Implement AI Podcast #86 - Why 90% of Enterprise AI Projects Fail (And How to Be in the 10%)

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Speaker A: AI, uh is not just enhancing the business model, but it actually will become the business model by 2030. And so that is a really different set of thinking because we talked about sort of letting go of this ability of driving incremental change and really thinking about how do you drive constant change in the way your business model operates.

Speaker D: Welcome to the Implement AI Podcast, the podcast where we explore the impact of AI on your business. I'm Piers Linney and alongside my co host and co founder of Implement AI Dr. Alex Shukler, we cover the real world applications and impact how, uh, AI can be practically applied to drive growth and efficiency in your organization. We cut through the jargon to focus on actionable strategies and use cases to highlight the transformational power of AI. Let's dive into into today's conversation. I'm, um, Piers.

Speaker E: I'm Alec.

Speaker D: Well, I've taken the time I was grilled by the, by the team at IBM, but we finally got Nicola Hodson on today who's the chair of IBM uk and she was the CEO of IBM UK and she had a very senior role at Microsoft as well. She's one of the most senior women in British technology on the board of Tech UK as well. I think what we're going to ask Raj, because we have done, we have engaged with IBM sort of socially sometimes, but also a potential partner perspective too about asking Nicola really about what's the, her view on AI obviously, but also what's her experience of working with sort of larger companies and is it different?

Speaker E: No, totally, you know, like very experienced business leader and it's great to like understand, you know, exactly what large enterprise CEOs are thinking about and talking about and so really looking forward to talking to her.

Speaker D: So if you're new to the podcast, implement AI podcast. If you're not new to the podcast then please leave a, like ring the bell on if you're watching on YouTube and uh, follow us if you enjoy the content and this, it's going to be a great chat with Nicola, I'm sure. Please leave review on Apple. So let's get Nicola in. Wow, Nicola, it's great to have you on M. We met I don't know how many times we met there, quite a few occasions. I've been down to IBM as well in Victoria. But tell us a bit about your background.

Speaker A: Okay, so I'm Nicola Hodson. It's great to be here and it's great to have uh, met you on various occasions including the last couple of trophies. I'm chair of IBM in the UK and Ireland. I'm deputy president of Tech UK and I'm also on a couple of external boards as well.

Speaker D: But your background. So you were the CEO of IBM

Speaker A: and then you were Microsoft M. Yes, that's right, yes. And uh, before that I spent a long time working at Microsoft.

Speaker D: So safe to say you want to be leaders in UK technology, especially female leaders. Shall I add that?

Speaker A: Yes.

Speaker D: So we talk a lot about AI whenever we meet and um, this podcast about the practicalities of it. I think what's really interesting about your experience is kind of moving up a scale. So we focus much on SMEs and corporates and small enterprise. We're having our first conversation with a local authority now, which would be interesting. But again your expertise and you've Got lots of data, you've done surveys and my experience of us engaging via because we have done as a potential partner is the enterprise. So how would you describe the state of sort of enterprise AI? Uh, because in our view you see a lot of big companies talking about it, probably more than SME or corporate. They've got the resources, they understand the value. If you move a dial by 1%, it falls straight through to your bottom line. Makes a big difference your share price as well. So how would you describe the state of enterprise AI?

Speaker A: If we start like at the top level, in a sense, what is on the agenda for a CEO, uh, and a board typ in a big organization there are a whole bunch of externalities, as you know. So there's the oil crisis, there are uh, supply chain challenges in chips and many other supply chains, energy prices, talent gaps. And then you've got a whole bunch of tech challenges. People are still doing digital transformation. You've got various regulators looking at cyber and making sure everyone is kept safe. And you've got then AI and a lot of external pressure on. Are you adopting? The board is leaning on the XCOM to make sure there's adoption. Uh, there's pressure from the ground up because early career people are coming into the workplace with AI skills because they're using it every day. And so amidst all of that, most organizations certainly that I've worked with and talked to somewhere through a journey of implementing AI and they've gone about it in different ways. Uh, some have just sort of let lots of projects bloom and they've tested out where have they got to. And then they've gotten to a certain point and said, hang on a minute, there are some challenges here. We need to make sure we got the guardrails in place. Others, typically more heavily regulated sectors have put all the guardrails in place first and then they've gone on to start implementing projects. But they're getting stuck at different phases, sometimes after the pilot stage when they realize either the data isn't in the right place and they need to do a bit more foundational work there, or that they want to make sure that they've got all of the governance and the governance is caught up. Uh, and then along in the middle of all of that came agentic AI and that created even more governance challenges. And so folks are pretty much all on the journey. You know, when you look at surveys, 70%, 80% are ah, doing something. Not so many have made it into genuine ROI yet.

Speaker D: And we heard it a lot. Don't we so quite a bit of research saying, you know, 90% of, um, AI projects sort of fail. And I think there's lots of reasons for that. Often we don't really know what it is. The end game is what the objective is. We find that quite a lot. And m. Do you think that in the enterprise space that, are they seeing this as a strategic sort of imperative, is something they have to do, or is it, you know, like digital transformation? You could do it now or next year or something? They feel that is a, ah, it's a huge competitive disadvantage not to do something.

Speaker A: I think there's so much activity, there's so much change, there's so much going on in the markets that it is impossible to ignore. And so I think everyone is feeling the imperative to move and to make sure they're moving quickly. Then it's about where are you going to drive impact most quickly? And have you got the skills and capabilities in your team to do that? But bear in mind, um, you know, a lot of sectors are regulated. And so they also have to think about the risk of moving too fast and not having all the right guardrails. They have to think about the reputational risk and the operational risk of moving and doing something with AI now and of standing still, by the way, because both offer different sets of risks. And so there are lots of considerations, I think not. Once you've gone beyond the pilot stage, as you think about scaling for a large organization, where's the accountability? Who can take decisions, when, how and on what? Is the AI accurate? Is the model accurate? How is the model taking the data? Is it giving you fair results? Can you transparently explain that to your regulator or to yourself? More importantly, is it drifting? Is it using good data? Is it using more energy or less energy? And all of those things important considerations for organizations, and then you've got all of the cultural stuff within your organization to think about in terms of how do people feel? Are they going to adopt what you put out there? Tech adoption's notoriously challenging and when you look at all of the press around AI, people can feel quite fearful. So I think it's also important to just take people through a measured journey of change alongside the tech change that you're driving as well. But the most successful organizations I've seen have picked a small number of use cases and they've driven them through from idea to pilot to implementation, started to see some results, convinced themselves that that's possible, and then gone back to take the next set of use cases before

Speaker D: we get Back into the conversation. A very quick word on implement AI. We provide managed AI workers through our workforce platform, an AI workforce as a service for SMEs, regulated businesses. And we have large enterprise customers too. Our highly configurable agents communicate Through Enterprise Voice, WhatsApp, SMS and email and they can use and operate across all of your systems, even when there's no API. Our interactive agents engage customers, our analyst agents surface insights and our action agents complete work. The outcome is very simple, more revenue, more capacity and um, better customer experiences. So start with a pilot to identify rapid ROI use cases, then scale whatever works. Go to implementai IO to learn more. That's quite comforting because that's what we've experienced outlook, isn't it?

Speaker E: No, totally. And I think the key thing is it's making sure that the leadership and the kind of operational management are aligned on the main stuff. Sometimes they can be a departmental priority, which is a tangent going off in a different direction. And there's no real ROI that can either be, uh, achieved from that. And it's making sure it's like, look, what are the quick wins you want to get and what will that mean for your company? Because I think horizon, planning for innovation, stuff like that used to be like, you know, different horizons you'd be looking at for like, you know, far in the future, how will things be? And different stuff. But now with AI, one of the biggest use cases we often see is what are all the insights you can get from within the data. Uh, like that you've already got that the things that you were missing you couldn't see before. And often we find some of the biggest wins are in doing things that people aren't even doing right now, actually, like do a bit of exploration and seeing first and then you can surface some of those things when you, and then see them through to production.

Speaker A: I think that's right. And then I think there are a lot of use cases that are well proven by now that are relatively, uh, straightforward to start on the journey. Now the journey can be easy or more complex, depending what state your tech is in and your data. Um, but the things like in the back office, you know, automation of operational hr, finance, admin, um, routine ops, those sorts of things, coding some real advantages to be gained that can be gotten right away. And then to your point, how do you use the AI to give you better insights? Generally speaking, the employee experience is much better when you're able to take away some of that routine work and give people more insight and allow them to apply their Judgment much more enriching in terms of job satisfaction.

Speaker D: And what about IBM? I mean we're a micro, micro, micro microcosm of IBM. So you've got this idea of, because I've seen you've got a platform and then you've got services. So you've got everything from almost like infrastructure and they've got an energetic platform to services. And what is it your, what is the main approach of your clients and customers? Is it platform or is it buying these services? Is it buy or build?

Speaker A: We have done a lot of work to adapt the way IBM operates itself as client zero. And to do that we've started on a journey some years ago. But looking at how do you take out operational complexity? I don't know depending sort of what sorts of organizations one works with. But organizations build complexity over time. They do. More people want to be involved in decisions, decision chains get longer, bureaucracy builds up over time. So you have to start, I think with taking away that operational complexity and looking at how do you simplify the workflow end to end rather than tweak little parts of it, which are the tasks that manual that you can automate and then how do you deploy AI across your operations to get the additional benefits. So that's how we've gone about improving productivity. If you improve productivity, of course you can free up capability and funds to drive more innovation and growth and that is what you're trying to do. So we've gone about it in the way most big organizations would in terms of having a small steering committee, a project office, a team that does discovery, small bringing in the business technology, um, the relevant functions and just looking at is there something here, can we evaluate it quickly? And then how do we use that to create something new and empower people to really start to do the work of eliminating the unnecessary steps, simplifying the processes and then automating what they do. But you have to create the capability in the flywheel to be able to do that. Oftentimes you have to be able to benchmark yourself externally. And you're really looking at how you rethink the organization to drive those savings and at the same time deliver better employee experiences. So if you then apply that across your own business, that supports you in taking the services and the platforms to market and they important to our customers as well. Um, IBM's built its strategy around AI for businesses, hybrid, cloud and latterly you will have seen a lot of news around Quantum and that's the way we're going forward. So that's what we're looking to help customers with. And it's super helpful to be able to talk personally for all of our employees in terms of the examples they've seen and how it helps them to do their work in it.

Speaker D: What's your view when we're made at Anthropy, which is like, if you haven't made an anthropoc, it's like the UK is kind of Davos down in Cornwall. Every year about 2,3000 people turn up there and talk about building a better Britain. We talk about technology quite a lot. And you mentioned there, quite interesting, which is about the fact that you see all this news now about the frontier models and is the US government going to sort of block them? And he made a point about small models that in most cases other organizations could use, even like you're saying, a hybrid cloud or hosted technology models which do the job but don't need to be as powerful as some expensive frontier model.

Speaker A: Yeah, I think if you, you and I will use AI at the weekend or during the day, whatever, to do clever things, silly things, whatever things you might be wishing to look at. And oftentimes you're using Internet scale language model, like, you know, pick any one of them. In most enterprise applications, you don't need a model with all of that data. Uh, you don't need to know the order of the presidents of the United States, the length of the Niger Delta, all of those things. It's not really relevant to most enterprise applications. And so one of the ways you can control the energy costs, the time it takes, the size of the amount of data in the model, making sure the data in the model is validated and indemnified, and making sure that you are able to control the model and manage its drift, et cetera, is to have smaller models just bespoke for the job in hand. The industry is moving more and more in that direction, I think, in my view. Uh, so if I take the example of procurement or hr, uh, what do you need to know in an HR scenario? If you think about your employees, you need to know what are your HR policies and procedures. Your model needs to know all about HR language around those policies and procedures, and then it needs to have been trained on that. And then you need to know about the different laws and HR regulations in the country that you're operating in, or multiple countries, as many businesses do, and that's probably it. So you don't need all of the rest of the world's data. You can do something small, bespoke, fit for purpose for that scenario and that you can get off the ground pretty quickly and you can be more confident that you're not having to worry about all of the other data that might be in the model. So that I think is a different way of thinking about enterprise applications.

Speaker D: So it's an outlook, isn't it? We're looking at obviously different models, also hosted models and then fine tune models because now you can fine tune models and uh, it's not expensive as it used to be and even I don't know if you've heard of this yet. But fine tune models which you then compress using, they call it quantum inspire technology.

Speaker A: If you take a model and you know the provenance of the data on which it's trained, and then you have that operating in your environment, whatever you consider to be your environment might be cloud, it might be on premise, whatever, and you put your data into it, you're much more confident about what's in there and you can fine tune the model to be very fit for purpose for what you're doing in, in your business. So yeah, I mean the innovation's crazy fast, isn't it? You know, the chips, the quantum, you've got the AI of models evolving, you've got the shape evolving, you've got agents popping up everywhere. I think what's not moving quite so fast is organizations ability to govern all of that. Um, you know, where are all these agents? Do you know what they're doing? Do you know what data they've got access to? Are they sufficiently bounded? How do they communicate with each other? What are they doing with the humans in your business? And how do you kind of manage all of that in a quite complex environment? And then how do you at the same time completely rethink the skilling and the way you're building your organization and your performance management and your culture around that? Because it's quite a complex set of changes I think beyond all of the tech and most enterprises can't absorb the pace of change in tech. So you know, interesting, very interesting times,

Speaker E: that's the main friction. So like I think when, if, when organizations like collaborate with IBM to like lead their AI transformation. Do you like is the first thing you start off with a whole project to like help identify what can be AI first, what can't be and taking everyone through a journey because you mentioned the questions about people understanding where their agents are. So what we actually find is many organizations, they just almost stuck in, you know, analysis paralysis basically. Right. So it'd be great to understand how you almost like you guys like lead, lead through that and what kind of education or programs or services you guys take them through.

Speaker A: It sort of depends where you, what the start point is. So yes there are programs of work that are big end to end transformations. There are others which outsourcing, um, chunks of capability where the AI might be applied in service centers and then there are others where you might be working on, I don't know, how do you govern, how do you build a governance layer that's appropriate? How do you orchestrate all of this AI? How do you knit it together in your systems? Where do you want to run it? And so we would be working with either our own consulting team or other partners across um, across the partner landscape to implement the tools and techniques. Customers will sometimes have tools that they're more familiar with or partners, tech partners that they particularly want to work with. So it's different environment by environment. But um, you know, I would say most big organizations are really rethinking how they operate and some are doing that function by function or process by process. And others are thinking more end to end, like how do I completely transform this business? So you see all of those different forms.

Speaker D: In terms Nicola, you've done, you've got some stats you mentioned before we started, special numbers. You do research at scale. So what's that found?

Speaker A: It's really interesting. So when you look at, I like to use like two or three different studies, but we have something called the Institute for Business Value and it surveys CEOs and typically a thousand plus CEOs on a global basis. The one I particularly like to start with is, uh, what's the organization, what's the enterprise like in 2030 and it was published in January this year. And it looks at AI as not just enhancing the business model, but it actually will become the business model 523. And so that is a really different set of thinking because we talked about sort of letting go of this ability of driving incremental change and really thinking about how do you drive constant change in the way your business model operates. And um, it looked at 2,000 different executives across all different industries. And it came with five conclusions which I really, really like. First, nearly 70% of execs think AI is going to remove some of the resources and skills gaps that they've been facing for the last few years, which has been a pretty consistent theme. Second, innovation is a competitive currency, so you have to be able to innovate. And that's where most CEOs think their competitive advantage is. Coming from. Third, you cannot deploy tech just to drive efficiency. It's got to drive efficiency and um, it's got to power continuous innovation. So to think, start to think of it as more of an engine to drive that efficient organization. Fourth, it's quarterly. No one's going to wait 12 months, two years for a big transformation program. Board isn't going to wait. A CEO isn't going to wait, and nor is your industry standing still. So again, away from the incremental, being bold in terms of how do you drive disruption. But the final point, which I really, really like is speed equals strategy. And in that scenario, how do you execute faster than the competition? That becomes a real challenge because most organizations are not that used to moving fast. They've been built for a different era. And so you're looking at how do you really rebuild your organization to move at the pace of AI?

Speaker D: It's also Locke always says organizations that uh, they struggle to move very, very quickly. And the point is, are you finding that this speed to scale is there? What we find is organizations, they don't really know where to start. That's the first thing. And then when they do start, they often they pick the wrong kind of use case. And one we find there, which in your report you talked about, it can't just be about efficiency. What we tend to find focus on is it's actually revenue, isn't it a lot?

Speaker E: Yeah, like revenue or it's like, you know, some customer capacity, customer support capacity or some issue like this. But like the key thing that we're always trying to look at is like, how do you just drive your, you know, your P and L to be better, basically, right. And where is the underutilized value? Like where are the inactive customers? And like I had a conversation with somebody today that we were talking with and like they've got, you know, very successful business. But then I said like, you know, do you want to drive sales? Actually our team is like overworked right now because they're operating so many systems. There's so many frictions in between those.

Speaker A: We.

Speaker E: What I actually want to do is remove some of the those frictions first of all. And so he talked about actually assistance for the team where they, if they need to order something, they just talk to the AI and it knows the answers to everything and rerouting stuff. Something I wouldn't generally give as a high value, basically. But for him it was like, actually I want to like take the team down intensity notch. Like that's like where one thing would be but then the other thing was like, right back in our wheelhouse is like, I got all these people that have opted in, but they're not buying from me. I'm like, yeah, great, let's, let's focus on that. Let's reactivate some of those people as well, right? Because then you can improve your, your wins. And also what I like about the, the employee experience, you can also prove the utility to them. So I think those two things can be quite a nice compliment where you've got something proving revenue and then the other thing, proving employee experience to bring them along the journey. Because I have seen as well, you know, we obviously deal with much smaller organizations, but there's a lot of friction. And sometimes the friction is like, because they're scared, they don't know what's going to happen and where's this going to go and they don't want to have things in. It was funny actually. We were sat outside of Piers, we were outside of Liverpool street station, isn't it right. And an analyst after our event, I think it was like last week in London and this guy walked past from a bank, he goes, you already worked for. See our T shirts implement AI. And he's like, uh, then at uh, the end of that, we were saying that okay, if you want to drive transformation in your organization. He goes, actually no, I don't because I'm an analyst and it'll take my job. So no, thank you.

Speaker A: It's interesting what you say. I mean, I think there is friction and the use case choice is really important, especially the early ones, because you have to prove it to yourselves and your board that you can actually do this stuff and drive some kind of an roi. It might be revenue, it might be cost reduction, it might in some cases be, be both. But, but to be able to do that and build the confidence. And to your point, they're often really simple things. So insurance claims, loads of data comes in, right? It's in different formats. People scribble things on pieces of paper. They might send an email, they're expressing sentiment. You might want to know what was going on at the time, what were the weather conditions. So in a claims agent analyzing all of that data is quite complicated. So uh, it's a use case there where AI can really, really help just taking all the data, summarizing it, putting some context around it and giving in the hands of the agent, then a set of data or insight that is much more decision ready. The agent's still taking the decision at the end of the day. But they're not doing all of that work of pulling the data together from many different sources. And you know, you make a really good point. Not every individual is working with a set of systems that are joined up in the first place. And so oftentimes your use cases might be just to sit across systems that don't really work that well, that make life much easier for people.

Speaker D: And do you find that they understand that like, speed is a competitive advantage? Because I picture in my mind, right, that uh, it's always been that SMEs do the small, difficult stuff and big companies do things at enormous scale. But if the large enterprises really get their hands around AI and understanding, do transform their businesses, they can almost come down to where the SMEs are playing. And then you always say that, oh well, the SMEs now got the power to go after the big companies. And I think it's that a battle for the middle ground. Do you kind of see that?

Speaker A: I've not really seen that unfold yet, but I think, you know, we've been talking about digital transformation for years, haven't we? And that's sort of been able to operate in that nirvana. So I suppose what you're talking about is an extension of a trend that's been there already. If I think about myself as a consumer, how do I want to engage with technology? And I'm going to engage with brands, I love brands, where I have a great experience as a customer and I'm going to be pretty selective about which ones I choose to work with. So if you make it easy for me and um, friction free, I'm much more likely to start to build a brand loyalty with you. But that then comes back to the point you were making earlier, which is how do you improve the customer experience, the front end of the business, you know, can you be more effective in your sales leads, your follow through, the way you appropriate what your customer wants?

Speaker D: Can you get like five minute callbacks? When we were working on with somebody, can you engage straight away and personalize it?

Speaker A: And that's going to feel like a really fantastic customer experience, isn't it? And so I think if you can crack that sweet spot of how you engage with customers in a better, faster way, then then you're into some really interesting space. However, I would say, I mean, I've been in the tech industry for decades and the products and services you design for big enterprise are not the same as the products and services that small businesses typically want. They're too big and rich in terms of capability, functionality, cost oftentimes. And so there probably is a sweet spot. But I think it's really important that it goes all the way through from product design to customer experience. Not just um, you know, can you engage with different customers in a better way. You've got to have a product or service or both that is fit for purpose for what, what that customer actually needs. Because we're all a lot more discerning than we used to be.

Speaker D: And it's going back to the importance of yeah, we kind of skipped over really skills. So we have, we have the challenge. You know you're smaller companies, you've got people in there that are very technically proficient and they want to dabble and build it and do it themselves. Management is saying don't do that, we need someone in here as their core competency. But then you've got the skills of people just understanding what the technology is and how to use it. So I know at IBM you've sort of been involved in the government program as well, but also as well as that, what have you done internally? Because IBM is a big organization, it's got legacy but you're also trying to operate this frontier and how have you sort of bridged that?

Speaker A: So a few things skilling if you think of it at ah different levels, uh, what needs to be done and then I'll come back to what we've done but we have something called IBM Skills Build and is part of the government initiative to reskill the population as well. We're looking to skill 30 million people globally by 2030 of which large number in AI skills that's all free learning available. We often partner with organisations like the government and others to make that available to people and we can all go and do that ourselves in our spare time with our employers support et cetera. And all of the tech industry offers similar training. So that is available out there for folks to get curious and get, and get stuck in. Most organizations including ourselves have then rolled out AI to their workforce across the organization and that again gives people the ability to get hands on, see how they can use it, test it out, test out whether it's valid in certain applications related to their job and just get a little bit more comfortable with it. That helps people get over the fear factor. Uh, we run something called every year the what's next challenge which people form into teams. I think a team is about up to 10. We have, I think we had 170 odd thousand people engaged in that last year. They take a challenge, they use AI to Build a model and address that challenge. And then there's a, uh, I mean, it's just like a giant hackathon. So those sorts of things can get people hands on and you can learn alongside engineers if you're working in a team with engineers, et cetera. So that's another good way. And then leaders and managers in the situation where they're often trying to encourage people to use tools, new tools, new AI. And that is, um, my experience, best done by role modeling. Here's how I did this. Have you tried it this way? Have you looked at this and, and just helping people to adopt that technology so that it's easier to use. I think if you're curious as a human and you go out and explore what's possible, like, it's pretty clever stuff at, uh, your fingertips. And so, you know, we talk about agents, but if you've done some basic stuff in Claude, you've probably built yourself an agent. You know, I was talking to one of my sons last week and they were talking about what they've been doing to do their exam prep and they're basically building a couple of agents to help with that. So there are very easy ways, I think, to help people get skilled. Some of it's about personal curiosity. Then you go back to how do you think about learning in your organization? And so a few years ago, before I came to IBM, I had the pleasure of driving the global transformation in sales, marketing and operations for Microsoft as big organization, many, many countries. And you know, one of the things I think is really important is, uh, you make learning a priority. This is consistent across the technology industry, by the way. Learning is an absolute priority. You help people create time to learn. Because if it's all squeezed into half an hour at the weekend, it's, that's not maybe the best way for people to do that. So different countries took different approaches. Some did a learning day, some did a learning half day or some got everyone together and they all did it as a group exercise or they did a big hackathon. But making that space for people to learn, to understand and to, you know, a lot of people like to get their LinkedIn badges, et cetera. Um, so that's important too. So you've got to make it an absolute priority.

Speaker D: And also, Nicole, what I try to get across people is the fact that it's not an IT project, it's not a course. That's the end. If it come back in three years, this is the big change now. This is going to never end. Um, and it's how you sort of build that into your business, actually.

Speaker A: Yeah, exactly. And what you really want is people who are curious. And then you've got to think about. Because, by the way, once you've got curious people, they're going to help you redesign those processes because they're going to come from either a sense of possibility or frustration. And um, if you co opt them, it's much easier to drive change. But then you need the culture and the systems to be up to speed with what people are trying to do, otherwise you get frustrated. Right. Another of my sons went into the workplace and the capability of the AI that being used by students and stuff isn't the capability you get in most workplaces yet. So, you know, you've got kind of pressure coming up from the bottom with all of these like curious, uh, early career folks who are doing this stuff. Not all of them, but some of them. You have to make sure you're a good employer for people like that who can bring in that ability from the get go into, into the business. Then you have to think about how do my human systems need to change? How do I evaluate someone who's working with AI versus someone who's not? How do I evaluate what sort of judgment and creativity is going in and critical thinking skills and how do I even train people in that world? I was talking to, uh, someone running a legal firm last week and I was asking how is your business changing as a result of AI and how are you thinking about juniors? And it was really interesting because there are legal tools that we all know that are doing a lot of the um, research, uh, that juniors would previously have done. In that scenario, then your job as a leader changes, right. Because you're then helping people to understand. Okay, here's what the research is saying now. Here's how I'm going to apply my critical thinking and judgment. So the demands on leaders actually are going up because you're going to have to train people in very different ways. You're going to have to think, rethink how do I evaluate them, how do I performance manage them, how do I create a culture where they can challenge. How do they know when they can challenge AI or not? Have I thought through my decision cycles? There's quite a lot of.

Speaker D: It's also about attracting talent. Yeah, attracting. My view, attracting talent is that unless you're doing all of that, you're not going to attract the best talent either.

Speaker A: You are not. No, absolutely not. No.

Speaker D: Right. Nicola, we've had you for nearly 40 minutes. We know you're busy. So let's end on a question then. So, you know, if you advising a, uh, business leader, business owner, it might be an SME or it could be a large enterprise, you know, about what they should be doing next. And they're kind of looking at it, thinking they may have run a few pilots, they're not quite sure yet. What would your advice, uh, be? And this is free advice for anyone listening from the chair of IBM uk.

Speaker A: So listen, if you've not got started, get started. Put your ground rules, your guardrails in place. First, pick a use case that's straightforward, which is either something that's high volume and high pain and relatively repetitive because it's an easy one to tackle. Get your team as part of that and if you don't have the time and the capability in house, find a really good partner to work with because, you know, many businesses don't have that if they have that spare capability, they'd be doing different things anyway. And so that would be the sort of line of thinking I would go through and just prove it to yourself. And once you've done one, the brain starts thinking, oh well this could apply over here. So you then start to create a flywheel. But there's nothing in the way of getting started. And for folk concerned there is still so much out there on Instagram, substack, all the other platforms, you can educate yourself. I would, you know, if you do absolutely nothing else, educate yourself and stay up to speed because that currency is really, really important and you start to spot opportunities and can then bring them into your business.

Speaker D: Well, Nicola, it's been amazing having you on. It's fascinating. It's comforting hearing that everything you're saying kind of resonates a lot, doesn't it? What we've experienced actually of what we say to our clients and customers as well. So thank you again for having you on and I'm sure we'll bump into you again soon. Thanks for joining us.

Speaker A: Thanks guys.

Speaker D: Great having Nicola on. I have to say that I'm glad to say alok that almost everything she said, there's some differences with big companies. Everything she said resonates with what we've experienced, what we've learned and what we're saying to our customers, isn't it?

Speaker E: Absolutely. I mean ultimately it's the same, it's just on a much bigger scale. But it's very good to hear and share some great insights around, you know, looking for like where the opportunities are basically.

Speaker D: Right. So it's quite, quite a long episode, but some great insights there. Whether you're an SME or a corporate or a large enterprise. I, uh, think we'll have some more large enterprise, um, guests on as well. Thank you for listening and we'll see you at the same time in about two weeks.

Speaker E: See you next time.

Speaker D: Thank you for joining us for this episode of the Implement AI Podcast. If you're interested in learning more about how we can assist you and your business in leveraging AI for growth and efficiency, visit our website at ImplementAI IO. Don't forget to subscribe to the Implement AI podcast on Apple, Podcasts on Spotify, or wherever you listen to. Stay updated on future episodes. Thank you for listening and we'll see you next time.

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