[Music] welcome everyone and thank you for joining us today my name is Don mccon I'm a director in barretts technology and Services Group I cover the human Capital Management sector alongside my colleagues Brett shock and Andy ladaro here in the United States as well as Seb Diller Simon Pearson and Helen Jones in Europe before we get started wanted to first level set on what what we had Beed view as the Three core verticals within HCM that's talent acquisition talent management and Workforce Administration Within These Three core vertical bared has extensive expertise across all the models from services to Tech enabled services and software and while our team has been covering those the sector for over 30 years we are looking forward to its continued and participating in its continued Evolution so with that said we're super excited to host this webinar today and dive into a topic that is front of mind for many of our clients that is how AI will impact human capital sector uh now I know this is a very complex topic with far-reaching considerations and implications um but we are privileged to be joined by our panel of experts to help shed some light on where it's headed I'm pleased to introduce Steve ctz HR transformation service leader at ernston Young Jamal Justice also from ernston young people advisory Services practice with a specific focus on AI applications and HR and Dr Eve Alexander Mona AI thought leader and associate professor from Imperial College of London each of these panelists have a very unique perspective based on the work they do in and around the intersection of AI and HCM so firstly gentlemen on behalf of bar thank you for joining us we're very fortunate to have you here with us today um and with that let's uh let's Jump Right In so EES let me start with you to set the stage generally around AI can you give us just a highlevel overview of where we are relative to the last two years in AI advancement no more than more than happy to um mostly I think in the past couple of years we've been going through kind of two AI revolutions um the first one is basically the Deep learning Revolution uh it's how the the availability of data and and computing power uh means that models machine learning models AI models they've now been able to basically learn directly from row data across data modalities uh text images tabular data Etc and and that has really led to you know dramatic Improvement in their capabilities their production capabilities uh because now model can basically learn directly from Ro unlabeled data and have been able to basically transfer Knowledge from from one task to the other and that had like you know quite a dramatic impact on the AI feied but also or AI was useful across businesses including in human Capital Management and then the second revolution is basically what we've seen uh in the last year or two was generative AI right and basically AI so far was mostly focusing on prediction models right given a picture data about a person uh can you make a prediction right is this a picture of a cat or does this email looks like spam uh but now models can go beyond prediction and actually generate content uh we've all used you know chat GPT but gen goes Way Beyond just text right now models are able to generate images uh audio video synthetic data and and that is like you know a dramatic uh changes um in you know how model are built and what they can be uh useful for I think when we wrote the report uh for was sped on an AI state of mind uh we mostly emphasized kind of three ways AI is impacting businesses um the first one uh is basically new insights production the ability to to forecast uh things the seven one is basically the automation of task right efficiency gain the ability to automate a number of um tasks to go faster and cheaper and then finally the last one are basically new product new services that that before AI would basically not have been uh not have been possible I think throughout this uh webinar I think we're going to come back to both you know what deep learning and gen enable in the way it is impacting uh businesses and in particular in human Capital Management we're going to talk about the range of uh applications um on the automation front right there's obvious one on consumer uh service Services uh answering FAQs handling you know routine queries uh on HR on like you know matching uh you know CV for example to to job adverts scheduling interviews writing adverts uh all of them are like you know dramatically improving uh productivity and freeing up uh human Talent uh and on the new inside front um you know we'll be talking more about you know consumer analytics helping to you know create you know personalized marketing strategies improving consumer experience but also risk management and how AI can help you know better again forecast predict and anticipate uh potential potential issues thanks Eevees and just coming off of your overview of where the technology land so far the the one thing that I really want to to make clear for our our audience viewing today is regardless of whether you're a Services organization or a technology organization or even if you use technology to deliver Services through it there's a little bit of everything for you and what we're going to cover today um a lot of the examples zes that you covered um around automation of routine tasks or generating new content or becoming predictive in nature using the advancements of AI and generative AI we just really want to make sure that you're able to take that and understand that irrespective of how you deliver to your clients there's a lot of opportunity for you here today yeah that's a really great Point Steve and evees a wonderful backdrop to what is it otherwise extremely complex uh environment so we've we we've boiled that down probably you know too far but it's a a great backdrop to uh to what you maybe I passed it over to Steve and Jamal at this point and guys that that you touched on some pretty incredible developments and functionality that that AI is going to bring to the table but from your seat maybe talk to us about what you're hearing from your clients on our end we're hearing kind of this increasing volume of both excitement and concern over the potential impacts that AI will have on their their businesses or portfolio companies um but I'm curious maybe specific to the HTM sector why should operators or investors in the HTM space really focus on deploying AI within their organizations and then maybe give us some discrete examples of where you're seeing AI have called the greatest impact uh within your your work absolutely Don and I'll I'll take this one first uh thank you EES I think we heard a tremendous story from EES about the evolution of this Tech the two other things I might add there is that H you know Ai and generative AI even is becoming increasingly democratized so it's in individual's hands to use and they're using it uh sometimes regardless of what the Enterprises uh approach is around Ai and whether or not it's desirable to use it so it's out there the other thing I'll I'll mention is that there's a tremendous amount of funding that we've seen come into this space as organizations are eager to experiment at because of the opportunity ahead of them to realize true business value from investments in this space so why is this important to us in this spa space one is that opportunity whether it be operational efficiency whether it be experience whether it be cost Effectiveness uh whether it be improved customer and employee insights there's tremendous opport real business value opportunity ahead of us two uh competitive consideration so whether you choose to go on this journey or you don't choose to go on this journey there's there's a there's an impact and companies that are able to leverage AI effectively to improve their services products or operations are going to be have an opportunity to be at a significant Advantage from those who don't the third one I'll mention is around risk so there are ways to do this right and there are ways to definitely not do this right and we've seen several examples in the news around companies who've made Mis steps in the space so it's important to to Really approach this space thoughtfully consider data data privacy considerations Regulatory Compliance a variety of those considerations as we go into this space so yes it's important to get into the space but to do so with intention responsibly and then if I talk about impact we talk about we we we're seeing product companies innovating their tool sets to capitalize on AI opportunities we're seeing Services Company companies innovating how services are delivered looking at people process Tech risk considerations and as we're seeing these Evolutions we're recognizing the uh tremendous positive and disruptive potential of this technology which means there's implications for how we manage change there's workforce planning implic lications there's implications for how we upskill res skill and think about our talent as generative AI becomes increasingly more integrated into our work so let me let me just give a a few examples of of Applied into the HTM Space by digging into some domain eras so when it comes to recruiting Ai and intelligent automation can be applied to automating monotonous tasks such as screening resumés and scheduling interviews it can generate insights to predict hiring Trends and anticipate Staffing needs and enabling companies to provide strategic value beyond the transactional aspects of recruitment it can help by putting together comprehensive job descriptions role requirements and expected responsibilities and it can also help us to remove language that reflects our unconscious bias things that are beyond our understanding that we may put into these these artifacts um and and in so doing help us to to be more effective at recruiting a diverse set of talent and avoiding the risk associated with doing that in inappropriately machine learning algorithms can learn from existing data to predict future outcomes such as which candidate is most likely to succeed as well so it's just a few examples in recruiting from a talent management perspective a major factor is ai's capacity for personalization so AI can offer a customized employee experience by tailoring Learning and Development programs benefits plans and even communication Styles and this High degree of personalization has a direct positive impact on employee satisfaction and retention and therefore Downstream from that the bottom line um it also has the capability to match Talent with required roles optimizing Talent allocation throughout the organization so just a few examples in talent management in workforce management AI can also be leveraged to automate routine workforce management tasks including time sheet management leave tracking benefits Administration compliance reporting it can analyze varied data sets and help to generate predictive models to Aid in smarter and quicker workforce planning and what I'm get really excited about is the opportunity around skills so identifying and inferring skills from various information that we can provide to it like resumés or LinkedIn Pages or a variety of individual specific information look at that broadly across the workforce and and Beyond a single organization to identify trending skills and then to help to anticipate the needs of the workforce in the future and how we can upskill rescale higher Etc to close gaps and get ahead of those Downstream needs and then I you know I'll mention a sort of in the payroll processing space the ability to automate dat data entry tasks to predict payroll costs help identify in surface anomalies or errors and then NPO Services analyzing vast quantities of data related to benefits Administration Regulatory Compliance and risk management to and helping to make those insights more digestible to professionals supporting these processes to better and advise and support and clients so in some AI Done Right can help us to do many things that we do today more effectively and efficiently and as EES so Appley pointed out make things that were previously not possible or too costly to do possible it can enhance the experience of employees working with technology ultimately drive towards better overall business performance and financial outcomes and that's why we should pay considerable attention to this space thanks Jamal and just to add a little bit onto that as well and I'm going back to my previous point but I want to go a little bit deeper into it around generative AI it's not just transforming day-to-day or business as usual activities but it really is it's reshaping the the role of HR as strategic partner in the organization and so whether again you're a Services organization or you're more on the technology product side if we think about kind of the value spectrum of activities what we're finding a lot of generative AI doing is allowing us to be able to um almost Outsource or utilize generative AI for more of the low value tasks in order to focus our people our Workforce or our products on more of the higher value tasks that move the needle in the function and so when we go back to some of the points made about it being proactive being able to predict and being able to personalize it's very powerful and we we'll get into a few examples here as we continue on through yeah that's a great great Point Steve and Jamal you touched on you know some very factful discret examples there we certainly have seen our clients across beo and RPO starting to deploy those same models like the the one that that struck me the most um outstanding the the process automation functionality but the fact that there are poo models out there that are taking in Ai and bring it into a sales motion and that that that program that software is able to effectively predict what topics or the way in which topics are delivered will actually correlate to better close rate so just the the broad application is is really stunning here uh but appreciate the the the color um you're kind of the same vein and Steve maybe turn this over to you just given your team's ongoing work again some discrete examples but you maybe not exactly with how it's being used with an HCM but where you see the most effort being exerted Jamal covered you know a lot of examples of how it can and will be applied but you practically day-to-day today how do you see your clients and where do you see them focusing the most attention today yeah absolutely um a few examples from our standpoint number one and yees and Jamal had mentioned this previously but the the ability to personalized content we're seeing that really take hold from a Learning and Development standpoint and so when we think about the personalization aspect and also the ability to digest information on an individual and we think about the linkage between what skills they possess the the career trajectory or the track that they're on and being able to pair that with a lot of the great learning technology in the space what we're seeing what we're working on as a firm and what others are working on is accelerating the personalization of that content and so from a learning standpoint a couple ways that I see that that play in um we're we're seeing generative taking a a strong stance and a strong um a strong model and actually helping develop that content um we're also seeing it from a matching standpoint when we think about again the skills and the learning required to to guide an individual on their career path a lot of the developments that we're seeing in these tools are around the personalization around the matchmaking or the marriage between that content and the individual and really what we're trying to do and just collectively the Wii as a group is because we have such a a recent focus in the trajectory and advancements in it around AI we're going after high impact learning experiences and thanks to generative AI what used to be almost on an individual basis we're now able to do it at scale and scale is a really important thing to talk about with respect to generative AI because that's really what's unleashing our power to be able to do it um in addition to learning and some of the content creation as well as personalization we're also seeing right now in a few client examples that even come to my mind is inserting AI in a way that allows you to create a digital Workforce or digital labor and what I mean by that is aside from what many of you may think when you think about generative AI or chat GPT kind of a and ask me anything type tool what we're also seeing AI uh in a digital worker format is really being a side of desk utility where we can take a discrete function whether it's uh within the HR function a recruiter or somebody in the learning space and being able to create a worker on the side of their desk that and I'll go into recruiting for a second if if there's a massive amount of manual work that still needs to happen whether it's in requisition creation or whether it's in matching Talent against the requirements of a role we're able to see that packaged and almost compartmentalized into a side of desk utility for an individual so I think what many of you will see moving forward whether it's embedded into your products or embedded into your services is how can I make an enhance an individual's contribution to an organization we want to make sure we they have massive impact and by standing up AI in a manner that has that side of desk that that digital worker accompanying them on a day-to-day they are they're more more simply and more really more effective at being able to move work kind of through that that cycle or that Spectrum um and then when we think about just overall Ai and the in the HCM space we're seeing these Trends steadily increase and so many of you that are ingesting this or even embedding it into your tools you're seeing a lot of AI powered Talent Suites um you're also seeing a lot of examples that Jamal is going to cover a little bit on HR analytics and Workforce productivity and so the main point that I wanted to make through these examples is whether it's learning I know I hit on that a little bit whether it's workforce management that Jamal talked about I do think digital labor is one of those trends that you will see in the Forefront and continue to see advances on and accelerating as we move through the next six months nine months and a year but Jamal what I'll do is do you want to talk to us a little bit more about worker productivity specifically yeah I'll talk about a few things um so one is we're seeing an uptick in leveraging this technology very common use cases around chat Bots and virtual assistance so whether that be to facilitate the the onboarding process can process and a answer employee questions quickly with the right tonality and the right content and to do so you know by not necessarily being explicitly programmed with a series of questions and answers but actually leveraging some of the natural language capabilities to to do perform these functions that's incredible in payroll and benefits what gets exciting to me is the ability for these chat Bots and virtual assistants to look at both unstructured and structured data so I can answer basic questions about payroll and benefits policies but then I can also get into the individual's record and understand you know like how much if I transition to a new organization midy year and I had 401K contributions at my previous organization and some at my new you know how much more can I contribute before I top out on my 401k contributions and at what percentage of my paycheck do I need to contribute to do that right that's a pretty sophisticated question but the art of the possible here is to be able to bring together that structured and unstructured data into these models and give it access to it in a way that allows it to answer those types of questions work worker productiv activity is huge right and we've seen tremendous investments from major players in and around this space uh to to advance what's possible here but when we talk about things like content generation so first drafts of email Communications when we talk about idation so I I need to go into an interview for a candidate for a particular type of position what are 10 questions that I can ask uh and especially related to managerial skills and sort of some of that foundational idation creating job descriptions removing bias uh making sure it's the the tonality in our Communications reflect the tonality of the organization and the tenants that we want to keep front and center and then also interestingly more more coaching right so I I draft a communication and I asked these tools what could I do to improve this communication and getting some of that coaching based upon you know the art of the possible and how I could communicate um really facilitates Improvement and and worker productivity you know Steve Steve also mentioned predictive modeling and EES as well uh you know they're very traditional example like continuing to leverage traditional techniques to develop uh predictive models around attrition this continues to be a topic that clients come back to and these models once done are not done in the larger scheme of things right they need to be maintained over time and they need to be made current to ensure that they're still predictive in nature based upon the data that they're they're consuming and it's and its reliability um and then the final thing that I'll mention related to this I think there's um you know excitement and trepidation around uh bias and risks around the technology um we've seen examples or heard of them in the news of of organizations who have uh Le trained these models on historic data on what makes a higher successful in a given role and because that foundational data was skewed towards a particular segment of the employee population it perpetuated a a a bias that you know they may not have been aware right it's absolutely critical that we are conscious of that as we develop and release these models because on the flip side the tools when appropriately used have the ability to help us to in understand bias that we may not even be aware of and to appropriately adjust for those bias es and that's the art possible so that's what I add to what you said Steve yeah thank you let me jump in there so I mean you touched on what I believe is probably a third rail live wire for a lot of clients that that the opportunity in front of them is is undeniable the application and impact on the organization is very clear um but not on the same scale but I personally when I get an iphone update I I I view that with some skepticism and hesitation and that's just updating software on my phone I have to imagine the prospect of rolling something like this out you know regardless of where it hits within the organization is an extremely daunting Prospect for a lot of for a lot of businesses even those that are you know more Tech leaning or even software focused who are naturally inclined to you'll be a little bit more on the the the Leaning edge of of this sort of thing but maybe Steve Jal if you wouldn't mind the last few minutes just talk through a few lessons and Jamal you you mentioned a few of them but few lessons you guys have seen or you had clients learn as they've kind of gone through this process and maybe kind of risks and challenges that you you're also seeing clients experience as they continue to push through into this new frontier perfect thanks Don one of the biggest lessons that we've seen learned so far is in and around the relationship between having a a corporate strategy on how you want to approach and Tackle implementing Ai and again whether it's more of a traditional steering it towards back office or putting it in into the product itself or even from a goto Market standpoint these are all different areas in which parts and portions of the organization I I'll call them towers for the purpos of this right are all running Full Speed Ahead but where I'm seeing a lot of clients struggle right now is in the alignment of what these towers in an organization are doing versus where it fits into the overall Enterprise strategy and so quite sounds simple a little bit more difficult in execution but making sure that you have that overall strategy and then what you're doing as an organization irrespective of if it's internally focused or go to market focused or even embedded in the product as a go to market itself being able to make sure that everyone's operating in Tandem and it's happening at a very quick Pace but it needs to be in lock step with the overall strategy of the organization and that leads to less conflicts down the road and it also makes sure that there aren't any questions around what's the return on the investment inspected and so that's one of the areas just making sure that alignment is in place between the overall strategy of the organization and the parts of it where you're looking to implement it another area too that I'll hit on is around Readiness you heard a lot of of I'll call them buzzwords but they're more than buzzwords they're critical to implementing generative AI is around data and around language models whether the data is structured or unstructured in nature one of the biggest things that we're seeing clients um struggle with now is jumping right away into an AI solution but not yet having a solid foundation from a data standpoint and those two are critical and I would say that the implementation and the move forward with generative AI is only ever going to be as good as the data structure and the data governance sitting underneath it and so one of the things that we look for our organizations to do is have a cohesive strategy on how are we going to structure the data how is it going to be compartmentalized how do our language models interact with it those are all the critical conversations that should be happening upfront rather than getting to a juncture in any type of a project to implement this to avoid misalignment and similar to those two points governance is another big uh a big topic on this is even after you have that strategy you have the alignment um you have expectations set throughout the organization how are you going to govern it from a point of starting on it to actually finalizing continuing to iterate making sure that again back to expectations the direction the organization wants to go is in line with everything that they're doing a governance structure is really critical and when I say proper governance structure I'm referring to top down intersecting through all of the areas in the organization which are moving forward with efforts so those are just a few examples and what we often find is that um there's a little bit of paralysis by analysis that goes on whereas organizations almost want to solve for everything in the Enterprise prior to actually getting started there is a happy medium there and Jamal is going to talk a little bit in a moment about um kind of a big bang approach versus more comp compartmentalized or iterative ways I'll use agile as an example of saying once we have that governance structure set once we have that data model and set what are some of the ways that we can we can move forward in an iterative manner that still give Real Time Value drops to the organization and so Jamal I'll turn it over to you a little bit to talk talk a little bit more about that yeah absolutely I couldn't agree more with everything you said Steve so there there's not necessarily A need to go so big all at once so while it maybe int tempting to to try to to do all these use cases simultaneously you know it's essential to have that foundational approach to understand Associated cost highest value Investments to really work through prioritized use cases understand risks and understand Readiness across the various factors that you mentioned Steve including organizational Readiness how ready are we from a talent perspective from a culture perspective to do these things um I agree I would use the word agile as well way as well um so adopting an agile mindset and taking ink incremental steps in AI adoption can prove to be beneficial it can reduce the risk be more cost cost efficient it allows for room for Learning and adjustments during that process and so what I'm seeing a lot of organizations do is focus on sort of that that overall as you mentioned cross business unit cross function legal risk compliance security it HR sort of foundation right and then ident identifying a series of use cases whether that's leveraging technology that has it embedded in the tech technology to advance those use cases in the context of the workflow and the work processes or it's developing something new a proof of concept that has high impact and you mentioned some of the opportunities in the learning space as an example of what you might consider poing because it can take a lot of that work out or enhance the quality or we're just seeing that our Workforce is exerting so much effort in this space and we want to improve that experience experience that they have so they can focus on higher value tasks right so that's what I would add to to what what you uh you mentioned in terms of Lessons Learned and then and then I would I would just maybe end with this point um you you probably can tell from everything that we've covered the pace of technological advancement is rapidly accelerating reshaping how businesses operate and AI is playing a major role here so organizations that are slow to adapt or choose not to utilize Ai and related Technologies are you know they're they're experiencing that risk of being left behind by their more agile Innovative competitors that are willing to take steps into this space to start to think through all the things that we've mentioned to start to execute proof of Concepts so that they have an intentional and thoughtful approach to responsibly deploying AI to address the opportunities uh in the marketplace place um the reality is this technology continues to be on an evolutionary journey and so we we've got to meet it where it is um and uh that still uh means that that you've seen hopefully and heard there are some some real meaningful value that can be derived today and foundations that you can put in place to help you to continue to benefit from investments in the space Domin so I would close with those final points got it know thank you for that NES maybe just last minute here obviously you sit kind of at a higher you level in terms of your perspective of the ai ai Universe here maybe just a couple learnings that you've seen along the way um given given your perspective no definitely I think honestly I'm I'm mostly going to pile on what Stephen Jamal said U you know data is extremely important alignment continuous governance uh but but but potentially more than anything else a really agile iterative uh approach um it's it's really you know AI covers a range of technology and and I I I see is is is basically that it's it can be hard when when you're not an expert to understand actually what's what's doable and and and easy to do today with AI and what is really really difficult task right as we see AI winning at at games like go and Starcraft and molecules and generating really impressive you know videos and answering questions uh yet it doesn't mean AI is good at everything right like you know self-driving cars they've been announced for you know a very long time they're not here uh robotics is still a really really hard problem U hallucination when it comes to chat GPT uh and and chatbots is is an open-ended uh problem it's an open problem today so I think it's it's really about this aile lit ative approach working with experts and and really trying to identify opportunities where AI as it exists today is going to be you know very good and and will bring will bring uh value I agree I think there are a lot of opportunities a lot of values and it's a question of really aligning what AI can can can do today uh with with the opportunities excellent well look this has certainly been an incredible conversation um I think we've we probably contextualize fair amount of uh content here in a relatively short period of time but think ultimately the takeaway here is that AI is undoubtedly here and will remain and we continue to evolve uh the HCM landscape uh but firstly again on behalf of be thank you to the three of you really appreciate you taking time out of your day to to join us uh it's a real pleasure to have you share your insights and on this critical topic um but thank you again on behalf of be to our panelists and thank you to our viewers uh please reach out if you have anything that you'd like to chat about on this this uh this webinar look forward to chatting in thank [Music] you