Putting Generative AI to Use in the Real World

Since OpenAI's ChatGPT became publicly available in November 2022, the world has been captivated by the possibilities of generative artificial intelligence (AI). But AI isn't new: companies have been developing AI and machine learning technologies for many years. One organization with a rich history in this area is IBM. As the hype about AI continues to build, it's crucial that firms implement the technology effectively for their business. IBM has decades of experience applying AI in real-world solutions to deliver tangible outcomes in industries such as HR and healthcare.

In our latest podcast episode, Bola Rotibi, CCS Insight's Chief of Enterprise Research, was joined by IBM's Andi Britt, Senior Partner and Talent Transformation Leader, and Jon Lester, Vice President for HR Technology, Data and AI, to delve into IBM's AI vision and the technology's impact on the enterprise market.

The discussion unpacked the practical applications and complexities of generative AI, offering a glimpse into the future to shape effective AI strategies.

The episode explores:

• Current applications of generative AI in the real world
• Challenges faced by early adopters of new technologies
• Ethical implications and considerations for businesses
• Predictions for the future of AI.

CCS Insight has produced a free report detailing the key points of the podcast discussion, available here.

Creators & Guests

Host

Bola Rotibi

Chief of Enterprise Research at CCS Insight

AB

Guest

Andi Britt

Senior Partner, Talent Transformation Leader

JL

Guest

Jon Lester

Vice President for HR Technology, Data and AI

What is The CCS Insight Podcast?

Insightful audio from the global tech advisory firm.

Bola
Welcome to the CCS Insight podcast. My name is Bola Rotibi, and I'm the Chief of Enterprise Research here at CCS Insight and your host for today's discussion on putting generative AI to use in the real world and investigating IBM's large language model journey. Joining me today from IBM are Andi Barrett, Senior Partner and Talent Transformation Leader, and Jon Lester, Vice President for HR, Technology, Data and AI. Hello to you both and welcome to the podcast!

Andi
Hi there. It's great to be here this afternoon.

Jon
Thanks for having me today.

Bola
Excellent. I think we're going to have a really great discussion. So, companies have been using artificial intelligence models and algorithms behind the scenes for many years for the predictive maintenance of connected systems and appliances to allow for better operational control and decision-making, automation of workflows and to support greater levels of personalized interactions. However, the fervour around artificial intelligence has centred on the launch of generative AI applications, and in particular, the launch of OpenAI's ChatGPT to the public in November 2022, which attracted 1 million users in the first week and over 100 million users within two months, forming the digital zeitgeist of today and driving the strategic IT investment agenda within many organizations across the landscape. Andi, your role is leading IBM's UKI Talent Transformation service line, providing talent management, digital HR, GenAI and organizational change management advisory services for clients. Therefore, I'd like to ask, what is GenAI?

Andi
Yeah. Great question. Let's start off with the definitions. Generative AI, or generative artificial intelligence, is a form of artificial intelligence that's capable of generating text, images, videos or other data and often in response to a question or prompt from a user. What generative AI does, or what the models do, is that they learn the patterns and structure of data. In fact, they've been trained on the entirety of the Internet in many cases, and then predict what the right answer or what the best next word is in response to a user prompt. And AI generates that data in a way that can be understood by human beings.

Bola
That's great. So, Jon, I'd like to bring you in. Why the hype?

Jon
I think, you know, we've been working and experimenting with GenAI now for just over 12 months and we genuinely believe the hype is real, by the way. What we've kind of seen is it goes across the way that every single person at IBM works, and we've kind of broken that down into three trends in a way.
The first trend is the idea that people do ad hoc tasks. How do I read a document and summarize it quicker? How do I take feedback from 20 people who've onboarded and see the good and the bad about onboarding into IBM, all looking at support tickets? And that kind of one-off task is now basically done very differently using GenAI.
Secondly, this idea about process automations, things like FAQs or policy search, or even doing a task like a job transfer, etc. We're now seeing GenAI come in and generate answers to those FAQs or processes as opposed to the creation of them.
And then finally we're seeing the emergence of a concept of a digital twin, which is how does a digital twin work with a human SME to automate a programme that may last multiple days or weeks across a year? And we're now seeing these digital twins using GenAI with a memory to basically perform those programmes significantly quicker.
So, for us the hype is definitely real. The challenge with GenAI is that it moves so quickly, and its capabilities get so much better and better all the time, that our thinking has to change all the time.
And even though we saw of these three kind of trends separately in the past, we're now thinking of them as they could all be performed on a single platform, whether it be a platform that Microsoft is looking to create, whether it be Amazon or even our own IBM watsonx is looking to create, is we won't think of "what different solutions do I choose to do a different task, process or programme", it's just one platform one place.

Bola
That's a really good point actually, because I mean, let's face it, I've spoken to, you know, sort of many different organizations, but also on a personal level, I get the thing is, I think for me, when I see the hype, I think it's almost the potential. I think people have seen, you know, the possibilities as what you've actually said, Jon. in terms of, you know, the sort of, the automation, the kind of like being able to just use natural language and just seeing the returns of the summarization, the information and the support it's been able to do. So, you know, even with, you know, some of the challenges, but it's I think it's almost captured the imagination.
And I the more I see for a lot of people, it's almost been made AI very accessible, you know. So, I think people have kind of seen the possibilities. So, absolutely. I totally agree with you, in the fact that, you know, it's capturing that the hype is actually really expanded because it's made it very accessible to everybody, and they see the possibilities and the benefits that they can get.
Andi, is that something that you're seeing from the clients that you have spoken to?

Andi
Well, I think even before we get to talking about clients, I think as everyday consumers, you know, my friends, my family, you know, my relatives have all had some exposure to experimenting with ChatGPT or generative AI solutions and we've all played around with it. And the fact that there's universal access, all of us can sort of test and experiment, and we've seen its power for ourselves.
And whatever we're into, you know, we've had a chance to put a question into ChatGPT and see how powerful it is in providing an answer or summarizing information or producing an answer in a particular style or tone. And so, I think we've all kind of ourselves seen, this is really exciting and powerful. Now, what we're starting to think through is, so how am I going to use it?
And that's true with most technology innovation. It's supply-driven often, not demand-led. By that I mean a new technology presents itself that says "I am capable of doing this", summarizing data, producing information in a particular format in seconds. And then it's up to us as users to think, how am I going to use that? What are the use cases? What are the places where that particular technology or capability would be really helpful in my personal or my professional life? And that's why I think there's so much hype around GenAI.

Bola
That is a really good point. And in fact, actually is a great segue to my next question. And Jon, I would like to bring you on this because your responsibilities include end-to-end human resources ownership for cloud data science, AI and automation technologies, as well as the digital HR strategy for IBM. You are also a thought leader in how automation and AI will transform the future of work.
And you've taken the lead in deploying AI and GenAI for IBM's workforce. And I've seen that you've recently published an article on the IBM website. So, this next question I think is ideal for you. And it's the question of where is generally being applied within IBM? And what does the company see as the biggest benefits being achieved?

Jon
That's a really good question. I think just going back on something, you and Andi talked about in making GenAI accessible, our vision is to make GenAI capabilities available to everybody as in democratize GenAI so that everybody across the whole of IBM and in society, in other organizations, can benefit from it. And for us, it goes beyond that kind of "how do I use traditional AI as a tool to do one thing?"
GenAI will help you do everything better. That's our view of it. So as I kind of said before, we see these three trends emerging around GenAI, that idea of a one-off ad-hoc task. And recently, our CHRO, as she does every year, kind of commissioned us to look at three particular focus areas. And what she did is she created a squad across HR, usually about 10 or 11 people in each squad, to go and investigate them.
One was things like, how do we keep evolving the role of the manager, how do we continue to drive consumer-grade experiences? And this idea of a two-way value prop, both for employer and employee, and these squads would traditionally go away and for example, create personas. They'd sit in a room, do design thinking, create these personas, and draw on walls.
It could even take them half a day to say, okay, here's the 10 personas within IBM that we now want to test these three ideas around. Using GenAI. You can create those personas in seconds. You can say to a large language model, give me 10 generic personas in IBM within different regions with different skill sets, different roles.
And literally within seconds you can see them be created in front of you. What's even better is you can then turn those personas into assistants who you can ask questions. So, for example, in our kind of particular area, you may create a payroll specialist persona and say to that payroll specialist, how can you now use GenAI in your job to move you to high-value work?
And the GenAI would give you a really good response in seconds. So, we also look at, as part of those squads, you've got to do research, and you have to kind of allocate a different person to read each document. Each document could be 20, 30, 40 pages long. What we're now saying is, why don't you just drop those documents into a large language model and basically interrogate them as a collective set of insights and again, within minutes, you now get, here's the top five trends that we're seeing in that space, both positive and negative. And across the whole lifecycle of this probably six- to nine-month project or series of projects, we think it can increase the value from our early kind of measurement of every single person, that team by 10 to 20% and make them more productive.
So, we're kind of saying any way we don't think about anybody doing a project, having this concept of a copilot to work alongside you can change the way you work fundamentally in day-to-day tasks. You know, if we look at that idea around assistants, you know, we've been using a tool called AskHR which is our HR digital assistant to transform the way that IBMers get support from HR. And it can now answer over 3,000 FAQs, it can pull insights from 4000 documents, and we've even now linked to underlying technologies, so you can now perform tasks through chat or a few clicks. And that in itself has reduced our overall tickets by about 60% in seven years. So traditional AI, natural language processing is brilliant for us. We've seen the value. So why are we now thinking about what can GenAI do to do that even better is we kind of hit a plateau in what that traditional AI can do. Last year, the number of tickets that we saw only reduced by about 1%. We think with GenAI it can now start to answer so more complex types of queries, it can generate content significantly quicker than a human can create content.
And for us, we're kind of analysing all of our different parts of HR and looking at things like payroll, compensation, benefits, they're very kind of compliant, but looking at things like learning, talent management, talent acquisition that may be less compliance-focused and saying, can we now replatform those FAQs those policy statements on top of a large language model and can we use AI to generate a more human-like, funnily enough, response?
Now, there are certain things like the compliance side where we're learning that GenAI may not be the right answer just yet because we can't have GenAI hallucinating, creating an incorrect answer around things like what is the paternity leave for somebody based in the UK? That can't be wrong, and if there's a possibility of GenAI hallucinating, we may say, actually, don't use GenAI for that answer, use traditional.
But what it's doing is it's enabling us to shift our whole baseline to a GenAI platform way quicker than we thought. And the feedback we get from the pilots we're running is really positive, saying the answer we get is great, we can deep dive, we can have a meaningful conversation with the large language models through assistants, and change the speed at which we get the right answers. With that caveat of some things we may not be ready yet to put in the GenAI platform.
And then finally, we've been talking about this concept of a hybrid workforce where humans and digital workers, digital twins, work hand-in-hand to move human-style value work. We built around seven different use cases, where that programme management, where we have things like HR partners working with these digital workers, is now changing things like a 12-month promotion cycle that we go through every quarter for our IBM consultants, and it's changing it from a 10 or 11 week programme to a five week programme, and probably taking about two-thirds of the asset out of our HR partners, where digital workers take away all of that and do it automatically. And we're actually giving significantly a number of hours back to managers to enable them to focus on their real jobs rather than just doing HR stuff.
So GenAI, we think, can significantly increase the speed at which we deploy these digital twins and provide significantly better and quicker insights to augment human decision-making things like managers around, well, who should I promote this time given I've got a limited budget, etc.?
So I think for us, there's so many different use cases, our idea now is as we measure the impact of this, we can now think about what's the business case for bringing GenAI in. And we are really excited about those business cases.

Bola
That's really a good point. And I like the fact that at the end when you talked about measure the impact, because, you know, a lot of the things you were saying is that, well, actually those you know, I could hear already some really great ideas on how that could be really helping both with the productivity of the workforce, but also the workflow and handovers and helping people to really kind of give them the time to kind of, interrogate and then get answers back.
Because often if you feel that you've got to search for the information in lots of different places, it can prevent people from asking questions and really get into the crux of an issue, right? So the ability to kind of get all of that information into, you know, so quickly, is great. So, there's lots of great information, but at the end of the day, it is about trying to kind of understand what is the impact?
How do we measure that? And what does that look like for, you know, the real value, the real value beyond just kind of like productivity, what's the business return? You know, which might be, you know, might not necessarily, you know, be fully monetary. It could be all sorts of things, I like the fact that and I think measurement needs to go hand in hand so it is great to hear you say that.
Andi, I mean, there's lots and lots of examples, you know, I mean, Jon has told us, you know, shown us an example of how you're doing it within IBM. But, you know, you look after skills and lots of different areas and talking to lots of different organizations, you know, what do you see? What other areas are you seeing in this?

Andi
Well, I think the first use case that many of our clients would want to explore, and we need to acknowledge that probably many of our clients aren't quite as advanced in their journey as Jon has outlined that we are at IBM. The first use case generally would be for some kind of conversational assistant. So, you know, a virtual assistant, a digital assistant that can answer employees' queries. And typically, what most clients will want to do is start off with something that can just answer basic Q&A, it might be, you know, responses on HR policies or, you know, sickness plans or benefits, entitlement, etc. but once we've got that nailed, because that's a classic use case for where a GenAI solution powered by a large language model really can give fabulous answers in seconds.
Then we'd move on to the second level, which is can we get the conversational assistant to actually do transactions? So, all the routine transactions that I, as an employee or a manager might do, whether it's booking vacation or whether it's transferring an employee from one department to another. Can I do that via a conversation with an assistant? Because that will save me, a manager, a lot of time.
And then the third sort of evolution is can we then get the conversational assistant who knows me and who knows my role to start nudging me. It could be at a fairly basic level, nudging me in terms of HR actions I need to take: I've got a probation review, or I've got to do my annual appraisal for my staff. Can it nudge me as to those activities in the HR calendar that I need to complete? And then we go to the next level. Can it start nudging me about the skills that I need to develop? Particular competencies that I should be, you know, building in order to make me more relevant to my organization or to the clients that I serve.
And that's the advantage of generative AI. It can literally scan all of our systems where we sort of track, you know, what the assignments and activities and jobs are that we need to perform. It knows my skill set, it can identify what the skills for the future are and it can begin to give me personalized recommendations on how I keep my skill set current.
So that's what I think clients are looking for in terms of a journey. Basic conversational assistant answering Q&A right through to a proactive assistant that's helping me manage my career within the organization.

Bola
I like that. I like that because it's the kind of like the first base. The other thing I would add, I was talking to somebody, a colleague of mine, and said that one thing I really quite like about, you know, the things that the benefits like I personally feel from some of the things that I can see within organizations, especially this, we both talk about, you know, these assistants and this kind of copilot, this support or helping you along.
But I always think that the nice thing about it is the fact that there's no judgment. So in other words, you don't have to be afraid of the question to ask, you know, or the task to do. And I think that's actually, you know, that kind of almost personal side because, you know, it's a kind of well I can ask silly questions, or I can ask the kind of tough questions.
And in fact, it will take all the information you know that it can access from the within the organization to give, you know, a very, you know, sort of summarized information that, you know, bares no judgment. And I only say that quite funnily because, you know what it's sometimes like you don't want to ask the question sometimes of colleagues and that's it gives you a nice clean answer without actually saying, well, that's a silly question, so I think that's quite funny.

Andi
And of course it's 24/7! You can ask that question any time of day or night. You're not going to inconvenience your virtual assistant by putting that question in at 4:00 in the morning when you're struggling to sleep because you think, oh gosh, I've got to do that, how do I do that? So, these assistants generally are fantastically helpful and convenient for our managers and our staff.

Bola
That is perfect. One thing I didn't hear: we've talked a lot about the really good benefits and it's positive and it's fantastic. And I know we mentioned earlier on around the potential challenges say like hallucination, giving the wrong answer. But have you seen any other challenges, either of you. Jon, have you seen any challenges, before I go to the next question, when we talk about responsible AI? I'd love to know, is there any immediate challenges that people should be aware of?

Jon
I think there is. And it kind of touches on what you just said. There is sometimes a bit of a fear of this, what is this GenAI capability going to do to me and my job and the way that I work? And do we need less of me to be able to do this work in the future?
And going back to the big hype around GenAI, I think initially a lot of people said, oh, 50% of jobs will disappear, new jobs may get created, but how do we make sure that we take our employees on this journey? And I think that is a really important element of this. We're seeing probably beyond the hype now, we may think maybe 10% of jobs may not still be here in five to 10 years, but we think that GenAI will create 10% of new jobs that didn't exist three or four years ago.
So we're starting to see a balance between that. But I think the majority of people will still do the same job that they do today. That domain expertise is really, really important. I think what we're definitely seeing though is where people embrace these new ideas, these new ways of thinking that GenAI can give them, they are the people that are going to be more successful in the GenAI era.
I think one thing we're saying to our staff across IBM is to make sure you understand what GenAI can do for you, because it will help you change your job as opposed to you being changed by GenAI. And that's a massively important thing.

Andi
And the other fear that I think many people have is that AI is either going to make a decision for me, or it's going to make a decision about me, and that's why it's really important, I think, that organizations need to establish some ethical principles as to how they use AI. And at IBM, we have a very clear principle that the purpose of AI is actually not to take the decision, it's to augment human decision-making.
It's going to help me as a person, as a practitioner, as an expert, as a clinician, as an SME. It's going to help me make the decision by analysing data, making some recommendations, but it won't make the decision. Ultimately, I, as a manager, get to decide if I want to rely on the AI or choose an alternative course of action.
I think that's really important because that will build people's trust in the system. If they know that it's a human who is being augmented, not AI making a decision.

Bola
That's a really good point. And I think it's a really important point because actually it's about pushing the empowerment side of it. You know, this is there to empower the human, help with their, augment their capabilities as opposed to the other way. And it actually brings us onto a really, really great point, which is, you know, how does one go about using GenAI responsibly and what kind of ethical guardrails should support its use?
Andi, you started on that, is there some more that you could talk about before I bring Jon in?

Andi
Yeah. So, I'm going to give you three basic principles. The first one which I stated at the outset: the purpose of AI is to augment human decision-making, is to augment the workforce, and that's why Jon talked about an augmented workforce is what we're aiming for here. AI is to support, it's not to take the decision.
The second principle is that data and insights generated by AI, they belong to their creator. They belong to the people that originally generated that source data, whether that is an artist, whether that's a researcher, whether that's a subject matter expert. The people who originally generated the information or the research insights. The data belongs to them, not to the AI engine that analyses and forms some recommendations from that data. And that's a really important principle to establish.
And the third one is that all new technology, but particularly in this era of AI systems, it has to be transparent and explainable. I.E., it's got to be clear to the user or the consumer how we see AI making that decision, on what basis is it making that recommendation. And at IBM, we have this vision that all AI tools and technologies used not just in any organization, but across government, across academic institutions, it should carry like a food label. You can't go into a supermarket today and buy food without seeing all the list of detailed ingredients and additives that make up that particular food product that you're buying. Well, we think it's exactly the same with AI. You should be able to see a food label that says who designed it? Why did they design it? What was the purpose? What data are they using? And how is this AI solution being monitored to make sure it's giving the right answers? And we think we need that transparent, explainable food label on every single piece of AI on the planet.

Bola
Well, those sound like fantastic principles to put in place. Jon, I mean, you know, you look after HR, so the ethical guardrails must be absolutely kind of like the base foundation. And, you know, obviously you both work for IBM, so these are, you know, you're building on top of what Andi is talking about. But are there any other specific things that you could add to this?

Jon
Yeah, and you're right in saying that, for us, AI in itself is not a new thing. It's been around for decades, we've been kind of dedicated to using AI responsibility as part of IBM's DNA. And interestingly, back in 2015, we actually appointed our first AI Ethics Global Leader, and we kind of subsequently published our principles of ethical AI, similar to what Andi talked about.
We also looked at how do we detect and mitigate bias in AI. In 2018 we really formalized this process, and we created an AI ethics board to ensure that every single AI use case had somewhere to go to seek advice, but also, to some extent, approval before we move any new AI or GenAI capability to production.
We now have to, every time we create something, so, our Ask HR digital assistant, our digital twins, we have to produce an AI fact sheet to make sure that every single person who uses these technology can go see, back to Andi's idea around, what is it doing with my data? How am I using it? What does it do behind the scenes?
A fact sheet basically tells you all that. And those principles around explainability, fairness, privacy, robustness and transparency are built into those fact sheets. So, every single IBMer can see them, from a HR perspective, we have to ensure that we're complying with the latest legislation. You know, there's a lot of changes in the EU recently, there's even more in the US, and we have a kind of, again, very simple rule that we always go back to: we keep humans in the loop. And AI does not make decisions about human beings. It can help us make better decisions.
And it's funny, I've actually probably over the last couple of years, I've been in front of that AI ethics board three times: the first was around our AI-based chat bot, the second was around the first digital twin we created around promotions, and the third one more recently was about as we replatform all of our solutions onto large language models, how do we make sure that large language model use in HR follows those legislation changes, but also those principles. And that board is fascinating and is kind of made up of our chief information security officer, our chief privacy officer, our chief legal counsel, our heads of various government, AI, regulatory affairs and a whole bunch of lawyers. So forget my title for a minute, and I was really scared the first time I went there. It's kind of, how do I present what I want to this group of very senior people who really know their stuff. And to be fair, I very quickly realized that I shouldn't be scared. That actually the board, they're not to stop us developing great AI or ethical GenAI. They're there to give us great advice about how do we get them live in an ethically compliant way.
And I actually see that those board members as really great partners to us in HR to make sure that we do things that not just the change the way we work, but do it in the right way. And that's really fundamental to the way that we work.

Bola
That's really good. In fact, actually, you both talked about having the transparency in place because actually transparency builds the trust. But then also having an oversight body or committee, you know, filled with kind of like, you know, skilled experts with relevant experience, but working in partnership and, you know, sort of to help along, and not there to kind of like, oh, you should, you know, with a big stick, but actually kind of looking and feeling, helping you kind of like, you know, expose anything and then to seeing how things are addressed and what your ethical impact and everything like that. So it actually should be a friendly kind of environment. So, I understand because if you have to go up in front of everyone, you do worry about, oh, are they going to really sort of come down on us and you're going to be defensive. But it sounds to me like its actually a very collegiate, it's really important, but it's very collegiate, very collaborative. And it's there to kind of help you out.

Andi
And it's worth, it's worthwhile saying that that principle is just so fundamental to IBM. I mean, if you think about our history, we've spent the last sort of 110 years ushering in new technology safely, to the corporate world, the business world, the world of government. And that's why we've been particularly focused on generative AI and all the controls and safeguards around it, because we know that in society and the world as a whole, there's a lot of anxiety about how this technology can be used.
And so, we've spent a lot of time just thinking about the principles, the ethics, the guidelines and the governance to actually help not just our own organization, but our clients and government organizations who really make the most of this technology. And to limit the risks, because it obviously has rich potential, but it's got potential risks. And so, we worked really hard to make sure that organizations and governments can trust the way that we're using and introducing this new tech.

Bola
Absolutely. And thank you very much Andi for jumping in there, because I think it's really actually it's a very fundamental, you know, principle, and it's an important thing to get across.
So, I'm coming to the end, we're come to the end of the conversation. There are loads of things we haven't talked about, so maybe that's something we may think about going forward, maybe something else in another podcast.
But what I would like to ask you both is what predictions do you have for generative AI's trajectory over the next 12 months? Jon, anything that comes to mind? Any trends?

Jon
Yeah, it's interesting, when I took the role on in IBM that I had two years ago now, and the first thing that my new boss asked me was, can you create a vision for the future of technology for, say, the next five to 10 years? And I thought I was very brave in saying no to her because she didn't know me very well at the time and you don't say no to a new boss that quickly in your career. And what I said is, look, anyone who's really trying to figure out what's happening in five to 10 years within AI and GenAI in particular is kind of guessing, because the speed at which we're moving is so much quicker. So, I really like the fact that you said the next 12 months, because we do have some pretty good ideas about that.
We genuinely believe that GenAI will accelerate the shift to a hybrid workforce. That we will see humans working with digital workers to help those humans be more productive, add more value, make quicker, better decisions, but also change the whole way that they as individuals work.
And just one example I want to give you, we've got somebody in my team who started her working career back in 2016, and she was on our HR support desk answering phone calls. And she gave brilliant customer service. Her customer SAT for every single person that she talked to was 99% or higher. And she got promoted very quickly within that team. But when we started the shift in particular toward AI-enabled support, she joined my team and became a conversation specialist, and she then learned how to create an experience through conversation that could support 1,000 people at a time, or even 10,000 people at a time, in the way that they did their work.
That person this year has now become a prompt engineer of a large language model. So, she herself has gone significantly up the value chain. She is now significantly higher paid, to be fair to her, because the stuff that she used to have to do doesn't need that attention from humans anymore, because AI and GenAI are going to do that better and better and better and quicker and quicker and quicker as we go forward.
So, seeing people like this person in the HR support team completely rethink her career, but have one domain specialism, which is creating great customer service, we're going to see more and more examples of that, and I think that's why I'm really excited about what GenAI can do over the next 12 months. We're moving from experimentation, lessons learned, sometimes failing, but failing fast hopefully. So we now have a pretty good idea of where the value is. We now can understand how humans can change their own work by enabling GenAI to help them do it better and quicker. And I think on that shift to a hybrid workforce: we thought it was probably still another year or two off, but we think it's going to happen this year if it hasn't already.

Bola
That is fantastic. It's very positive. I love that, I love that positivity. And in fact, that actually is really good sort of foil for people who are worried about sort of GenAI and AI in general, but seeing that, the example you gave of that person and how they've actually completely kind of gone up the value chain and delivered and gained from it as well. It's absolutely fantastic. Andi, anything you'd like to add?

Andi
Yeah, I think for me over the next 12 months, I think business leaders are going to begin to see that generative AI and its associated technologies is not just about finding use cases and plugging it in. This is actually about changing your whole operating model, the way that you show up as a business, indeed, your very essence as to what it is your business does.
Whether you're an airline, a CPG business, whether you're a retailer, whether you are a financial services organization, increasingly you're going to see, actually, we've got to be a tech company. That our business at the heart of it is tech, and therefore we're going to have to change the way that we work and way we operate as a business because of the way that technology doesn't just change the way that we work, as Jon highlighted it, change the products and services that we can deliver, the way we serve our customers, the way that we manage our supply chain. So, I think CEOs and business leaders are going to see that this is far more fundamental. It's going to change the DNA of who we are as a business.

Bola
That's a great point actually, and we talked in the past around digital transformation and modernization and moving to the new ways of working. And I think GenAI, certainly for me, is kind of part of that story, that trajectory, part of the arsenal that you will use to kind of help that transformation process.
One of the things I see as a, you know, as an interesting trend is that, I think what we're starting to see, and we haven't really talked about it in terms of the tools and the solutions, and maybe that's something we can think about going forward as maybe another discussion to maybe look at the technology underpinning it, and especially the things that are coming in from IBM, especially with all the capability that IBM has in the watsonx solution, you know, platform to supporting generative AI models.
But I think I wonder what will happen, certainly in the next 12 months, is that I think more and more people will start playing with this. They will start experimenting it, but they'll start to actually see it across specific areas of focus. I think that's what we you know, you both talked about HR, we talked about skills, but I think we're going to start seeing it in sort of other parts of the business. And people will start to bring together, sort of practices, you know, whether it's best practices, you know, working strategies. And what we see in the in the next 12 months will definitely be different from where we are today. But I think we're going to start to see some real functional capabilities come to the fore.
Guys, this has been a fantastic conversation and I know we're coming to the end of it and I would just like to say big thank you to Jon and Andi. And I want to say to our audience, make sure to tune in to our next CCS Insight podcast. But until then, goodbye.

Andi
Great. Goodbye. Thank you, Bola.

Jon
Thank you both, it's been a pleasure to speak to you today. Thank you.