Time to Hire

EP 27 Talent Acquisition Tech Strategists on AI and Automation Transforming Recruitment Processes

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In this insightful episode of Time to Hire, host Lamees Abourahma speaks with Agile One executives Andrea Barre (VP of RPO) and Jordan Morrow (SVP of Data and AI Transformation) about transforming talent acquisition through data analytics. Barre and Morrow discuss how organizations can pivot from reactive recruitment to strategic workforce planning by using high-quality data and AI. They explore essential metrics for optimizing hiring processes, strategies for aligning talent acquisition with business goals, and the critical role of data literacy across all organizational levels. Barre and Morrow emphasize that successful implementation requires more than just technology—it demands a culture where people understand and effectively use data. 

The conversation covers:

  • Key metrics organizations should track to optimize their hiring processes
  • The transition from point solutions to holistic ecosystem approaches in data strategy
  • How RPO providers can bridge the gap between talent acquisition, business objectives, and data teams
  • Practical strategies for overcoming the challenges in implementing data-driven talent acquisition
  • The essential role of AI and automation as enablers that augment human capabilities rather than replace them
  • The importance of data literacy across all levels of an organization for successful implementation

This conversation provides valuable guidance for TA and HR leaders seeking to future-proof their talent acquisition strategies in today's workplace.

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Welcome to the Time to Hire podcast from the Recruitment Process Outsourcing Association. I'm your host, Lemice Amparama. And today, I'm joined by two experts in the field of data and analytics in talent acquisition: Andrea Barre, Vice President of RPO at Agile One, and Jordan Morrow, Senior Vice President of Data and AI Transformation at Agile One. In our discussion, Andrea and Jordan share insights on how the use of data and analytics has transformed talent acquisition strategies in recent years.

We explore the key metrics organizations should track, the role of data in strategic workforce planning, and the challenges companies face in implementing a data-driven talent acquisition approach. Andrea and Jordan also highlight the importance of aligning talent acquisition with business goals as well as the critical role of AI and automation in future-proofing talent acquisition.

Let's dive into the conversation and learn how data and analytics are shaping the future of talent acquisition.

All right, so we're gonna start by just looking at the power of data in talent acquisition and help us understand how the use of data and analytics has transformed talent acquisition strategies in recent years.

Andrea Barre: Yeah. What I have found is over the past few years, data analytics has really shifted TA from a reactive transactional function to stepping into more of a strategic business partner, which is really exciting - this idea of being a business enabler. Organizations now have the ability to leverage real-time insights to optimize sourcing, enhance candidate engagement, and really improve overall hiring decisions. And that's from the data analytics side. But then we also have this predictive analytics, which is allowing companies to anticipate hiring needs, which is incredibly exciting, which has ultimately reduced time to fill and improves this idea of workforce planning.

Lamees Abourahma: Jordan, anything before I ask the next question?

Jordan Morrow: I'll add one thing. And the only thing I would add to that is in the evolution of what's happening with AI, it's only going to be as good as the data is in an organization. So to Andrea's point, it feeds the analytics, but it also feeds the AI. And so without that very strong foundation and good, clean data, good luck making AI work for any talent acquisition company.

Lamees Abourahma: Perfect. I think that segues well into my next question. So what would you say are the key metrics that organizations should use to track and optimize their hiring process?

Jordan Morrow: So for me, oh, sorry, Andrea,

Andrea Barre: Go ahead, go ahead.

Jordan Morrow: No, I'll go real quick and then turn it to you, Andrea. For me, when I take a look at metrics and all of that, it really boils down almost to industry. And right now we're seeing almost two channels that are emerging within data analytics and AI. There's point solutions, which would be like resume parsing, candidate matching, then there's ecosystem solutioning, which means you're building a maturity model at these organizations that will have different metrics at the different levels of the maturity model. And you're building a holistic approach, not just RPO, but total talent management. That term has been around a while, strategic workforce planning, that term has been around a while. To do that right, we have to shift away from point solutions and get ecosystem building. And that's where that foundation of data goes. I'll turn it to Andrea here.

Andrea Barre: Yeah, I don't think that the metrics have changed from what TA has looked at holistically or could have been looking at holistically for a long time, right? Like we're looking at time to fill. We're measuring the efficiency and the effectiveness of a hiring process, right? Quality of hire - really what's the impact of those new hires on business performance? I think the difference is that we're taking these and again, using them more from the strategic business enabler than just this reporting metrics standpoint. When we're looking at source effectiveness, are we using the right channels? Are we putting our money in the right place to drive the best candidates? Candidate experience scores, how is that candidate journey? Not just for the candidates we're hiring, but the other candidates out in the marketplace that we're turning down, right? Are they having a positive and engaging hiring journey? Offer acceptance rates, which really help us understand the barriers to success, retention rates. So that kind of ties into that quality of hire - what's the long-term hiring effectiveness? The one thing that I think is a little different in the way that we're looking at things and changing is this idea of turnover and additional headcount trends, right? How can I look at what's happened historically from a geographic standpoint or a line of business standpoint? Where is our organization seeing turnover and where have we invested historically to predict what we're gonna do in the future?

Lamees Abourahma: And that also segues us into the next set of questions. And we wanna look at how data supports strategic workforce planning, right? And I feel like this is a loaded question because with everything that's happening, is it actually even possible to effectively plan workforce? But how can data and analytics help?

Jordan Morrow: Oh my goodness, this I could talk for a lot longer than we have on this, but we'll keep it concise and then see if Andrea has anything. I do think it is possible, but we're not just talent, but in industry across the world, what we find is the strategies are very weak, if I could say that. I don't wanna say poor, but part of the issue that you run into is the strategy around using data and analytics for workforce planning is too broad. Like you just said, it's loaded. There's a lot that can be done there. And I think we have to shift the focus away from tools and technology to business outcomes and use cases. Because when you do it that way, it doesn't matter what the data analytics and AI technology you're doing around you, you're still focused in on the right problems that you're solving. Data AI and analytics like Andrea has been saying becomes an enabler. So when we think about data strategy for talent acquisition, it's already done. If people think about it right, what I mean by that is organizations have their talent strategies in place. The data strategy that supports that is how will we use data to accomplish that? That's as simple as it is. We don't want to put buzzwords into data strategy around talent acquisition because that's not going to accomplish anything. So when we change our focus away from what is our data and analytics strategy to our data and analytics strategy is how we're using data and analytics to support our talent strategy, we have a whole different picture. It's not just reporting, it's not just reactive. It's very proactive because we're not focused in on technology solutions. We're focusing on solutions for talent needs. Then we put the technology in place, whether it's analytics, whether it's AI, whether whatever it is, it's because the focus shifted away from technology to the problems we're solving and the goals we're trying to meet, then data and analytics, that strategy is much easier to do because you're not focused on technology that in a month from now could become obsolete.

Andrea Barre: I think additionally, something that you said even when you asked the question - it's this idea that maybe things are changing too rapidly and so we can't workforce plan and this is a problem that I've seen for many, many, many years, right? There's this idea that we want workforce planning, but nobody can do it. I used to joke that I've yet to see one organization really do it. And this kind of jumps to your next question, but this is where I think RPO providers can come in and truly help and where we need to change, right? And not just RPO providers, but TA in general needs to change. There's this idea within TA that the business units need to workforce plan better. TA has the data and TA needs to come to the table and say historically this is what we have seen. Is this what's going on in the business now? What it's been is TA comes to the table and says what's going on, what are you planning on? They don't know how to do that. And truly the business has business to run, right? Like they have widgets to make and widgets to sell and TA needs to come to the table and say this is what you've hired historically and where you've hired it and what the outcomes have been. What's changed, right? Are you still going to be hiring in this location? Are you still needing this job title instead of coming in and saying what do you need? It's easier to say yes I still need this or oh no we've actually shifted our plant strategy to this new location. So I think if we can use the data that we already have hopefully.

Lamees Abourahma: So, you also want to align your talent acquisition with your business goals. And what I heard is that gap is about 12 to 18 months. That's surprising for either of you. They don't align very well, so their business goals for many businesses, they don't align. The talent acquisition is lagging to catching up and that's a big gap.

Andrea Barre: I think that depends on the partnership between TA and the business, right? Does TA have a seat at the table or is TA looked at as this reactive? If TA has a seat at the table and is proactively coming with that data and information to help the business make decisions, also does TA know what the business decisions are or the business goals and operations are. I think that's a huge piece of it. So it's not surprising and I've seen it much better than that. But the enablers to being much better is TA has to have a seat at the table and TA has to be coming to the table with the right data and asking the right questions.

Jordan Morrow: And I'm going to piggyback on Andrea - it's almost like a triangle. You've got the business, you've got TA, the data team better be at that table too, but not just data professionals are trained in data. They're not trained in business principles and TA principles. So there is an aspect of change management and learning that the data professionals need to go through so they can have the right conversations. But when you combine that triangle of the data team and to Andrea's point, the TA team aligning with the business and they're all at the table together, it is not necessarily gonna be this big lag because not only will you have TA lag in the business, like you said, but what if data is lagging TA? You've now even made that even longer. And if that's the case, the way the speed of change is happening, those companies might lag behind. But if you put everybody at the table and have good open frank conversations about needs, wants, what's happening, trends and good data, much better conversations can occur to create that planning much more effectively.

Andrea Barre: I think fundamentally too and something that we've done at Agile One that is really helpful and insightful is making sure that all parties at that table are trained in understanding the data and how to use that data to tell the story right. As Jordan said the data team knows the data but do they know the business goals and how to tell the story with that data. I'd make the assumptions that the business team knows where the business is going and probably is better at using data. Jordan's laughing. He's like, "Oh, maybe not." But really, does TA have that ability too? So there's making sure that when everyone sits at that table, can they have those conversations? Can they understand the data? And can they use the data to help tell the business case and business story. So that key training around data enablement, data storytelling, Jordan has three books on the topics. I could just name all three of their titles, but since Agile One has done that within our teams, we've been so much more effective when we're sitting at that table.

Jordan Morrow: Yeah, and I'll piggyback. I'm laughing because I think it's very interesting to hear TA in the business being siloed apart because that's been the data space for so long is data and business siloed apart. And one of my first directives - I started in February last year - as Andrea is mentioning was, I'm going to increase the literacy of the company, the rising tide lifts all boats, right? And so if I don't have people who can use the data that we're building and the AI that we're building, what good is it? And increasing that communication between the groups to that point is our effectiveness for any client that we're working with.

Lamees Abourahma: Yeah, awesome. So education is a key, is what I'm hearing, and this is what we're doing with our conversation today. Jordan, you introduced the triangle, the data, TA and business. Where does RPO fit, Andrea?

Andrea Barre: Maybe in the middle. Fundamentally, they sit within the TA space. I would say that RPO maybe is the lines between TA and business and then TA and data. This opportunity to build partnerships and communications and help educate. I can't tell you how often I'm sitting with my partners and some of our organizations are really well trained in data literacy and some are not. And so helping them understand, this is what the data is showing us. And sometimes with an RPO, we bring a tracking of the data that they don't previously have. And so just having that access to that information to be able to look at the trends and where we're going is really helpful. Sometimes, especially through like an implementation where - and I really maybe like this idea of where the lines of the triangle, right - we're helping TA explain to the data team what we need and how it needs to be set up, right. And how often we need it and what it needs to look like so we're really that enabler in that piece, and then we're helping TA communicate and get that data so that it does align with the business outcomes. So, I guess we're the lines on the triangle.

Jordan Morrow: I'm going to echo that. And I think it's perfect. Yeah, because those are super elevator. Yeah, absolutely. We're going to have to talk after and figure out how to make this more concrete.

Lamees Abourahma: Yeah, I'm very visual. So I love what you guys built with that triangle and lines.

Andrea Barre: We're the wormhole between the two.

Lamees Abourahma: Well, awesome. So to us challenges now. So since if data is so great and analytics are so great, why are organizations not adopting that? What might be some of the challenges that companies face in implementing a data-driven talent acquisition approach?

Jordan Morrow: So this is something. So when I started pioneering data literacy all the way back in 2016, I would argue that these challenges still remain. We're seeing increases to Andrea's point. Some organizations have figured it out better than others, but I'll use an analogy. Imagine that we have an organization of 10,000 employees. 90 to 99% of them are not data professionals. Maybe 50 to 100 will be purely real data professionals. That means 9,900 or 9,900 plus are not. That's not their background. It's not what they've been tasked with. And because of that, when we democratize data and put it into their hands, it's not their comfort level, it's not their skill set. They're good at reading charts and descriptive analytics, but there's four levels of analytics. And that's really where the literacy comes into play. So when we look at organizations, I would argue that a lot of times what they've done for data-driven processes is they buy a tool and think that's gonna be the solution. But in my 80 to 90% of data-driven success deals with people. Are they skilled enough to do it? Are they comfortable to do it? Do they know how to take data and make decisions with it? Is the culture ready for it? Do we have holdouts? And maybe underpinning all of it is how strong is the communication around it, right? Business professionals don't necessarily speak data and data professionals don't necessarily speak business. And so the moment you do that - and by the way, neither of them probably speak really well, TA to Andrea's point - they are those enablers. So the biggest challenge is you can have the perfect tech, the perfect data, you can have perfect AI. If people don't have the skills to use it, nor do they want to, or they don't want to, good luck. And so it really boils down to the challenges are people side. And that goes all the way from the C suite, understanding how to build good use case strategy, then the communication on down, not just buying tools and leaving people to their devices, but buying tools and really creating strong adoption change management and learning practices. So that again, out of analogy, the rising tide is going to lift all boats because people have the skill set to drive through it. But that culture, that is the linchpin. I'll use the example Andrea mentioned. We, again, I started early in this, but I was lucky. I was an advisor basically to Agile One before I even started here. So I had trust built around me. They know what I was about. I came in and I launched workshops and there was trust around me. There has to be good communication at organizations if they're going to do this. How many of us like mandatory training emails? We don't like them. So you can't just send an email out saying you have training. It's - there's all this cohesive ecosystem that has to be built up to overcome those challenges.

Andrea Barre: And I'm gonna take it even a step ahead of time and, you know, I'm in operations and deliveries. Of course, my answer is gonna be more there, right? But there's an accountability that Jordan mentioned that are, you know, garbage in, garbage out. If our, from a TA standpoint, if our recruiters aren't documenting everything they're doing because they're managing, you know, 50 recs and talking to hiring managers and interviewing, I understand how and why it happens, but if they don't understand why it's important that they document, I talked to this candidate, if they're not putting the right information in the ATS or the CRM that we get the good funnel metrics out, then it doesn't matter how we're using the data because it's not accurate data. The other piece that I see really often is not just our, you know, our producers and our team members documenting and putting the things in the system, but our systems talking to each other so often. As Jordan says, we buy these tools, but we don't allow them to integrate. And if they're not integrating, we're not getting the data or the best information. And so how can I tell, you know, a source ROI, if the APIs between the tools aren't working, that I can tell where we're hiring candidates from? So we have to set it up for success from the beginning, and then we have to explain to our recruiters and give them the time in their day to be able to document effectively.

Lamees Abourahma: I'll update both perspectives on the keys to success. If you will, Jordan touched on people as the key and Andrea, it's a process and operation as a key. How about AI and technology? What's the role of AI in shaping that talent acquisition analytics?

Jordan Morrow: I think AI is one of the best enablers and what I call augmenters of the people in organizations. AI needs to be human-centered, right? It is fast moving, it's evolving, it is getting even better, but the key is it doesn't have the human touch. So when you hear me talk about it, I think AI continues to come in into those channels. Point solutions like we talked about resume matching, all of that, and the ecosystem approach, but in both sides of it, the human needs to be there. So the way we describe it here is let it supercharge the human. Let it be your superhuman power. I use it so much and it is such an efficient way to drive through it but I have the literacy behind it because it's what I do, right? I spend the majority of my day as AI focus at Agile One and with that it means, okay what technology is gonna help the solutions? Where do we need it? This is why there's such a divide. You have so many vendors out there hyping it up, the buzz, everything. I ignore that sort of thing. I'll study it and learn about it, but ignore it because when I come down to what we're building and doing, I look at the problems we're solving, I look at the use cases we're doing, and we ensure that people know how to use it. When we do it in that manner, AI becomes a critical part of the RPO process, of the TA process, of helping the business operate and the people are a part of it, not a superseded part of it. I think that's the fear in TA and in candidates and hiring and all this right now is what is AI gonna do to jobs? Well, if your focus is right on use cases and problem solving, AI is a part of the process just like in the mid '90s. Did we think the internet would be what it is today? In 2007, when the iPhone came out, it's synonymous with things now. Let's make AI be that? Get rid of the hype in the buzz and make it a part of talent acquisition and help companies in their maturity to get there.

Andrea Barre: I think just like we talk about TA being a business enabler to help the business go after their goals better, AI is a business enabler for our recruiters and our TA team to be able to focus their time on the places that they really need to. AI can come in and take a look at the 150 applicants I had on a job application and we're seeing applications consistently increase because of AI, because applicants can go in and use AI to apply to 500 jobs, half of which or 9/10 of which they might not be qualified for. So a recruiter has more work to do just to screen to talk to the candidates that they really need to talk to. It can also help from a candidate experience, right? If I am not pre-screened and AI helped me apply to a thousand jobs that I'm not qualified for and a recruiter didn't use AI to screen me out, so then I have to spend my time scheduling in an interview just to be told I'm not a fit for this job, it's a better candidate experience as well if we use these tools correctly. But if I am the right fit, AI allowed, you know, was allowed to screen me, that a recruiter could talk to me, and a recruiter only talked to the best candidates that fit. That's such a better experience for everyone involved. And it's going to be able to do it faster. My caveat to that is, again, you have to let the systems talk, because we're not going to be able to measure the effectiveness of that AI if I don't know how many were screened, I don't know how many were down selected. So that's kind of the caveat and I'll pull that back to the data side as well.

Lamees Abourahma: I'm thinking back of that triangle and visual and probably would fit AI on each of these nodes of the triangle. And every line, right? Right.

Jordan Morrow: Yeah, because it should take away, like Andrea said, if we can utilize AI to take away some of the mundane and some of the time-consuming pieces, it frees up people to be more strategic, more creative, more visionary, more empowered, and it fits everywhere. And it becomes necessary, and that goes again with the people side and the learning, and can they use it right? But it fits everywhere. I love it, as Andrea probably knows, probably a little too much. I love AI and what it can do, but we go at it from that practical use case perspective so that it's providing people good solutions.

Lamees Abourahma: Sounds good. Well, we are at the end of the time. I asked for, so any final thoughts? We didn't get to go through the whole interview. Any final thoughts either for you when I shared that we did not capture?

Andrea Barre: Yeah, I think, so just coming into that idea of like future-proofing TA and the thing that, you know, we talk a lot about the data and the AI. I think automation is a huge piece in this too. So again, this enablement for TA to be spending their time, you know, the biggest bang for their buck. So I think organizations and RPOs and providers really need to embrace this idea of AI and automation. Automation. Jordan helped me set up some tools just myself. I know I need to use these data driven tools, but getting them set up takes so much time. So having the automation tools that I can go in and then use the data to understand what it's telling me and what's going on, this idea of really investing in the predictive analytics tools. And Agile One has some really interesting things coming out in the very near future that I'm really excited about. But this idea of developing workforce agility is really important to me as we go forward. We need to use this data. It's like, you know, using the force, use your powers of good, right? We need to use this data to upscale and reskill our employees proactively. So these are the kind of things that I'm thinking about as we go into the future.

Jordan Morrow: A shout out for your nerdiness, Andrea, and bringing Star Wars in there. I'll be very quick, 'cause I know we'll write on it, but she nails it here for me. As you'll learn from me very often, Andrea, the human matters to me so much. Our critical thinking skills, the cognitive ability, but to future-proof your workforce and the TA roles and all of that, we need to help people understand they don't need to be AI engineers, and they don't need to be data scientists. We need to increase their comfort and confidence in using these tools to empower their jobs. And if we do that, not focused on tools, not focused on the technical and all that advanced, we can help them harness these things for better.

Lamees Abourahma: Well, I love this. I think it's been very informative conversation. And Jordan, are you available to help me with some automation?

Jordan Morrow: Absolutely, Andrea knows it, it'll hurt me. I love this, it's not working for me. Like over the and I counted out a lot of things and so absolutely happy to help.

Lamees Abourahma: Wonderful, but thank you both for your time. This has been fantastic. Thank you everyone. Have a great day.

I hope you enjoyed this episode of the Time to Hire podcast from the Recruitment Process Outsourcing Association. Give us a review wherever you listen to the podcast and always stay connected, stay engaged, and stay informed of what's happening in the talent and recruiting world by tuning into the RPOA, the place to go for RPO.