[Music] what I'm going to kind of cover today is to um really talk a bit about the history around people analytics um where it's come from where it's kind of heading today um and then we'll kind of talk through some of the examples and some of the challenges that I think many HR functions are facing um um in their kind of people analyics Journey before we then start to touch on some of the best practices and learnings um and then sort of talking around obviously some of the advances uh in relation to um artificial intelligence and and and and then touching on some of the challenges and ethics around some of these things but um obviously a lot of art of what possible has has never been greater but um obviously um it's still quite early days um as well so um as I say we'll try and go through and um really focus on some of the Practical examples um everything in the presentation I put a lot of um you know links to the sources and things so um if people do would like a copy you know you can reach out and we we could potentially share that after um so um without further Ado um as I said I think look um I think it's never been such an exciting time to be in HR um you know as it is today with um you know the art of what's possible has has never really been greater um a lot of people I'm sure you've heard of um Concepts like analytics before Big Data artificial intelligence open AI uh machine learning robotics um you know these are I think you know many conferences now are talking a lot about these kind of Concepts so um what I really try and focus on today is you know what does this mean in the context of of HR um so I always like to start around um people analytics because I think there's many different terms that people call this um some call this HR analytics others call it people analytics um some would call it human capital analytics um um and I think more recently A lot of people are starting to call it Workforce analytics because it's not just about people anymore as Ai and robots um start to make up part of the workforce um but how I Define this um I like to describe it in in relation to four domains so you have the domain of HR your it systems statistics and science um for me the interface between HR and systems this is where your HR Information Systems come in so whether you're you know on Modern cloudbased systems like the workday success factors or the like that's typically where a lot of that um operational in system reporting would sit um the interface between the systems and statistics this is typically where your computer scientist would sit uh you then have your behavioral economists that sit between the interface of Science and statistics um people like myself as a as a ched or organizational psychologist um I kind of would see myself between the science and and the domain of HR um but really for me um there's a big sweet spot I think around data scientists and these been very popular um certainly in the past um 3 to five years because they sit across um you know science statistics and systems but really people analy is is really the center of all of these uh worlds colliding so it's the interface of combining people strategy science statistics and systems um and really HR is the domain in which we apply to improve the effectiveness of people related decision making and human resource strategy um so if you think on the systems you got your haa systems apps data capture and visualizations uh how your applying statistical um techniques um as well as that scientific methodology research survey experimental design and so forth um so I always kind of say that really it's that diversity of all of these views and domains coming together which which really make um that um diversity of views and and and also it can be quite challenging for leaders obviously managing such diverse teams um but yeah um that's how I would Define this this and um yeah that's that's often a question I get asked um so the and this is a very wellknown I think um concept but um you know obviously analytics is not a new area or domain I think it's um it's it's certainly uh been used a lot within businesses in their operations applying to customer services and marketing and sales but um I I think it's fair to say HR is is a lot more immature in this area in terms of adoption uh to other areas um and and this is a common uh challenge that is actually been coined um HR is often hitting the wall in terms of HR measurement and and what I mean by that is that we tend to rely on the very reactionary operational measures um and descriptive analytics rather than um you know really looking at more predictive and prescriptive analytics capabilities so I think um for me a good example of this would be a metric like regrettable turnover so often this is how of your top performers uh have left um from the previous year whereas you know these days you can start to actually predict what's the likelihood of um employes leaving over the next 12 months leveraging you know things like random Forest machine learning um to look at look at these kind of things so that that hopefully gives you a bit of an example around you know retrospectively looking at things that have already happened and due to the operational nature of that versus leveraging some of these more advanced analytics capabilities to help HR Peak around the corner and start to answer some of these more strategic questions um so to kind of highlight that in in another way there are various kind of maturity models um and you know one one that kind of I i' like to highlight is that you know you typically look at um you know what's happened why is it happening to what might be happening in the future and as I've said before HR tends to react after an events happened versus where I think the real power is how we can help HR as a function know what's coming before it happens um and and this is where you know if you overlay things like the Josh bsen maturity model um and I'm just kind of highlighting this around well if you look at some of the uh measurements here from your operational reporting to more advanced and then Advanced analytics and Predictive Analytics um the the percentag is there on the right just showing well from organizations that have been served they how much how many organizations are actually uh spending their time in those areas so I think really and look I know these numbers have been updated um but you know there's still very few organizations um really doing a lot around that Predictive Analytics piece um so so hopefully that just kind of gives you a bit of U an assessment around like I say how we're trying to move from descriptive to more predictive and prescriptive insights um we know from research around what are some of the top drivers of people analytics maturity um and just some of the call outs here that you know they tend to have uh more mature organizations are two times more likely to have a data Council responsible for data governance I myself has have been a divisional data um Steward for for the HR function so it's it's a really important role to ensure that how we're using and leveraging HR information um and obviously overlaying you the various privacy um you know overlays and and jurisdictions that your your organization relied in um because again you know really important that you've got good strong data governance around how HR information's been shared across the organization um mature organizations also tend to hand strong Partnerships with business units and corporate functions uh for me um a really key important stakeholder is your obviously your chro your Chief HR officer and your um HR business partners because they're the window into the organization they have a lot of the business context um to Overlay with the data that you're getting to ensure that look you know was that intended or not um so I think it's really important that you you're fostering and nurturing those relationships and how understanding how HR information is being leveraged and um put into various decision forums through through your organization and then finally um and you'll hear a lot about this where organizations realize that look how do you start to get their data out of the limited hands in your specialist people analytics or your HR reporting team uh to really have that scale across the organization um and and that's where you start talking things around you know ensuring that you have an organizational culture of data driven decision making and this isn't just about training and uplifting your HR stakeholders in terms of how to leverage um data but the broader organization and I've seen in countless examples where actually they're providing data literacy programs for all of their employees so as you start to create you know all these great amazing products how do you ensure that people are leveraging understanding using them in in the desired way um so I think uh the the first example I shared was like commonly referred to as the first wall um in terms of HR measurement and um we have Patrick kulan and and Frank who introduced concept of uh the second wall uh which is really about the maturity of deployment related to people analytics outcomes so um what I mean by that is that despite the growth in the adoption of more Advanced Analytical methods in HR most of the advanced analytics work is is really done on a more ad hoc basis or a one-off project basis so what they're really calling for is that how advanced people onic outcomes you know have been T typically delivered in a one-off report or PowerPoint um how can we start to um you know create the scale across the organization so and what I like here is that you you kind of start to Overlay around some of the skills and capabilities so if you look at that descriptive analytics this is typically where your data analyst would be versus your more advanced analytics that's where you bring in your data science capabilities and then when you start to think around deployed analytics this is where you kind of start to get into your machine learning um engineers and and so forth and and really ensuring that you've got good created sustainable data models that are not just one-offs um that could really be scaled across the organization so um really important uh point there um Insight 222 I'm sure many of you will be familiar with um amazing David green and his organization Insight 222 they've done a bunch of great research um and I really like this uh paper where they kind of highlight the characteristics of leading companies in people analytics and you'll see here and they kind of categorize these in in relation to you know investment around the influence the the business priorities skills ethics and then the value um and you'll see a nice little kind of quadrant model on the right there where you can start to uh really how you would measure democratize and personalize this and U again there's a link there where you can kind of go in and and and look at this kind of paper um but the idea is that you know this really helps aim to help CH hro and people anals leaders really understand what their current position is and really help pinpoint the characteristics that will enable them to deliver more value and I suppose on the delivering more value um we there was a very Pinnacle paper by Thomas rasmason uh along with u Mike rich and Dave or um so Dave or obviously deemed by many as the uh modern of HR um and this is a really great paper where they really call for um more guidance for impact and what I really like about this is that um how do we start to increase the impact for all stakeholders um and I think really um and again a link to this uh Journal article but um what they're essentially saying is look don't get to bogg down in the maturity models instead you know really start to go straight to answering the questions of value for your business um to ensure that your people Don anx is aligned and focused um really on the impact that to help shape more practical Solutions um for your business so so as I say what really I think the what they're saying here is don't don't really um be an island um go engage your business speak to your business really understand well how are they currently using information where are the gaps what are their key pain points and and as this article is really saying is you know don't don't start to you know um try and apply really fancy statistics and models often I think the most simplest way is the most effective um so um as I say I think the true value is you can have the most advanced analytics capabilities in the world but at the end of the day if no one's really taking that insight and driving any action off the back of it it's it's kind of all all for nothing really so I think I really like this article because again it just says look let's just go straight to answering the questions of the business and what's important to them so uh to bring this to life um I thought I'd give you two kind of examples um and this is kind of off our um aile hyro analytics product um but there's kind of two personas or stakeholder perspectives I like to think about and and firstly being that strategic layer so this is typically your executive level so this could be your board um this could be your group executive uh Team or your Chief HR officer and and your HR leadership team so um the idea is that you know what are some of the common questions that they really care about and look there's a lot of research here this is an example from Forbes in relation to the top 10 essential HR metrics that execs care about um and you know um often we get asked a lot is well where do you start and again go to the questions that they care about um and um how really I think you know the the power of you know HR information is that you want to be able to just blend this with other information so you know work with your Finance team bring in your your your Finance overlay in terms of cost um you know bring in your business kis to actually look at well as you're working on those key strategic people uh levers you know how is that then um addressing your your business strategy me personally I'm not a huge fan of when HR tries to have its own dedicated strategy that they're then trying to force on the business to me um the business strategy is the strategy and hr's role is how do we enable and support the business to be able to execute and deliver on that business strategy um so so I think that's just from a as suppose strategic layer so my advice to you is that again what are those key decision forums and meetings um how is HR information currently being used um you know is it just at the back of a pack you know and it's not really talked about much um or actually using this in a strategic way to start inform um you know how we going to win on that uh that that business strategy and one of those key people leevers that we need to to kind of uh pull to ensure that we we execute on that um the other really critical layer is the operational layer and and I think to the the second war that we talked about scale I think a lot of um organizations really struggle with this one um and that's because often what I see is that the way that the data and reports are generated it's because it's so manual and often in in Excel um you you know the teams really struggle um to really um provide a consistent set of reporting across the whole organization um so they may only be able to just uh Supply this for the the most senior leaders and for exact LT um and not really be able to dissimilate that down across the organization to all people leaders but um you know one of the things that um I found very powerful and and one of the stories I'll share is that um actually is part of my onboarding when I had the role uh at C CBA um and I'll never forget I I shadowed one of our regional branch managers uh around the CBD um of that's the Sydney Sydney City center it's one of the hottest days in the year so I'll never forget it um and the the actual regional manager never spent very rarely spent a lot of time in his office he actually walked around and visited many of the branches and his key concern was that due to the operational nature of the business there's a lot of people in calling in sick unwell didn't show up and you needed a minimum amount of people in order to keep a branch open and and from his point of view if you close branches it's going to the bottom line so it was a really fascinating time because you know after speaking to him I was saying well what insights would be really important for you and and I think the key thing was what wanting near real time insights so rather than having a static monthly or quality snapshot actually could I get real time data from the day before um and actually being able to um help inform and help manage you know the management of his branches and just the operational nature there and then taking it to that next level um you can start to think around more proactive prompting that's a big thing now in HR so again rather than reacting from something that's already happened you can then prompt and remind leaders to say hey um do you know that there a team member that's due for mandatary learning if they don't complete that they're likely to have B consequences and get dinged on their bonus or somebody's returning from at Le legally speaking as a manager these are things that you need to ensure that you're doing so the idea is that you want to set people up for success and give them um those kind of nudges and prompts and and things so um so there's a lot you can do there and if you think around the humble line manager really that's how you're going to influence the organization and that for me is the low hanging fruit um is that if you can start to get the right data at the right time in their hands to action um that's where you really really start to to make an impact so so I think having an eye on those lenses around the Strategic is really important one of those key things that they care about they're often very lag metrics in nature um but then how you know how for your organization and you're getting that scale and getting the information into the hands of those humble line manager so they can drive those day-to-day behaviors and decisions so um yeah so look I'll I'll pause there because I realized I've been talking a lot um before I go into the next section but just wondering a man if there's any questions that we've had from R so far uh if you have any questions please type your questions in the meeting chat or you can unmute now and ask your questions so we have a question from uh yeah so great session so what kind of projects would you recommend in order to break the wall from descriptive analytics to predictive currently we're working on an nutrition prediction model um in our business so um great question um so for me this is where I would look at some high value use cases right and one thing that again like maturity models are great but at the same time you know not everything is linear it's not like a maslo hierarchy of needs and you need to follow sequentially um there is nothing stopping you now doing a Predictive Analytics use case um and I love the fact that you're looking an attrition prediction model um that's one that I found is is quite popular um and so um you know what so so my advice to you is um find something that's really going to address a business need or problem and show something where you can demonstrate a return on investment so an attrition prediction model is is a good one um so using something like a random Forest machine learning algorithm to understand what are some of the risk factors of well why people stay in an organization versus why they leave um and then you can start to then overlay and essentially put a risk or or likelihood of people leaving um in the next uh 12 months um and the the great thing is you could use that as part of your talent review um and say well look would you care or not is that an opportunity to bank natural attrition and have zero people impact if you know that that area is going to get automated or offshor um or actually that's a critical role and there's no ready now successor uh we probably want to go in and do something about that and then you could actually use that to say well evaluate the effectiveness of those interventions did you manage to uh uh prevent that outcome or not um so so I think my advice to you is just think around well what are some of those high value use cases um obviously pilot it in a particular area of the business so do do it small don't just go big bang um and and then in in my experience that's when you start to show some of that value get some of that excitement that can then lead to some additional investment and resource to then think around how would you scale that across the organization um so hopefully that kind of answers your question um but yeah do do put in again if if not um maybe one of the questions so we saying do you have any suggestions on key leader characteristics insights in in a model um probably what I'm trying to understand what you mean by that so do you have any suggestions on key leader characteristic insights model so you are you trying to understand what makes an effective leader is that is that your question Anders yes okay um yeah look again there's a there's a lot of research on this um I would recommend um I might be a bit controversial and actually say that you know if you look at a project by Google actually it was called project Aristotle they spent a huge amount of money and resource and had an army of people looking at what makes an effective leaders and teams um and this is where the term coin psychological safety came about because they actually found out the characteristics of the teams didn't really say much but what they did find were things like having good psychological safety so this is you know know be being able to have clear transparent conversations really speak your mind without that fear of consequence that was really really important so if I was going to recommend um there's been a lot of research but I think having good psychological safety is really key so I think leaders being able to Foster that so people can speak freely and I think we've all been in those meetings where you know people are talking and no one really speaks up and then something goes wrong and it's oh I knew that I saw that coming so so I think yeah um that's probably one example that I'd kind of call out um you know psychological safety um and yeah is is a really key key enabler I think and that's something I've always looked to Foster within my teams and just getting rid of this whole hierarchical you know uh scenarios this is why people are moving to more agile teams that are empowered and and things and so forth so anyway I can sport talk for days about this but I realized I got a lot more slides to get through but I will stop again later and and take more questions and hopefully we'll have a big trick of time at the end as well um so in terms of the next section um I'm going to just really start to touch on you know harnessing artificial intelligence through Revolution is HR um I think look for me I always like to start with um what's been coined as the fourth Industrial Revolution um and this is really blurring the lines between people technology and really fusing the the physical digital and biological worlds um and the the world economic Forum really explo has you know there's a lot of research here and again there's a source link here to Conor papers I'm an recovering academic so I like to read um but essentially they um they really call how this is um really a fusion of advances in the artificial intelligence AI robotics The Internet of Things web 3 blockchain etc etc and the reality is the speed breadth and depth of this revolution is really forcing us to rethink how countries develop how organizations create value and even what it means to be human um and so really the fourth Industrial Revolution is more than just about technology driven change um it's about an opportunity to help everyone including leaders policy makers and people from all in groups Nations to really harness covering Technologies in order to create a really inclusive human- centered uh future so you know so a lot of positive intent but we'll put a pin in that and and come back um on that a bit later um I think it would be remiss of me if I if I didn't mention the the Gartner uh 2023 hype cycle for HR transformation I think look so many HR functions now really going under a digital transformation agenda and look I think look it's predicted within 5 to 10 years HR Technologies will really be an essential neighbor of this transformation um and it shouldn't really come as a shock to people that look it's with its ability to automate improve operations transform user experience so a lot of people you know I think employee experience is a very topical thing right now a lot of conferences um and really providing insights to make better more informed decision making I think look ar is going to be um front and center to that transformation agenda um so what is AI um look there's there's many branches to this but look it's a branch of computer science concerned with building SM machines capable of Performing tasks that typically require human intelligence so it's really the ability of the digital computer or computer controlled robot to perform tasks that are commonly associated with intelligent beings like us um and look um AI is really red defining to that end what it is to be human what it is to work machines can really augment Us in all sorts of interesting ways um and AI will lead to new economies transform cities and and so forth and just as a few of the examples here I'm sure you've heard of open AI chat gbt you've got co-pilot uh from Microsoft um now assist Josh Bin's just launched his own um products uh as well and and so really there is a huge arms race right now for a lot of HR Tech suppliers to deploy geni into into their products um so um but at the same time I I do think and I think this is an interesting article from the Boston controlling group um where there although people and strategy planning and analytics were ranked as the foremost future focused topics in the realm of people management um there was a clearly a striking disconnect however given that only 35% of HR professionals uh really agree that their organization's people management function effectively leverages digital Technologies and again only 30% report that HR harness data analytics to anticipate people related challenges so a bit of a disconnect there so although it's voted number one um not many functions are really are really doing that and and as well uh there's another survey um a recent one by Mercer end of last year for um a chief hro and um 41% of those really Express the desire for greater depth in dollar rinks and around 60% see technology and automization becoming much more prevent in in the future so and again I think this has really been exacerbated accelerated as a result of things like chat gbt um and yeah so I I think this is something that's going to dominate uh many HR conferences as I say a lot of chro are really starting to look at how they can leverage this um in in the HR domain so um but before we kind of go on um I think for me um really before we get too excited and start to invest and look at the art of the what's possible in terms of these products and suppliers um I really think there's just getting some of those key foundations right are really critical and and um I could probably spend a whole hour just talking to this slide um I often refer to this as your HR data ecosystem um so really if you think about how are you currently collecting HR information um where is that being stored what systems you know what um and then how are you enhancing and curating that to then how is that being consumed and shared across your organization and look the reality is um HR has often been very underinvested in technology so so um you know the reality is that um there's very rarely endtoend process owners um you might have a modern HCM solution but very rarely all the data is in there and the nature of HR it's buil of functional silos each solo tends to have its own system of record um and thus they don't often talk to each other and what I mean by that is that there's a lack of integration so we're trying to then take all of this information Stitch it together in Excel and try and generate some reports and things from that um so really understanding your data ecosystem and and getting the investment to to really try and uh modernize that and put in the interv intervention sorry Integrations um and as you go on your analytics journey and I'd be speaking to your um Enterprise it um function because you know what what is what's their strategy um and I guarantee you most HR functions they're going to be on a lot of legacy and Tech debt that people keep adding to um but everyone's moving to kind of more modern cloud-based um analytic Solutions so this is often AWS oror and you got things like daa bricks snowflakes you may or may not heard of these things but essentially they're D lakes or D lake houses um and really how is the HR function are we starting to kind of migrate and and build into that environment so what I'd be saying is do a bit of a current state assessment around where you are um and then based on where you want to be strategically um you can start to build that interim State so um yeah just an important part of that puzzle and when you start to think around you know build versus buy and so forth but um as I say a lot I can talk to that and apologies if that's a bit technical but this is a really important piece of that puzzle and essentially in a very lame and simple term um don't go off and get it too excited and buy go straight to the technology and get a data scientist you you really need to get some of the foundational data foundational things right here because at the end of it's rubbish in rubbish out um so I think really understanding this and and how that looks for your organization is is a really important starting point um how am I doing for time okay I better a crack on um so look um I I I'll touch on a few of these things but look there's a future do survey we know um Big Data cloud computing AI um again um these things are we know are expected to uh be big trends in the future we're seeing it now so I think none of these things are going to be a surprise for you uh but essentially this is just a survey around technology adoption over the next um few years is um and and again I think a lot of these things are really coming to to life and what does that mean for HR well um again good old Boston consuling Point great paper here is that according to them geni will transform HR into more strategic function um and why is that well um it means that uh following impact areas so it's going to help dramatically increase self-service um you're going to get productivity gains and experience enhancements um because you can start to be much more personalized rather than this kind of one siiz fits-all approach um and to that end you know being truly personalized always on delivery for HR service so you know with the chat Bots and you know the geni you know they don't take breaks they can be on 247 essentially um and also you get a much more comprehensive data driven Talent ecosystem um so um and and that's where many companies are starting to invest to better understand the employee skill skills and drive Talent upskilling career planning and so forth so really the question now is how to use this skills data to be more meaningful Talent decisions across the business and and so forth and to just bring that a lot to life a bit um here's an example of a you know how the um that productivity gain is going to M remain so this this is an example of a HR business partner scenario here suggests that you're going to unlock anything from around 25 to 30% of that our business partners time through geni deployments um including the chat Bots and automated Solutions um and that model would lean heavily on the HR ciler and manager to upskill staff through tech-based nudges U and the results is that you know HR bussiness partners are going to be more proactive better able to support more employees given um the reduction in administrative work essentially so hopefully that kind of brings it to life uh a little bit um but the the challenge though and I think this is where I wanted to kind of touch on next is that that a lot of potential but it is still very early days and so a couple examples here and this isn't actually an example around asking the wrong question is risky um so this is a prompt hacking example on chat gbt um and um basically um the example here by Steve samartino um is basically what he showed is ultimately it's about how you ask a question um and so the example here is that um he went and asked um chat gbt what what are some of the the great kind of um sites to Pirate music uh and obviously um chat gbt replied and said look no this is illegal um we're not going to share that so all he did was actually flip the question round and uh said look well what are some of the s to avoid because they have pirated movid and music content and then chat gbt um proceeded to provide a list of all of those things so so I think this example here is just really showing the the importance of the the the end user in how they're engaging the context and the prompting um with with these things there there are definitely um weaknesses and and hacks and way to kind of work around things so uh another example is a viral um AI resume hack so um there's an example here by Daniel Filman he figured out a sneaky way to get a resume pass chat gbt's image recognition capabilities so he found that you could use white text um on a white background to trick the AI image model um like the one on chat gbt to then select his CV um so again another example they have patched this if if people are wanting to to try that but again just another example um and just to finish um this is actually one that is only a month or so old but um just shows I think this is quite a pivotal um this has a lot of implications for for AI so Bas basically um mafat um had booked a flight with Air Canada following his grandmother's um uh passing and during he kind of engaged with um the airlines AI chatbot and the chatbot incorrectly assured him that um there was a bant discount for postt travel claims so long as they were made within 90 days so he proceeded to do that and actually um the company said look no that's not the case we're not going to honor that um it went to a tribunal and um the tribunal actually dimissed the argument and I think the the argument on the employer or the organization was that it's not a legal entity it can't make these kind of decisions but the tribunal basically said look um emphasize that chatbots are an integral part of company's digital interface and thus the company is ultimately responsible for the accuracy of the information that they provide so as AI Technologies become increasingly embedded in our everyday decision and business operation this rolling really marks a crucial movement um for reflecting on the ethical and legal standards governing AI in interactions with our consumers uh so um not directly relevant to kind of HR but um I think look the the the important points here is that um yeah the these kind I think you're going to see a lot more of these kind of um tribunals and lawsuits and things and I suppose to that end you know there's a lot of examples where HR have have got this wrong so you know I think just in Australia as an example you know the Australian data breaches a spike to nearly 20% the end of last year um Bank details TFM personal details of applicants have potentially been compromised payroll HR information so again there's a lot of nervousness of organizations that you know people are starting to upload confidential documents into chat DVD and you know proprietary information and things so there there is a lot of worry and concern around how we're going to control this um how this information be used what are the decisions off the back of that so again lots of positives but again maybe tempers some of that enthusiasm around W that it is still early days um for this so um so just again touching on that I think the key insight there is that we really need to consider some of the legal and ethical implications of AI biases you know unfortunately as the technology is built by humans um existing human biases um are often then transferred into these artificial intelligent models so again a few examples here around Amazon scrapping sexist AI hiring tool workday um is currently going through a law so um been accused of its AI um function discriminating against applicants and and things um so really my message here is that you know HR really should consider the legal and ethical implications of AI biases within the tools so my recommendation is that you know make reducing AI bias determining Factor when choosing your HR technology um and particularly if it's a black box and pety I think we need a lot more transparency into the models and the algorithms don't take things on face value you want to know and question the effectiveness and you know what were the sample you know how is it considering for some of the diversity factors and things like this so um yeah um again how I do for time um probably a couple more slides and then I was hoping to give you a bit of a a demo of a virtual agent and then we can kind of jump into some questions um just to kind of um continue on the topic of Ethics um uh there's a great piece by Unilever um and a lot of organizations are starting to create clearer policies around Ai and ethics um and I really like the one by unever because um you know they've already have a quite a well- defined approach um to information securing in data privacy so they really wanted to apply that to to ethics um and just put in place a lot more Assurance around Ai and the process there and and what I like here is that firstly they say look we'll never blame the system there must be a unil owned accountable um and we will use our best efforts to systematically monitor models and the performance of our AI to ensure that it maintains uh uh efficiency um um and also importantly any decisions that have a significant life impact on an individual they will not be fully automated so it's a little bit like um if those of you familiar with um GD um gdpr type legislation it's um where you know individuals you know um they can request speaking to a human so if there's been an automated decision saying your application it doesn't meet the Mark it's a no um people can request to say well I don't want a robot or an an AI model telling me that I want a human to come in review and validate so I see a lot of these kind of things that yes it can be really good and help automate that but um it will still not negate the need of people to be able to review and bet that um because again there's always going to be outliers right and it's never going to work 100% of the time um so to that end as well um there's a great piece of work I attended a a webinar with qual tri so they do a lot of um surveying and things and they did a big um uh study where I think they surveyed over 35,000 employees um and what they're finding here was really interesting is that look employees um state that they're open to AI so long as it's not too personal so what they mean by that is if you look at things like writing tasks and a personal assistant or virtual aging people are largely comfortable with that um however they're not so comfortable when it comes to things like doing their performance appraisals and job interviews so I think really the message here is that we should be prioritizing so again coming back to that use case I think around where would we leverage this I think we really should be prioritizing building trust and leveraging AI to really enhance human connection rather than replace and perform more personalized things such as performance approvals and job interviews and my last slide um before I kind of go into a bit of a kind of a demo on a virtual agent is that um my personal belief and prediction is that and it's kind of this is my quote above um is that I believe distinct human qualities such as awareness compassion empathy will become more important than ever um and so what my the concept I don't you've heard of the concept of purple people I think this was a term coined by deoe where essentially um purple people are the individuals who blend the the red of technical expertise with the blue of business Acumen to create purple sets of skills so I often refer to them as the analytical translators so they can really bridge the gap between kind of really technical people and then business stakeholders um so I think for me um that's a really important capability um and and so look no matter how accurate or comprehensive the Insight is the the value it provides really depends on theis receivers um willingness to use it so again it comes back to that article around insight to impact um so thus if they're skeptical if they're fearful or resistant to information they're really unlikely to leverage it so thus um the concept of emotional intelligence or EQ will become more important than ever to really help identify what are the hidden issues and how you going to deliver insights to the stakeholder on the um um on the other end how they going to receive receive Embrace and leverage I take action off the back of that so I think the good news is for everyone AI is not really going to be able to do this well so I think it's those innately human qualities that are going to be more important than ever so I think the the the the hope and the expectation is that this will really help in augment work and and help automate a lot of those administrative tasks and things and and free up time to invest on the more strategic and more initely human qualities of work um but again still early days um and and I think you still need to apply things with a healthy level of skepticism um but um obviously a lot of excitement and opportunity here that we can start to take advantage of and and to just finish with I thought you know um we could give a bit of a a demo or example from from our product um we we've actually created a virtual agent um so this um this essentially for those who aren't familiar um it if you imagine things like chat gbt um companies can start to buy their own license bring it into their Eco system so in other words it's it's it's firewall no information's going outside the organization and then that way you can start to upload your you know your policies procedures your knowledge articles uh as well as your data and Reporting um and then start to query that in a very natural language way like engaging with a chatbot um so to kind of show you that and bring that to life um here's a bit of a scenario and by the way this is a real example um but obviously hypothetical organization um so um the scenario here is that the CEO of a global Finance Company um has just had a onetoone with his executive of supply chain to discuss the company's diversity goals and many organizations have a five-year diversity goal often you know they want increased female representation senior levels to 40% by 2025 and you'll see on the report here the supply chains at 27% there's a year away from that and so CEO is not happy what are you going to do about it so um the executive supply chain calls up his HR business partner and says look I need a meeting in three days I've got to come up with a plan I I need your help and advice so um this is where the virtual agent comes in um and the HR business partner says look right I'm having a meeting with our supply chain executive please can you tell me some of the key headline diversity inclusion statistics for this business so the um virtual agent says absolutely not a problem here's a summary here's the key headlines here's a link to the LAX report and actually we will um bring up that report automatically filtered for the person you're interested in and here's your information um so I think great next question well what is our policy so what is our Global policy we're a global organization um can you tell me that so again um because we've got all the policies in there we can say well here's the document here's the page um and we can send you a link directly to that and here's again a summary of what that says um and in this hypothetical scenario um the next thing is well actually we're a global organization but I'd say 90% of the supply chain Workforce is based in um Australia so could you tell me what are some of the the the local legal and compliance rules that we we need to be aware of within Australia and and this is where it gets really um interesting because you know there are various National and state-based um requirements so you've got things like the the the equity opportunity laws workplace gender equality disability discrimination um so again each of these kind of legislations it can summarize for you provide you a link of what that legislation is so so hopefully you know within seconds if you imagine if I was in the the hands of the the the the CH you know the the HR business partner supporting this person I would have to go wage through all of those policy documents I would have to go and engage the reporting team and get all those static reports i' then have to go and consult with the legal team to get some advice so this is where I feel that this is becomes really powerful to be able to provide those kind of insights and really help the HR business partners have those key strategic conversations and not just kind of focus on doing all of that admin and pulling and they probably scrunch together all the information but they've not had time to actually process that turn that into the Insight what are the actions that you want to take off the back of that and so forth so for me this is where I think the real power of this technology can be applied and obviously this is a use case for diversity inclusion but you could apply this across all the different human Capital Areas all the cyclical HR processes but again with all of this with temper that caveat this that you'd still want to run this byy um your your legal team and you know put Health warnings and things um but this this is where I think it gets a bit exciting and remember this would be within your own corporate license so again you have full control over what you're feeding this but again my final point is before you start to invest in great Technologies like this you need to get those data foundations right if you've got outdated knowledge articles you got four or five different policies the the insights and and things here are just going to be very inaccurate so again you need to start and get those data foundations right before you kind of reward and start to look at look at some of these things so with that said I think I'll probably draw a close there um and probably got plenty of time for questions so yeah I think that's that's all my slides thank you everybody [Music]