The Global CIO of IFS on Redefining IT from Infrastructure to Intelligence

Speaker A: IT and technology is not all about software. It is the full stack. Look at the infrastructure cabling that we're putting under the seas, all the data centers that are being built. Our capacity has doubled since 2022. It is absolutely everything that goes with that. We still have IT in our language. IT and the business. The reality is it is the business. The there are no businesses in the world that can operate without it. AI is not a tool, it is a platform. So start building the platform, thinking into it. It's not just a collection of tools that people randomly use. You need to think about what is the next platform we need to create really effectively enable AI as part of the organization.

Speaker B: Welcome to the new automation mindset where AI automation and integr integration come together. I'm your host, Markus Zern, and as chief Strategy officer and part of the founding executive team at Workado, it is my mission to find these top innovators in AI automation and integration and share their journeys with all of you. Today. I have, uh, with us here, Helena Nemon was a really accomplished, uh, IT leader. Like lots of, uh, different companies, also different industries. Fujitsu Cancer Research uk. That's an interesting one. Thomson Reuters, Unimonitor, uh, International Endava. Now, uh, ifs. So Helena, first of all, welcome. And you know, I'm really curious. So 20 years of being an IT leader and then if I saw correctly, another 10 years in technology, uh, as well. You know what, what have you learned over all of these years? I mean, I'm sure you've seen lots and lots of IT situations, uh, maybe some where you felt, you know, that's right and you know, maybe that's not so right. Any expertise, knowledge, you know, everyone's hungry for that. You know, what can you share?

Speaker A: Well, thank you Markus for having me and thank, um, you for aging me there as well. So you are right. Sometimes it feels fascinating when you, when you tally it all up and you go, oh goodness, I've been in technology for 30 years. And you are right. What, what a 30 years it has been. We've seen everything from the birth of the Internet, uh, where everybody just thought it was going to be used for communications and messaging. And one of the first big lessons to learn there is the wow. We thought a technology was going to be used for something and look at what we now have in the ecosystems, you know, the digital ecosystem, digital economy. We have business models, we offerings we never even thought about. Um, so I think that is definitely one of, one of the big takeaways and the other big Takeaway is we are again in one of those really big points where, one of those crossroads with AI. And I'm hoping that there are loads of really enthusiastic technology leaders who have been around as long as I have and even longer who look and draw from their experience of what happened with the Internet and the digital revolution and what is going to happen with AI and the AI revolution. Because I think the reality is, and I think we all recognize it is the Internet was big, AI is going to be even bigger. And the more expertise and experience and stories we can share to make the experience good and smooth for everybody, I think that's going to be one of the key learning points and key inputs I would interject into this next phase, like 100%.

Speaker B: I'm actually really curious. So people are now starting to talk about AI transformations. Obviously we're in the very early innings of all of this, but look, I couldn't agree more. So I started my career in 95. So what you mentioned when you brought my memories back to the Internet, uh, I've been there, seen that. Uh, and then I would argue that in my career, like, you know, our careers, AI is probably the biggest thing that has ever happened. I mean, you know, I started in uh, a BPR business process redesign. So I was drawing these, these visio diagrams of business processes and so on. AI. And these AI agents are now like almost like fundamentally changing, almost turning upside down like how you think about business processes, which is, uh, it hasn't happened in 30 years, but help us a little bit on the way the different steps of how we got there. So Internet you mentioned, right? I think, uh, so having been a CIO, I imagine like SaaS applications where obviously the SaaS revolution was a big thing. Anything else, Anything else that has led us, I mean, I'm always trying to like learn from history and what we've seen. You know, I'll ask you in a second second. The AI transformation. And for a long time we talked about digital transformation, right? Uh, I think digital transformation was inspired by the Internet, uh, coming around and so on, and what Amazon and all these consumer Internet companies did. But anything else, Any other kind of key milestones that you think we need to remember when we start thinking about AI?

Speaker A: Well, there's, there's a number you kind of mentioned the software as a service, infrastructure as a service, platforms as a service. You know that we have got so many service orientated, uh, offerings out there right now. And I think that is one of the, the keys in the thinking we need to bring um, there are then underlying lots of trends which I think we can learn as well. You know, there was, there was a, there was a point where it was all about infrastructure. That's what it was. You know, it was, it was the tin and the cables and you sat in the basement basically is, is what a lot of, a lot of my colleagues will, will remember. And um, right now we're coming a full circle again. Um, you know, everybody thought that while the software as a service revolution would happen, it's all about software. But actually it's currently I'm flipping back into it's all about hardware. It's all about hardware in a different way. It's in the way of the consumption, the storage, the processing power. Um, everything that is, is happening, uh, in that space. Look at the infrastructure cabling that we're putting under the seas. All the data centers that are being built. Our capacity has doubled since 2022. And all of that is driven by the software revolution, the software revolution, uh, of software as a service, but also now moving into AI. And I think one of the lessons and reminders for us to think about is that IT and technology is not all about software. It is the full stack. It is absolutely everything that goes with that. And we've had the digital transformation, the digital revolution. And you're right, people are now starting to talk about AI transformation. And um, the business models are going to be more complex as the technology evolves. We are going to have traditional organizations, we are going to have the digitally native organizations organizations, and we're going to have AI native organizations. I can't wait to see what those business models are going to be. They're going to be just, wow, some of them that come out. So I think, you know, there are many smaller and bigger trends that are sitting on top of each other that I think we as IT leaders need to try and uh, keep on top on and top off and see the bigger picture and understand how we maximize the benefits from them and not get pulled in to some of the, dare I say, fads, but actually really focus on the bigger picture, uh, with an eye on the AI piece which is going to change how we do our work.

Speaker B: You made a really good comment with the infrastructure and more the software world and how it kind of changed a little bit. And maybe, I mean that was actually quite insightful with the coming full circle. Um, I'm curious. So the one thing that I told you, I did the BPR stuff and so I was really, and this was as a consultant, so I was Mostly working with the business side of the house. Less so, um, was almost like it. Who then, you know, was supposed to implement what we built. That relationship with the internal business customers. How has that changed over the years or maybe what kind of different forms and shapes have you seen in the different companies that you've been in? I'm curious, has that always been the same? Has that changed over time? Is it different today than it used to be? Uh, back in the 90s? I'm curious.

Speaker A: There's a different approach each organization take to it, but there are some very clear, you know, lessons, um, that we've, we've learned, uh, not just as an industry but you know, the entire humanity which is uh, technology is key to a lot of things and it is about evolving different stakeholders. In the conversations, you know, you talked earlier about um, uh, processes and obviously we're talking about new automation mindset and the business process view on how things are changing, Changing. You're right. It very much used to be seen as a super advanced piece of work. If a business process was written down to a level of detail that it could then take and put it in place and automate it in that way. That flow, that was something that was seen advanced 20 years ago. Now we expect it as a norm. Um, whereas the piece that we need to start looking at more and the piece that we need to start focusing on more is not the process as much, but it's the end result. We do processes and we follow processes to consume data. We're there to input and we're there to output. And the focus now is definitely changing more into the input and output and the data from the process mapping thinking. However, if you look at all of the workflow solutions and the Q is in the name really, we are looking at all of our IT and all of our solutions in that process model, in that workflow thinking and the AI initiatives and the AI thinking that is coming around that is as you say, it's going to flip it on its head. It's going to completely invert uh, our business processes into something completely different. I think those are some of the big learnings because you can't do any of those without having good stakeholder management and an ah, open mind. And I think that is the big transformation we've seen in IT and approaches to technology over the years.

Speaker B: How do you feel? Uh, you know, I felt there was always somewhat, a little bit of attention between the business side and the IT side, at least when I was doing it. On the process side, the work uh, do you feel that has, uh, gotten better or stayed the same or gotten closer? Uh, Gardner, the industry analyst, they talk about the Fusion team of how the business and it ideally work together. Do you feel like we're getting there, or over your career that has fused more together, or has it stayed the same or is it gotten worse? I'm curious what you're doing. Not different. People perceive.

Speaker A: Yes. So. So I think there's many perceptions on that. So I, I like the Fusion teams, You know, we used to call them purple people, um, the blues and the reds. Your ability to straddle both the, the business side, um, and the IT side. And I think that is the absolute key. We still have it in our language, IT and the business. The reality is it is the business. There are no businesses in the world effectively anymore. And I'm making a generalization there that can operate without it. So it's funny how the language of IT and, um, the business have still persisted. And that's one of my, I have to say, one of my pet hates. Marcus. I find it really disappointing that we have ingrained in the language into our thinking, which, you know, kind of goes then into some of the, the AI and the large language models, because it shows how easily a certain thinking and, um, way of behavior becomes a pattern that is so common that everybody uses it and it's not right. So that is still there. It will be brilliant to see it gone. Yes, there's always a bit of friction and conflict, but again, you know, there's friction and conflict with, with all various different functions. Um, because everybody has got their own agenda. It's, I think the, the conversation is much more moving into what does the organization need, what does the company need? And that's, that's then that the senior leadership's responsibility to pushing that message down into the wider organization of we have no silos, let's not talk about the silos. And, um, we have a common agenda.

Speaker B: I couldn't agree more. And I think maybe now with AI and so on, people realize how important the technology really is for the business. So it's not the. Not as you described it in the beginning, not the people who just deal with the infrastructure, uh, down in the cellar somewhere, hidden away, uh, but it becomes really part of being the business. Uh, so tell me a little bit, um, you know, if you're, you're an IT leader, uh, today I've, I've talked to IT leaders. One of them, um, told me the other day, said, like, you know, Marcus, I've had Similar to you and me, I had my 20, 30 year career and uh, I feel like I've done everything. I've done the digital transformation, I've done the huge ERP project. Uh, you know, I, I was with a big company, I was with a startup that grew very quickly. You know, the one thing I want to have on my resume is that AI transformation I want to do. I think the one thing I want to do now, I want to be in a company and really apply AI so that the company almost like changes for the better and then I'm happy. But I'm curious, AI transformation. What do you think when you hear that or what's going through your mind? Uh, do you see something that is different from what was there in the past? Is it yet another kind of similar digital transformation with all the things that come with it, or do you see something completely new there, uh, doing an AI transformation in the company?

Speaker A: So yeah, you're absolutely right by the way. Just picking up on the point there. AI. I am super passionate about AI and I've been watching it for the past uh, 10 or so years and it's brilliant to see it coming to fruition. Um, I think the, the AI transformation is going to be digital transformation on steroids. Um, but I also think it's going to be quite disruptive to a lot of people, not everybody. There's a lot of people who are disruptors. There are a lot of people who are going to really embrace the change. That's the piece that everybody's a little bit cautious about because with AI you're talking about not just using technology but having technology co workers. So you've got agentic co workers. Um, ah, so you've got agents, uh, who you're working with and agents who you might be managing. And notice the word there, I've actually just humanized them by using the word who. Um, and there's the concept there of everybody automating. And um, you know, when you're starting to think about everybody automating, there are people who are really scared about that because they might not be familiar with the automation, automation. They might be afraid they get the automation wrong or they might be afraid to use other people's automations and agents because they don't quite know how they're being formed or created because they're not humans. With humans we can, we can look each other in the eye and, and we can have that rapport and that conversation. But when you're starting to talk about agents and automation which you cannot touch uh, and you cannot see the whites of their eyes. That can be a very, very scary piece uh, for a lot of individuals. And I think part of that is, is taking a step further which is yes, we are going to have agent tech workforce, we are going to have agent colleagues. Um, but then you've also got the different side of things which is um, the manufacturing and the heavy industries and how they're using IoT, um, and robots to remove humans from harm's way or to enable a more 24 7. So you might have a manager who is managing physical robots and um, and you might have a manager role that is managing virtual robots, um, as in agents. Um, so I think it is a really scary transformation because we are, we are as humans we are quite hierarchical. We have built those organization constructs to suit our thinking and that kind of line management and structures that we enjoy. And the AI piece is going to massively flatten that and change that. Mhm.

Speaker B: I think you said something really important. Uh, and I do feel that most people don't understand that yet and sometimes always confused when I say the same thing. I always feel like these new uh, LLMs, the large language models, the new generative AI is almost human. Like it's actually very, very different. I think when people use the word automate, they think of robots, they think of machines, they think of something that is almost like the opposite of human. Like it's something that does something just deterministically. Typically, uh, it's something that is a very simple task, um, at high volume. So it's like an engine in your car or something. That's how they think of a robot. Uh, even also in process automation, I think people, again the word automation is in there. So it becomes this engine, this conglomeration of metal parts that does something right. Uh, but it is almost the opposite of being ah, a human. Now we got these AI agents, uh, and we're automating the business as well. But it's completely different. It's now, I mean these things are wicked smart. They react almost as humans. Uh, you tell them, you get a response and you tell them that you wanted something differently. And they said okay, well let's change it up. Almost like a human behavior, they make mistakes. Some of them, they hallucinate just like all of us. Um, they're almost like, I think you gotta oversee them. I think, I imagine there's probably gonna be some HR department one day for these agents because you have to do one on ones with them. They have to get a behavior, uh, or like a performance review or. A lot of this is actually, you gotta think of, these are colleagues, these are people. And I think we're struggling with that. I believe, uh, a lot of people struggle with that because it's not what automation used to be. I don't know, does that make sense? I actually really wonder what that um, you know, some people come to me, they go, well you know, this gotta work immediately. Uh, you know, this is when we automate the business process. It's gotta be, uh, you know, can't make many mistakes and you know, if you hire someone brand new out of college to do a job, like would you have them do something completely independently from day one without any supervision? Of course you would not. You train them and you expect that it'll take some time and you know that after a while they do amazing things. But uh, you have to manage them. I think you have to manage these LLMs the same way.

Speaker A: Yeah, no, I would completely agree. And I think, you know, you touched upon the point of saying that uh, we've created robots to do repetitive same tasks. Now that is really easy to automate, but it's also really make easy to make it so that it fails very rarely. Um, whereas you know, when you're creating those agents, we are making this assumption that there can be no hallucinations. However, that's clearly not the case because they're learning from us and um, we're not perfect. So how can we make them perfect? And there's a big line that we are going to have to cross in a trust perspective that when we talk about agents, they are not perfect, they are never going to be perfect. And it's our responsibility as humanity to go, well, why ask the questions? Not to be afraid to challenge. And you know, you made me smile then when you talked about the HR function. You're right. These are going to need a performance, uh, review and they're going to have to be adjusted. Some of them might have to be switched off permanently. And whose job is it? Is it the engineer or is it the HR function or is it somebody else? Um, so it's, there are going to be loads of challenges, um, which are going to be fascinating to watch how they, how they evolve.

Speaker B: Yeah, I mean there are some benefits. I don't know if you noticed. I mean the one thing that, that uh, is surprising when you, when you have these interactions with like a chatgpt or something and then you give them some instructions, you don't like it and you say like, hey, can you modify this like this and this, uh, it just does it like, you know, it's not like how my teenage son, uh, behaves. Right. So compared to humans, they seem to be very, uh, susceptive, uh, for feedback. It actually works quite well. Not stubborn at all. You know, we always talked about digital transformations were all about change management. You know, we got the technology, but how do we get people then to use it? It seems like these agents, the new AI agents, they all of that, there seems to be a short circuit. Almost like, you know, you tell them to do it differently, oops, they do it differently. So that's, uh, that's quite interesting. Maybe, maybe, uh, maybe a step forward. Um, look, I'm, I'm, I'm wondering what as IT people like, what do you think this will look like? Because, you know, I told you I started with business processes. I actually then had a little bit, well, I had a career in product management, uh, building software. And you know, I was sitting there writing these requirements documents. Right. Um, it seems like today with these agents where before you had to like, almost program, you had to give them step by step what they needed to do. Now you just write the requirements document in English language and then you feed it to that LLM brain and it'll figure out the process. So almost like it becomes like product manager level. Almost like, you know, you can create a business process by writing an essay about it, if that makes sense, right? Or like writing different essays about it. How do you think about this? I mean, as an IT organization, uh, are we ready for that? I mean, do you feel we have all the right resources? You know, what do people maybe have to relearn also, uh, to make these agents work?

Speaker A: So no, I don't think we're ready. I don't think we're ready at all. Um, and I don't think it's just the IT folk who are not ready. Um, yes, you're right. It's easy to take the requirements document and feed it into the machine and say, design me the perfect process. The question we should be asking is why should we even have a process? Um, ah, so it goes back to the data and the input and the output, which is, I have got this problem to solve. Uh, let's say I want to look at a data set relating to a customer. Can you please give me, um, a view of the customer and all of the, based on all of the function functions we have in the organization and um, tell me what data those functions need at what point of time. So it's deconstructing that, that conversation. Now when I say it's not just the IT folk. Yeah, we're definitely not quite ready for that thinking. But, but you just look at the corporate world at large. Um, everything we've structured is around a cycle. You have your month end cycle, you have your governance cycles, you have your quarterly releases if you're a publicly listed company, for example. Um, and there is an awful lot of governance um, that we have put around and adhere to which is, which is based on historical data. So go back to the month ends, you know, we do the month end close. That was for the month that just took place. And it always takes a few extra days to do that and, and then the numbers come out and all of that. Um, so being able to bypass that governance model I think is we can do it, but I think it's not quite in our mindset to be able to do it yet because we still hang on to those governance models as the credibility for the organization. Uh, so how do we really change that thinking is going to be one of the big questions for the next few decades. Is, is. Is my view. Mhm.

Speaker B: Yeah. I think you made this point uh, earlier with you uh, really need to think about the outcomes of not, not about the steps, how you get somewhere, but what you want to really accomplish. And uh, I think you have to unlearn almost like what we've done in the past because we kind of, almost like we're very prescriptive of like this is step after step after step what you have to do. And I think we got to think a lot more about what we really want to accomplish and then, and not so focused on every single little detail. Uh, you know, it seems to me like knowledge management, you know, uh, how you describe. Because the agent will need to learn from something. I mean again thinking, taking the, taking the agent, again as a human, uh, almost synonymous with a human being. Uh, if they come into an organization and the knowledge around them is scattered and siloed and maybe ambivalent, I think they're going to have a really hard time becoming productive, uh, working towards the outcome. My sense is that in a lot of companies there is still a lot of this ambivalent knowledge. Maybe, maybe a lot of knowledge is locked in the heads of employees that are there. I wonder if what the AI transformation, if you want to do a really successful AI transformation, uh, almost like prepare your company to be really easy for an incoming LLM to be effective. I think we probably have to do a lot in organizing that knowledge that exists in a company in a consumable way. Does that make sense?

Speaker A: It does, it does. So um, let's, let's say you let um, an agent free on your company network and you give it a purpose. Say, say let's say there is a project, um, trying to come up with a clever name here but I'm not going to, so I'm just going to call it Project X. So you've got your Project X, you give it its instructions. You say can you go and find me everything you can about Project X? And um, can you optimize the project plan and um, the stakeholder management for me please? Now if you've got that data in your networks, yes, you can absolutely do that. What it does miss is, is then the human coaching it with that extra edge. So there's, then the human input comes into play. And you know once, once you, once you've got that basic of that information you could have the humans around that project talking to the agent and doing that knowledge share which automatically then enhances the overall information. The agent has got to give that more better information. But it does um, it does mean that you need to have the confidence to let it loose first of all and to be able to treat it as a peer, um, and to be able to question and query, um, but also to have the trust. And this is where humans, we're, we're always like this, you know, more knowledge is power. Are we going to step into the model where we also see agents as threats and we don't share our knowledge because knowledge is power and if that happens, well you've only got a half baked agents. You haven't got something that can be super powerful and help you with your work. So there is a lot of, lot of really challenging conversations we are going to have around this. Mhm.

Speaker B: It's fascinating to think about it. It's a whole brave new world uh, ahead of us. I'm curious Helena, in so if we as IT leaders are starting out in this brave new world, obviously we're not going to change the world in one day. Uh, but what do you see like you know, maybe this year or next year, where will be the early steps getting to AI transformation? Because I think you know what we're talking about, it's going to take time. Uh, but where do you see or maybe what would you recommend uh, to fellow IT leaders? Where to start or maybe also where not to start.

Speaker A: So I think looking at it and starting from it is you have to experiment with this. You cannot hide away I think that's the big big ask for, for all of the IT fork out there. This is, we, we can't hide from this so we might as well embrace it and, and move it forward with it. Uh, because tell you if, if, if every, there's people in your organizations who are already doing this, they might be doing their own free time, uh, they might be doing it on company resources, but they are doing it. So it is creating those safe places to do it to encourage people to do it. Even take the steps of promoting hackathons for people to really get comfortable with this. Um, one of the big things is also is uh, democracy in AI, um, making sure that everybody has got the capability to have AI tooling. Um, ah, and to also just not see. And this is very much as part of the new automation uh mindset which is the AI is not a tool, it is a platform. So start building the platform, thinking into it. It's not just a collection of tools that people randomly use. You need to think about what is the next platform we need to create really effectively enable AI as part of uh, part of the organization. And I think those are kind of the few learnings, experiments, give space to experiment, encourage. Yes, it's not always going to be super safe or super successful. I tell you what, neither was, neither was um, digital transformation. There's been quite a few spectacular clangers around that. So why would we expect the AI transformation piece to be any different? Um, but we have to try and we have to literally lean into it.

Speaker B: Any functions where you think um, we might see uh, the proper use of AI first I'm thinking like look, let's say I'm making cars out of sheet uh, metal in a factory. It seems to me like the good old dumb robot, very predictable, will probably still be around for a long time because I don't think you want to have someone, some AI agent come up with a new design every day or something. I mean it seems like deterministic processes will still be there. Uh, uh, but do you see any uh, functions, any areas where I think the genai is the most um, most useful or maybe most appropriate?

Speaker A: Now that's an interesting question because I actually think that there's a couple of key functions in all organizations where gen AI is going to be super powerful. Um, but both these functions by nature what they do tend to be the most risk adverse functions in the organization. And by that I mean legal finance. Yes. So both of those uh, uh, functions and the processes and the governance we've structured around them have been created to have no failures and hence why there's a lot of humans there and there's four I principles, all of this. But if you look at the processes that you have in legal and in finance, they are the ones that are uh, have been super solid and unchanged and really mature for years. So it's an interesting chicken and egg situation where the most risk adverse organizations that really are challenged by the governance are the ones that are probably most ready to have that AI revolution. And that is a significant step and a very interesting and challenging conversation for the leadership teams to be having on how do we do this? Um, uh, you know it's the step away from uh, RPA and business process outsourcing. It's the logical next step. And if we look at how many individuals we have got coming into those professions as well on a yearly basis, what does that mean to those interns? How do they learn if they can't get their hands on this and actually practice it? So yeah, that's, I think that's, that's a big corporate challenge that we've, we've got. And um, there's one thing, there is one thing that I would, I would always kind of shy away from and, and this is probably, this is a human learning rather than it learning. Um, we've got our chat GPTs and co pilots and they're quite happily recommending uh, to us on. Would you like to respond, would you like me to respond to this email on your behalf? Actually I think that's the piece. Whereas we need to keep the human element in it, make sure that we are taking the care and attention to communicate with our fellow colleagues rather than have a AI generated response to it. So yes, it's, it's, it's interesting the easy bits we are doing because we're lazy and the difficult bits we are stuck with because you know there is that risk, adversity and the 4i principle of governance around it.

Speaker B: Yeah, it's really interesting. So there is uh, I mean it's, it's, it's uh, interesting. You pick up on legal and, and finance. Um, legal. I've actually been really surprised of the advances I would say because you know the big thing in legal was always there was this legal contract like this 20, 30 page papers and it was always like in it speak it was unstructured data. A human had to process it, a computer couldn't process it. It couldn't figure out what are the structured pieces of data in there. Um, I think that gap has broken. We're now seeing that the LLM can take a 30 page contract and actually break it down into its structured pieces which is phenomenal. I mean there's a lot of things that before you couldn't automate that actually now you can. So that is cool. But uh, the finance organization I think you'd 100% right. And you know I had another guest from uh, from Bain and Company consulting company that actually said you know there is um, quantitative research that actually does say that uh, AI gen AI is underutilized in finance for exactly the reasons you mentioned which is there's a lack of trust, there's a lack of uh, you know they're very risk averse and they don't want to go there. Like the, the smallest amount of uh, variability just scares them off. I uh, think it's a big opportunity. Uh you know the one thing we're seeing which is kind of interesting is um, the most complex processes or maybe, maybe process is the again the wrong way to even think about it in the new world. But uh, you know I'll give you an example like more support kind of functions. Like we're working with a big automotive supplier, uh it's in the spare parts business and there is something around like a uh, pricing help desk of you know there's different channels, different discounts, very, very big assortment of parts and so on. And if you write down in a process the rules that exist they're mind boggling, they're just really really complicated and so people couldn't really automate it because it was too complex. Uh now with the LLMs they seem to do a really good job uh like wherever there's complexity and a little bit of uh, something that is hard to just describe in a flowchart. I um, think the LLM seem to really add a lot of value. Uh so it's almost counterintuitive. The more complex things are easier to do now with the Genai components where before it was always the, the simple high volume things. Um so uh, that's really interesting M

Speaker A: and expanding on that. AI is brilliant in a difficult, it's brilliant in the dirty, it's brilliant in the dull. Um, so it's how do we use it and what do we utilize it for? Um and we are going to be grappling with these challenges for the years to come. There's no point hiding away from that um because it is a huge transition from all different kinds types of jobs. Um, I've seen quite a few research pieces recently which is talking about what kind of jobs, um, will we'll be using or replace partially with AI and the jobs that remain are the ones where we still need the human dexterity. Um, there's a lot of manual work, um, ah, a lot of creative work. Um, that way you need that dexterity that only we can have because it's, it is just near impossible right now to replicate a human in a robotic form, um, ah, or replicate our brain in, in that AI agentic form. Uh, so there's a huge amount of opportunities, uh, and a huge amount of development challenges to have. It's going to be a longish process I believe.

Speaker B: Yeah, listen, this is fascinating. I mean it really, really really is. And I think it also shows that you know we wrote this book, the New Automation Mindset and I would say like we thought like hey, we got it all figured out after like 20 years of experience and so on and then the AI transformation, the AI revolution comes about and I think we're learning all from scratch again how to do that and I think that makes it fun. Um, and I know uh, this conversation, I think maybe it went a little differently but I really do think that uh, how to go about AI transformation is maybe the biggest question for IT leaders for the next 10 years. How to do this. Right. Uh, I have a feeling maybe we figured out digital transformation by now and there's very clear um, guidelines. What to do, what not to do. I think AI transformation, I think we're all learning together. I'm curious, uh, I mean first of all I want to say thank you for spending the time here and I think raising some really uh, interesting points and topics. Any parting kind of advice for IT leaders or anything like when we think about AI transformation, what, what you would recommend your, your fellow IT executives should or maybe should not do, um, definitely

Speaker A: should not be afraid. We've done this before. This is not the first new technology that's come around and this is not going to be the last new technology that comes around. Um, and I think all of us have got an awful lot of knowledge and experience that we can lean on. Yes, it's got a new label, yes, it's got a new badge. Not all of the things that we, we have done are going the same way. And you know, I'll give you a good example. It's uh, um, when we're doing system implementations when it was on prem compared to when we're doing software as a service implementations, yes, the gap is not quite as big in that world and the step to AI is bigger, but there's still some of the basics, basic learnings and we should not forget those because we will be making some of the same, the same mistakes around it. So definitely not being afraid. And um, I think there's the one that we absolutely need to do is to have a laser focus on AI and not boil the ocean with it. What it is currently, it's going to be completely different in six months time and completely different in 18 months time. I would recommend creating no long term plans around it, but take it every day as it comes and be ready to reevaluate on a exceptionally short cycle.

Speaker B: Perfect. No, I think I'll uh, I don't think I'm going to add anything to this. I think, I think this is really sound, uh, advice and I want to, I want to thank you for your time, Helena. Really appreciate it.

Speaker A: Thank you very much for having me, Marcus. I enjoyed it and I hope our listeners will too.

Speaker B: Thank you so much for tuning into today's episode. Please make sure to rate today's episode. Leave us a comment with your thoughts and subscribe to the show so you will never miss an episode. I'll see you next time.