Episode 103: How AI can supercharge your 2025

Speaker 1  00:02
You welcome to the CIM Marketing podcast. The contents and views expressed by individuals in the CIM Marketing Podcast are their own and do not necessarily represent the views of the companies they work for. We hope you enjoy the episode.

Speaker 2  00:16
Hello everybody, and welcome to the CIM Marketing podcast. And happy new year. And to start this new year, we have a big brand, the big blue. We are joined today by IBM, and to join us, from IBM, very excited to say, are Emma Flinter, who is marketing transformation lead at UK and Ireland, at the big blue, and Jay Trestain, global lead for the intelligent content supply chain and marketing transformation at IBM, and also a tech women 100 winner. Jay, Emma, how are you?

Speaker 3  00:51
Thank you, Ben, thank you for the invitation to be here. Yeah, fantastic. Delighted to join you. 

Speaker 2  00:56
It's great to have you on the show. And what better topic, topic of the moment. Only yesterday, we saw the prime minister himself talking about bringing AI into public services and how he said it was going to transform those services. And was met with some enthusiasm and some skepticism, some agnosticism. People really don't know where this thing is going or where we're at with it. So I'm going to ask you the questions on most marketers lips. Is this still at the testing stage?t

Speaker 4  01:26
I think it depends where you look. So what we're seeing across industries is a lot of very thoughtful experimentation using AI and generative AI to really understand what the potential could be in a production environment aligned to marketing and marketeers also taking that experimentation actually making it real. And that comes with different challenges, depending on the approach organizations take to this kind of technology, not just necessarily from a marketing standpoint, but from a technology, security, privacy, Ethics and Standards perspective. So I think we're seeing a mixture of very intentional, thoughtful experimentation and also very real and tenacious attempts to scale into production, the approach of which varies depending on the organization landscape you're operating in.

Speaker 2  02:25
And I presume, with the kind of technology we're using, there are an infinite numbers of AI,

Speaker 4  02:29
yeah, new, new stuff comes out every day, which makes it both exciting and understandably for some really quite intimidating. So it's a rapidly evolving space,

Speaker 2  02:39
if we are talking about the difference between implementation and experimentation. How is IBM utilizing AI itself in its own marketing in terms of implementation stuff that it's already using?

Speaker 3  02:53
So that's a super question for two reasons. One, because I can share with you exactly how we're using it ourselves. And secondly, because, in essence, what IBM marketing has been doing to ourselves in terms of how we transform as an organization, and in terms of how we engage with clients, our ecosystem, our partners, we've been on a journey for the last couple of years where our IBM IX consultants have been a partner to IBM marketing to help them on that journey. So you're getting a combination of, there's some new technology out there, we're looking at it, we're going to explore it and see what happens. But you're also marrying that very much with, what are the data requirements to be able to take advantage of the promise or potential of AI. What are the process and workflow changes that can be accelerated or supported by AI? And in some cases, you know, more traditional machine learning and basic automation that will really lead to some outcomes and outputs. So you know, at the very exciting, sexy level of generative AI, we've had really incredible experience testing how, for example, Adobe Firefly, in terms of visual assets, can be weaved into a content creation workflow. To look at the creative end of that experience, that whole experiment for us, which was just at the same time that Adobe launched Firefly, resulted in our first pilot of how we would take advantage of visual generative AI capabilities in a campaign around the masters. Many listeners will hear about IBM more in the context of how we partner with lots of large sports organizations like Wimbledon around the world. So this was the golfing masters, and we used Adobe Firefly to help generate visual images, and then also Watson x to a, b test. Text, how both the images and the text with the images would perform in a social outreach campaign. And this pilot, within two weeks, had delivered back 26 times engagement in the social campaign that we had generated in collaboration with the generative AI tools and our own large language capability as well. So as you can imagine, when a pilot, in and of itself performs at that level, people get really excited. But what it also did was of that 26x increased engagement we were hitting and getting into marketing qualified lead status with C levels in new accounts that we wanted to reach, and existing accounts that we already have relationships with, that we have not interacted with before. So a visually engaging, well tested social campaign gave us a line of engagement and permission to reach out to further with senior level stakeholders and organizations outside of your traditional CIO CTO type space.

Speaker 2  06:11
It opened doors, Jason, it reaches parts that traditional marketing has not been able to reach 100% and

Speaker 4  06:19
I think this is where we get really excited about generative AI and try to steer people away from the kind of shock and all headlines where people, you know, there's concern about loss of jobs and placing humans, and actually, where we're seeing the most impact and effect of generative AI, particularly in the creative space, is to reduce the overhead of non strategic productivity, sucking tasks, ideation is a big one. Management of time is a big one. Versioning of creative assets for various channel uses, all of this stuff that is timely and costly can be sped up, effectively, reduction of overhead, freeing up people's time to spend doing, actually, what it is they love doing, which is being truly creative. And for us, that's a real win, and something we're leaning very heavily into, rather than completely eradicating job functions,

Speaker 2  07:16
what you're saying is it's killing the mundane. Let's be let's be honest, all marketers, all professions of all kinds, have a certain degree of mundanity in in them, you know, even if it's, you know, arranging schedules or, as you say, handling versioning control something pretty much everybody does. And what you're seeing is that this is killing the mundane. I have to say. When I heard Emma, giving that outline earlier, I thought this is fantastic and very exciting, but a lot of marketers who are listening to this show might also find it a little bit scary. And I take your point about killing the mundane, but does that also, in the near term, kill some junior marketing jobs?

Speaker 4  07:59
I think it's a point about evolution in the same way any job function evolves with the advent of some kind of productivity tool or technology enabler, I think that everybody needs to recognize the movement of the industry and adapt to that. So I think if there is resistance at those more junior marketing levels to embrace the technology shift that is happening, then naturally there will be a relevance question. But actually, my experience is those junior marketers who are coming into the industry are embracing at a rate of pace with real excitement, because actually for them, it means they're not going to have to do those hard yards of graph that traditionally they would have had to do to prove their capability. There they, you know, they're jumping two, three rungs up of the capability ladder with the technology and tooling that's available to them. So I think, you know, Ben, personally, speaking, it's a matter of perspective. If you're embracing what's coming, then you're going to have the best possible time if you're fighting against the tide of change, it's going to be hard. Of course, it's

Speaker 2  09:05
interesting, though, isn't Emma, the term was interesting point that Jay's making that actually what you're seeing is that people coming in at the bottom rung, the junior rung, tend to actually be, if anything, more enthusiastic about it. Because instead of doing a lot of the donkey work that marketers would have had to do two two or three decades ago, they're immediately into that creative space, which is presumably the admission of most marketers, is to be in that space.

Speaker 3  09:27
Well, yeah, so as a marketer of three decades experience, you know, I liken this moment to Marlon Brando in the old movie of on the waterfront, where he says, I could have been somebody. I could have been a contender, because for me, I've lost so many hours of my life looking at spreadsheets. So you know, you know, the reality is that, you know, personalization is so important to consumers and to whichever end user, whatever industry you're in, they want to be engaged with in the best way. That they've experienced it from any other industry. And the reality is we as marketers, regardless of where we're working, we simply cannot keep up with the demands of what's expected of us, at speed, at scale, at quality, within brand compliance requirements, within regulatory requirements, etc, and at the same time, manage the volume of those tedious, horrendous distribution list cross checking, moving from really, you know, really simple so called personalization, which is, you know, not simply able to get my name right. So, you know, the level of the demands are absolutely massive, and we as humans will not be able to keep up with that. And then, equally with the application of all of these really great digital engagement and social media and other types of I keep hearing about how you know that the likes of your virtual reality is now going into a whole new level of in store experiences, etc, etc. We need these tools in order for us to be able to realize our potential. And the place where I see that most frequently, eagerly and natural talent wise embraced is at the younger cohort, and then from an older cohort, which I now, you know, have to put myself into. I'm bringing context on experience, and looking at, well, what ways will we help our younger colleagues to appreciate some of the lessons we did learn from the way that we did marketing in the past? However, I started and the internet was a thing, and now young people are starting and generative AI is a thing. And you know, in 1520, years, who knows what the next thing will be, but like this, is something that will give us an ability to not just keep up, but move ahead in where we want to differentiate either ourselves or our brands or our companies.

Speaker 4  11:52
And just to build on your point, Emma and Ben, actually, just to a slightly different way to answer that question, and shout out now to some incredible young professionals in the IBM community that have recognized and embraced what's coming with generative AI and created a generative AI society where young professionals come together to hive mind learning and by bringing experts In and clients and partners and that level of proactivity and tenacity, in my experience, is not limited to IBM. There's a hunger from young people coming into the workforce to differentiate themselves, to be competitive, and the tools, the organizations, the support, is there for them to do so. And you know, congratulations to the team for what they're doing. It's an incredibly um supported and very thriving community. And my experience is that's not a one off. There's lots of those pockets of ambition to be front of mind and front of the transformation that's

Speaker 2  12:53
coming. It's interesting, isn't it, because every big industrial change actually did not destroy the jobs that people said it correct. Revolution didn't destroy jobs. Video did not, of course, kill the Radio Star. We're doing a radio show now. So the positive outlook, the positive vision, is that this won't kill jobs. It will increase creativity. It will increase people's capacity to do interesting things and reduce their need to do mundane things all as Emma puts it stare at spreadsheets all day. It will, of course, or it should clear time in people's calendars. You know, if you're a marketer and you're saying, Well, okay, what do I currently do Monday to Friday? I'd say maybe a day and a half of that five day week. I'm doing the mundane stuff we've just slagged off for the last five minutes. You can expect with this technology, from what you're saying here, to get some of this time back. And if you're a marketer, and you do get that day, that day and a half back, what should you be doing with that time?

Speaker 4  13:50
Well, I'd say for me, Ben, we've gone straight in with that time saving with generative AI being the answer. Actually, there are steps CMOS can take before even deploying the generative AI silver bullet, because the reality is and it goes back to that brilliant opening question that you set you asked me, which is, you know, are we implementing? Are we in experimentation? And the reality is in our experience for enterprise level CMO, in order to really harness the potential of generative AI, you need to get your foundation layers in place first, and things like optimizing workflows just by defining best practice. What's the gold standard for intake request and who, and how should the data quality checks be done and who? And how do we approve things, and can we reduce any miscommunication through using a work management platform like Workfront as an example? So there are steps to take that would and does dramatically free up time to be more productive before you even need to pull the generative base. AI trigger, and actually, by getting those foundation layers right, either in parallel or ahead of when you are ready to move from experimentation into scale with generative AI, the aggregated benefits are far greater. And I'll give you my favorite but most boring example to highlight this point, one of the great non strategic spends for CMOS is the creation of derivative assets, that is the vast number of variations of things to fulfill the variety of needs of, you know, formatting channels, etc. Currently, more typically, that is done by agency, by physical intervention, and the technology, as we know, is available to do that in a fraction of the time, at the fraction of the cost.

Speaker 2  15:52
I love this example because it resonates with me. Going back a few years. I remember the amount of time we used to spend on agency side, the content agency turning double PDFs into single PDFs, and vice versa. And whatever that, whatever the client wanted, was always, always the opposite of what you'd send them. Exactly right. How many hours in a year we used to bin basically doing those conversions. Yeah, he's cringe worthy, but that was the reality. Then, of course, exactly. It's easier to do now, but there are still so many of those similar examples. I don't think it's all I think it's it is mundane, but I think it's interestingly mundane because it resonates, you know, turning a square asset into a rectangle asset.

Speaker 4  16:40
But behind that technology, though, there's an even more mundane point, which is the metadata associated with those assets. If you know your aspiration is to have an automated and intelligent system for content production and distribution, then you know you need to have a robust taxonomy that enables retrieval of pre existing assets from a single asset management system. And if you don't have that in place, the wheels fall off it before you even get started. So that's what I mean about you know, it is non strategic spend, but large spend and a really boring starting point. But if you get those foundations right when it comes to wanting to scale content production exponentially, then you can all of your capacity increases in line with that. So it's the steps to take prior to the generative AI Silver Bullet isn't

Speaker 2  17:29
your direct the foundation right before you go into the sort of generative generation, because otherwise you won't get the full benefits from it, time savings and vast cost savings to be made on really boring stuff, and we love doing

Speaker 3  17:47
it as well, because, you know, there's a lot of stuff, so that's the boring but high impact, high cost saving value approach. Then there's the impact on personalization, as experienced by your end user, because these variations and derivations actually give that personalized experience, which has been very challenging to author once publish anywhere, customized and tailored to you. And then on the other end, we as marketers have been focused on this holy grail of personalization since, you know, before the turn of the millennium, and we've been aspiring to it, ambitious about getting there, and talking about it to such an extent that often people say, I'm sick of that topic. It'll never happen. We're never going to get there, whereas now, because you have to lay the foundations correctly in collaboration with your data colleagues and your security colleagues and other parts of your organization. The not sexy, but very important work that was very low and down on the priorities list, very low on anybody's backlog, has now come back up to the surface, because if you're going to take advantage of a brand, large language model, all of these various different tools that you're going to apply to be able to add visual elements, etc, etc, and have all that meta tagging and taxonomy that Jay just talked about. You need that data foundation in place. And then the other thing too is, you know, I was around for the marketing platforms revolution, which, you know, back when it was Scott Brinker and his, you know, 500 the 5000 that's now in, you know, the multiples and the multiples and the multiples, he's totally changed how he's tracking. It's happening year on year. And in with Mark tech, otherwise known as frankenstack Spaghetti Junction, with whatever way you want to look at it. Now we're in a place where we can actually get to a destination that we've had in our sites for a very long time, but only if we do some of this grunt work, which can also be accelerated and sped up by the way that other parts of our organization use AI. It's just bang for our buck on both sides, and that's

Speaker 2  19:59
takes real leadership. Ship, isn't it from the CMO to say, look, we want to go for this stuff. You know, we're sick of the donkey work as well. You know, we know you're all sick of the donkey work. Talking to the business. In order to get benefits from this, we've got to make these necessary changes now, so the things that Jay and Emma have just described can be done and done quickly and efficiently and elegantly once we bring the technology forward. It's quite a hard thing, even for really good CMOS, to get across to a business, isn't it? How do you explain that? How do you explain that sort of need to get the strategy right. Need to get the foundations right and the nuts and bolts of the business right in simple terms that sort of non marketers, non AI departments, non tech departments, if you like, can easily understand and get on board

Speaker 4  20:41
with talking numbers. So our recommendation to our CMO stakeholders is always to start with a business challenge that you're trying to solve for at macro level, signings. Increase revenue, increase GP, increase, whatever the mission is, start there, then from an approach and implementation perspective, make sure your early phases of embracing this kind of transformation directly correlates to those big KPI numbers that your CEO and your other colleagues will find important. Then benchmark, how did you perform before any form of transformation, and how is the transformation providing improvements that directly line up to those big KPIs. Once you can build that kind of advocacy and momentum through showing not telling, then it becomes a much easier conversation to have. But the reality is, is that Emma's already alluded to in terms of liaising and and aligning to different colleagues in your organization, the potential for transformation usually means needing to go beyond the boundaries of what traditionally was start and finish as a CMO budget or sphere of responsibility. So that does require leadership, as you say, Ben ambition and a clear goal that your senior leadership team can unequivocally get behind. Because it becomes incremental and hard. This stuff is hard to do, but if you can stay clear to a North Star vision and prove and show rather than tell, everything else becomes a lot easier.

Speaker 3  22:22
One of the examples I loved from one of our clients, and then also from our own internal organization, for the additional benefits that they are demonstrating, whether that's greater engagement or a higher performance of a particular campaign in market, or just, you know, the ability to fine tune and adapt on the go your content and your campaign in order to perform better, rather than wait for six months for the results and then say, well, I would have done it this way had I had these insights earlier. We are seeing our teams where, yes, you're achieving the savings, but part of it is a bit of a risk reward and incentive, because we're seeing marketing then be given budget back to invest in further areas that they want to apply generative AI to, and investment in skills. When you have the capability at your fingertips, you need to know how to take advantage of it, and for the most part, marketing platforms and other digital platforms, investments didn't always succeed because there was anything wrong, particularly with the platform itself. It was much more to do with do you have the right skills? Have you done the right change management? Do you have the right level of engagement? Are you giving the data points back to your C suite that matter to them, and are you also helping your teams? Yes, they're getting more time back. And you ask the question about, how are they spending their time instead? Well, continue to develop your skills in order to be able to embrace the next big thing that will come down the line. Interesting, use that

Speaker 2  23:56
time for skills development. Don't just fill it with more tasks, because this is only going to get more and more and more and more and more the technology is going to increase. So make sure your skills are keeping up with that talent. You use that time for self development, personal professional development. It's interesting, and of course, we got talk about AI without talking about ethics of AI and the trust around it. One of the things that I like doing in my wife doesn't like doing is using voice control on the music systems in my house and the music systems in my house can recognize my voice perfectly and get it right first time, 99% of the time and get it right first time about 40% of The time on my wife orders it to play music, and the reason is, I'm told, is because the people who trained these products, and I won't name the brand, but generally males, so they learn to recognize male voices. And that's a slightly facile, you might say, trivial example. Yeah, but it kind of speaks to some of the issues with potential inbuilt bias in AI, which is that they are mostly programmed, which is perhaps unusual this given the personal and on this show, but mostly program with people who look and sound like me, and if they look and sound like me, they probably reflect the sorts of views that people like me tend to have, and that's a thing, isn't it? Jatra strain that actually AIS at the moment, are essentially trained by a bunch of guys,

Speaker 4  25:31
yes, and that's a problem, but that's been a perennial problem for inequality for generations. And what's different now, it should be more different now is the awareness of the risks and dangers that that introduced, that baked in bias, and what potential catastrophes that could have for young people and anybody that doesn't look like you. Ben Franklin, this is not a new problem. The potential consequences are wide reaching. But what I truly appreciate about the company that Emma and I work for is the unwavering dedication to calling that out and interesting, empowering our customers to harness the power of the technology with full visibility of the risks and being able to moderate for that with their own data sets, checking for bias and inequality, having robust governance in place, so that even with the best of intentions and planning in advance for training models, you still go back and check again, and you still go back and check again, and that needs to be a constant vigilant evolution. It is never done.

Speaker 2  26:57
It's fascinating the way that Jay frames that though, isn't it Emma that she says that actually, you know, to try to deny that this is a problem is wrong. It is a problem. It's an ongoing problem. It's a technical version of a problem that's existed for decades, centuries, in fact. But the thing is to face into it and say, we have this challenge. How are we going to mitigate it? And how are we going to, eventually, we hope, overcome it?

Speaker 3  27:20
Yeah, and because, and again, because we're speaking as marketers, and we're in that space of trust between us as a brand and our customers and our consumers. That doubly reinforces what Jay has just been talking about in the context of how we lean in, because tech bros influenced the evolution of technology and digital etc, in a particular way we now know this. It happened in the absence of explicitly calling this out. What we do going forward happens because we are have these guardrails governance compliance, which can refer to industry regulatory or regional regulatory requirements. And then also, as Jay shared, initially, you fine tune a model, and then you continually monitor it and enhance it. And you're doing it for the reasons of A, ensuring that you're managing, mitigating, removing that bias. B, you're also doing it because you want to avoid averages coming through and from the recommendations that generative AI produces. Because what you could end up with is the certain type of set of recommendations that are seen as the you know, for want of better waves, just like a harmonized blah,

Speaker 2  28:40
you end up with a bland middle of the road if you carry

Speaker 3  28:43
exactly so you should do it because it's the right thing to do. You should do it because, and I know, you know, it's like America innovates, Europe regulates and other parts of the world imitation. I'm not going to say where they are, but you do it because it's the right thing to do. It's brand trust compliance thing to do, you're very targeted, having your blinkers open to where there may be weaknesses or lessons to be learned, and then most of all, it's about us helping you be able to do your job better. It's about us helping customers be able to make choices better. So it should be empowering.

Speaker 4  29:17
Ben, it feels like a massive topic, and it is and it's fraught with danger and sensitivity, but we all have a part to play, and the first thing anybody listening to this podcast can do is look around the respective tables that they're sat at and ask what level of representation is there. And if there's not enough, do something about it. You start there, it snowballs, it

Speaker 2  29:43
snowballs. And it's a very quick, easy action, but it's a quick and well defined action that you can take immediately, right? I mean, it's interesting, apart from the sort of overarching ethics of having tech bro designed AIS, which are aimed at other bros, shall we say. I. So one of the issues we've had with AI in some businesses, I was reading some research of the day from a business school in Switzerland is adoption. And in order to get people in businesses to adopt this technology, they have to trust it, otherwise they just won't use it. And actually, this segues from there, doesn't it that if you've got an AI that's constantly recommending things or suggesting courses of actions which perhaps don't fit the demographic of its user. You're not going to get that trust. You're not going to foster that trust. You need to get it adopted in businesses.

Speaker 3  30:35
You hear people all the time talking about, well, you know, I did this with chat GPT, but, you know, it was, it was okay, but it wasn't quite what I was looking for, or it'll never be as good as and, you know, I think there are two elements to that adoption. Actually, there's three. One is around that skills enablement and just experimenting and playing with it yourself. Then the second one is making sure that you're engaging with the right kind of generative AI for the right kind of activity or outcome you're seeking. And then the third is, because things are changing so rapidly, make sure you're using the latest and greatest version. So, you know, the way, you know, we used to have, like, years before, you'd see certain evolutions. Well now, you know, Gen AI year is like maybe two to three months in terms of the tooling and how the tooling improves. So that's why you need to keep going back and checking what you're doing. But I will always use, you know, maybe chat GPT to never have a blank page. I'll use IBM. Granite is one of the IBM's llms, and that's very fact based, so I can do a check on whether or not I'm getting something accurate. But then I would use something like an anthropic to really help me be creative. You know, one gets me away from the white page. One actually proves that I'm using the right data or the right facts, and the third one makes it look beautiful or sound beautiful or read beautifully. So I think, you know, just exposing yourself to what way do you engage? What do you use? And how can you best combine multimodal engagement to get the kind of outcomes that you want? And I think that will help us stay on top of it,

Speaker 2  32:07
a portfolio approach, and horses for courses. And just because you've tried using one al for one task, and it was an impressive I remember I once tested, I thought I was testing chat GPT when it first came out, by asking it the best pubs within two miles of my house, and his recommendations were very bland and very middle of the road, and nobody who lived here would have given those recommendations. I thought, Oh, this is load of rubbish. But since, like you, I found good things that it can do that's helpful in my job, that's the same for many other tasks and tools. You know, this episode is called how AI can supercharge your 2025 so before we go today, I'm going to ask you both for three ways in which you think you can use AI as marketers to help supercharge your year ahead. And we'll start with you, Jay, what are your top tips?

Speaker 4  32:53
Great question. So I'm going to give you one from the perspective of marketing, one from the perspective of a CMO, and one that I personally am doing. So from the perspective of a marketeer, I would encourage the approach that Emma has just described, play, get comfortable, embrace and be agile of change, because the landscape is evolving so rapidly that you know you should be flexible to that and enjoy it, embrace it as much as possible. That's my marketeer top tip. Cmo, top tip is get the foundations right, because once your organization is ready to invest or has invested in the infrastructure in a way that enables scaled distribution of generative AI technologies in your estate, in your organization, you're going to need the foundations in place to really unleash take the handbrake off that so that you know, for the examples we've given around increased volume of derivatives, how do you enable that increase in volume to be felt impactfully, positively by your intended audiences? And then my third top tip, which is, what am I doing? I am looking to redesign how I turn up and do my job by questioning pretty much every hour I spend and get frustrated, to find better, more effective ways that I can do more of what I love doing, which is stuff like this. I'm interrogating myself and driving transformation for myself.

Speaker 2  34:28
That's a great takeaway. Interrogate yourself. Are you sitting there bored to tears of the task you're doing? Emma's spreadsheets, and ask yourself, Do I need to be doing this? Is there a better way? There's something out there to help me. Emma Flinter, can you follow those fantastic three tips.

Speaker 3  34:42
Well, I think they're just brilliant because so I think I said earlier that, you know, we've been looking at 2025 as the year you go from Wow to how. And I've been looking at, where can I show the value, where can I save time doing the mundane? And then where am I going to invest? At this capacity, bandwidth, time to do what I actually really care about myself. So thematically, I think I'm very well aligned with my wonderful colleague, Jay. We're soul sisters in that context, for sure. And then we're also, I would say the other thing which I've just been so amazed by is, you know, you can either grab snacks of it, or full time listening to podcasts like these. But you know, my other big recommendation is things change so quickly, you need to have those foundations, right, but continually be building on your foundation. So I've got a couple of areas that I or podcasts in particular I really enjoy. So I love pivot. And that's Scott Galloway, so your absolute marketing guru at the heart of everything, and Cara Swisher, that one of the leading interviewers for the likes of Steve Jobs and Bill Gates and and some other tech bros along the way. You know, I'm old. This is so exciting and so energizing.

Speaker 2  36:01
You're both far too modest, because neither of you mentioned your upcoming round tables with CIM. So if you're interested in what our two ladies today have introduced on this show, some of their tips, recommendation, their advice and their insights. Do get involved on the CIM roundtables. Find out more about those they're coming up, but that's some fantastic insight from you both. That's Jada strain and Emma Flinter from IBM, the big blue with some big ideas. And so thank you very much to both of you. Happy New Year, and I hope you'll come back on the show maybe in a year, and update us on all the latest tools and how this implementation that IBM has been getting on Absolutely. Thank you. Thank you, Ben, it's been great having on your show. Thank you very much indeed.

Speaker 1  36:48
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