The conversation centered around AI’s potential to improve talent acquisition and management. Speakers emphasized the importance of ethical considerations, transparency, and trustworthiness in AI systems. They emphasized the need for standardized requirements for AI quality and governance and effective communication with stakeholders. Speakers also discussed the importance of aligning AI strategies across business functions and creating forums for conversation and experimentation. Additionally, they emphasized the need for compliance with AI regulations, particularly in the context of HR.

AI’s role in talent management with industry leaders.

• Speakers and Adrienne are guests discussing AI’s role in talent management.
• AI can help bridge the gap in internal mobility and career pathing.
• Adrian from Vodafone discusses the company’s vision to improve connectivity and its scale and scope across 22 countries.
• Adrian chooses a train as his preferred mode of transportation due to its pragmatic advantages.

AI integration in daily lives and work, including automation and personalized learning.

• Speakers is a senior manager at Deloitte Consulting in Germany, specializing in digital ethics and AI governance.
• Speakers’s team recently transitioned to Deloitte and has been working on various projects related to trust and integrity in new technologies.
• AI is already embedded in daily lives and work, from personalized news feeds to email spam filters.
• AI use cases are expanding in enterprises, including automation, productivity improvement, and HR optimization.

Using AI in HR, including talent acquisition and personalized learning.

• Vodafone uses AI to simplify talent acquisition and democratize access to learning.
• Speakers: Define what you want to do, then find the right partners for the value proposition.
• Rebecca Warren: Trust issues with AI output, consider bias, accuracy, and ethics.

Trust in AI, including creepiness factor, governance, and purpose.

• Speakers discuss the “creepiness factor” of AI and the need for trustworthy alternatives
• Rebecca Warren agrees, emphasizing the importance of asking hard questions and implementing governance measures (14 words)
• Rebecca Warren: AI can help reduce bias, but it’s important to consider ethical implications.
• Speakers: Trust is key when deploying AI, and relevance is crucial for positive outcomes.

Aligning AI initiatives across departments for business success.

• Warren and Speakers discuss aligning across organizations for holistic solutions.
• Speakers discuss AI implementation across functions, recognizing unique challenges and opportunities for each department.
• Enterprise-wide conversation and peer review facilitate continuous improvement and scalability of AI solutions.

AI adoption in organizations focuses on communication, trust, and transparency.

• Speakers emphasize the importance of early involvement and socialization for successful AI deployment in smaller organizations.
• Speakers discuss AI foundation and business model, pricing, and scaling.
• Rebecca Warren asks about communication of transformation to organizations and clients.
• Speakers emphasize the importance of communication and trust in AI implementation.
• Speakers emphasize importance of partnering with vendors for AI technology 
• Transparency and communication are key to successful AI deployment 

Using AI in recruitment and performance analysis has potential benefits and limitations.

• Speakers explain how AI enhances decision-making by providing quick access to relevant information.
• Speakers and Rebecca Warren discuss the importance of human interaction in creating trust with AI technology.
• Rebecca Warren: AI transforms recruitment, but humans must be in the loop.
• Speakers: AI helps recruiters with informed decisions, but data quality is crucial.
• Speakers discuss using AI to help recruiters mine data and provide insights while still involving humans in the decision-making process.
• Speakers highlight the importance of defining the purpose and rationale for using AI on performance data and ensuring that humans are involved in the decision-making process.

AI in HR, including recruitment and skills assessment, with a focus on EU regulations.

• Speakers discuss using AI to streamline the recruitment process, providing pre-configured interview questions and guiding framework.
• Speakers also highlight the importance of identifying employees with relevant skills for AI-related roles, even if they are currently in different departments or areas of expertise.
• HR leaders must consider new EU AI regulations when creating future talent strategies.
• Risk-based approach to AI use cases includes prohibited use cases, high-risk use cases, and limited risk use cases.

Implementing AI in HR, focusing on trust, transparency, and human connection.

• Speakers emphasize the importance of early partnerships with experts in security, cybersecurity, and privacy when starting an AI journey.
• Speakers stress the need to get data, culture, and governance processes right before implementing AI, as they make or break the technology.
• “Put people first when rolling out AI technology, prioritize culture and training.” (Rebecca Warren and Speakers)
• Speakers emphasize trust, transparency, and open-mindedness in building a culture of learning and development.
• Speakers encourage listeners to share their insights and learn from each other in the conversation.

Rebecca Warren 00:02
Good morning. Good afternoon. Good evening; wherever you are in the world, we’re super excited to be together again for another Talent Table webinar. My name is Rebecca Warren, and I will be your host for this event. A couple of housekeeping things we’re going to start with, and then we will dive right in. So, if you look to the bottom of your screen, you’ll see some widgets that you can use during the event. There’s also further related reading in the resources section; you can ask us questions through the Q&A. So, if we get an opportunity, we may answer it live. Otherwise, we will potentially take that away and get back to you later. We also have our talent table coming up for the next month, which is looking at elevating talent with guests from Coca-Cola, Euro Pacific Partners, and the Josh Bersin Company. So, not to take away from the excitement of this event, but you can already register for the event coming up next month. So today, we are super excited to discuss how AI can help your tech practices. And we have a couple of fantastic guests to help us talk about that. So AI is a topic that seems to be everywhere, in it a whole lot of different flavors and understanding of what it is and what it isn’t. So we’re going to be looking at AI in this webinar from a talent management lens. Now, with businesses focused on internal mobility and employees seeking career pathing, HR functions are realizing how valuable AI can be to bridge the gap and unlock that talent potential. But AI can be so much more and also so much more confusing, and how to use it when and how often. It’s rapidly though becoming a game changer in enhancing employee engagement, surfacing potential talent, digital succession planning, and building a talent intelligence roadmap for many companies. And so we have two amazing leaders who are here to tell us more about that. Welcome, Isabel. And Adrienne, we’re so excited to have you with us. So if you could, we will. Yeah, absolutely. So if you could, we’ll start with Adrian, if you could introduce yourself, your name, your organization, your role. And then we have the GET TO KNOW YOU guest question, which I will throw out. If you had to choose one mode of transportation, would you choose a plane, a train or a cruise ship? I’ve given you such fabulous options. All right, Adrian will flip it to you to go ahead.

Adrian Boruz 02:50
All right, thank you. Well, I’m Adrian, I work for Vodafone. We launched the first the first mobile network in 1985 in the UK. Ever since then, the Vodafone vision has been to improve people’s lives through connectivity. What that meant is that following this purpose, which stands true today, we grow and move from being a traditional telco into our next-generation connectivity digital services provider. So that means that we have operations or partnerships in 22 countries across Europe and Africa. And also, we partner with mobile networks in 43 other countries outside our footprint. So, in terms of scale, there are about 98,000 employees across these countries. And they speak about nine different languages. So scale and scope. And certainly that brings opportunities as well as challenges and inflection points for deployed air. My specific roles is I support these operations, specifically from a people technology perspective. That specifically means I have a group of I have a team and with my colleagues two things we are one we are responsible for the governance and privatization of solar design, with humans in the centers across the technology for those solutions that support our people. So that’s the remit of my team would never come to me to say how travel is that the question? Are we past this?

Isabel Gadea 04:49
Yeah.

Rebecca Warren 04:50
Go ahead and tell me what you pick.

Adrian Boruz 04:56
Because I can’t, I can’t. My point is that we played in a smaller city that doesn’t have an airport, so there’s a strong pragmatic criteria they use.

Rebecca Warren 05:09
Well, and you have options, right? You have a lot of train options. They’re very different in the US, depending on your location.

Adrian Boruz 05:19
And venue environmental factors.

Rebecca Warren 05:22
I love it. I love it. All right, Isabel, over to you.

Isabel Gadea 05:27
I’m Isabel. I’m a senior manager at Deloitte Consulting in Germany, based in Germany, and actually, Deloitte, what do we do? So we are doing as a professional services firm, we are advising typically on all topics related to trust, and our technology. And actually my team is located where we combine these two things, mainly. So I’m completely specialized in the topic of digital ethics and AI governance. So how do we create and implement trust in AI and integrity in new technologies, that’s basically what I’m doing. So my whole team just transitioned to Deloitte one and a half years ago. And previous to that, we also did consulting and advisory work on that topic, but also a whole lot of research. So a lot in AI in healthcare AI, like us, everything people related, and how do we actually put humans in the center of our work, and what needs to be guidelines and God rates, so that, you know, we can actually balance innovation and control some sort. And my favorite kind of transforming transportation is also the train. So I’m based in Germany, so the past weeks, we had a lot of footballs or soccer players on the train as well. So that was a kind of, so I’m, I’m a huge, huge fan of sports. So that was nice. And also, next week, I’m going by train from Germany to Paris for the Olympics. So, especially within Europe, that’s a nice way to have seamless transportation. Yeah.

Rebecca Warren 07:21
Yeah, on the cruise ship, you get to eat whatever you want, and activities are planned for you. Are you sure you don’t want to change your answers?

Isabel Gadea 07:31
So the environmental issue was I agree.

Rebecca Warren 07:36
Thank you so much for sharing that. And jealous that you’re getting a chance to do the whole Olympics adventure. That’s gonna be amazing. Yeah, all right. Yeah, yes. Hopefully, we’ll be able to hear some of your stories after the Olympics have ended. So exciting stuff. Okay, so let’s jump in. And let’s talk about AI. So, let’s start talking about first about what it is and what it isn’t. Right. There’s a lot of things that people think they know, maybe they’re using it in certain ways, little bits and pieces. There’s the everyday AI that we use, right search or on social media, chat. GPT has become such a cool little thing now. AI for HR, maybe some talent insights, or using some AI in the background. So it’s about, I’d love to send this to you first, talk to me a little bit about how AI is already embedded in how we work, right? It’s really not a brand-new thing that just showed up last week. Talk to me a little bit more about what that looks like in terms of embedded in our space already.

Isabel Gadea 08:56
Yeah, so actually, the integration of AI in our daily lives and work has been seamless, right? So you know, maybe first we got like a lot of awareness. In our, let’s say, more personal lives. Yeah, it’s like AI algorithms powering Facebook’s news feeds, for example, or Google search results, or, let’s say, email spam filters, that are run by AI. But in enterprises and in business, let’s say we distinguish the past years there was always AI but more in very specific use cases and when very specialized areas. And what we see now, of course, with the upcoming of check GPT but also more you know, the advancements of AI is that we see in our common working processes also in the nontech environment. We see a lot more AI use cases coming up being it and added into software that is already there. Yeah. So in whatever vendor you choose, or in very specialized, very special or specialized solutions solutions. That, yeah, coming on top of that. And, you know, it’s, of course, a lot of that is for automation gains, improving productivity, better decision making also, and particularly in HR. So, I spin, it’s being used to analyze employee performance data optimized schedules improve, you know, the whole recruitment process to develop personalized learning, learnings, and development programs. So there are a lot of opportunities out there also for the HR departments.

Rebecca Warren 10:55
Yeah, I love that. And I think there’s, I think what you said is exactly right. It’s embedded in ways that we don’t even realize.

Isabel Gadea 11:03
Yeah, yeah.

Adrian Boruz 11:10
Yeah, I mean, for us, in terms of, from our, from our industry perspective, business insight, this, I think everybody because you use AI in our, in our, in our naturally, people, leaders, managers will ask the question, how can we? How can we expedite our strategy? So for example, for us in Vodafone? Our refresh strategy is about customer simplicity and growth. So, under that, obviously, there’s a lot happening. And so the natural question is to say, how do we, how do you make sure we get the right people in the right roles, where quickly to serve the customers? You know, how do we make sure that we leverage AI wherever possible and ethical to do so to simplify the way we work, how we organize ourselves, you know, how do we might be using AI as a way to make a step change in terms of behavior, and leadership? So might we be using, for example, conversational AI as a way for people to practice part of their development, different types of compensation? So there are multiple aspects that come into play. And we’ve been using AI Vodaphone for a couple of years now, with with great results. So we’ve experimented with, like Elizabeth said, you know, talent acquisition has been a key function.

Adrian Boruz 12:31
For all companies, the standard acquisition is an area to focus on in that space all how can I democratize talent so that our recruiters reach to the full story? Can y’all hear me in our database? Yes, we can. We can see you, but he can hear you. And also, we looked at personalized learning. How can we help?

Rebecca Warren 13:09
So I’m going to send that next question back on over to Adrian, I apologize. My internet is a little sketchy. So Adrian, you had talked about that using it strategically? And I think that’s really important. So, how do you stay informed on when to use the right AI solution to help you make those accurate decisions?

Adrian Boruz 13:37
It’s a very big question. I think we started with any technology from what are you trying to achieve? And so I think there is a lot of a lot of knowledge around what is what isn’t. There are a lot of courses out there, even Cheju PTK, that’s if we want to try and interrogate that; for us, like with previous technology, we started from what is it that you want to achieve? So what is that what what is it that you want to focus on in a way that adds value? So then we started to look at what adds value and then value versus complexity and risk matrix. So we went through a rigorous process of question what you want to do, what is it from what you want to do that can be enabled and make sense in terms of scope? And ethically, can we do it? And then we’ll look at an apt way to do that. Sometimes, the answer was uncomfortable in the sense that you may need to bring together multiple partners and multiple vendors who have never worked before together to actually create that one platform. So that was our quest. We ask big questions. We get asked big answers back, and then you have to look at it and see now how we deal with it. And you’re very lucky we found a long process of searching for All the right use cases, but also for the right partners to get to the value proposition that worked for them and work for us to create a unified platform, for example, for skills. And that’s critical. So I think the short answer in summary to your question is, number one, define what it is that you want to do. And then be prepared to deal with the unseen by the big answer. But then find the right partners and be able to bring them together to actually collaborate and find the right value proposition for everybody. For us and for the partners.

Rebecca Warren 15:37
Yeah, what a great call out to say it might be uncomfortable, but we have to look at all of the options and consider all of the pieces before we actually make that decision. And Isabel, I think you probably can help us answer that the the idea of how can I trust, right, like, the bias, accuracy issues, all of those things? Like there’s always that little question in the back of your head? Like, is this real? Is this right? How do we trust the output?

Isabel Gadea 16:09
I’m. Actually, we have a term even in academics; we have a term for it; it’s called the creepiness factor of AI. Yes. So every time something is new, you know, it seems creepy. And, you know, how can I trust AI is also a question How can I trust the alternative? You know, so how is it actually being done? Some process is being done today. And compared to that, can we trust AI? 100%? Probably not. Yeah. And you shouldn’t? No matter what role you have in, you know, for the specific use case, but probably, we will never, you know, trust 100% Like any process. So, you know, the thing is always, what is the alternative? And we do have a lot of new governance measures that will come up with the EU AI Act, at least for Europe. Yeah, that. So, we have standardized requirements for AI quality and AI governance that will be implemented in the long run. So you know, I said, being from a standardization and quality perspective, kind of a new topic. But you know, for highly regulated areas and industries, we already came up with standard solutions, and we only have to regulate what really needs to be regulated. So, you know, he is very, you know, has a lot of impact on people’s development. So that’s, you know, and we have very personalized data that is being processed. So, of course, it’s also in the focus. But everything that has a lot of impact on, you know, human lives and human developments, of course, and human heads is, is in the center of some regulation and standardization, not only in Europe, but around the world. Also in the US and other markets. There are standards coming up popping up. So that is being worked on. And I think, you know, we see numbers, also in studies going up for trust towards AI, because also of regulation. Yeah, not that I say that I’m the biggest fan of any regulation. But you know, it’s part of the deal, that we get some quality standards also in the development processes there. Yeah.

Rebecca Warren 18:33
Yeah, that’s it; I love what you said about what I can trust as an alternative. If we don’t use this, then what so what a great tie-in to do the uncomfortable things, ask the hard questions, question, the alternative if you don’t do that, and then I think that’s a great call out about the governance, right, everybody is starting to weigh in on the right usage, making sure that there are some guardrails and some controls. So I think there are a lot of great things that are going to continue to come to help us feel more comfortable around the the accuracy and the ability to eliminate or reduce bias through using AI.

Isabel Gadea 19:16
Maybe one additional point, you know, also it’s about awareness. Yeah, so AI is new to most, you know, people that try to use AI in the end, so that’s why the creepiness factor comes up. Yeah, experts, tech experts, stallions usually have that feeling because they know what they are working with. So, you know, clear guidelines, clear communication, what’s the strategy behind AI? Why why are we using it? What are the opportunities? How will probably my role change? You know, when AI comes in, what are the positive side effects of that but also what other risks that are like a key, also to gain that trust also.

Adrian Boruz 19:58
I think there’s one point that I’d like to add as well. I’m Rebecca Watson probably comes under the broader umbrella of trust. But it’s also about choosing the purpose to deploying AI for right. So, just because the AI can do something, it doesn’t mean it should do that. So right, I mean, choosing your choosing you’re the part of the process or the process, you’re deploying AI very, very well. So then it works well in the context you want it to work? Well, I think that’s a fundamental part, even start versus the rest of the question. So we were given an example, a recent example from what we do now, which is whoever perspective, so yes, absolutely. If you’re a junior and together, you can use feedback data to send any recommendation because you’re talking about use cases. And recommendations. Great. But the next question is, should you be doing that? You know, is your input data strong enough? We are still battling with it. So we’re doing analysis on the data? Is the data specific enough? Is it and what experience do you want to create? So you might be doing the right thing even? But is it the right experience and ultimate right outcome that you have? So once you send a recommendation to people, you’ve just got to send another 10 learner recommendation, but in the in the, in the prioritization of how people should be spending their time they have another 10, from politics and other tech from some other source. What’s really relevant to them? How do we help people to make the most of the little time they have, or they want to allocate for learning or for development or funny careers? So it’s very, very rare. And so I guess the point of trust, for me is also about the relevance of where you deploy AI first, and what experience do you want to offer? And once you define that, kind of the questions around trust become narrower and narrower, and more and more more controllable, I guess.

Rebecca Warren 21:59
Yeah, what a good call out. Can AI do it? And should ai do it? Right, like, where is that line? I love that you said that. And so tagging along that line, Adrian, as we think about the strategy, and you talked about making sure that you know what outcomes you’re looking for, and then back into your solutions? How do you think about aligning with the overall business? I know, in previous organizations that I’ve worked in, folks have said, like, oh, well, this department is using this technology, and this department is doing this, and this department is doing that. And sometimes, or have two separate contracts with the exact same company because they’re kind of doing the same thing. Like, how do you align across the organization to make sure that you’re not working at cross purposes? Or maybe potentially missing out on some alignment? How do you get it, finance, HR, everybody aligned to make sure that you’re putting in that holistic solution, rather than just everybody kind of doing their own thing?

Adrian Boruz 23:07
I guess I’m spending most of my time. I think for me, our our learning is start as early as possible. So share idea early on, has been an experience, but also, I guess, the practical or give us a little bit of stats on our journey. So we kind of found that the answer was AI, by by the, by the fact that you’re coming out of the cloud transformation. And you’re asking the question, Who are we now done in terms of the experience, if you want to create how we want to enable or empower our people and therefore accelerate our strategy, business strategy, not just, it just strategies to accelerate the business? So when you start asking these questions in engaging with, with business colleagues, so managers in place, we found out that we shaved about 260, close to 300 user stories, which are all about things we have to evolve, how now we build on the foundation of trusting foundational data we created. And then when you looked at this, you say, Well, what’s yours? You know, why can’t you deal with it have? And kind of the answer was, well, we let’s experiment with it. And then we learn it to the point what I said earlier, which is there is the answer. But is it one vendor, is it multiple vendors so then we had to create partnerships across vendors and create the right journeys. And in all of his process, we didn’t it wasn’t an HR process. He was using it and finance and business and HR together. That me and some of my colleagues lead and then there was we build a foundation for AI as we start looking at Gen AI and also continue to experiment with AI we also have company wide steer costs where I seek and we learn from each other and sometimes we have have healthy conversations as to why you think that so you might have, you know, legal might won’t be doing a chatbot. Because they use janai to to, you know, supply chain might be doing the same customer facing function might be doing the same. So you have multiple, multiple environments where you have to recognize that everybody is experiencing we all learning about it. So this is a place for experimentation, which will happen potentially in silos. But once you create the forums of conversation, listicles and operational groups on AI, which is not HSC, it’s there, but it’s across the business, you start to find them and ask us the question, how do you go from experiential learning, and experimenting and learning about that technology as well, the context of the technology, data controls are the things and then scale up at company level, which is a different question. So I think phase wise, you kind of recognize, you know, each function will ask their own questions, because they are specific to the function. So identify the scope and the place where AI is best to support and enable and compliment humans. And then we’re going to say, okay, so if this is what you’re thinking, how do we scale it up enterprise level, therefore, we now we simplify even more, and optimize before it’s a continuous peer review, with this way, continuous conversation, multiple levels, and some very solid, constructive debates, to say, what’s the right thing to do? So there isn’t all of the use cases. On Ginia, and AI go for this home privacy, security and other business part of the business. And we’re having this conversation and this visibility across everything. That’s how we do it doesn’t mean we didn’t write, want to learn from other companies. But it’s our answer for now. Right? It’s a continuous improvement question.

Isabel Gadea 26:59
But I can really speak to that, because I see and when I talk also in different projects to our clients, and we see that it’s a much more interdisciplinary effort to get to scale AI in the organization than it was for previous, let’s say, software or technology transformations. So it typically starts as you said, the expert exploration starts use case by use case, right? And how do you actually enable certain functions also to to think in the direction of use cases? what is possible with AI? Yeah, that is the first challenge here. But then you kind of have the backlog? And then how do you actually combine and get everyone together? Because there are not that many people were actually specialized in AI? So how do you get them together? For knowledge sharing for experience sharing, and then, you know, get to really get to AI deployment and really get the value out of the experimentation phase? Yeah.

Rebecca Warren 28:04
Yeah, that’s great. Adrian, I took a note where you really said start early and socialize. I think that’s such a great point is to get as many people involved and understand it early on, so that you don’t feel like you’re continuing to have to backtrack or explain or justify right, pull people in early proactively as opposed to springing it on them in a way that maybe they are not as open to it. So let me ask a question. And Isabel, I think this probably goes to you, especially in the work that you do, it came in from one of our attendees. So from your experiences relating to deploying AI, what’s relevant for smaller and more agile organizations? And I think I have a couple of thoughts on that. But it’s about I’d love to hear you talk about what does that mean, for smaller companies who maybe could move a little more quickly?

Isabel Gadea 29:01
Yeah, yeah. Actually, I have a background in working in startups. So I come also, very personally, from the very agile and small organizations. And sometimes I missed the speed of decision making. Yeah, so I think that’s a huge advantage. Yeah. So no, very long processes. But still, it’s, you know, what, the, you know, what is your foundation? Actually your AI foundation? What, you know, do you actually just want to enhance processes? Or is your business model kind of based and relies on a on AI software? Yeah. So if you just want to use it for for your own processes and to enhance the processes, then it’s probably a topic of pricing, you know, what is what is the best vendor? How can you negotiate prices and and licenses and stuff? Because that’s also kind of a challenge sometimes. I Um, but if your business model really is based on AI, you know, then this, how can you actually get the most out of it? How do you build your foundation up for scaling? Yeah. So, you know, kind of the basics, also documentation, validation testing, and stuff like that. How is that set up? And how can you actually also give that to the market and make it an advantage? Yeah. Because there, you know, there is a lot of uncertainty around AI. So if you can really pick out your USPS in terms of, you know, how did you develop it? Do you really? Do you really take a problem your clients problems with it? Then you have a big advantage to the to the bigger organizations, because you’re just faster? Yeah.

Rebecca Warren 30:54
Yeah, makes sense. So let’s, I want to talk a little bit about communication. So we’ve talked about building trust, we’ve talked about internally socializing, asking the questions about what are you trying to achieve? What do you want to accomplish? What are your goals, right? You don’t just want to say, let me just throw in this new cool tool, just because it’s new and cool, right? So if we talk about communication, and this is a two part question. So Adrian, I’ll throw this to you first. How do you recommend the transformation piece be communicated to the to your organization? How do you communicate it to the business? So folks don’t get scared by it? And then the other part of the question is, do you also share with your clients, your customers? What you’re doing? Like, should they know the ways that you’re using AI to make you more efficient as an organization? Or is that something that you feel like should be kind of kept underneath the surface? Does that make sense?

Adrian Boruz 32:07
You does there multiple questions into one? Let me try. No, no, I’m sorry.

Rebecca Warren 32:10
He might be talking for the next 20 minutes.

Adrian Boruz 32:15
Look, I think, communication wise, clearly interested in keeping up with some great studies on this, I’m sure they will have I haven’t looked at, which is something about me not about the research on the right. Look, I think it’s clear from a lot of research and from interaction with colleagues on empirical evidence that people are a little bit wary about AI. I think having some solid principles in place that you can communicate internally, what AI is about and what it’s not. So it’s about augmenting human intelligence and not replacing it. I think that’s an important piece to carry through in the communication internally. And equally, again, I know I’m repeating myself for this, but focus on what he does, and why is needed as opposed to, you know, oh, we do it because it’s cold, because it’s here, because people don’t want that. But they don’t want any technology just because it’s there. They want to see what is it is going to do for us how he’s going to make our life better. So as an example is, you know, one of the impossible questions in HR, but also that supports any company strategies. How do you understand this gives you and the skills you want to develop? That is extremely difficult to do without support and enablement from AI? And look, in HR, we’ve been trying to do that for 2020 years now, you know, to what competencies and multiple other things. So deploying AI we’re actually trying to do to address a problem, that really is a problem. And people will know that heads when they told them what you want. Right? So that becomes quite easy, because there’s a clear partnership between humans and machine, so to speak. And so it’s not a problem, right. And then also, I think it’s important to create trust and trust for everything that Isabella has already mentioned. And I think he’s having transparency in the layer of protection and quantity. Partnering well with the vendors will they have their own test and follow industry level regulation on controls like EUR USD, or in your act in New York acting in the States is extremely important and being able to really work with vendors who can provide the technology and have the ability to explain the prediction that AI has. So it’s not a black box as such, but it’s actually something that can have a human to human conversation about I think these are very important. And then people embrace it. I think there are other stuff So show that people have an appetite to learn about this technology more than ever before. And I think also the opportunity here is different from all the other technologies evolution going for, from Excel to on prem technology from on prem to cloud. Now, where they are now, in jail Genet of AI, the difference here is that in reality, all the other technology transformation ahead was kind of done by some group of people called group of people, for the rest of the people, we implemented big enterprise, we implemented big technology. Now, there is a chance to actually listen to people and say, Hey, I think the compiler can help me here do my job better. So crowdsourcing ideas and creating a community spirit around it just makes your communication when you implement even easier and better. So it’s a different way of doing things, I think, this time around for this level of transformation. You’re last question. So I think though, I’ve answered two or three questions you had before. Around the last time around should customer? No, I think depends was relevant to them. But if, if if, if you deploy technology that supports customers, one of the important principles with AI is to signal and be transparent when somebody human enters a space, what is managed by AI, so you never trick a customer or recording anybody into thinking that they get an answer from a human being, but actually they have it from from a flat machine. And being able to provide the source of information is also critical. So I think transparency expandability. And getting it right, in terms of where you deploy AI and why these are the three things I think, are fundamental, and then the communication will be easy.

Isabel Gadea 36:51
And, you know, it’s not that AI typically does an automated decision, but rather, it’s a decision support. So it enhances your information that you have and speeds up, you know, your the time that you would rather spend on let’s say, research or whatever, and that you would check us for and I liked what you said, because we have that in our internal AI tools as well as well that we have the source and the link to the exact page in some source documents that we can refer to. So my role is not that I trust fully the output, but rather that I say, okay, I can double check it quick, because I get like summary. Yeah. But if there are formal requirements, then of course, I’m, I still have the responsibility that I say, okay, is this information correct, but it’s much quicker, because I get directly directed to the, to the, to the exact abstract where the information is pulled from. And that helps. And also what we know from communication research is actually the more human the explanation is, so the more human the interaction, and the explanation the communication is, the more it creates trust. So if you just put like a small disclaimer, oh, a is used here, and then that capacity because of them or that, so it’s more like a legal compliance thing, then, you know, it’s okay. It’s better than nothing, at least when an AI incident happens. Because we see these Yeah, these are the ones that come in the media. Of course, a lot of it is around bias in HR, when it comes into to the media. So disclaimers are better than nothing. But when you really have like that personal conversation or through video or through maybe avatars also the more human the the interaction, the more it clicks, the more it stays, and the more trust it creates, actually.

Rebecca Warren 38:43
Yeah, a great point to say AI is being used in some form or fashion, right, like we are using technology, you don’t have to go into all the details. But at least that disclaimer, I think that’s great. And Adrian, something that just really was a lightbulb for me that you said, which was great is all of the other transformations that have happened with technology have been more static, right? They’ve been like, Hey, we’re going to implement this new thing. And now we’re moving to a dynamic implementation of something that we don’t really have control over. Right, like because it’s continuing, it’s growing. It’s learning. The way AI works is so different from like creating an Excel spreadsheet, right, that is static, you can you know that if you add cell A and B, you’re gonna get C, but AI is a little bit less formulaic. So that dynamic idea of what’s coming in is a very different transformation that we’re seeing that it hasn’t really been seen ever, right. I mean, I think that was just really a light bulb for me. So I appreciate that context. So there’s a question that came in here. Uh, about using AI to make recruitment decisions or analyze performance data. So I will leave this open to either one of you if there is a point of view that you have. The question was, how are you leveraging AI to make recruitment decisions or analyze performance data, we could also open that up to say, Should you, but I’ll leave that open for either one of you to tackle on using AI to make recruitment decisions, or analyze performance data.

Adrian Boruz 40:32
I mean, we are using AI to fool equipment, but not to make recruitment decisions. So it’s important in fact, for anything to do with it, anything to do with people decisions, and the EU Act also tells us that the humans have to be in the loop. So but I think the opportunities presented by by the AI in recruitment spaces, I think about it through the lens of a recruiter, right, so you’re going to get a job. And you want to fill in a job, and you’re going to go in and search the database. Now search would be as good as it is with your vendor. But by by by all intents and purposes, you will be limited in many ways. Your data might be quite right, you might have migrated the data from another ATS or whatever. So what AI really does is help so imagine I have a big pool of data. It helps to provide more informed information to recruiters say here is now I helped you to do an impossible task you would never go through over a million of records that somebody would have any point in time, like large company will have any point in time in the database of candidates. And so what you do have was a recruiter, when you when you have AI in the system is an ability for somebody to help you to mine all the data and say, Hey, based on the information provided by the candidate, and based on their preferences of privacy. And however, here’s what you might want to consider. And then you could still go and look for all the different screens you have, right. But it’s a it’s an information point is a copilot for you to give you help, and really help you or us as an organization to democratize talent to talk about it, because really is about reaching the group of people, you might not actually use other reach. Otherwise, if searches was perfect in a system, Google wouldn’t exist at all, but we know it exists and makes money. So we know search isn’t really right. And so adding AI on that, it just helps you get better. Visibility to people, right? Obviously, it’s a good tool for is a good tool for that as well. So, in many ways, our experience has been by working with with with AI, and actually specifically with with eight foot in da we had fantastic, fantastic feedback from candidates we had, you know, we increase our speed, double the speed to hire of the right candidates in roles, and generally overall fantastic experience. So, but importantly, humans are always a little absurd recruiter makes a decision with the hiring managers with a panel of interviews, not the not AI. Yeah. Performance data is very sensitive, and is very, very important. For what purpose do you want to use it? And who needs to look at what and what are you trying to achieve? And I think that is still taking shape for most companies in terms of the the rationale for for using AI on performance data, I think that I haven’t seen a company that if there is a company, I would love to understand how they were not using that for performance. And I think potentially I can see a future for it, but it needs to be defined very, very well. In the end, whatever decision is being made, it will be made by humans. Rather than then.

Isabel Gadea 44:16
Yeah, I also have two examples actually. So in in recruitment in recruiting, but not recruiting decisions. Yeah, but rather you know, preparing that’s also the point it doesn’t take any decision but while you know supporting the whole recruiting process basically it’s an interview process so we have a whole journey I platform and then we have like a pre configured journey it so I don’t have to prompt anymore myself but there’s like an recruiting interview with use case or tool that I can use and I can say okay, who am I what position Am I hiring or interviewing? What kind of interview is it? What am I looking for? So is it More team fit, is it the functional expertise, the technical expertise, and then I get like the instantly I get, like the whole, the whole guiding framework for the interview and pre configured questions, etc. And also it’s you know, and it’s very, very based on the scientific methodology out of how do you best interview people to get the most out of it. So that actually helps. And it’s just, you know, everyone takes interviews, but maybe some of us, you know, do it regularly, some of us less regularly. And, you know, what do I need to consider? And what questions do I need to ask, what is my actual role? How does it come together, so that is where I will help instantly, basically, because it just, you know, quickens up the process. And the second is, you know, skills, AI, something that, you know, are one of our clients, now, much more also deploy and work on AI. So our skill profiles also need to be much more, in a sense AI based, so what, you know, how do our job roles and skills look now? And what do we need in the future? And, you know, maybe we have people that are in different roles, but you know, it’s kind of similar. And the skill set is kind of similar, but you know, you wouldn’t, you wouldn’t just imagine that that’s, that could be, you know, with like, just little upskilling, that would be aI relevant. So how do you actually detect these people with the right skills that, you know, you can then put more in, for example, just, for example, on the data science and AI field, but maybe working in some other department right now in some other area of expertise?

Rebecca Warren 46:50
Yeah, that’s great. And that actually ties into one of the questions I wanted to make sure that we covered. And we’ve talked a little bit about this in terms of compliance and governance and making sure that we’ve got the right guardrails and controls in place, but especially with the EU on AI regulation. That’s that’s the forefront of everybody’s mind. Right? What does that look like? So what do HR leaders and the broader business need to be thinking about with that in place, when they’re creating their future talent strategies? Any thoughts on those new guardrails that are in place?

Isabel Gadea 47:34
So maybe a quick overview on the UI X, actually, it’s a risk-based approach. So, you have a risk pyramid. Yeah. On top, you have like the prohibited AI use cases, yeah, that we can probably leave out for most of the businesses, then we have the high risk use cases, let’s say for for the corporate world, HR is a huge topic there. So everything in recruiting everything in contracting, you know, performance monitoring, and stuff like that is very relevant. And of course, everything you know, that is, let’s say, product-related safety components in medical devices, etc., also, but you know, here, typically, HR, then chatbots are, let’s say the limited risk. So that’s one step beneath limited risk, everything AI-related, where you have an interaction, where you have, let’s say, an AI, human interaction. chatbots are a primary example there. And you know, you have high for the high risk use cases, you have obligations that you need to fulfill in terms of AI quality and governance. So you have organization and measures that you have to put in place, for example, we said it several times already that the human is in the loop. We said an AI doesn’t make automated decisions, probably in the end, it’s not allowed to do according to the AI Act. So that’s basically the thing that is regulated there because it could take automated decisions if we wanted to. Yeah, but we have a human in the loop. That is something that the UI Act actually regulates there. But also technically quality measures and transparency, obligations that come with it. Yeah. And they are, of course, it’s something that you need to prepare to.

Adrian Boruz 49:22
But I think for me, what I would add to what you already discussed, I think, to be honest, if you quick, we kind of the whole discussion was how you really apply, you are into into implementing AI. But just to add to what is about I think, apart from the compliance, but I think as you prepare and start an AI journey, there are a few things probably is good to think about. One is how do you create quick and early partnerships with the experts in in the company so the security, the cybersecurity the privacy, people who are experts into understanding specifically how the legislation applies to the specific use case Do you want to have that can be quite uncomfortable because you have to be open to talk about things that are very early on in the idea is not easily as a collaboration is about being open to challenges and iterating your use case and your ideas. So I think that’s an important one starting early on, and being open minded about so you may get challenged back newsfeeds, because you haven’t thought it through, I think if you if you see those interruptions, and also the regulation is a is a enabler, which I think most people do. Certainly, in HR briefing, most business leaders overall, regardless of the nature of the company, small company, large company, they do, I think, doing that first time, and I think the other part, I would say, is don’t underestimate the importance of culture before you start in with AI. So what I mean by that was AI will evolve, what technology will be there, all the stuff, culture and data will make or break it, in my view. And so getting the data, right, aligned to your use case. So making sure that the input is right, what is AI in the end, right is a piece of technology that is able to infer something from the input and provide predictions or generic content, really, I’m oversimplifying, but but if you think it in that way, you can say, I have to prepare my input very, very well. i The use case, the data and people who can help me to think it through, so that the output is why expected to be and then the output has to then align to what we said earlier, you know, expendability, in a human way. And so data, culture and early engagement, and your governance processes are fundamental, and specifically talk about culture is not a fluffy word, but it’s specifically about collaborating well, making sure that people share ideas, and they share when they find a good use case for for a co pilot, as opposed to thinking oh, but if I’m, if I’m sharing this, maybe you lose my job, because I showed that the co pilot can do my job. So I think it’s very important to get that right. The culture of experimenting, learning, fast collaborating, and staying open minded about use cases, I think that and data makes or breaks the agent.

Rebecca Warren 52:23
Fabulous, that is such good points in there. I have pages of notes from all of the salient things that you both have shared. We’re coming towards the end of our time, I have six more questions, which I’m not going to ask you. I’m going to ask you just one as we wrap up. I know agents like shoe, not ready for all of those. So the last question that I want to roll out here and really appreciate everyone else adding questions from the audience. Thank you for throwing those out there. Those have been helpful as we’ve gone through this conversation here. So my last question, and we’ll throw those to Isabelle first, and then to Adrian is, what’s the one piece of advice you would give to HR leaders when they’re thinking about rolling out AI technology? Hmm.

Isabel Gadea 53:13
So the one piece of advice also, adding to Adrian’s last point is really put the people first. Yeah. So just rolling out AI, it doesn’t mean that it, you know, really, really drives all the value that you have hoped for. Yeah. So really making expectations clear. And really, you know, sparked that excitement in the end. Yeah. So that, that really is, and building the culture of, you know, really using AI in a meaningful way. Yeah. And then, you know, I think it’s not so much about the fear of losing the job, but really of the excitement, how can I do it even better? And, you know, and what’s the next step then of technology that we can see out there? And that’s the exciting part also. Yeah.

Adrian Boruz 54:07
Pushing away, but I think is a frame that I would like to offer, which is to say, you know, so there are two answers to this one is a technology piece, which is designed for humans, except that humans and technology will collaborate in the future. And so they forget the culture, right, in terms of collaborating, learning, fast experimenting. So there’s something about culture about design. But there’s a third element that cuts across all areas of the business, which is about training people in the company to be aI ready. And so making sure that you’ve got you’ve got your curriculum right for people to access a different level, there might be an potentially different level of expertise for different type of roles depends on the kind of roles they may have. But overall, being able to communicate and offer to people and risk meanings strategy to say, hey, my role might be different, or maybe different opportunities generated by the fact that the other day, so how they get ready for that, how they get ready for the jobs of the future, I think that still sits into the portfolio of HR overall, which we kind of wear two hats in this in this scenario. So technology, design, culture, and partner well, and data. And the training piece is super important. Get ready for the future. upskill risky for the future, but also helping people to understand that technology here now and how we can interact with it doesn’t have to become engineers, but the basics of how we can interact. I think everybody needs to know that.

Rebecca Warren 55:42
Yeah. So this has been fabulous. So I have four words or things to take away from what you’ve shared. So the things that that have impacted me, and the things that I have noted is focus on trust, right, build trust, focus on transparency, be open minded. And the other side of that Adrian is what you talked about is it’s okay to be uncomfortable, right. So be open minded, and then keep that human connection. Those are those takeaways that I’ve learned from you all, which have been fascinating. So appreciate the time. Appreciate the conversation. Please stay connected to us. If you have questions for any of us. We are available on LinkedIn. I would also encourage you to join our next talent table coming up in August where we’re going to be talking about elevating talent with a couple of folks from Coca Cola and the Josh Burson company. So Isabel Adrienne, thank you so much round of applause for all of your your insights and your the conversation here has been fabulous. So thank you so much. Any last thoughts that you want to throw out there in the last 30 seconds?

Adrian Boruz 56:57
No, I’m looking forward to learning from the people on the call how we do it. Thank you for having us. I love it.

57:08
All right.

Rebecca Warren 57:10
Enjoy the rest of your morning, evening, afternoon, the rest of your day! Tthank you all so much. Have a great rest of your rest of your day.