As AI reshapes the HR landscape, many organisations are still grappling with how to turn big investments into real business outcomes. Could talent orchestration be the gap between investment and actionable outcomes?  

In this episode of the Digital HR Leaders podcast, David Green is joined by Jason Scheckner, Senior Director of Business Strategy for HiredScore at Workday, to reveal how talent orchestration is the key to unlocking AI’s full potential and transforming HR operations into a strategic powerhouse. 

Packed with actionable insights, this episode delves into: 

• What talent orchestration is and how it builds on the foundation of talent intelligence 

• How it connects various HR systems and data to improve processes beyond traditional methods like data lakes and systems of record 

• Why so many organisations are struggling to make real strides with AI, despite it being a top priority for executives 

• Practical examples of how talent orchestration is enhancing internal mobility and employee retention 

• Key strategies for ensuring AI used in HR remains ethical and safe 

• Metrics and outcomes to gauge the success of talent orchestration efforts 

This episode, sponsored by Workday, is essential for HR leaders looking to transform their people strategies through AI-driven talent orchestration and offers practical takeaways on how to leverage these tools for organisational success. 

Workday is a leading provider of enterprise cloud applications for HR and finance, recognised as a leader in the Gartner Magic Quadrant for Cloud HCM Suites. 

Organisations ranging from medium-sized businesses to more than 50% of the Fortune 500— including Netflix, Sanofi, AstraZeneca, and Rolls Royce—have chosen Workday to build their HR systems and implement Workforce Analytics solutions. Join them and learn more at workday.com 

Learn more about

• Insight222: Insight222 

• MyHRFuture Academy: MyHRFuture 

• HiredScore: HiredScore 

[0:00:00] David Green: In recent years, particularly since the launch of ChatGPT two years ago, we've seen a lot of excitement around Gen AI, and more widely AI in HR.  Nevertheless, many organisations are still trying to figure out how to turn that investment into real actionable outcomes.  So, what's the secret to bridging that gap?  Could it be talent orchestration?  I'm David Green, and today on the Digital HR Leaders podcast, I'm going to explore this very question with Jason Scheckner, Senior Director of Business Strategy for HiredScore at Workday.   

Jason has been at the forefront of HR tech for over a decade, having played a pivotal role in scaling HiredScore, which is now part of Workday's ecosystem.  And today, I'm delighted to welcome him onto the show to discuss how talent orchestration can transform the way organisations connect their HR systems, leverage AI, and achieve measurable business outcomes.  We'll dive into what talent orchestration really is, how it builds on the foundation of talent intelligence, and explore practical examples of how it's being used to drive internal mobility and improve employee retention.  So, let's get started by learning about Jason's background. 

[0:01:28] Jason Scheckner: My background has been really back to 2008, I joined the HR tech ecosystem.  I did my first startup back then.  I've since done two or three other companies in the startup space, two of which were also HR-tech-focused.  So, vast majority of my career, this Workday is now my fourth company that's in the HCM space.  But most excitingly and relevant to this conversation, I joined HiredScore as the 11th employee back in 2017.  So when I joined, again, very small number of employees, I think we had three or four customers at that point.  And my focus really, when I joined and agreed to sign up with Athena, was really about, how do we take the story to market about what we're working on?  So, we built out the go-to-market side.  That includes sales, marketing, customer, partnerships.  And then my last two years really, David, last time we connected, I was in a role called a Chief Business Officer, and that was really focused on the intersection of our customers, our partnerships, and actually our innovation.  And so, I got to work across our customer base, focused on really sort of what were the big challenges they were solving, how AI could unlock those.  So, got to travel really all across the country, world, sitting with our customers, focused on the next generation of their transformation initiatives.   

Then this spring, we were acquired by Workday, which has been a really exciting transition.  And then, I've since moved into a role called Senior Director of Business Strategy, which again largely focuses on how HiredScore can connect to the Workday ecosystem.  So, that's me, it's been a fun journey and excited to be with you today. 

[0:03:05] David Green: Could you share a little bit more about what talent orchestration is for our listeners, and how that builds on the foundation of talent intelligence? 

[0:03:13] Jason Scheckner: Yeah, of course.  And yeah, Rising was amazing.  I mean it was, just as a side note, I mean having been to it in the past as a partner was one thing, but to go as an employee and sort of be on that side of it, it was a really amazing event.  But yeah, I think your question around talent orchestration, again, we made a really important decision about three or four years ago when the market talent intelligence started to emerge.  There's this really tough decision around, did we want to become a system of record?  A lot of our clients were happy with us and they said, "Well, maybe you could build some of the functionality that some of these other companies are working on", and there's no shortage of those solutions.   

I give credit to Athena really in our team.  We decided that the world didn't need another system of record, and really what we're going to focus on was leveraging the systems customers already have and connecting to that data, connecting to those processes, the systems, and leveraging that data rather than giving them another system and putting that back to work for the customer.  So, if we think of talent intelligence, David, it's sort of been this all-encompassing catch-all for everything from skills to CRMs to talent marketplaces and a whole bunch of other things.  And again, great examples are what they're achieving is bringing AI insights or automation into the ecosystem often.  So, no problem with talent intelligence overall.  I think it was the natural evolution from where platforms were.   

But where talent orchestration takes the next step is, it's really about outcomes.  So, insights are great, automation is great, but at the end of the day we care about, are we fundamentally changing the way the businesses are driving talent?  And so, in order to do that, you have to connect to multiple systems really well, you have to understand different data types, you have to understand various personas, policies, procedures; and then ultimately you have to tie that back to a business objective.  And so, when you do that successfully, you go really beyond the insights into the outcomes phase, which is what I was talking about.  And so, maybe a practical example is, in the world of talent intelligence, an employee for instance would have to go to a talent marketplace, they'd have to input their skills or upload information.  And once they did that, they might get an insight about jobs that they could possibly move to in a few years.  Okay, great.  And I think a lot of companies were excited about that, and that's a cool feature and functionality, and employees got excited about it.   

Orchestration, an example of how it's an evolution is, without the employee telling me anything, I can consume all the data across the system.  So, I can take your HCM data, I can take any skills data, your talent profile, learning data, even application data on the other side of systems.  I can unify them.  I can meet the employee where they are in a system like Microsoft Teams, I don't even need them to go into a platform.  I can understand all the mobility rules, policies, procedures around moving, fully understand compliance, GDPR, all the fun privacy regulations, and give them recommendations that are highly relevant without them having to give me more information.  So, I'm achieving the outcome, but I didn't need something else to get there.  And it's an outcome in the form of an employee, for instance, moving to a new role.  So, it's my best way of kind of giving a practical example of how orchestration is a bit different, but there's countless use cases across the enterprise.  And now that we're part of Workday, I think even broader things that we can do beyond what we were doing before.   

[0:06:40] David Green: So it sounds like, because let's be honest, many organisations, their HR tech ecosystem has kind of evolved over the years and for whatever reason, they've got what they've got.  And in some cases, they would have spent a lot of time and money implementing these solutions.  What it sounds like you're doing with talent orchestration is, you're actually meeting the individual organisation where they are, and through your technology, helping them in a way get the most out of their investments without any major re-surgery.  Is that kind of where you would say you were? 

[0:07:19] Jason Scheckner: Yeah, and I don't know how many consultants or SIs are listening to your podcast, but in theory, some of the orchestration means that they don't have to re-optimise their system to achieve the next business objective, right?  So, if their workflow that they've built is what it is, normally to go to the next business change or let's say the business goals change, I might need to change, I might need to modify the workflow, I might need to change permissions, I might need to institute new policies, orchestration simplifies a lot of that.  So, I can coordinate that down if the business, let's just say we go from hiring surplus to hiring freeze, that's a tough thing for companies to adjust to.  But in orchestration, you can just change the SLA and focus for the recruiters and what they're targeted with, and you can drive that down through orchestration.  So, I don't need to reinvent the systems, I don't need to rethink the business processes or anything like that, as a simple example.  And that's a practical one, because again, HR objectives shift quite often.  But again, it's how do I connect back to the business management? 

The other thing that orchestration we're seeing does is, it takes a lot of the change management hassle out of the equation.  This falls on HR to -- for instance, a great example, there's a lot of HR processes are driven through non-HR personas.  So, think of the manager, the employee.  But HR doesn't own those individuals, right?  So, how do you get them to adhere to the policies and procedures if they're not required to do so.  And so, orchestration can do that really well.  So, if I meet a user in Teams, it's a very UI-less experience.  I can update policies, procedures in real time.  And if they're opening a new role next week, it'll remind them what the new policy procedure is related to their role.  So, I don't have to re-teach that, re-roll out new training and things like that.  I can easily integrate that into the process. 

[0:09:15] David Green: Jason, one of the data points you highlighted in, I think it was from BCG at your recent presentation at Workday Rising, which really resonated with me, was that 89% of executives rank AI as a top priority, but only 34% are happy with their progress.  So, there's quite a gap there between expectation and reality, I guess.  Why do you think companies are struggling to make real strides with AI, and how can talent orchestration help bridge that divide? 

[0:10:51] Jason Scheckner: So, it's actually really fascinating.  On one hand, we have to applaud companies because they are trying to experiment, right?  And because of the AI, what we saw in the last few years with the absolute wave of AI technology and the early easy access, the entry point to AI became easier ChatGPT and everything else, we saw really a great way that companies could start working on these different projects.  So, on one hand I want to say, "That's great", we should encourage and applaud that.  The challenge that we're seeing is, where companies are choosing to experiment is sometimes counterintuitive, meaning if I'm a pharmaceutical company, it's great for me to deploy AI use cases for things like drug development or drug release, or something like that, because I understand how that's going to impact my business and drive outcomes, etc.  But what you end up having happen is you've got different engineers and things like that.   

So, when they put together a list of experiments, like I had one customer that -- and again, it was a cool initiative.  They said, "I want every department to come up with several ideas for how AI can be deployed in their department".  And so, HR comes up with a few great ideas, they bring it to the committee and the committee says, "We can build that".  And so, you get people building in a domain that they don't have expertise.  And so, what you often get is you get a PoC that works or looks good, like I can write a job, or I can write this thing, or I can synthesise this information, whatever it is with these large LLMs that they have access to.  But they don't anticipate the scale challenges of that.  And that is really where the rubber hits the road.  So, in HR, you have things like compliance, you have global regulations, I mean the need to adapt to different languages and for different user experiences.  I mean, the data types are fascinating because once they get into the weeds of, well, the data, even in Workday -- by the way, Workday is an amalgamation of an HCM platform, a recruiting platform, now a continued platform, which is great.  You have one platform and one data structure, but there's still different object types, and so I still have to connect to different APIs.   

So, there's that, and then just the amount of processing that has to happen to make that work.  There's understanding the workflows and processes, obviously the compliance and all the regulatory shifts that are happening and keeping that up to speed.  And so, I think particularly HR, I guess my point is they don't anticipate, and I imagine this is similar in places like finance and other highly regulated areas, but unless you're an expert at that, anticipating all those challenges, you probably don't foresee those.  And I think that's, in HR in particular, why companies will experiment, or like you have the Amazon case from, whatever it is, it's now old, I think 2018, but where they publish the findings where they do these things, and then they run into bias issues and other things like that, and then abandon the projects.  So, I think that's one major thing, just the difficulty and the lack of understanding of what's going to come up.   

Then, I think the other thing is, we're still in an economic global environment that requires prudence in terms of how we spend our money and getting results.  And so, if you balance experimentation with cost outcomes, it's a tough environment, meaning like there's not a lot of appetite to keep going with experimentation if we're not getting business outcomes.  And so, I think that pressure also means the experiments don't have time to maybe progress to a point of value, because if it's not giving me something, get rid of it, work on something else.  So, there's less desire, and this is why orchestration is great, because we've already done the legacy R&D that's so hard to connect all the systems and the objects and the workflows and the policies and the rules, and it already reads all that.  So, instead of having all that barrier, we can get right to the hard stuff and then start actually experimenting beyond that.  And so, we've got customers now that are on their third, fourth, fifth AI use case, and they're starting to come to us with problems and saying, "Hey, could we orchestrate this?" which I love to hear. 

[0:15:09] David Green: Yeah, and I guess it's the same with everything, isn't it?  When it's a new shiny thing, for want of a better phrase, it's about, "Yeah, but let's apply it to a real business challenge that we've got".  So, as you said, that in a period where maybe many organisations are careful about what they're experimenting on, and they want to see the value from it, if we connect AI to some of the business challenges that we're trying to solve as organisations, then we're more likely to see some impact from it and some value from it, and then we can invest more as we move forward. 

[0:15:44] Jason Scheckner: Yeah, and one other thing I've observed is, even if you go beyond the customers, and so they have their own initiatives, right, and then even if you look at whether it's the Copilots, or all the different models that are coming out, so customers have their own LLMs, you've got obviously quite a bit of generative AI in the areas of the vendor ecosystems.  But what we're seeing actually is that a lot of the functionality that's coming out are addressing -- and they're good use cases.  Again, I want to be clear, I don't want to knock any of the technology, I think it's awesome.  But it's addressing things like writing a job or, "Can I better synthesise interview notes?" or, "Can I find an answer to an employee's question easily?"  I love all that stuff, but I will say the following.   

When you look at it on a transformation scale, let's just say it's a huge company, maybe they're writing 5,000, 10,000 job descriptions a year.  But at an employee level, you or I, David, might do that once or twice.  So, it's nice, and that one time I do have to write a job, yeah, it could be better.  But in terms of changing how I work every day, it's probably not fundamentally changing, transforming the organisation outcome.  I'm not saying if you add up those increments over a bunch of different tasks, that isn't valuable, maybe 1% to 3% gains, but it's not transforming an entire way a role works.  And so again, I think that's where we see that the scale of impact is much more significant as we look at some of these deeper augmentations or disruptions than the orchestrations.  That's our early observation so far.  And it ties back to, again, that outcome of that pressure to have the initiatives actually deliver value.  That's where you also see a disconnect, I think.   

[0:17:30] David Green: Let's think about talent orchestration and areas of employee retention and internal mobility, because I know these are two areas which are a big focus, I think, for you and the team, but also for a lot of the companies that we're working with.  Tell us a little bit about that.  What does talent orchestration mean in those two areas?  And if you're able to share some case studies, I think that would really help listeners really see the opportunities there.   

[0:17:56] Jason Scheckner: Yeah, of course.  So, what's exciting about this, and this is actually where I think the combination of Workday plus HiredScore is very exciting, because Workday already had solutions, like they have a module called Talent Optimization, which sits on top of Core HCM.  And so, it's got functionality like their career hub and their Flex Teams, and you can attach learning in there and other things, like performance and manager insights.  And so, there's a variety of successions in there.  There's all sorts of great things that are already packaged in there, again for the manager, for the employee to build on top of the great stuff that Workday does.  Where HiredScore had been approaching it, again prior to the acquisition, is we had looked at, what is wrong with -- why did some of these talent internal mobility initiatives suffer?  And I go back to the data island problem for a second, and I say, "Well, the data says less than 20% of people updating their skills and less than 12% of people proactively looking for new roles".  So, right off the bat, you have a data input challenge before you even get started, which is if I'm going to get employees moving and that's dependent on them giving me more information, that's why you've seen other vendors emerge now that are starting to anticipate what skills the employee might have and pre-populate those.  So, there's ways they're trying to solve that, but that's a fundamental issue.   

So, what HiredScore does, we actually took a different approach and we said, "Well, in orchestration, we know actually a lot about the employee.  It's just sitting in different places".  So, in the HCM, for instance, I know everything about what job they have, what division they're in, how long they've been in that role, and that's just their basic employee profile in every single HCM system in the world.  You keep that data about your employees, right?  And so from that, you can almost structure a resume even where there isn't one, or a CV even where there isn't one, because we know which job they held, how long, what division, maybe even to some extent, what skills.  Okay, so that's one thing.  Then I can also look at, a lot of these systems have an employee profile construct.  So, this is where the employee can go in, add their preferences, are they open to relocation, what skills do they have?  So, if that data exists, and we just said that it won't always exist, but if it does exist, I can take that. 

Then there's actually a third fascinating component, which is application data.  When an employee applies like, David, if you were hired by a company a year ago, we have the application from a year ago, again within GDPR retention policies and all that.  Now also, if you're applying to new jobs, that is an intent point, and it's a new data set.  So, I can take all of those different information, I can amalgamate that, and for most employees, that gives me an understanding of the employee, at least for everyone I at least have their HCM data.  So, without asking them information, I now went from 20% to 100% of people that I can activate essentially, at some level.  So, right off the bat, I've got huge gains, right?   

Then, what I can do is, what's fascinating about human behaviour is, when you ask for something to give something, you get more resistance.  But if you give something and then ask for something, you actually get a more interesting contribution rate.  And so, we're seeing that if we say, "Hey, employee, here's a couple of jobs we recommend that are fit for you.  And oh, by the way, if you tell us a few more things, I can even tune these recommendations more", now you're getting better skills data, now you're getting better other intent data, and that benefits the company as a whole, not just in this effort, but across all the other aspects of their module, right, like in Workday, for example, and other areas that the manager might be looking at the employee skills, based on performance reviews and whatever it may be.  So, now it benefits everybody.  The cool thing about that is, so we're uplifting and so that creates a loop.  So, next time around, the AI has more data again, and can be more effective.   

The other thing is that, what I saw a lot of the solutions focus on, it goes back to that intelligence versus orchestration, a lot of it was about, again, things like the talent marketplace or career pathing.  If I come in and I put in my information, again I had to give you the information, but if I did that, you might be able to tell me where I could move in the future, and I think that's really cool.  Again, love it.  It gives people hope and confidence about where they can go.  The problem practically though is, I have a customer I talk to says, "We can't figure out our workforce plans for this year, let alone the workforce plan for three years from now, to make sure they deliver on all the roles that people wanted to move to that the system told them".  So, I do think you're going to see a disconnect between all these things that what they project people can be and whether companies can deliver on that.   

So, we're kind of focused on the here and now of like, where can they go now?  What learning could they do now?  What projects are available now?  Because that's going to start to build the progress, and then that's going to make them more marketable for the future.  So, it's a little bit more of that current supply demand versus the future.  I'm not saying the future isn't important, and Workday's actually got some cool technology there, but we're here to sort of focus on the things that are going to deliver the outcomes, and that's moving an employee now that so they're not moving to another company tomorrow.   

The last thing that we did, and this is actually fascinating, is that we invested in post-pandemic in our collaboration platforms like Microsoft Teams.  A big bet there, David, was if you can meet the employee where they are, we use this expression that said, "The new UI is no UI".  And so, if I can meet the employee where they are, and I don't have to ask them to go anywhere, but I can meet them, I'm also going to get a higher success rate.  So, you asked about success examples.  We're getting right now like one out of two employees that we serve a recommendation to, will apply for a job.  We're seeing a 40% increase in one of our customers.  I just did a case study at HR Tech and Rising with a big pharmaceutical company, and they talked about a 40% increase in internal applications as a result of launching this initiative.  So, now you've got the classic feedback you get from employee surveys, it's easier to apply externally than it is internally, like it's easier to find a job.  And so, we're immediately solving tangible things that are on that sort of complaint list.   

Then when they do apply, we can even now, using that orchestration, alert the recruiter that a high value employee just applies, so they don't get missed in the black hole.  With orchestration, if they do get rejected after the interview, you could even alert their manager to say, "Hey, your high-value employee just was rejected.  And so, maybe you want to consider a mentorship or other recommendations, maybe learning for them".  So, this is where it gets really exciting because you can actually achieve those long-term business objectives.  But it's not about going to another system, it's taking the data you already have, taking the information, and I'm not even getting into all the rules layers and all the stuff you can embed underneath this, the campaign types.  So, you can even, for instance, outreach to an impacted employee, which would be a much different message than a top performer, for obvious reasons.  But you want to get that right, and that's all about the message content, the experience.  So, long answer, but this is what we're working on. 

[0:25:17] David Green: We talked a little bit about AI, and you referenced this a little bit, but how does your approach at HiredScore ensure that the AI driving the talent orchestration remains ethical, remains safe?  And I know practices that Workday have been putting in adheres to responsible AI.   

[0:26:29] Jason Scheckner: Well, first of all, let me say that I think this is one of the things that made the acquisition super-attractive for both parties, was that Workday had a very strong, responsible AI framework ahead of it.  They've touted for years their one data structure and platform, and so I think that really set us up well.  Now HiredScore, the benefit for us was that we had been in the market since 2012, pretty much last 12 years, as AI for HR only, so we didn't have other use cases.  And I emphasise that because going back to even the examples of customers building on their own, there were so many lessons learned over 12 years and costs that went into building these models and doing it the right way, but the architecture was there from day one.  And it's actually made it easy, because Athena and the team built it that way, it's made it easy to adapt to a lot of the changes.  So, when new laws come out, we're not scrambling to figure out how we retrofit those laws into it.  A lot of the framework's already there to adjust, and I can give you some examples.   

But I would say there's four key tenets that we talk about in the responsible AI framework that are really important, especially with all the Gen AI LLMs and all things coming out.  So, our models actually historically are machine learning models, so we're not using typically generative AI.  The reason that's important is not because Gen AI is bad, but because a lot of it is open source, you have a sort of black-box element to where did the decision come from.  And so, in our case, all of our models are in-house.  So, if we ever did identify an issue, and fortunately that's not really the case, but if we ever did identify an issue, we would know how to solve it, derive the problem, and modify it.  So, that's really important when you're coming talking about this kind of AI use case.  So, that's one major thing.   

The second thing is that all of our AI is transparent.  We talk about transparency in a couple of levels, David.  So, the first is the user level.  So, the actual AI was designed to tell recruiters, managers, whomever's interacting with it, what did we like about that relative to the job?  So, if we're serving a recommendation to a recruiter, we actually allow them to turn the AI inside out and see, what do we know about the job?  How do we understand the structure of the job?  How do we understand the CV resume?  And what was their matching criteria, whether it's skills or experience or certifications, whatever it may be?  So, it highlights those elements for them, allows them to have confidence in the decision.  We also publish all of that back to the core platform for audit purposes.  So, the grades, the prioritisation, that all goes back into the system automatically, and so their customer can run whatever analysis they'd like to.  And more companies are doing that as they launch like AI COEs, and so it makes it easy for them to actually do their own analysis.   

The third element is actually a proprietary method that we've done, and this goes back to I think the danger of a lot of the methods, which is that the training set can influence bias.  And so, we take customer data to drive the models for rediscovery and things like that.  Now, that part is fine.  The challenge with the customer data set is it could have inherent bias in it.  So, one of the things we do, David, is something called balanced learning.  So, we actually use a down-sampling technique to modify learning rates, such that we're learning even where, for instance, not to their fault, women had applied at a lower rate than men historically, which maybe meant men were hired.  We down-sample so we don't learn from that characteristic in any way, shape, or form that that was positively attributed in the model.  Now, even though we also remove race, ethnicity, gender, nationality, any of those kinds of disability data from the AI, it never touches it, we still do this as a method to ensure we're not accidentally learning a characteristic that could be attributed to any of those at-risk categories.   

Then, the last thing we do is, everything that we're doing is fully in compliance with all regulation law.  So, whether it's in the US, that's the OFCCP, or EEOC in Europe, they now have the high-sensitivity law; in New York, you have statewide laws, now the New York 144 Law.  And there's versions of these that are more privacy-oriented, some are more ethical constraint, some are about compliance with government laws.  The flavours are different, but the general themes govern somewhere around, again, this ethical or safety parameters.  We've got everything built into the AI, from even opt-outs.  We publish all of our findings annually on our website.  We test and publish that publicly.  That's actually a requirement of 144.  So, this is all in the background here.  And again, credit to really Athena.  She's dedicated about a third of her time historically to compliance, regulation, ethics, safety.  So, it's been a real cornerstone for us.  And so, it was really easy for us to make that transition to the larger Workday ecosystem with that foundation.  But those are some of the kind of core elements that we're known for.   

[0:31:32] David Green: Yeah, and it's so important, isn't it, not just because it's the right thing to do, but because there's more and more regulation coming in.  So, whether you're a technology firm operating in the space, or whether you're an organisation deploying some of this technology, it's something that we have to stay fully up to speed with and anticipate, like it sounds like you've done at HiredScore all the way through your journey to do that.  And as you said, Athena, and for those listening, Athena is the CEO at HiredScore, she's spending a third of her time on that.  And it really indicates the level of importance around this topic.   

[0:32:07] Jason Scheckner: Yeah.  And I think the adaptability to it as it goes on, because this is where, you know, a new law comes out and you have to now retool your whole thing to comply with that, because you were taking in data that you're not allowed to take or you have now a requirement.  And the other thing that we've done that, again, I think is quite cool is, we take the strictest interpretation.  So if, for instance, OFCCP has a thing that doesn't apply in the EU, but we think it's actually the right thing for fairness and ethics, we'll still apply that globally.  So, another example of that was the opt-out functionality that the 144 Law required, which was that candidates now needed to know that AI was in place, but they had the right to opt out.  The EU Act, for instance, doesn't require the opt-out, but we still offer it to customers, not because of the law requirement, but because we actually like that ability to say, "Hey, we're being transparent here.  We'll give you this chance".  And it's actually played well for things like workers' council approvals and things like that, because it's not a requirement, but they like that it's got the same posture as, like, a GDPR, or something like that, that really takes into account personal privacy and consent. 

[0:33:12] David Green: I really like that, because it's almost like a lot of this regulation will have good practices that maybe could be better employed elsewhere in other jurisdictions, and it allows you to provide more of that consistent experience for employees wherever they happen to reside within a global organisation, and hopefully in some respects maybe even simplifies it a little bit for that organisation as well, perhaps.  So, I like that, I think that's a nice touch.  Jason, moving on, in terms of we talked about outcomes, and it's about making these insights and helping translate them into outcomes, and one measure of outcome is obviously how we measure the effectiveness of this.  So, on that topic, how can we measure the effectiveness of our talent orchestration efforts?  What kind of metrics or outcomes do you typically focus on to help your clients gauge success? 

[0:34:13] Jason Scheckner: Yeah, so listen, every client, as you know from being in the space, the clients will decide there's a lot of different things they can define.  But what we've found over time is that there are generally some common categories.  So, it doesn't mean that these are exclusive categories, these are probably just the most common.  And these aren't guarantees that I'm going to share with you, but these are averages across the customer base.  So, every customer is different based on their particular problems, challenges, timing, etc, data sets.  But typically, we categorise the savings areas.  And by the way, we don't just talk about outcomes, David, we call them iconic outcomes.   

[0:34:51] David Green: Okay, I like that. 

[0:34:51] Jason Scheckner: So, we're trying to move the needle big time here.  All right, so we talk about recruiter capacity and productivity.  So, again, it can be expressed different ways.  At GM, they estimated that that translated into 23,000 hours of FTE savings, okay, so that was one expression of it.  Another big global pharma company described as 140% recruiter capacity increase.  So, they had 400 recruiters that is now 40% better off in terms of now, what could you do with that?  You could assign them more responsibility, you could have them focus on other tasks.  Again, that's up to the company.  We're not dictating what they do with that capacity.  The third example we see is actually sort of role leapfrogging, meaning they're now thinking of, what are the roles of the future?  So, we had one client in the airline business that said, "We're going to actually take that savings and we're going to reallocate those recruiters to focus on internal mobility".  So, they created a career office for their employees, for example.  So, companies will treat the savings different ways, but the savings are inevitably about capacity and productivity that are gained from their recruiting organisation.   

So, on the mobility side, you're really looking at the re-enablement of their data.  So, you either have external candidate rediscovery, and that has impact on candidate experience, recruitment, marketing, savings, and agency savings.  We had one healthcare customer in the US estimate tens of millions of dollars of agency avoidance post-pandemic as a result of rediscovering talent in their own system, so that's pretty cool.  And then on the internal mobility side, you've got the employee experience and sentiment as a result of what we just talked about.  And then, you have the increase in internal applies, so again, keeping your employees.  And then, you have the conversion of employee recommendations.  So, that's really the rate at which employees are actually moving and expressing interest in those jobs.   

So, those are the general ones.  I mean, there's a lot more.  We look at all sorts of interesting things, but those are probably the most common that we talk about a lot.   

[0:36:52] David Green: Where do you see talent orchestration going in the next few years?  What's the next frontier you're exploring at HiredScore?  And most importantly, how can organisations start preparing now? 

[0:37:05] Jason Scheckner: Well, I think what's cool is once customers start to understand how orchestration works, they actually will start to come and say, "Is this a good use case?"  And so, even our roadmap starts to get furnished a little bit by our customers' ideas.  But some of the areas we're excited about that are already currently talked about in the public roadmap for HiredScore and Workday, we're going to be looking at democratised succession planning, so how you take what was typically reserved for the top 500 to 1000 employees of big companies and make that available to any manager who wants to think about the future of their team.  We're talking about recruiting managers as an interesting unlock.  So, there's a lot of technology for candidates, for employees, for recruiters, but the recruiting managers and the leaders are often overlooked.  And I find that a lot of organisational outcomes are driven through the managers, and they're an overlooked group.  And so, we think the recruiting manager, talent managers, they can be a really interesting unlock.  And so, how could you have an AI copilot, coach, agent, whatever you want to call it, assisting that person better understand capacity, bottlenecks, challenges in their current team roles, whatever it may be.  So, we think that's interesting.   

Athena's got some really cool ideas on workforce planning.  It's early, but now that we're part of Workday, there's so much you can do there, and every customer seems to feel like they're not successful in this area.  Some really cool stuff on the contingent side, so we're starting to connect to systems like VNDLY, contemplate contingent workers.  Not only can we deploy the AI on top of prioritisation and rediscovery there, but could you actually take somebody coming off project, move them over to a recruiter inbox for a job that they've posted?  Could you think about, at the point of recreation, is this job better suited for a project, a contingent assignment, or is it a full-time role?  So, you can invert that both ways.  And then there's some really cool stuff.  Customers have started to come to us on agile performance management.  We're hearing a lot about this, but it's difficult to achieve.  And so, orchestration seems like it potentially would be well designed to actually handle this agile concept, because it's really hard to build into a system and encourage the manager to always do it.  But orchestration could actually be quite effective there.   

So, these are just some of the areas.  I'm probably missing a few, but I know we don't have so much time, but a lot of exciting stuff.  And that's just on the HR side.  Again, now that we're part of Workday, I don't know where we'll go from there in terms of some of the other components, but they have a broad AI roadmap, which you saw.  And certainly, I think we'll continue to look at areas where we can connect data and types. 

[0:39:48] David Green: How can organisations use workforce data to drive culture, inclusion, and engagement? 

[0:39:55] Jason Scheckner: If we think about data as the sort of connection to these culture, engagement, inclusion efforts, then I think what we would agree to is that impact is the result; again, it goes back to this action.  And so, if we take the earlier supposition that orchestration is well-suited to drive action, then I think what it can encourage is the right behaviours to drive the outcomes.  So, for example, we're now part of Workday, so I've actually started to look at a lot of the Peakon data, which is coming from employee sentiment surveys.  So, it's great to actually understand a group's sense of belonging, engagement levels, but the next step of that is actually some sort of action to respond to that.  So, again, we now have the employee telling us that something is good, bad, whatever it may be, and actually the Peakon data is fascinating.  I'm actually finding it really valuable as a manager.   

But then how do you orchestrate something off of that, whether it's remind the manager, give the manager a warning, actually put together a plan for them on how they could prioritise these things.  So, it's not leaving up the manager to interpret the data, they'll actually process the insight, come up with the plan, action the plan.  Like, there's so many steps in there that sort of leave the outcome up to chance, or strong change management.  Either way, it's hard.  But by the time everything's agreed on design, I think it's been six months, nothing's changed, the employee's still dissatisfied, whatever, and I think the AI can really, really do that at scale.  So, that's just one example that I see practically a really cool opportunity to actually drive more engagement and actually let AI do the work for delivering the desired outcome through the orchestration.   

[0:41:50] David Green: Before we go, Jason, can you share with listeners how they can contact you on social media, presumably LinkedIn is one of those options, and find out more about the work that you're doing at HiredScore and at Workday as well? 

[0:42:03] Jason Scheckner: Yeah, so in terms of me, I'm easily contactable on LinkedIn, I'm just @JasonScheckner, you can search me, or in Dot I think it's JScheckner.  Same on Twitter, although I'm not as active as I'd like to be there, X, or whatever we call it these days.  So, that's @JScheckner.  In terms of if there's interest in anything we talked about related to Workday, if they're an existing customer, I highly recommend they'll just connect with their account teams.  If they're a prospective customer, same process.  There's prospective teams, and they have ways that they can start to pull in.  There's all wonderful solutions consultants and experts now trained on HiredScore.  Certainly, they can always ask for me and I'm happy to step into whatever I can where possible.  But yeah, that's my recommendation on how to find more.  And as always, this has been a really, really fun conversation. 

[0:43:03] David Green: Well, it's safe to say that Jason gave us a lot to think about in terms of how talent orchestration can bridge the gap between AI investment and business impact.  So, thank you again, Jason, for joining me today.  If you enjoyed this episode, please subscribe and leave us a five-star review on your favourite podcast platform.  And if you would like to stay connected with us at Insight222, you can follow us on LinkedIn, visit our website at insight222.com, and sign up for our weekly newsletter at myHRfuture.com.  That's all for now, thank you for tuning in and we'll be back next week with another episode of the Digital HR Leaders podcast.  Until then, take care and stay well.