התמלול המלא של Closing the Gap Between Humans and AI with Ursula Tavender and Dagmar Albers

innovation than the large corporate environments that can afford investing in it perhaps more. Dagmar Albers: I really think it's tricky and large organization because They go through so much change and so much change management, so many restructurings, you know, at one point, I call it the French attitude because they do this, you know, at one point and then seen it all done it all will go away anyway. So they really don't massively engage with it. And because there's so much corporate speech, I think it's it's harder there, you know, and sometimes and it's It really usually starts with acknowledging the resistance rather than trying to override it, right? And I think that's easier in small companies than in big companies or middle-sized companies. What often is labeled as resistance, think, is quite often this whole thing about they don't know what looking... Theodora: Well, they don't know. Dagmar Albers: Yeah, what they don't know and what good looks, what good actually looks like, some English words I can't say anymore, know, they don't trust the system to be fair, you know, there's this whole fear loss of competence and identity. I think that's actually exaggerated in the moment with AI because it's so fast. It's just like so alien really, right? And I think there's a lot of that going on. So... I think it all starts with actually restoring orientation, really, giving clarity on what is actually the end of the journey. How is it going to look like? What's our vision? How might it look like? Even though we don't even know that yet because it's so fast. It doesn't make sense what I'm waffling around here. Ursula Tavender: I think you're onto something really important there Dagmar because what we notice very often is that there is a real lack of clarity at a leadership level and that filters down everywhere and I think to a certain extent SMEs are in a position, advantageous position because they are more agile, they're able to be more agile than the bigger corporate businesses right and because of that what they can do is they can consult their people, they can really enable their people's voices to be heard on a much I guess more like down to the minutiae scale because they've got fewer people, they've got more access to those people. Whereas in larger organisations, it's much more difficult to and much more cumbersome to engage in an activity which will really ensure that everybody's voice is heard. It's obviously possible in larger organisations do an incredible job of that. I think because larger organisations have the people infrastructure to be able to devote resource to this work of change, which SMEs don't necessarily always have so much Theodora: Thank Ursula Tavender: depending on their size of course. There's swings and roundabouts, I think there's advantages to be able to move quicker as a more agile, smaller business, but also in larger corporates they've got more resource so they can engage in more listening potentially. So yeah, it's a mixed bag, I think that's what we're arriving at, isn't it, is that it's a really mixed bag. Theodora: Yeah. Dagmar Albers: Yeah. Ursula Tavender: But what we would say, our approach is always to start with that clarity and that kind strategic level conversation at a leadership level to make sure that everybody is aligned on ultimately, what is AI for in this business? What is this change for us? What does it mean for us? And as Dagmar says, what does success look like? Dagmar Albers: My favorite sentence can come in now, like, if you don't know where you're going, how do you know that you have actually arrived? I think it's such a powerful sentence, I absolutely love that one. Theodora: accident. Ursula Tavender: Yeah. Theodora: Absolutely. understanding the destination before you start the journey, Dagmar Albers: Bye. Ursula Tavender: And it often gets missed, know, people jump in because I think especially when things are so fast moving, you've got a huge, you know, chasm of difference between the ways that people are engaging with AI technologies. There's, you know... a lot of shadow AI happening where there is no direction yet from businesses, particularly smaller businesses, where they don't yet have a strategy. We're starting to see some businesses developing a strategic approach to embracing AI, but still there's a lot of it being owned by one team, particularly the technological team within the business. So people aren't involved yet. And we've spoken to even larger organisations, larger SMEs that are yet to understand the connection between the people aspect of this transition. Theodora: Mm-hmm. Ursula Tavender: transformation and the technological aspect and marry the two together. Theodora: Absolutely. But do you think a lot of it is actually also, you know, such an overwhelming, noisy space right now, right? And we have so many tech providers promising the world. I know, since I started the Vaxxine November, I probably get about five to ten pitches every single week from different AI tech stacks promising, you know, they're going to do it all for me. They're going to outsource this, outsource that. you know, better this, better that, they're going to improve my ROI by 373 % to be exact. you know, I think it's, yeah, but it's also when you don't have enough experience with it, it's quite easy, right, to get a little bit lost and kind of look after every shiny little thing that comes your way, right? And what do I try? What do I do? And do we almost go to the other end where we start trialling everything and we actually forget to do the actual work or what the purpose of it is, right? Ursula Tavender: I think it really depends on the individual, doesn't it? Because you can easily get sucked into a rabbit hole of that, you know, there are tools for everything. And ultimately that's why we go back to the clarity piece, because, you know, if you have a clear aim for what you either as a function or as a business, what you

need to achieve, and you bring in the right expertise to be able to help you understand what it is that AI can do to support those goals. Theodora: Mm. Ursula Tavender: that can help reduce that overwhelm because the number of tools is infinite and the potential and the capability is now so... it's not just rapidly advancing, but it's already established. So that makes the digital gap very big in some circumstances, particularly at leadership level. And it's really a big problem. That AI capability gap is a really big emerging problem among senior leaders because they are either paralysed by the choice. Theodora: Mm-hmm. Ursula Tavender: or overwhelmed with a number of different options and that can manifest in ways which do have a detrimental effect on the overall business and on the team. it is a real issue, isn't it? And I think how you deal with that is really dependent on who you are as a person and how you deal with that overwhelm and that paralysis. Theodora: Absolutely. talking about, you know, lot of leadership and perhaps a lot of the individuals not necessarily knowing what to do with it, I guess, you know, what would you say is probably the most common thing that professionals get wrong when it comes to managing their career in this particular environment and in this super fast paced, ever changing industry. Dagmar Albers: I think a lot of people making still a lot of assumptions and a lot of people think or assume that someone else is curating their relevance which I found quite fascinating and a lot of professionals I talked to and I speak to they're still optimizing their own rule, right? Rather than looking at the things which are really important, which is curiosity, in my opinion, adaptability, empathy, judgment, and did I mention agility already? Can't mention it off enough. I think this is way more important these days, you know, and they start to focus on skills, but that is changing so fast. You know, that there are other Theodora: softer skills. Dagmar Albers: things softer skills which are more important now in my opinion you know and because these rules now they mutate much much faster than rule descriptions i don't think rule descriptions they are usually anyway as you know you know quite often utter nonsense you know but now they are getting really old-fashioned you know and all these i think for me in the moment mindset and attitude is what really counts rather than what kind of experience you have bloody blah you know i think that Theodora: I know. Dagmar Albers: That's way more important in the moment. Don't know what you think, Ursula. Ursula Tavender: Yeah, I completely agree with everything that you said. think, you know, it's very important to remember that in a world where there is so much noise around jobs being taken, if not soon, then eventually by AI technology, that the human side of work and our human qualities will never be replaced by... no matter how sophisticated AI becomes. And those qualities of relationship building and influence and how we build our visibility, how we mentor others, how we lead, how we make decisions, know, how we build culture and how we maintain those ethics around what we do every day. Theodora: Yeah. Mm-hmm. Ursula Tavender: If we can concentrate our effort as professional people on developing those capabilities, then establishing a partnership with AI tools and technologies, plus those unique human skills, strengths and capabilities is where we'll be able to supercharge our career growth in the next however many years. think that's a big, you know, it's a core part of future-proofing our value, right? Is, you know, how do we... upskill ourselves so that we can keep abreast of technological development and make sure that those digital divides are not exacerbated, but also how do we really focus on those areas where we bring something very unique to the table? I think that, yeah, that combination is where people can supercharge their relevance. Theodora: Thank Dagmar Albers: Yeah, comes back to curiosity for me testing, learning, adapting all the way through and my favorite tool, which we quite often teach in one of our workshops, which you know is like, what is really the worst that can happen? Theodora: Mm-hmm. Ursula Tavender: Yep. Dagmar Albers: and how realistic is it, you know? And if you go along these lines, you know, and you start to test and you learn, you and you adapt, you know, you're not afraid because what really can go wrong, you know, I think that is what needs to happen more and more now, right? And I think I hope people can get, and again, for me, that's a mindset, you know, just... Theodora: Okay. Mm-hmm. Dagmar Albers: starting and playing really. So there's a whole theory of play too. it actually gives you in the moment, it's quite a positive in my opinion, the opportunity to play a bit more around, know, which usually you're not allowed to in bigger companies or small companies. I like that. Ursula Tavender: Yeah. Theodora: 100%. Absolutely. think, I may add my opinion, I know I've asked you the question but I've got opinions on this and I must also, me, opinions. I think we're also in a space, obviously we've got five generations in the workspace right now that all embrace or look at AI in very different ways. Ursula Tavender: Yeah, yeah. Thank you. Dagmar Albers: Do you? Thanks Theodora: and who are all brought up in different ways from a culture of work perspective and ethic of work. And I think what I am seeing right now from a recruiter's perspective, I'm seeing an awful lot of people who have been perhaps a little bit too loyal to, you know, their previous corporations, to the previous teams who kind of did what they were told, who, you know, sat in their little lane, which is there was nothing wrong with it. You know, it's what we were all taught really up to a certain point in life, right? do as you're told, you will be seen, your time will come. you know, don't be too loud, don't make

a fuss of it, you will be seen, you will be given the next promotion, you will be given the next project and so on. However, I think, unfortunately, those will be at the moment, type of individuals and careers that are the hardest to adapt to this current marketplace, right? Because if you spend 15, 20 years within a single organisation, within a single business unit, you haven't demonstrated curiosity, agility, flexibility. You haven't really experienced anything but those specific SOPs, that specific team culture, that specific corporate structure. And it is increasingly difficult for you then to demonstrate that adaptability and to learn and to play because you never learned how to play outside of your lane. Ursula Tavender: Yeah, because it's not convenient, is it, for that traditional structure, for people to think outside of it, and it's not necessary. So I think that's all part of this evolution, isn't it, into this new frontier, really, which has been, you we stood on the precipice of it the start of COVID, right, where we demanded agility from people in a way that had never been expected of them before. And that's continued. And I think that what I see is two very definite schools of thought or definite camps of people where, you know, there are, there has Theodora: Mm-hmm. Ursula Tavender: been a real sort of capitalisation on that moment to play and to be curious and to develop those proactive ways of being and taking ownership over your own skills growth and career development rather than waiting for opportunities to present themselves versus those who have, you know, Theodora: Mm-hmm. Ursula Tavender: stayed on a hope and a prayer, know, it's kind of, yeah, there are differences and there are definite generational differences and mindset differences and you're absolutely, you know, absolutely right about that. But what's really fascinating to me is that among both of those groups of people, the fact remains that if you do just take the approach of your head down, work hard, know, and success will come, Theodora: Yeah. Ursula Tavender: that's been proven. many, many times over to be completely false. it actually, you know, creating a career that looks whatever your version of success looks like for you, you know, to achieve that, we need a combination of visibility and proactive sharing. So self-advocacy and proactive sharing of the work that we do. We need to be able to build visibility so that the right people see the work that we do because we assume we're very self-focused, right, as human beings. Theodora: Mm-hmm. Ursula Tavender: we naturally assume that our work is obvious and that success will follow as a result of the obvious work that we do. And that's just not the case at all. working hard is absolutely imperative. I'm not questioning that in the slightest, but we do need to push ourselves to be more visible, to amplify our own achievements, to be able to talk about what we do in a way that is fact based and that Theodora:

Hello. Ursula Tavender: forms a lot of the career development work that we have done historically is all around supporting people to build those self-advocacy and those ownership skills over their own career visibility and their own strategic growth because without that combination of visibility and personal branding and you know talking about our achievements getting seen in the right places and getting heard in the right rooms alongside that hard work you know that's how career success happens. Theodora: Yeah. Dagmar Albers: Yeah, but I think that is no more important than ever because now it's really interesting because now with AI and all these Claude and ChatGBT and Co-Pilot, know, it's really, there's a real danger that performance reviews get really confusing because there might be a lot of senior leaders who actually do not look into quality of work any longer. If you don't talk about it in particular, but more about the speed, you know, and speed is not necessarily the quality. Theodora: Bye. Dagmar Albers: Right? And it's nice to work with a pilot, you know, but you still need to have a lot of human proper input in it, right? Data in, data out, you know, and the quality of data plays a huge role and that comes from us, human beings, right? So if you are then on top, are not able to talk about what you have achieved, then it's getting really, really difficult to be seen, right? Or to be seen for what you really have achieved. So it can go very, very quickly wrong because performance is not necessarily about speed, it's about the quality of the output you're producing. Theodora: 100%. So obviously we still absolutely need that human element, right? And the critical thinking, which I think, you know, is harder and harder to identify these days. Ursula Tavender: but there are ways of being really strategic about that and that's a lot of the work that we do and what we're seeing working really well is when guardrails and principles are put in place that determine within a team Theodora: Hmm. Mm-hmm. Ursula Tavender: when oversight is needed, when we need to use our judgment, when what AI is used for and what it is not used for. And I think when you give people that understanding, and you can ask for that, if you're working in a team where you're not in a position to be able to put that kind of thing in place, be proactive, use that as an opportunity to take that to your leaders and say, need this structure because again, going back to the point of actually Theodora: Yeah. Ursula Tavender: most people are using AI even if they're not being open with their employer about what they're using it for. Where's your data going? Where's the governance? Where's the risk strategy around the sensitive information that's being fed into AIs? that's a whole kind of different conversation about the risk appetite and tolerance within a business for that kind of governance. yeah, it's... Dagmar Albers: Yes? Theodora: Well. Thank minefield. Ursula Tavender: It's a fascinating, it is a minefield, it is a minefield, but I think, you know, as I say, what we're seeing working really well, and this is something which is not new, it's not rocket science, but putting those guardrails in place is a way to A, identify where we as humans. Theodora: Mm-hmm. Ursula Tavender: add the most value, where as you said right at the beginning Theo, know, our repetitive tasks, our admin, you know, reporting, you know, those things that people have been experimenting for on a widespread way of, you know, reducing the administrative burden after meetings, for example. You know, it's an easy one, support with drafting email, first drafts of research, that kind of thing. You know, it's a no-brainer for... Theodora: Hmm Ursula Tavender: AI to be able to support with those tasks. But beyond that, as you say, you know, there are tools that can help with absolutely everything within a specific role and a specific team and a specific function. So what would that look like? And that's why team conversations are such an important part of this transformation, because people have always known what will help in their role if you ask them. You know, if you ask them. Theodora: No. Mm-hmm. Mm-hmm. Yeah. Yeah. If you ask them, I think that's probably Dagmar Albers: Yeah. Theodora: one of the biggest parts of it, right? If you ask them. Ursula Tavender: They've got all the answers. Dagmar Albers: But that is what I find so fascinating is I think a Voodoo LinkedIn post about that lately is that coming from a background as customer experience, right, and customer insights, companies spent millions. billions on that kind of stuff, know, generating customer insights and new ideas and innovation and right here where you are. But I hardly ever do any employee journeys or employee insights or anything like that. And I found that really fascinating because without your employees, you literally don't go anywhere. Right. So it's really, really fascinates me massively that they spend so much money on customer insights and customer experience, but not on employee experience. Theodora: Bye. No. Dagmar Albers: other than every now and then a little bit of a questionnaire where they do some pulse checking or whatever, however they call it, you know, which is anyway not... depends on how you ask the questions too. Theodora: I could talk about that at quite some length in my experience. Yeah, absolutely. I see some follow ups here in our future. But I think there's a lot of tension at the moment, in the way we, when we speak about the way we utilise AI. Specifically, I think there is a huge amount of tension around using AI to scale and then losing that human judgment to, you know, Ursula Tavender: Again, that's another future episode. Dagmar Albers: Yep, Theodora: make a great hire or decide who gets promoted or how we collect the data and how much do we give A.I. access to? How much do we let it do for us? And where does it become a burden rather than a

support? Dagmar Albers: Yeah, I would say depends, Ursula Tavender: So that is it. Yeah, that is a big question and I think it's one that leaders within a business can answer. as a kind of collective conversation within that business. There isn't a one size fits all answer to that question. And I think that's the difficult thing about this entire conversation is there is no, here you go, here's a clean process to follow because every organization is different. Every, you know, you've got different levels of maturity. You've got different personalities navigating these transformations. And I think that that makes it quite, quite complex. So yeah, there isn't a, it will look different for everybody and that's okay. But I think it also makes Theodora: Mm-hmm. Ursula Tavender: it bit more difficult really, infinitely more difficult actually, because it is, you can't sit here and say here's the answer. Theodora: Yeah. Dagmar Albers: Yeah, and I think, I mean, what is AI really for? I mean, and we talk here about language models and stuff like this, because AI is such a vast thing, we are not even tapping into it. And it exists already since the 50s, doesn't it? What AI should do and what we talk about is absorbing repetition and should do this pattern recognition, right? So that we as humans can focus more on Theodora: Bye. Dagmar Albers: interpretation and ethics and trade-offs and stakeholder impact and all that kind of stuff. But again, that only works, like Östler said, if organizations really clearly define where judgment actually sits. So I think that's where it starts, and rewarded and then also visibly. Because I don't think that there's a risk that AI is replacing judgment, not at all, because We will always do that as human beings, you know, but organizations are at risk to fail to protect, I think, and to value the judgment, you know, once, especially once the output is starting to accelerate even faster and faster, you know, that's where falls. Yeah. Theodora: Thank Ursula Tavender: Well then there's the point around. Then there's the point around bias, because ultimately if systems are biased to begin with, then all AI does is amplify and accelerate and extend those biases, right? So, you know, if you are able to examine systems for bias before scaling, you know, that's the ideal, isn't it? But at any point, systems can be taken apart and examined for bias. But, you know, it needs a multi-stakeholder approach to do that because, you Theodora: Mm-hmm. Absolutely. Dagmar Albers: Exactly. Theodora: BOOM! Ursula Tavender: know, if we have the same people sitting around the table, then, you know, now we're getting into the sort of D and I lens, which is a really interesting angle for us, because our work originated with the goal of creating more equitable structures within the workplace, right? And that's the way we approach this phase of our work is how do we ensure that we scale AI enablement in a way which ensures and protects equity for every Theodora: . Ursula Tavender: And that is a minefield because systems and tools at the moment in the main, I'm speaking broadly, but in the main they are bolted into existing workflows. Theodora: Mm-hmm. Ursula Tavender: Our approach is to reverse engineer the workflows so we redefine and reimagine how work happens and how decisions are made, putting those guardrails in place so that the systems are inherently equitable to begin with. Whereas at the moment what's happening as tools are kind of retrofitted into, they're sort of bolted onto existing workflows, all that's going to happen is biases are going to be amplified and your example of recruitment is a great one. Theodora: Mm-hmm. Mm-hmm. Yeah. Yeah, so I think before obviously before we actually start recording, right, we were talking about the fact that there's this argument, right? have I been ultra rejected by AI? Right. And quite often you're not because nobody actually lets AI or to reject people on their behalf or candidates on their behalf. However, if you do have a scoring system in place and you're looking at 200 applications, 10 of them have been scored a nine out of 10, and then you have 90 of them that have been scored two out of three, I am pretty much 100 % certain that nobody will sit there and read every two out of 10 CVs and decide to move forward because you've already, you're looking at something that is already biased for you, right? And it's kind of made the decision on your behalf do is click the button and then the decision wasn't human, the decision was made by AI and I think ultimately where the ethical issue, there's obviously huge ethical implications here right, but we deal at the moment with an awful lot of individuals who never built the skill of looking for a job because prior to these last couple of years a lot of people in middle and upper management who find themselves displaced in this current job market never really had to proactively pursue a new opportunity. they were previously sought after, they were headhunted, they were contacted, they never really had to travel the whole of the internet and figure out how to write their CV and then they've got so much conflicting advice at the moment, use chat GPT, pay somebody five grand to write your CV for you and those are absolute jackals that I have bought but they are confused and then I think you have somebody who's worked as advisor for the past 15 years who has displaced last year who hasn't written a CV in 15 years who's completely overwhelmed who either sends you a 20 page long CV or sends you an AI written CV because somebody told them this is what needs to happen and in either of those scenarios it will probably be scored quite low. But they will probably have some really fantastic experience that they just don't know how to put on paper, how to put in their applications. So those people will automatically be taken out of the process. I think this is where the biggest issue is. You we have AI

speaking with AI to a certain degree and the actual human communication is becoming obsolete at that level because ultimately we still have jobs to fill and we still have candidates who need jobs. Yet somehow we still have jobs to fill and we still have candidates who need jobs. Ursula Tavender: Yeah. Theodora: Thanks. Ursula Tavender: Yeah, it's one extreme to the other, isn't it? We've almost arrived at a point, and that's an extreme example, but where we've lost that balance between the human, what AI can do for us and how it can support processes, but remove the human element, which totally bastardizes the process, right? Theodora: Yeah. Absolutely. Ursula Tavender: So I think these mistakes are kind of inevitable with any sort of massive change. You know, we saw it when after Covid when some organisations moved to a kind of work from anywhere. You know, they went with an extreme form of flexibility, which works for some organisations very, very well. And in others, it wasn't led very well. So it didn't, you know, it was one extreme to the other. So, you know, with any kind of radical change to ways of working, there are always these teething issues and this Theodora: Bye. Mm-hmm. Ursula Tavender: of learning and you know I think you know Doug my talked about testing learning and adapting and that is always our advice with with any business that is trialing human and AI you know partnership ways of working is is set up low-risk pilots where you can assess the impact because I think if you if you move straight off to a process which is so radically different then you've got like as you say we hear this a lot in focus groups and in other because I was doing a key Theodora: I know. Ursula Tavender: know, a couple of weeks ago, and there was a number of businesses in the room talking about their recruitment processes, talking about exactly this topic of, you know, people are effectively, you know, creating AI generated applications to, to get through the AI driven process. And we've also got the problem of younger applicants who then get to interview and are not equipped with the communication skills to be able to Theodora: Mm-hmm. Mm-hmm. Ursula Tavender: stand on their own two feet in an interview to get through that process. It's a whole kind of worms, that process, isn't it? So yeah, it's a really interesting time from that point of view. If you look at the human lens, it's a really interesting time, isn't it? Theodora: Mm-hmm. It truly is. And I think, you know, it is not just individuals who perhaps amplify their experience, right. But it's actually there's a lot of fraudulent applications that are happening on certain niches and parts of the market. And these are areas, you know, that have had fraudulent applications since the beginning of time to a certain degree. But now they're getting smarter, they're getting more agile, they too are AI enabled and they are getting very, very good at fraud. And it is it's even harder to detect it. earlier on. So it's becoming more and more complex when it comes to recruitment. it's not something that necessarily every job seeker would understand that actually, you know, and I get why people feel so negatively towards recruitment teams at times. And it's easy kind of to a certain degree, right? Kind of sit there and look, I've looked at LinkedIn, it's had 300 applications, I got an automated rejection three hours after I applied, a bot rejected me. But the reality is, you know, you've got somebody who works on 40 requisites that each have 200 applications that doesn't have the bandwidth to review manually 200 CVs per application. So the question I guess that we're all trying collectively to resolve is how do we marry the two? How do we make the process equitable? How do we take the bias out of it, but also support our own teams when it comes to recruitment capacity? How do we reach that balance in between where we both offer a good candidate experience and candidates understand why they've been rejected. but also protect our internal talent acquisition and recruitment teams from burnout as well because no human can actually process that many CVs at any given time and it's impossible to give every single applicant feedback as to why their application was rejected as much as we'd love to, right? Ursula Tavender: Yeah. And that's where the trialling comes in to me, is trialling different approaches and researching what is happening in your sector and outside of your sector as well, because I think that peer-to-peer sharing and exchange of knowledge and ideas and experiences and here's what worked and that didn't work for us and here's the corner that we can help you to cut because of us sharing our experience. think get out there, find out what is happening in your sector and outside of it because it is about Theodora: No. Yeah. Ursula Tavender: experimentation and we need to develop a mindset of experimentation to meet this moment because there is no defined pathway and with things changing so rapidly there's always room for shifting and changing and I think that that is for some people especially if you sit at the intersection of age and career stage and experience and even down to the sector that tolerance for experimentation varies massively, but I think if you can take that approach that A, be willing to learn and keep your knowledge up to date of what is happening and then be prepared to experiment with different ways and you will find an approach that works better because from what you're describing, that sounds like it's not working for lots of people at both ends of the process. Yeah, it's about... Theodora: Bye-bye. Hahaha Ursula Tavender: being experimentational, isn't it? know, and being adventurous with the way that we do and being prepared to sort of be quite, I know if you've always talked about like out of the box thinking and it's really boring, but you know, it does require that, you know, and we've got tools that can help us

to think differently now. We can, you know, use them to find ways that are broader than our own perspective. And that's really exciting. Theodora: Hmm. Absolutely, think, statistically speaking, think under 50 % of professionals really have used AI at work over the last six months or so. Despite all of us, know, kind of collectively constantly talking about AI, why do you think adoption is still low? Ursula Tavender: to run. Dagmar Albers: Because as you said, there's so much noise out of there. I look at LinkedIn. It really does my head in. And I think noise creates pressure and not confidence. And I think that that's one part of the crooks, you know? And also, again, because there are no guardrails, quite often no guardrails or anything in place, you know? People are really unsure. So what's allowed? What's expected? Theodora: Bye. Mm-hmm. Dagmar Albers: I mean, they don't even know what's expected, you know, and that is really bad, you know, or how the use of AI even will be judged because we are judgmental, you know, that's how we are, you know. Theodora: No. and Dagmar Albers: And I even met some teams who said, so I'm a communication specialist. That's my job. I know best how to write something, something, you know, and actually I is cheating too. So that's a completely different lens, you know. But if you have unclear standards, you know. Theodora: Hmm. Dagmar Albers: then people start to self-regulate. And what I see, what really fascinates me, within this HBA where you and I are actually both volunteering for, see a lot of the younger people, before they do anything wrong, they rather don't do anything. And I find that whole attitude really fascinating too, because I'm having this, I ask for forgiveness and not for permission kind of attitude still, but I'm also a bit old. Theodora: Mm-hmm. Mmm. Yes. Dagmar Albers: so that makes it easier and I personally don't give a talk what people think about me anymore which is very very helpful and very liberating actually you know but the younger generation because of all the pressure and because everything is so hard and because there are so many assumptions out there and because everything is so much more visible and the social media can go on and on and on and on here. I think that's one of the reasons and that just is just too much. know, the pressure is too much and that really rubs you on confidence. I mean, look at the younger generation, they're all dressed in the same way. I think that also comes from peer pressure. Theodora: Bye! I mean, I don't know, I think, you know, I was going to say I also dress like everybody else did in the 90s and the 90s, you know. Ursula Tavender: It's all safe. Dagmar Albers: But that's what you do, you know, it takes a long time until you find yourself and self-confidence, you know, and also to overcome this, what do your parents tell you, particularly my mother, what will the neighbors think, you know, this whole attitude thing. yeah. Ursula Tavender: What's also really fascinating is the data that's emerging around the differences in the ways that people are engaging or not with these technologies based on their gender, based on their race, based on their socioeconomic status. based on their neurodiversity or disability, their age, and there are differences that are staggering. If you happen to be a woman who is over 45, you're about 50 % less than likely than your male counterpart to confidently use AI in the workplace. And so this is what we talk about so much with the differences and the divides in the ways that colleagues are engaging with these technologies, because Theodora: No. Mm-hmm. Ursula Tavender: without a strategic approach to like a bespoke learning pathway almost for everybody in place, which we haven't, you that's not widespread yet, but it will be. And that's one of the things that we advocate is creating these personalities or these kind of avatars profiles within your colleague population as a leader that identify how people feel about using these technologies because there are, as you've so clearly outlined Dagmar, you there's issues Theodora: Okay. Bye bye. Ursula Tavender: with different levels of trust and confidence and risk tolerance and all of those kind of different aspects that dictate whether somebody is likely to think, well, if I use this and it's obvious that I use it, you know, we were at a conference recently where somebody said I have in my email footer, you know, a disclaimer that I don't use AI, like everything is authentically me. This is just me. I won't use it. so, you know, it's an interesting angle, isn't it? so many processes now are being changed to incorporate and so many workflows are rightly being adapted to be more efficient and sped up. I think it's, you see the same pattern over the course of history, right? You look at when email was introduced or when computers were introduced, when fax machines were introduced, there's all this hype and noise around, we're done for. And then things settle down and they adapt and they change and people realize that it's not the Theodora: and Bye. You Dagmar Albers: Thanks Ursula Tavender: of humanity. it feels like, you know, that's without underestimating the scale of this change, which is obviously unprecedented, and you know, not like anything else that we've seen within any of our lifetimes. You know, I do think there is a real... benefit in understanding the differences in the ways that people are approaching this and the different mindsets that they have because that tells us so much about how we can support them to and increase that engagement. Dagmar Albers: But I think if leaders start to actually to admit uncertainty, which is of course very hard, I think that's a good starting point. know, it's just like, I don't know either, you know, it's just learn and just it's just like start to experiment, you know, and just also normalizing learning. I think that would help massively, you know, and take

everybody with you on the journey. I mean, when we introduced how to work flexibly properly. Theodora: Mmm. Dagmar Albers: back at Pfizer to close the gender pay gap, it was the same thing. As you said, you have five generations at work, you know, and people have totally different attitudes and idea how to work flexibly, how do you meet the individual need. the team need and the business need. We had of course some people who'd been very cheeky who said, okay, great. That means I can now take every Monday afternoon off and go cycling. No, you can't. That's not flexible working. Flexible working doesn't mean you can't do it. Theodora: Bye! That's just not working. Ursula Tavender: Yeah! Dagmar Albers: Exactly, that we can come in flexible, know. And you had some elderly ladies, particularly admins, who said, no, I can't do that, I can't do that, you know. And who'd been all the time like, so I can't even go and make myself a cup of tea or I can't even go to the loo because that is the moment when my leader will call me and then I will get caught out. You know, that's not what it is about, you know. And this whole... Theodora: You Dagmar Albers: rethinking what is actually work. It's not about how many hours you work, it's actually what outcome you produce. And the fascinating thing is we see all the same similarities happening with AI now. It's not how fast you work or how much more you can do, it's the quality of the output you're producing actually. Just because you work a bit faster doesn't mean it's better. Theodora: and and Ursula Tavender: And that's why the guardrails are so... Dagmar Albers: Yeah. Ursula Tavender: And the principles, the adoption principles are so important in the same way that they were with flexibility, you know, because it gives people that virtual permission slip, that sort of psychological safety to be able to experiment. And it all, it all starts with that leadership clarity and that those kind of, what is AI for? What are we using it for? What are we not using it for? And, and when, when does human oversight matter? When, you know, can AI take care of decision-making and when is it not appropriate for that to happen? And I think that is starting. Dagmar Albers: Yeah. Theodora: Absolutely. Yeah. What's the purpose? Ursula Tavender: starting there is key to increasing that. Dagmar Albers: Next. Ursula Tavender: among the population, know, raising those numbers of people that do confidently feel that they can experiment with AI and not feel like it's cheating and not feel like they trust it because the lack of clarity and the lack of direction is where, you know, people are looking to leadership right now to say, you know, we don't have answers to these questions. I feel like I should use this on my personal laptop, but not on my work laptop. You know, we've got a few co-pilot licenses that are sort of scattered among key people in the business. Theodora: I don't know. Mmm. Yep. Ursula Tavender: they happen to be early

adopters so what's happening to the you know the the people who are not those among those early adopter cohorts. Pilots are relying on volunteers which naturally self you know the people who are not inclined to be the first to engage are naturally self-selecting out of those processes and we're hardwiring bias into the adoption process. Theodora: Bye. Dagmar Albers: And what is? And for me again, like flexible working, one of the biggest problem with working flexibly properly is actually burn out and not that the work is not getting done really that. And I can see that now here too, because I spoke lately to somebody who said, I'm totally overwhelmed because everything is so much faster. I'm getting more and more more work dumped on me. Right. So instead of having time then to do thinking work. Theodora: Mm-hmm. Mm-hmm. Mm-hmm. Dagmar Albers: they're getting more and more other tasks on top, know. So again, you need to watch out that people don't burn out because they have the feeling they need to do more more and more and more, you know, and never rest or never take time off. Same as the flexible working. And I need to put a bit more thinking behind that, what's really going on there. But it's kind of emerging already, which is fascinating too. Theodora: No. Absolutely. I spoke to somebody the other day who said, you know, I've just spent the whole weekend playing around with Claude and I've done so many dashboards and I've analyzed so many things and I've fitted so much data and it was absolutely fascinating. I said, OK, wow, what business need has that address? It's like none, but it was great. And he's just learned that he can do so much more with it, but really hasn't actually aided him in his day to day job. But it was fascinating, right? So that's probably taking the plane to the other extreme. Dagmar Albers: Hahaha! Ursula Tavender: But you... Dagmar Albers: No! Ursula Tavender: But you know what, think that is so valuable and that's a really valid point because having the time to experiment is where confidence is built. Dagmar Albers: Yes. Ursula Tavender: and that mindset develops, right? So the companies that are giving their people an hour a week in a learning lab, in a drop-in session, to be able to experiment, to talk to other colleagues who happen to be able to build agents or whatever it might be that they have done that experimentation, that person has taken it on themselves to do that. If that's led by the business and you are given that time, again, it changes your mindset about how to engage, how to, know, it's that that's Dagmar Albers: it Theodora: Thank Ursula Tavender: psychological safety is another building block of that permission to experiment, make mistakes. If there's a clear message from leadership that we are going to be embracing more of these technologies, so we want you to innovate, we want you to be part of this shift with us, it stands to reason that you're going to build higher levels of confidence. It sends

a very strong message, doesn't it? But if that's happening in the shadows with no purpose, no clarity of direction, Theodora: Okay. Ursula Tavender: There's risk. Why would you do that? Theodora: Absolutely. Dagmar Albers: Yeah. Theodora: Well, this has been absolutely fascinating and I I tend to ask majority of the guests I've had so far who just also happen to be entrepreneurs is what has entrepreneurship taught you about yourself since you started on this journey? Dagmar Albers: Good question. I'm speechless. Doesn't happen very often. I think, no, I think trusting each other is really non-negotiable. think, because we'd be doing it together, right? And I think trust is a huge, huge thing. And that is absolutely the one thing which is absolutely non-negotiable, right? And never making assumptions and... Theodora: Nothing. I knew it all. course. and then Dagmar Albers: and just asking for help. I think that's also important. Because I come from the corporate world, Ursula has much more experience being an entrepreneur. She's doing it much longer than I am. But also because we totally different personalities. but do have exactly the same values. And I found that interesting. Having the same values really made us click and makes us work so well together. Right. And I think we feed from each other in a really, nice way. And the way we work together is just pleasant. It's just non-stressful. It's just really Theodora: Bye. Dagmar Albers: so lovely that it's actually fun, you know, and I wouldn't work any longer with people who are too aggressive or where I put down the phone afterwards and think so I don't want to this experience anymore, you know, that's why I left the corporate world. I know, doesn't make sense what I'm saying here. Theodora: Absolutely, yes. Ursula Tavender: I think for me it's all of that but if you zoom out a little bit it's about, the point you made for me is about independence and interdependence because I grew up thinking I had to do everything myself and had a strong sort of idea that you know, it was about individualised perfectionism, if you know what I mean, and I've been recovering from that for a very long time. So aligning with the right partnerships is such an accelerator because if you try and do everything yourself, you're slowing yourself down. That's just my experience and my view. So when I... Theodora: Nothing else? Mm-hmm. and Ursula Tavender: have found these partnerships that as you say Dagmar so in sync in terms of values like we are yin and yang like we could completely night and day but we complement each other so beautifully and and it is because of the fact that fundamentally at our core as people we share the same values and because of that magical combination in a partner of same values but different skills that is an accelerator so using the right partner in an entrepreneurial sense is an absolute accelerator. And that's been a really big learning for me because I had in my head, not even consciously, just that, you know, it was all on me. And so learning that it isn't and that I can rely on other people and I can allow, you I can stand in my own lane and then let others, you know, I don't have to be good at everything. That's, you know, that's really. Dagmar Albers: But Ursula Tavender: goes against Dagmar Albers: also... Ursula Tavender: a lot of the messaging, I think, about entrepreneurialism. Theodora: I think for us Taipei perfectionists in recovery it's a very hard lesson to learn, to ask for help and also delegation and understanding that if you want something done right you don't just have to do it yourself. Ursula Tavender: Yeah. Yeah. Dagmar Albers: but also having Theodora: thank you. Dagmar Albers: the gut saying if you realize that with somebody else it doesn't work, having the guts to stop and in time, you know, and also learning to be a bit more selfish, you know, to see what's good with whom you really want to work and what you really want to do and just... be on your toes all the time. Again, test, learn, adapt, because I mean, our business in between was at a dead end because of the geopolitical situation. And then we had to reinvent ourselves. We had to change from innovation equity to innovation in, you know, and we could do that because Theodora: Mm-hmm. Dagmar Albers: We have such an open relationship. We talk about everything very openly. because you make risk mitigation all the time through. So we are always proactive. We are never reactive. So we thought, OK, that's an opportunity. It's not a disaster. We could have gone into doom and gloom. But we just turned it completely around very, very quickly. And there was never any negativity about it. Theodora: yeah.