career was, well, my first career was I started in recruitment and then I sort of worked my way into workforce strategy. So in my previous career I was responsible for sort of large global skill talent acquisition teams, talent mobility, workforce strategy, and our diversity and inclusion efforts at our company. And I had no idea who any of my people were. So we would be asking people on a regular basis, please give us your data, tell us who you are because we want to move you onto new projects or we want to be able to mobilize you around the workforce. But we had no idea who anyone was. No one would complete this information. So we weren’t able to move folks from projects and assignments that needed to be redeployed into meaningful work. Yet we were sort of hiring thousands of people on one side of my business while letting folks go on the other side.
So that was really kind of the back sort of burning problem that I was having at the time, and I just couldn’t understand how we couldn’t solve this given the technology advancements everywhere else across organizations that the people space just seem to lack innovation to sort of really drive that data decision making support. So we started Reejig a couple of years ago, three founders. Sort of my expertise is in sort of workforce strategy and I’ve really lived and breathed this problem my whole career. And then Shujia Zhang, she’s got a PhD in machine learning. So she’s an expert in the data, the ethical AI, and Mike Reed is CTO and delivery and he’s responsible for cyber and software and that sort of main infrastructure. So three of us together are really sort of solving this problem with such a beautiful perspective of the world as well, given that we’ve all sort of coming from different skill sets ourselves. So really Reejig is born from a place of we are so obsessed with solving this problem because we’ve had it and we’ve designed a technology to really enable organizations to do this at skill now.
Mark:
As a corollary, it’s an interesting career path you’ve had. And I wonder if you could focus on that for a minute and tell me about how you went from the front lines of HR into being a technology CEO.
Siobhan:
It’s a very non-traditional pathway. If I knew that this where life would end up, I would not have got that right. No algorithm would ever probably recommend me into this job. But it’s interesting. So when you started in my early career in recruitment, and I think any listener, [inaudible 00:04:31] never intended to end up in recruitment. It’s not a path that you kind of wake up and go, I’m going to be a recruiter. That happened. But what the beautiful thing about being a recruiter is that you get this really diverse blend of skills from influencing to selling, to data analysis when you think about market mapping and all of the stuff that we were doing there as well from a manual perspective. And then that led to what sort of we would’ve called in my company resource management. So it’s kind of mobility the way that people are describing talent marketplace right now.
So I then took over really looking after that resource management side of the business, which was really great because it gave me that commercial lens. So I was now sitting with the business and for every sort of time we didn’t have folks in opportunities, the business actually wouldn’t do so well. And that was really my first kind of lean into really figuring out that, hold on a second. When we talk about having a seat at the table from a HR perspective, it gives me that sort of look into, hold on, I know why I can be valuable here and if I can help move my people to opportunity, I can actually unlock potential for my business.
And that was really the start of that commercial lens. Look, I don’t think that I ever dreamt of ever being in this situation, but weirdly, if you look at the skills that I have collected, a lot of them that I had in my previous career I’m using today. And there’s a lot of skills that we collect as practitioners from whether it’s the strategy to the influencing, to the change management, to really driving that business delivery to the strategy that I picked up. So the path wasn’t traditional, I don’t have technology background, but interestingly, I think the more that the space and the talent and HR work tech is changing, we’re seeing a lot more talent professionals really lean in to start to understand, especially with the generative AI, right?
Everyone, every single person that I know is talking about this and the impacts of this on work, on tech, on jobs. So there was really an evolution for me of really falling down a rabbit hole when it came to ethical AI and understanding the impacts of using that in decision-making support when it came to people. So there was this kind of chain sequence of events for me that led to then me being in that CEO role. I think there’s a way I would describe always just on time learning. In this role, I’m looking at everything from go to market, to building teams, to technology strategy, to investment and VC, to all of this. And it’s not skills that you would ever have picked up, right, from a practitioner’s point of view. So it’s kind of like just on time learning to get there, which really shows the point of these micro bite-sized pieces of learning to be able to evolve your skillset in what we’re doing today. So still learning as a CEO, but it’s definitely not a traditional path that we see CEOs coming from in the HR perspective.
Mark:
Your background, it’s given you knowledge, it’s given you experience with recruiting in HR. How does that sort of fit into your approach to running the company?
Siobhan:
Yeah, and it’s a really good question because what I learned, I probably have had to unlearn quite a lot if I’m honest. So I’ll give you an example. When we were hiring, we grew really quickly and we hired really fast. And you kind of think that you’re going to bring in people and then it’s all great, but actually the recruitment part was actually not the hardest part for us. It was the when you’re building a company and actually figuring out what folks will do when they get there, became the biggest problem to solve for me at that time. If you’re building a company, you’re kind of building the plane while jumping off the cliff and you hire all these people in and suddenly you actually have to figure out how to actually get folks to an ability to know what they need to do every day.
And that was one part where in my old career, we would’ve just hired and give them over to the business and we wouldn’t have really had to think too much other than the entry point of the onboarding. But no, we had to design structures and systems and documentation pathways and workflows and policies and all of this stuff that I had never really got involved in before, which was really interesting. And if we did not do that fast enough, folks would be impacted from an engagement perspective. So I think a lot of what my previous career had taught me was very much so about bringing folks in and getting great people, but then there was that sort of handover point that I’d never actually got involved in. So I had to painfully learn that experience of then not being a manager. On the other side of the fence, not actually receiving the talent, and then how do we unlock the potential of these individuals?
So that was a really big learning for me around designing a business where actually folks knew what they needed to do to be successful and that the hiring part was only tiny, tiny part of the success story because actually bringing them in and giving them the tools and enablement to be able to feel successful in the role was really critical. And that was a really big steep learning curve for me in this experience that we had to figure out. And we’re still trying to figure it out because I’m sure you’ll know the remote culture building is probably one of the hardest things I’ve ever had to do from a people perspective. Really when we think about where my workforce is, we’re all over the world, all in different time zones. And that’s been a massive learning cohort and I’m sure other folks are finding the same experience. It’s just such a different way of having to build that culture. So if anyone’s got any tips, would love them.
Mark:
Shifting subjects a little bit. A year ago there was a lot of talk about AI and a lot of work being done on it, but then ChatGPT came out in November and all of a sudden it’s everywhere. And I’m kind of curious about where you think that technology is in terms of being picked up and leveraged by HR functions. They seem to be using it a lot, but are they using it a lot and are they going about it the right way and using it the right way?
Siobhan:
I think the time that we are in right now, Mark as practitioners, is possibly one of the most exciting things that will ever happen in our careers. We will never see transformation as aggressively as this, I think in my career anyway, which is incredibly exciting, but also comes with that stomach of nervousness and the uncertainty and the unknown. And I think the one thing I would say is that yes, there’s a lot of talk and a lot of hype and whether you’re looking in the HR space or whether it’s from everyone talking about it in the news and everywhere you kind of turn, it’s everywhere. There is an incredible opportunity for us to really think about, a couple of different ways of thinking about this in the HR space. There’s the practical use case of enabling you to have a copilot to take some of your repetitive tasks and free you up to do meaningful work.
So that’s a really exciting thing. Think about you’re driving along in your car and you’ve got someone in the passenger side seat kind of giving you a steer of what’s coming up. We should be using this as a thought partner, right, in terms of that backing and forth thing. And then also giving away our tasks that are kind of repetitive. We’re trialing a lot of it in our internal ops right now in my team, and we’re seeing some incredible successes across every single department. And that’s all really exciting. But then the shadow side of this, which we have to lean into, especially when we’re thinking about from the people space, whether a human or a robot makes a decision and if you discriminate, you are liable, right?
You break the law. Facts. Human or robot doesn’t matter. That’s as simple as it should be in our heads. Right. And I think there’s a couple of not concerns that I would have. There’s a couple of things I would say folks need to be aware of, and I think from a HR perspective, we absolutely need to educate and educate as fast as we can to understand the use case of this, how we could use it. And then we need to talk to risk and think about the risk of what is the risk of this on my current employees and what is the risk on this on future employees?
…
So this is an incredible tool for us to think about from a people perspective, but the shadow side of this is we need to consider the risk. So the questions that I would be asking myself in my old role is, well, what is the risk of this on my current people and what is the risk for this on my future employees? When you think about the large language models that are in the market, these are not large language models that have been brought into an environment and are secure. These are publicly open access models. So any usage of this that gives away any personal data, you should not do because you cannot unravel that. With GDPR and with laws around the world when it comes to privacy as well, you don’t have consent to put any information into this because you cannot unravel that algorithm.
So there’s a lot of things that need to be considered that if you start giving it data that you shouldn’t be, that will cause you and your organization a lot of harm because you should not be doing that. So I think there’s two parts to it. Absolutely educate yourself as fast as you can on the potential good. And then think about the shadow side of this on the impact of your current people, the impact of your future people, and then what are the implications when it comes to privacy and security and the risk of the data usage within this as well. From a Reejig perspective, we do not give it any people data. We will not give it any people data. We’ve got a blanket rule across the company that we will not give it any people decision. So our algorithms are pared by independently audited ethical AI, which will not have the large language models from public domains in there.
And that’s so that we can control what’s happening with the data so that we have an audit trail of exactly decision making in that algorithm itself. I think just be very careful. There’s a lot of vendors now adopting this and everyone’s putting it in their product to say that they’ve got ChatGPT inside their product, and that’s sure exciting and great from a marketing buzzword, but the risk component of that for your organization is something that needs to be checked. And you’re going to see the relationship between the people team and the risk and privacy team being one of these new best friend relationships in companies now because these folks have got to partner up when it comes to making good and fair decisions of technology usage within the organization. But what I can tell you is every CEO around the world and every board around the world is now talking about how can we optimize our workforce?
Where can we bring this technology into our workforce today to find meaningful ways of unlocking potential within our organization? Look at productivity levels now in the US. Look at the conversation around our people are not productive like we are in potential recessionary conditions. Every CEO and board has this front of mind, and what they’re trying to figure out right now is, how can I use this in my workforce without causing any harm? So the practitioners that are listening to this absolutely up skill yourself as fast as possible to get that view because this is something that will absolutely drive competitiveness within your organization and is something that you will adopt. But it’s just making sure you adopt it in that safe way as well to make sure that you’re not causing any harm.
Mark:
Well, thank you very much. I mean, it’s been really fascinating talking with you, and I’m really grateful for your time.
Siobhan:
No problem at all.
Image: iStock
