Unlocking Gen AI Potential in Human Resources – Transcript

Anthony Snowball:

It’s easy to get excited about the art of the possible, but then you have to think about reinforcing the foundational principles of data and technology – so making sure you pick the right areas to focus the technology and also ensuring that you have the readiness in place to really support it.

Announcer:

Welcome to The Hackett Group’s “Business Excelleration^™ Podcast.” Week after week, you’ll hear from top experts on how to achieve Digital World Class performance.

Anthony Snowball:

Welcome to a new episode of our podcast series. I’m your host Anthony Snowball. I have the pleasure of leading our Gen AI session today. I am the AI market leader for Hackett, having worked for Hackett for over 21 years, and intend to facilitate through a discussion on all things AI within human resources with a concentration on Gen AI. Today, we delve into the fascinating world of generative AI and its transformative potential within the human resources arena. I’m joined by two distinguished guests – Lee Derryberry and Franco Girimonte – who will share their insights and experiences in this emerging and exciting subject of generative artificial intelligence and the related AI profile technologies. What we really want to discuss is how these technologies are disrupting the human resources organizations – their ability to operate while reinventing what’s possible. I’m excited for this conversation, and I can’t wait to get into it. So let’s dive right in. Lee and Franco first, welcome to you both. Thanks for being here with us today.

Lee Derryberry:

Thank you so much.

Franco Girimonte:

Thank you, Anthony. Thank you for inviting us.

Anthony Snowball:

Wonderful to have you here. And I know everyone’s excited to hear about really how generative AI can improve performance within the human resources arena. And we’d love to really start right out of the gate here with a question on what is the generative AI impact within HR? What’s the reality we’re talking about here so people can really understand?

Franco Girimonte:

Yeah. Anthony, that’s a great question. There’s a lot of really misunderstanding out there. I think with all the popular press around Gen AI itself – ChatGPT and other tools that are out there – it’s a lot more expansive than that. If you think about it, this capability is at the center of a lot of opportunities to automate things, and you could see automation around not just generative AI, but you have the ability to do things like totally improve your ability for conversational assistance, which are tools that have been around inside the HR function in the past. But now if you augment it with the Gen AI capability, it’s almost like turbocharging your conversational assistant capabilities because it builds off of knowledge from your knowledge articles, but builds off a lot of information that’s also out there within your data fabric that you’re leveraging to train these tools.

You’re seeing it within predictive automation as well – task automation, the traditional RPA stuff that we saw in the past. You’re embedding this kind of capability together as well. Intelligent optimization where you’re using these tools to look at opportunities to optimize things from the end-to-end process – all the way across. In the past, it used to be a lot of, let’s say process mining type work. It’s now shifting into more intelligent optimization, so leveraging the Gen AI to do the process mining for you to see where there are opportunities. So when you think about it, again, that broad expanse of capability – the sky’s the limit in terms of building out a more productive capability or what I would call capability build within HR. There’s so much out there that HR could be doing that they haven’t had the resources in the past to do so with this capability. It’s going to free up a lot of capacity for HR to do the things that they’ve always wanted to do for their internal customers or stakeholders that they’ve wanted to serve.

Anthony Snowball:

Incredible. Yeah. I would agree with you there. And it’s interesting you said broad capabilities, Franco. And it’s interesting in our ongoing conversations with clients we often hear it’s one – a mysticism around AI. Where do I deploy it and for what benefits? And really where can I successfully get a return? And so we often hear where do we start? And when we think about implementation, how do we initiate the journey? Is it more of a top-down approach? Do we accumulate use cases? I really value your respective points of view on this.

Lee Derryberry:

Absolutely. And I would say, Anthony, the approach is not that different than how we’ve seen teams take agile approaches even when they looked at automation – even when they looked at broader transformation efforts. So really the first step is just education – listening to this podcast, looking for content intelligence about what’s out there and what the organization is hoping to achieve, doing some level setting there. The second part is really understanding where and how you deliver value back to the business. So that’s the agile piece of this, is we have to be very clear on what is this specific opportunity for an individual organization. So certainly while we know what the trends are around some of those specific use cases, we need to look at it in the context of the organization and their individual readiness to do that. There’s a lot of foundational work that needs to be done.

It’s fun to talk about AI and all of the capabilities that can be. But if you don’t have clean data, you don’t have one source of truth. It’s hard to jump straight into the AI conversation. So really understanding the opportunity and then evaluating specific use cases within the HR space. We’ll talk about some of those interesting ones today to pique some interest around what that might be. That’s a very clear step. And then the last piece is a road map. Again, we’ve got to think agile. We need to move quickly. We need to have a focus on the business value that we are providing, and how are we going to do that. We have to communicate that clearly to the organization. If there are going to be efforts that are coming from the whole enterprise as opposed to HR leading the way or finance leading the way, we need to be really clear on what that priority is and who owns these initiatives, so that as there’s questions – as there’s concerns – there’s a central place for our people to go for updates.

Franco Girimonte:

Yeah. And Anthony, I would just add to that as well. I think I like Lee’s approach around just the prioritization aspect of this. We’re not here to, I think, boil the ocean with applying this everywhere and anywhere. I think you have to take a very targeted approach because you’ve got to build momentum for this. People have to see the results of these kinds of use cases as they’re not only identified but then obviously implemented as well. When they see the results ­– the benefits, the fruits of their labor – I guess you could say that will motivate more people. And like Lee said, that will give you a business case or a case for change on changing other things. I’ve had clients come up to me and say, “I’m not sure we’re going to be able to leverage Gen AI. We’re a very decentralized HR function. We need to figure out ways where we can do more standardization, and the only way to get a lot more standardization is if you have some sort of centralized governance and so on.” So there’s a lot of interest out there to one – find those right use cases that really tell the story so that the rest of the organization can get on board. And I think that that’s a key theme to keep in mind.

Anthony Snowball:

Interesting points. Both of you raised a common theme there, which was really around the client context. Where can the Gen AI technology deliver the greatest impact for that specific client and targeting those areas? So it sounds like really it’s understanding their performance profile where they can get the best returns from Gen AI technologies. And since we don’t have a client to discuss here, as you think about this generally and in your vast experience in working with clients, where are you seeing clients get the best return though from Gen AI if you speak about it more on a universal sense rather than a specific client situation?

Lee Derryberry:

Absolutely. I would love to say that I have clients going after AI agents. They’re not quite there yet. So what we see I would say are more foundational applications or use cases that again generate a lot of value. One of the common ones is around job descriptions. So how can you use an embedded Gen AI solution, whether it’s within your HCM for Oracle, SAP if you have Microsoft? The technology is there to be able to generate a really good job description. It’s probably 80% there with the responsibilities, the key capabilities or competencies that a role needs to have, and you can leverage AI to really get a head start on that. In the past, that would take a lot of research and a lot of human hours to pull together what are the core responsibilities – how is this role changing in the future?

The other benefit on this one of going with Gen AI to start to solution that is then the ability to connect it downstream to your ATS and starting to look at how are you looking at applicant resumes against these job descriptions. Whether it’s internal candidates or external candidates, how do we start to match up who we’re looking for from a job description perspective and then look at a prioritized selected list of candidates that might be a good fit. Another table stakes one that I see happening a lot is HR policy support. So, again, where we would typically see a lot of human hours crafting these policies, figuring out what’s different about the state of California versus Massachusetts, or how are the laws and policies in Germany different from those in Australia? There is a lot of power in native Gen AI that can start to establish those policies so that when you’re looking at enabling a chatbot or even just your HR portal, you’re able to really lay this intricate foundation of knowledge so that your people can very easily interact to get just-in-time questions that are very specific to even their location in certain examples.

Franco Girimonte:

And Anthony, I’d just like to point out a couple examples too. I think I always think of the embedded applications – AI applications – that are very helpful. You’ve heard of tools like a Microsoft Copilot or other products where they’re embedding the Gen AI into things. There have been references … I’ve heard clients tell me that are starting to explore those opportunities where they almost see they’ve gained a digital assistant in that capability. And so they save anywhere from 5% to 20% in terms of capacity build for themselves or productivity improvements for themselves. So there’s opportunity there.

I think anywhere in the space of shared services – HR shared services – where you could see these conversational assistants be helpful not only in just answering employee questions like, “Hey, how much vacation time do I have left in my balance?” Or, “What do I need to do? I just had a baby.” And these conversational assistants can answer the information and then obviously send it to you after the fact. But they could also do things like self-service aids. If you’re a manager that’s never recruited anybody over a year and you have to go in and put in a job rack, wow, it’s been a year since I’ve put in a job rack. I forget what the process is, which is one of the biggest stumbling blocks that we see to things like self-service or employer-manager self-service. You have these Gen AI tools that literally will sit on top and guide you every step of the way that can allow you to start guiding you on what information you need before you start the process. And as you’re filling it through, they’ll guide you and help you with filling in that job acquisition.

Anthony Snowball:

All great points and some really tactical examples, which is what makes Gen AI so exciting. The use of conversational assistance, you described the use of LLMs – large language models – for managing specific policies by country. All of this gets me excited. I’m already excited about Gen AI, so it’s just wonderful to hear these stories. And you hear from clients directly where they’re experimenting and deploying this, and I thought it was a really important point you made upfront. And really the last exchange, which was focused on understanding the client context and what value can they derive from these technologies because it’s easy to get excited about the art of the possible, but then you have to think about reinforcing Lee’s point – the foundational principles of data and technology. So making sure you pick the right areas to focus the technology and also ensuring that you have the readiness in place to really support it.

So in your experience, and as you’ve worked with clients, there are varying returns. I know we think about returns in a lot of different ways, but with Gen AI, we really like to think about it as capacity creation, productivity and throughput – maybe unit savings here and there. But given the scenarios that you just shared – where clients have deployed it after understanding where they get the best value, what benefits and opportunities are they achieving – at least those are clients that you’re talking with and supporting.

Franco Girimonte:

If I think back, one of my clients last year, they went headfirst into this Gen AI capability. And one of the areas they targeted after we did some assessment work for them was in their talent acquisition space. It was clear they were struggling with talent acquisition. It’s a health care organization. As you know, talent acquisition is really tough. It’s recruiting doctors, recruiting nurses – all the health care professionals and so on. It’s a very, very tough job – tough industry to be talent acquisition or a recruiter. And they went in and they looked at all different aspects of the recruiting thing. I’ll say the benefits that they achieved were not just only some financial benefits, but they seen huge other types of benefits. So I’ll give you an example. On the financial side, they were able to … well first of all, they leveraged Gen AI in a lot of different ways to help with a lot of menial tasks like scheduling to speed up the whole recruiting process. They helped it with gathering data and intelligence on various individuals to get better prepared for an interview. They leveraged it for outreach to candidates, especially around the sourcing and keeping the candidates warm and potentially for an opportunity down the road and so on. So there’s whole magnitudes of areas where they were able to leverage the Gen AI capability just in the recruiting area on recruiting and sourcing and onboarding.

And the savings they achieved were mostly, again, external costs. Costs that they were using for contract recruiters because they couldn’t keep up with the demand. They were using recruiting process outsourcers as well. They eliminated those contracts as well. Hard dollar savings that they were able to achieve as part of that. And the other thing, though, on the benefit side to the recruiters itself, they found that recruiting turnover had dropped from 25% to 6%. Why? Because he made the lives of recruiters so much better.

They had higher engagement. They eliminated a lot of those menial tasks. The recruiters were able to recruit. Do the outreach. Reach out with people. Save time. All these things. They were able to do the things they love and passionate about versus the things that they didn’t love and hated. They were able to get the Gen AI to do a lot of that stuff for them. So you saw not only the monetary benefits, but you saw the improvement of engagement and retention of recruiters and also productivity improvements like their time to fill went from 65 days to 35 days, which is a huge competitive environment, especially in the health care industry – really helpful and beneficial to them.

Anthony Snowball:

That’s a great example. Yes. Where it was all multidimensional benefit realization, it sounds like job satisfaction by focusing on the activities that you enjoy, outsourcing those that are more administrative to the technology improved cycle times. I would assume with that improved fulfillment and candidate identification and hiring. So wonderful example there. It sounds as though that’s one that may be an embedded solution where there’s technology that’s incorporated into the platform itself. And I know there are examples as well that go along with more of a native-type approach in the LLMs and the knowledge centers. So pairing the art of the possible with what’s already available in technologies and then what can be built using configurable platforms or off the shelf of Gen AI solutions is really when this gets exciting. And so we’re very motivated – very excited to see clients continue to advance.

I guess one of the challenges to that advancement is what lessons are to be learned. There are many clients that are out there that are intimidated. There’s a mysticism to Gen AI. There’s maybe some concern about the call it self-directed nature of Gen AI. Also, there’s a technology data element that has to fuse with the use case that needs to be considered. Do we have the right technology in place? Do we have the right data? Is it clean and accessible? So what challenges – other than those that I mentioned or maybe even reinforcing some of them – do you see clients really struggling with when they’re trying to jump-start their implementation of Gen AI? And are there rather than more specific implementation concerns, are there even broader policy concerns that an organization should be prepared for?

Franco Girimonte:

Obviously, there’s a lot happening that you see in the media around what’s going on. I think governments as well are struggling to keep up with this technology and how to position it for various regions around the world or countries around the world as well. But foremost, I think is one of the areas I see is around data privacy – a real concern there. These tools in a lot of ways have to be trained. And I think there are ways to ring-fence the data in such a way that protects your own data, but also obviously not putting it out there for every other AI tool in the universe to start leveraging. So there are some concerns there.

I think the other piece of it is this technical expertise. I think there’s just a tsunami of people looking for this skill set – not only just skill sets in individual everyday professionals working within the HR function about how to leverage the tools that are put in their labs for them to start leveraging. Like coming up with genitive prompts or something like that that they can use to start leveraging the tool in the most efficient way. But also what’s that visionary thought process you have to go through to how to start thinking about enabling these kinds of capabilities in the process? If you think about it end to end, where do we start to leverage this type of capability? I think that technical understanding is somewhat lacking out there in the marketplace.

I think the other piece of real challenge is integrating it with existing AI tools. You can imagine it’s almost like a checkerboard environment there where you’ve got rows and columns and stuff like that. You’ve got different kinds of capabilities. Every software vendor out there in the world is building some sort of Gen AI capability inside or embedding it in their tool. So what tools are selected? What Gen AI capabilities are turned on and so on? And then how do you train it? What data is leveraged for all these various tools cross platforms within a platform and so on.

These are all things that have to be dealt with as part of your understanding or assessment of your capability and building up that road map. And so we’d like to help clients with that road map in terms of navigating that minefield in the most effective way. So that, again, this is a high-impact capability. I think back of self-service back in the day where clients have told me, yeah, we tried self-service. We launched it poorly, and we haven’t looked at it ever since. We don’t want the same scenario to play out with Gen AI because it’s really a huge impact on the organization. And I think if clients were to had a bad experience and give up on it right away, that would be a bad decision in my mind.

Anthony Snowball:

And just building on that point, Franco, it’s interesting to hear from clients with very thoughtful and constructive questions around, do we have to think about policy and use in terms of how we deploy Gen AI inside our own firm as we use technologies like ChatGPT, like Gemini, like Llama 3? As we incorporate these technologies, are there concerns for our own intellectual property? Are there concerns for the sharing what could be personally identifying information? So in addition to what you described as a logical approach and even some ethical considerations, it’s interesting to hear from clients around their concerns on what information may leave their organization and wind up in a public domain. It’s interesting to hear those concerns because valid and even there are some concerns out there around bias in some of these capabilities. So curious Lee and Franco, just where you’re hearing clients as they start to tackle these issues – how they’re addressing protecting the data, protecting the IP and concepts to do so, and would love to hear your points of view and client experience on that.

Lee Derryberry:

Absolutely. It’s a good point. I feel like there’s a lot of information about all the good and excitement that AI brings to the table, and that’s certainly true. What else is true is that can be scary for employees and people who are seeing their organization use some of these new tools and maybe have some real questions and concerns about that. One of the things that we’ve seen is a dedicated employee feedback and grievance mechanism. So that as the organization is transparent, which is very important, let me double emphasize that – that as the organization communicates how generative AI is being used in processes in HR and in other functions to our people, we owe it to be transparent, and we owe it to them to be able to provide feedback on where they have concerns. As an IO psychologist, I absolutely cannot ignore the inherent bias that exists in these algorithms.

AI is good at learning from historical behaviors and seeing patterns. So you take an example of AI, looking at performance over time and helping to select candidates based on that or resumes based on that. Well, if the organization has not historically had a diverse slate, then there’s going to be a lot of bias in the candidates that it selects. So it’s absolutely critical that there is human oversight. That there are checks and balances as these algorithms and data are created and generated. That there are very clear rules and regulations against that so that we’re really starting to look at bias mitigation because it is happening. We need to understand how and where, and the severity to be able to plan around it.

I would say the other thing that I’ve seen is somewhat of a review board – just like you might have a performance review board – where you have tricky challenges that might come up that you look at holistically with a group of executives to provide an objective balance perspective. That is also something that we’ve seen clients lean to so that when employees have big concerns or we know that there has been bias that we need to correct or adjust for that. We have that review governance board that can take that into consideration. We know about PII, GDPR – there’s all kinds of acronyms we could throw out that that board needs to be aware of – but there must be clear governance on how we’re going to handle data privacy and individual concerns that can sometimes turn into thematic concerns when we look at feedback from employees.

Anthony Snowball:

Wonderful to hear the protections that clients are taking to ensure that they maximize the impact of Gen AI and minimize the risk to the organization more broadly. And you both have described what I think is a thoughtful process. Really it’s starting with a top-down mindset and understanding scanning the HR organization or even the enterprise more broadly, where can I get the best return from Gen AI or more broadly the AI profile technologies? And what you’ve described Lee in your most recent answer is really creating a governance board or a sense of governance sounds like with representation from not only HR but IT – maybe legal. And then even Franco to your point, and reinforced by Lee identifying those areas that you can deploy this growing lake of opportunities, even maybe bordering on ocean, as you said, boiling the ocean earlier. And really determining which use cases are going to deliver the best return for each individual client.

So assessing yourselves – where can I get the best return? And then understanding to each of your points respectively as well, can I use existing investments in platform technologies that I already pay for and SaaS capabilities, or should I be thinking about new native solutions like large language models or a conversational assistance? And then working your way through the readiness of that. And that includes the governance aspect and that includes making sure you’ve got a cybersecurity and a technology perspective on your readiness. So that takes us then … you’ve worked our way through from a top-down experience to identify the right use cases that are best for a client and helping them really determine or you to think as a client through what’s best for your organization. But then it’s where have we seen success and who can we hold up as a model? Maybe not a client name, but specifically clients you’ve seen where they’ve followed a process similar to what we’ve outlined here and where they’ve achieved real results that they’re excited with and that they’ve been touting out there in the marketplace. I’d love to hear from any examples you two might have. I’ll certainly share some that I’ve seen as well.

Franco Girimonte:

I think Anthony, I think the case I mentioned earlier about the client where they were able to leverage this capability across the end-to-end spectrum of or process of the recruiting process and onboarding. I think that’s a clear example of where you could see that they were able to leverage those things. I know one example I think really crystallized for me that a client shared with me they were using. They were away on a holiday, and they had a meeting first thing Monday morning with somebody internally inside their organization. They couldn’t remember what the meeting was about, and it’s like, “Oh my gosh, I’ve been away from holiday,” and stuff like that. She literally went into her Gen AI capability. In this case she was using Microsoft Copilot and she said, “Hey, could you go back and look back at the last two months of emails I’ve had with this individual, summarize what we’ve been going back and forth on, and give me some key themes on why are we having this meeting for?”

And basically within 30 seconds she got a response and said, yep, these are the email conversations you’ve been having with … These are the key messages from those email conversations that you had all agreed to get together first thing in the morning when you got back from vacation to discuss this, this, this. She was telling me, “Franco, I could not believe how this literally saved me probably 30, 35 minutes of time. One example and I got it achieved within 30 seconds. It made me so much more prepared for that meeting going into that, and I didn’t have to break into my laptop on a Sunday night after coming back from vacation to do all that work to get prepared for that meeting.” So that to me is a great example. It’s obviously not a … if you take that same example and you multiply that by thousands and thousands of instances a day across organizations, you could see how much more productive employees could be and more effective in connections or meetings that they’re having with employees.

Anthony Snowball:

Yeah. The productivity impact of what you’ve described because that really is a time-saver at the individual level. But to your subtle message there, once you extrapolate that across the entire employee base, that’s a dramatic productivity benefit that can free time up and allow people to focus on more strategic-oriented activities and thinking. So great example there. The other that comes to mind in working with clients is really … you mentioned it earlier, the conversational assistance and the ability to, as employees have questions, interact with more of an assistant on, I’ve got a new dependent, I’ve got a question around 401k match or on the provider side of things and saving for retirement and getting immediate response and freeing up time from the HR organization and some of its employee interaction responsibilities. Terrific opportunity there as well.

So it’s interesting because clients are all experimenting, and the ones that are successful are those that are taking a pretty disciplined process to this rather than allowing the popcorn kernels to pop around HR and people to experiment on their own. Instead, it’s a more structured, disciplined approach based on what the two of you have outlined here. And similarly, where you’re seeing success, there’s the necessary governance that’s mixed in with the entrepreneurship to bring this to life. I think we’ve talked a lot about use cases and even case studies, but another is custom solutioning you can build. And one that we’ve seen that we’ve built here at Hackett that’s interesting for clients when we share it is within the span of about a three-week period. Literally taking ChatGPT, Foro, Turbo, connecting it with an API into a LinkedIn database, and then querying that database using the generic prompt and being able to say, I’m after an HR admin resource or an HR employee engagement resource or in finance. I’m looking for someone with accounting experience ­– a CPA, 10 years, they’ve worked at Big 4 for five of those years, and these are the qualities and competencies I’m looking for, and please summarize them.

It’s amazing what generative AI can do when you build a native solution like that, connecting into a structured database like a LinkedIn. Immediate response time to prioritize and rank order three candidates that are the best fit based on the competence you provided. And so those are the things that really blow your mind, and I’m sure you two see a lot of those in your daily life working with clients.

Franco Girimonte:

There’s also situations where I’ve seen where clients I think came up with horrible use cases. I’ve had a situation – native solution, right – in terms of Gen AI capability. And they literally had 52 cases that they had identified, and they said, “Hey, Franco, do you mind just taking a quick look and seeing?” And you can clearly see, first of all, they had no real understanding, I think, anyways of what Gen AI … what it’s capable of. They were selecting a lot of use cases like a one-time event annually or something like that. Obviously, Gen AI could be applied to those types of use cases in the future and stuff like that.

But when you’re first out of the gate, right, you want to have that capability that we’ve been pushing onto clients in terms of having a very structured, methodical approach – backed up with good data and insights from the capabilities we have in our AI explorer tools and so forth – and our people to really identify the best use cases so that you can, again, build momentum, get stakeholders more engaged, and so on to want to start leveraging these technologies versus when I looked at those 52 use cases. Literally, I found one that was probably a decent one to start with, and then I had to suggest a couple more for them to look at on top of the 52 that they had already identified. So there are some lessons learned that you can see there that we’re seeing clients go through.

Anthony Snowball:     

Interesting. Yeah. That’s fun to hear and fun to watch as this technology emerges. And speaking about emerging and evolving, we’ve witnessed in our careers an unbelievable explosion in really digital solutions and Gen AI being the most recent to get notoriety and grow. And you described, I think, Franco upfront that our definition of AI is … while we’ve talked a lot about Gen AI here today. It’s inclusive of some of the legacy technologies that have been around for a while – the task automation, the predictive automation, the intelligent data capture, the intelligent optimization, and really six different AI profile technologies. We’ve talked about really Gen AI by and large. But I’m curious what each of you think for the future. Elon Musk paints a pretty grim picture as we fast-forward and our existentialism may be a threat if we evolve decades – maybe centuries – down the path. But in the near term, what do you see for the evolution of Gen AI within HR? What are some near-term steps? And hopefully they’re much more positive than some of the doom and gloom positions out there.

Lee Derryberry:

I would say so. I would say cautiously optimistic is what I see on that front. We have a number of clients that are still looking at automation, frankly. They’re still looking at how do we get more transactional work – that process-based rule checklist work done more efficiently? And maybe they didn’t get on the boat when RPA was the hot topic. I would say the fear driving it now is my clients don’t want to be behind. And so they’re sharing this effort of, “Hey, where is this going in five to seven years? Because I’ve seen the data. I’ve seen how much cost is going to be taken out. I’ve seen how much more work can be done by a technology instead of by a person.” And I think it’s wise to start to look at that now because there are big gains coming, and it presents a phenomenal opportunity to realign your talent. What a great opportunity as Franco shared of that example for the recruiter to get better job satisfaction where you can actually do the creative work. The work of your brain to think about and solve problems differently based on the context of your organization.

And I know Anthony that’s been a theme throughout our conversation here is your context matters. And that’s something that Gen AI right now is struggling to solve for. Maybe with these AI agents we will get there soon enough. But the human brain still brings so much context and creativity to the table that we can’t replace that. So what a great opportunity to streamline – standardize – that process. Right now we see shared services can be 65% in some cases with our more advanced clients. All of transactional work. We’re expecting to see that go up to over 80% as we look at this AI really being embraced. So what a great opportunity to take that talent and think about how we can use them differently in our future state organization to make good investments. Maybe you want to go after strategic workforce planning, which is on a lot of people’s minds. How about moving them into that space to really start building capabilities that have maybe been on the wish list for a while, that we didn’t necessarily have additional people that we could dedicate to that.

Franco Girimonte:

Every HR professional out there is looking to say, “Hey, I want to help develop employees. I want to help leaders lead employees. I want to engage employees. I want to build the human capital capability within an organization.” But when then you look to the realities of the role, all the stuff that Lee mentioned around that – all that transactional work – those are all distractions and take away time from that HR professional. So we want to get back to the real purpose of the role, which is to develop, grow, nurture, guide, coach. All the things that we want to do as HR professionals to help our leaders and our organization, our employees, really truly evolve the human capital capability within an organization to really reach that potential. When you think of things like innovation and ideation and all the things that you need to have in terms of a capability, that’s time that we’re just not spending enough time on with our employees in the past. I think this will be a true game changer for the HR professional, freeing up that capacity to do the things we’re all passionate about wanting to do, which is help our leaders and help our employees.

Anthony Snowball:

Well, I must say you paint a very positive picture, and at least I feel as though very well-informed on how really AI broadly and Gen AI specifically can relate within the HR function. You’ve done a beautiful job of describing the motivation, which is to allow companies to become more competitive and to drive greater enjoyment from their employees by having them focus on the most important tasks. And in general, I would suggest that there’s just a great opportunity to be structured and disciplined about how you go about this and understanding where you can get the best return from the AI technologies measured in capacity or productivity or even just softer benefits of experience. And at the same time realize that there’s more to come. That the glide path of this technology is inspiring. There’s big tech companies that are behind these technologies, so they’re going to continue to receive investment – continue to grow.

And so really the intent is to translate what’s being built into practical within the human resource arena. You both have done a wonderful job of spelling out what that vision looks like here in the present day and also projecting a future forward of what we can expect. I am told, while I can talk about this for hours and love every minute of it, we are officially out of time. So first, I want to tip the cap to each of you – Lee, to you, and Franco, to you. Been an honor to discuss really the Gen AI potential within HR. And I want to thank you for joining me today and sharing all of your incredible insights.

Lee Derryberry:

Absolutely. Happy to engage in the discussion and looking forward to more talks.

Franco Girimonte:

Yeah. Thank you, Anthony. Same here.

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