S6 Episode 4: The opportunity for Enterprise AI with Darshan Chandarana and Julia Howes from PWC

Speaker 1: 0:01

Welcome to the Actionable Futurist podcast, a show all about the near-term future, with practical and actionable advice from a range of global experts to help you stay ahead of the curve. Every episode answers the question what's the future of? With voices and opinions that need to be heard. Your host is international keynote speaker and Actionable Futurist, andrew Grill.

Speaker 3: 0:29

In today's episode, I'm joined by two amazing guests from PWC Dushan Chandarana, partner and emerging technologies leader, and Julia Howes, a director, who helps companies use data to make better decisions about their people. We're here to talk about the exciting opportunities in the enterprise AI space. Welcome both, but Dushan. Interestingly, around the same time, I was working at BAA Systems in Australia. You're working for them here in London. What an amazing coincidence. Perhaps you can introduce yourself and tell us how you made your way to PWC and what your area of focus is at the moment.

Speaker 2: 1:02

So how did I start off my life? I started off as a computer scientist, so that's how I trained and software engineering was my major. Went off to work for BAA and we sort of talked about what that was back then. This was many, many years ago, so I was working on head-up displays for various fighter aircraft, did that for a while, kind of enjoyed it. It was a good grounding and a good apprenticeship really. But then just went into other roles, went to work for banks in the technology space. Went to work for technology companies in the technology space, surprisingly, and then went to work for Consonci's Been at PWC for about six years now and loving every minute of it really.

Speaker 3: 1:41

And Julia what was your path here and what are the areas you specialise in? I love Copilot. I've actually turned it on my Office 365 tenant, so we'll talk more about that, maybe give you some tips on how to use it even more efficiently. So you're both working across multiple industries. So, darshan, is there any one industry that's truly leveraging the power of AI at the moment?

Speaker 2: 2:41

I think all the industries are very interested. Where I think we're seeing the most traction is where there's a lot of client-facing activity. So financial services have been using AI, with the capital AI, for quite some time and their pivot into Gen AI hasn't been that difficult for them. Really. There's a lot of experimentation going on and so on. The other area that I'm seeing quite a lot of activity is retail and consumer especially on the retail side.

Speaker 2: 3:05

So long story short, those are the two areas I think that we're seeing the biggest amount of traction, but actually it's Julia's area and other areas like HR, like Contact Centre, that straddle all industries that are seeing the biggest disruption. Marketing is another area that's seeing quite a lot of disruption.

Speaker 3: 3:21

So I've spoken to a number of guests about how we need responsible AI Darshan maybe. How would you describe this and how does it work in practice?

Speaker 2: 3:29

That is a I'm not even going to say $64,000 question. That is a monumental question. So we've been in the responsible AI space for quite some time. You can go online, you can check out the papers and all that sort of good stuff. But we truly believe that responsible AI is a fantastic framework to think about as you start going on your journey. And that responsible AI piece let's just take the high level piece here Just because you can do something using a technology doesn't mean you should right. So, really understanding that, understanding the societal impact, understanding your people's impact, your employees impact and your customers impact, is where that framework kind of fits in. And yeah, there's quite a lot of information around it, but it's that just being mindful. I think that's the best we describe being mindful of what you're trying to do, why you're trying to do it and what the long term ramifications of that, of those decisions, would be.

Speaker 3: 4:20

Now it's fair to say that Gen AI and ChatGPT a couple of years ago peaked everyone's interest. You said that a lot of your customers have been doing AI with a capital A for a while. For those listening that know they need to get into it. Where do they start?

Speaker 2: 4:31

Another great question. The best way to describe it is it sounds a bit consulting-y, but I'll say anyway, just understand what your strategy is going to be. Really figure out why you want to do these things. What you're going to do, Figure out where it's going to have the best impact, whether it's a commercial impact, whether it's a customer engagement impact, whether it's a loyalty impact. Figure out for you what's the thing that you want to start with from that sort of lens the value lens. And once you understand that value lens, then you can start on the journey of understanding the use cases, understanding the patterns, understanding the tooling. What we have seen over the last year or so is just a scramble around the tooling, a scramble around the use cases, but actually not a solid business case behind it. So lots of experimentation, not very much stuff going into production.

Speaker 3: 5:20

Julie, I love the fact that you're focusing on the people element, because we all know that AI is going to impact people and we need people to train AI. I read an article you wrote entitled Gen AI Needs an Intelligent Approach to Adoption. What are the components of that approach?

Speaker 4: 5:33

Picking up on the value side as well. We're seeing that a lot of organisations will struggle to hit their value levers if they don't get good employee adoption. But at the same time, I think there's not been a technology or a change that's been as complex. So that's why we talk about this intelligent adoption approach and I think it's understanding at its heart that it's not going to be linear. There's going to be lots of peaks and troughs as employees get excited and then get access to tools and then maybe get disappointed, but then the tools learn and get better, so then there's a new wave of excitement, and so I think when we talk about intelligent adoption, we take a very data-led approach.

Speaker 4: 6:15

Of course, it is about being responsive to employees in that journey and understanding when they're hitting those troughs and what they need around it to help them. But I think it's also really not assuming that everyone's the same. So very personalised to the mindset and the individual person, and not thinking that everyone in a certain function or a certain persona that's organisational based is going to be the same. So really getting to the individual and their own mindsets, their own history, their own background that they bring to AI, because it's a very personal change.

Speaker 3: 6:53

Now you mentioned tools and I understand you developed your own Genovo AI platform, chatpwc, great name. How did you develop it? Why did you develop it and, importantly, how does it help your consultants be more productive with clients?

Speaker 2: 7:05

I think ChatPWC is just the starting point for us. How we developed it, what we developed? Essentially, we've taken an open AI model and then fine-tuned it, put a ring fence around it so we can use it without pushing our data out into the public domain. So it does enable our people to use the technology, get familiar with the technology actually even things like prompt engineering, for example Just getting used to that on a day-to-day basis. That's a really good use case for it.

Speaker 2: 7:32

Over time, we'll see many, many more tools coming out for very specific reasons. We have a partnership with Harvey, for example, which is a legal version of it, again based on the open AI piece. But open AI isn't the only platform out there, and so we are constantly horizon scanning as well to see what else is out there, what we should be doing. We have partnerships with Google. We have partnerships with AWS as well. Their technology is coming on leaps and bounds too.

Speaker 2: 7:57

So a lot of horizon scanning going on, a lot of tool building going on, and I think that that's, as an advisory firm, that's really important for us. We need to understand the widest possible picture, because each client will be different. Each client will have a different need, a different requirement and as much as we'd love to say that this is the one silver bullet, one tool that solves everything, that's not really going to be the case. So, understanding that horizon scanning piece, understanding what's coming up and just keeping abreast of what's going on and how fast the pace is at the moment, that's an important piece for all of our people to take into consideration.

Speaker 3: 8:32

So what are the sort of things that customers are asking you about when it comes to generative AI?

Speaker 2: 8:36

The first question is what is it Truly in that sort of? We've got lots of people who have used it or use chat GPT from their own personal use. You know like let's go create an agenda for this or let's create a holiday schedule for that. You know just simple things like that. How do you then take that into business? How will it help me and what will it do and how do I release that to my population inside the organization? Those are sort of the basic questions that we get asked on a regular basis. But also that level set. You know you've got the non-exec directors, you've got the board, you've got sort of practitioners. They've all got a slightly different view of what Gen AI is and how it might work. So just doing that level set across the entire organization is also something as an advisory firm we get asked to do quite a bit.

Speaker 3: 9:20

I think that's so important because it's the one technology that actually the board can play with. My parents in Adelaide, australia, have heard about chat GPT. How did they hear about it? It was on the news, so I'm sure they've all played with it, and I encourage my clients also play with it with something for work and play with it with something for your hobby. And then the penny drops ah, that's what we can use it for. Do you find that's dangerous that the board's actually played with the technology and maybe thought it can do X when it can do Y, and that level set becomes even more important than, say, cloud or IoT that they can't really touch and feel.

Speaker 2: 9:48

I think anyone who's played around with the technology will have a personal view and a personal opinion, and it's great that they have that, because that generates passion, that generates that sort of view of I actually want to do something with this.

Speaker 2: 9:59

With some of the other technologies you mentioned cloud, iot, some of the other bits they're a little bit esoteric, you know. They're like well, what does that do for me at home, what does that do for me here? And you have to kind of well, you're using cloud every day. If you're using email, if you're using photo sharing, you're using cloud. You don't have to go through that anymore. You've already got a sort of level of understanding. Now you can also get bad habits that you have to sort of break. What can it do and what can't it do comes back to that responsible piece, but it also comes down to what is it that you want to do in your business? So for us, I like the fact that people have played around with it and have some level of knowledge, but it's that level set that's still quite important that we need to do.

Speaker 3: 10:37

And Julie. We mentioned Co-Pilot. Probably a lot of people are listening to this podcast. Don't know what they are. We're gonna see more and more than the three of us are at the leading bleeding edge of this world playing with it. So, first of all, what are Co-Pilots? What can they do Specifically? What have you been doing in the Co-Pilot space?

Speaker 4: 10:51

Co-Pilot is a series of GNAI tools that Microsoft have launched, and I think the first thing to note is there's multiple versions of them.

Speaker 4: 11:00

So the main one that people touch straight away is Co-Pilot for Microsoft 365. And I think they're quite clever in the way that they've named the tool. So it is this assistant that helps you with your work, and that's the whole way that they've positioned the Co-Pilot. So it's an assistant on your journey as an employee, in what you do day to day. So the Co-Pilot in Microsoft 365 works across PowerPoint, word, teams, et cetera, and helps you summarize information or prepare first drafts of different reports. But then there's a series of other Co-Pilots that are coming through the Microsoft ecosystem, so in things like Power Platform and GitHub. So we're gonna see lots of them, and I think this is the big issue for employee adoption and for organizations is that you can't always be a breath of everything all of them at once, because the pace at which they're coming out is so fast. And now I think in the Co-Pilot infrastructure you can easily create your own Co-Pilot, so the speed at which these are being created is phenomenal.

Speaker 3: 12:02

Some of our listeners may have seen that this year's Super Bowl there was an ad for a Microsoft for Co-Pilot, so now people are talking about this, they'll probably be asking people at Microsoft what it means. That for me is, when it's on a Super Bowl like crypto, all the ads last year were crypto. This year, co-pilot was an ad, so it's a thing.

Speaker 4: 12:20

It's a thing exactly, and I think because there's Co-Pilot, which they've rebranded in Bing, so it is an everyday tool. Now. You don't have to be in an office environment to be using Co-Pilots.

Speaker 3: 12:32

When I speak with clients, I talk about the need for an AI executive council to help coordinate AI strategy and execution across the enterprise. So, Julia, do you agree with me and are you seeing clients establish these, as they understand how deeply AI is going to impact every part of their business?

Speaker 4: 12:46

We do agree. I think the issue, like everything, is in the execution and set up of them. So we're seeing very good examples and then maybe some poorly executed examples. I think maybe in the future those councils may not be needed as AI becomes a core part of we don't need an email council, for example.

Speaker 4: 13:05

Exactly, but in the short term, yes, I think there's a strong need for them. But I think the biggest danger that organisations face when they set them up is not being clear on its purpose. So, I think, being very clear on what the purpose is and how it interfaces with other processes and committees in the organisation, and making sure it has the right autonomy. So there is a little bit to think about to execute them well. But, yeah, I think, given the pace of change, the multidisciplinary focus that we need, it's a very good thing to put in place.

Speaker 2: 13:40

I mean, the council term is kind of grandiose, isn't it really? We've seen a lot of clients who have set up working groups, at least right. So think of it in multiple levels. The council fees, absolutely. I think that that's going to be really important over the next couple of years or so, and then it'll just be mainstream. But as a starting point, a lot of folk have already got the working groups with a multidisciplinary team, so it's not one person that's in charge of it and they're going to dictate how it's working across the organisation. They are a working group with multiple lines of service, multiple business units, whatever else already embedded in, and I love to see that.

Speaker 3: 14:15

I think it's also a coordination piece. I've spoken to clients where they uncover other people doing the same thing, and when I was at Tolstern Australia remember I was in a room like we are today I brought six groups in all doing something around small business. We decided at the end of the meeting we're going to do it once rather than six times, so part of it is like shadow IT. People are playing with it. We've got a chat EPT, we've got some open AI. Do you think there's a danger, though, that if you don't have that coordination, people just go rogue, and then we've got issues with GDPR and data leakage and all those sort of things? I mean, what are the things that customers, what are the things that clients should look out for when even playing with and experimenting with AI projects?

Speaker 4: 14:52

There's a lot of experimentation happening. I think one of the biggest dangers is that organizations almost have too many use cases and those use cases are quite siloed. So I think there is a role to play in the coordination. It's a tough balance because you want to encourage experimentation. I think it's very hard to unlock the value without having that experimentation. But at the same time, where we're seeing better success in organizations is where they look at a use case and an application and it might say be in a function like HR, but then they're able to apply it across the business and so they think about how a similar and that's why we've started to use the phrase pattern could actually be applied across finance processes or marketing processes in a similar way. So it's not just the danger of going rogue, but it's actually the danger of missing out on the opportunity if we don't have more coordination across the different groups. But again, it's that balance of sharing but not controlling and stopping the experimentation that's the difficult balance to strike.

Speaker 2: 15:58

Couldn't agree more and really that balance of allowing your user base to experiment, to play around with technologies. You're going to get some fantastic ideas coming out of that as well. So too much control can stifle ingenuity and innovation, but too little, as you said, you will see sort of rogue stuff going on over the place. The worst situation is that you repeat effort and spend money when you don't need to right, and that's not a good place to be because that erodes confidence in the technology.

Speaker 3: 16:28

So, julie, what are some of the common challenges that organizations face when they're integrating AI into their business strategies, and how do you help them overcome these challenges?

Speaker 4: 16:36

The universal challenges that we see in the short term probably fall into two buckets at the moment. One is this concept of value. So there's been a lot of initial experimentation and organizations have been comfortable to do that with small numbers. If I take Copilot as an example, roll it out to the 300 users, see what value, see if they enjoy using it, and so it's employee reaction. That's been the measure up until now. Now that's pivoting into. So what's the actual ROI?

Speaker 4: 17:07

So there's a lot of focus on how do we actually determine the real value here, particularly if we scale it, and I think what's really hard with a lot of the AI tools that we're looking at at the moment is that they're not substantially changing a job. I would just good news for employees, but from an ROI perspective, we've got to think about it quite differently to FTE reduction, and so if you have incremental improvements in time, how do you unlock value from that? And I think what organizations are struggling with is that link between some efficiency and time saving on one hand but more productive employees on the other, and actually quantifying and even describing that. So that's a huge area for organizations at the moment, and the other one that's the foundation piece is obviously around data leakage, data privacy, ensuring that these tools don't allow our employees to access the wrong information in the wrong way. And without having enough confidence in that, it's really hard to even start in this area.

Speaker 4: 18:15

So I mean, how do we help? We're a big proponent in safe, short experiments. I think it's really hard on the value side to sit in a room and come up with 150 use cases. Yet we've heard of lots of organizations that have done that, but then I don't know where you go with that. And then, at the same time, where we've seen the best, the fastest adoption, the fastest identification of the data issues is where there's a safe group of employees that are testing it and now uncover the issues. So we're a big believer in small, safe tests to uncover the value and the data issues.

Speaker 3: 18:54

I'm sure listeners are interested in. Where are those quick wins? I mean, initial studies have shown the effective use of GenAI. You'll find in-custom interface purposes like customer loyalty, satisfaction retention, reducing customer churn. That impacts both the bottom and top line. But Darshan, are you seeing other areas? Or are these the ones where there are quick wins? I mean, I love the safe quick experiments, but are there areas that people should just focus on because that's a no-brainer as to where they should apply these tools?

Speaker 2: 19:19

There are a few areas that are coming up time and time again, so we've mentioned them a few times in some of the material that we push out. It's marketing. Marketing is definitely being disrupted quite a lot. Things like creating copy, for example, creating images. That's fundamentally changing with AI and the multimodal elements of AI.

Speaker 2: 19:38

I think customer service and the contact center that's the other area that we're seeing a lot of experimentation in, and I think that's going to be a big bang, and it's not about just reducing the number of people in your contact center and having a bot do everything for you. It's actually augmenting the human in lots of cases. That's the initial cases, I'm sure, and we'll see a better experience for the person phoning in, a quicker time to resolve, for the person that's phoning in or putting in a message, and I think that's a good thing. And then from that we can learn and figure out what's the best way to do these sorts of things in the long term. And the model is also getting better on a daily basis, so there's more applicability to other use cases.

Speaker 2: 20:18

But frankly, right now, marketing, contact center, hr those are the areas, those shared service areas where you augment the human, keep the people in the loop. They're the areas that we're seeing the biggest bang for our buck, but also you mentioned it, julia it's going to be where AI gets embedded into technologies. That's the other area that people are now starting to look at, and AI has taken up a lot of oxygen in every single media outlet, every single thing that you can think about. There are other technologies out there.

Speaker 3: 20:48

No, yeah, there are a couple Really. Oh my goodness.

Speaker 2: 20:51

Just one or two maybe, and they're going to see a resurgence because of AI, Things like IoT. We could do lots with IoT and IoT has been around for a while Now. You've got AI and IoT and the way that you can interrogate that data is changed. You'll see a resurgence of IoT. You'll see a resurgence of blockchain. You'll see a resurgence of some of the other technologies around the edges, and then you'll see a few new technologies popping up that couldn't have worked without AI.

Speaker 3: 21:18

Data quality in the enterprise has been a constant challenge long before Gen AI. So what do you see from clients when they realize they don't have sufficient quality of data or to properly train the models, or the data simply isn't up to scratch, or what I call AI ready? Let me rephrase that. So how important is data quality and what are clients doing to bring it up to speed? So it's AI ready Without good data.

Speaker 2: 21:42

there is no good AI there just isn't. If you don't get your data strategy right, if you don't get the sort of quality of your data to a point where it's actually valuable, then just putting AI on top of it it's not going to fix anything. You'll just get to a bad decision quicker, really. So getting your data strategy 100%, that's what you should be doing. How you get there might change. There are tools now that are available that are based on AI, that can help you scrub and clean, etc. But also, do you really need to boil the ocean and fix all your data in the entire organization before you use AI, or do you fix it in one area? Unlock that potential with AI and then use that as a catalyst to change the other data? Those are the conversations that we're having now.

Speaker 3: 22:25

Large language models or LLMs. First of all, maybe you could give us your own definition of what it means, but what are you seeing when it comes to domain or industry specific LLMs? Where will we see them evolve in specific industries?

Speaker 2: 22:37

LLMs. That's a very complicated thing, so I might not go into the technical details of how you build your LLM from scratch, but there are a number out there. We are not building from scratch LLMs here directly. We don't need to do that right now. There are plenty of options out there and we work with most of the players that are out there. So we've got the open AI piece, we've got the Google piece, we've got all the lovely stuff that's coming out and hugging face and available in Amazon and Bedrock and so on. So for us it's let's just use the base foundational elements and there'll be different ones that we use for different purposes. I really like the meta one, for example. I think that the LLM models are very good model for certain things, but actually understanding the whole tapestry of what's available to you is going to be quite key. But then how you take that forward and then creating that specialization.

Speaker 2: 23:24

We started to see that in industry already and we've mentioned Harvey already a couple of times. Harvey is essentially. When it first came out, it was the legal version of chat GPT. All right, fantastic use case actually plays really well with a lot of clients who have an internal function around that space. Let's move that forward. What else could they do?

Speaker 2: 23:46

Well, you could see Harvey moving into a couple of other areas quite adjacent to the legal space very, very quickly. We've mentioned the fact that you can create your own GPTs as well, because OpenAI have allowed you to do that. You'll continue to see that and you'll see these specialized models coming out. Remember, one model isn't going to fix everything for you either, and you've got to get used to the fact that you might have multiple models in your organization and you might stack models on top of each other to get to the outcome you want. So, for us, industry is the way to go. We've seen it in cloud. It's a similar pattern that we've seen in a lot of other industries, a lot of other technologies, so we'll see that coming into fruition over the next 12 months, I'm sure.

Speaker 3: 24:24

And Julie. In that people soft skills space, are these LNMs becoming important, that we can tune them for specific roles and functions?

Speaker 4: 24:30

At the moment I would say yes. So, if I continue with our Harvey example, that's played a very specific and easy to adopt use case for legal teams or those people that want to do research type tasks. So I think it does help with employee adoption because it's speaking the language or it's attuned to the tasks that they would do. It will be interesting as it expands, though, and as we start to look at use case or patterns in one area and try to apply them to the other. So it'll be a combination of how do we ensure that we get the most value out of certain use cases, but then how do we have the underlying data? That is specific to my role or functional discipline area. But, yes, I think the combination of kind of interdisciplinary thinking, but with subject specific content, is going to be kind of the utopia that we're looking for.

Speaker 3: 25:26

Question for both of you what's the most unique problem you've seen solved by AI to date, by your clients?

Speaker 2: 25:32

Some of the use cases that we're seeing do have a massive commercial impact. So it would be wrong of me to sort of blurt out some of the things that I'm doing with the investment banks. But they're happening.

Speaker 2: 25:42

They are absolutely happening. I think financial services are probably leading the way again, because they've been using the AI models for quite some time, whether it's algo trading or whatever. They're just used to it. The regulator's been on that journey as well, so I can see that being a big, big use case. But let's just go back to it Contact center. The contact center will be fundamentally different in 12 to 18 months time than it is today. Once AI becomes mainstream. That's going to be the biggest unlocking of value for most of our clients, because you can serve more clients. You can serve them in a more meaningful way and get to a resolution quicker. But the great thing is you're also tracking all of this. It's really hard to track phone calls. Very easy to track interactions here and make your service better and actually get rid of some of the problems before they even happen. I think that would be the big one that I'll see a lot of working over the next 12, 18 months.

Speaker 4: 26:33

The benefit of the work that I do with Copilot is that it's the universal tool.

Speaker 4: 26:37

So in some ways I'm looking at it more from how do lots of different people use it without having necessarily a strong technical background, and I think at first, when people pick up a tool like Copilot, there's a lot of summary of information, summary of meetings, summary of documents.

Speaker 4: 26:57

I think where it's very interesting now is to see how everyday users start to actually generate content with it. So that's the more interesting, I think, use case, and it is, though, breaking it down into first drafts or small pieces of work. So the art to Copilot is not in the grand gesture, it's actually in kind of small tasks being broken down effectively, but then, when you look at that kind of value chain, how much more effective the output or quick the output is. I think the nicest use case I've heard of with Copilot is really around employees that have either a non-English speaking background working in an English speaking team, or that have certain like learning disabilities that may hinder their ability to understand meetings that are spoken quickly, etc. And so there's a really very nice kind of use case where employees have just feel very empowered and bought them up to a standard that they just see immediate results.

Speaker 3: 28:01

I'll give you one of mine. I use an AI called otta ottaai to transcribe meetings. This podcast will be transcribed with otta Only two months ago that I work out. I can talk to it. I can go hey, add otta. What are the key components? Give me some quotes from this and I'm using some of this from my book and, rather than going through 80 hours of podcast recordings, I can actually get it to pull out quotes and summarise and those sort of things, and that saved me so much time and I'm now finding I'm pushing it harder and harder to do things. So my co-pollets I'm using are actually making my own job and writing a book much easier. So I think it's about playing with. Actually saw someone last night was on a board meeting. They were using otta to record the meeting. I said did you know about the add otta? No, so I think a lot of our harm moments are going to happen as people try and push the technology further and further. Are you seeing that?

Speaker 4: 28:44

Yes, absolutely so. Again, I think we see it in things like meeting summaries. So you know, recap this meeting gives you a nice summary, but I think it's when you actually then see things like what was the quality of this meeting, how could we improve the meeting, what was the opinions of this person, what was their kind of feedback on X, y and Z? And so when you actually have a conversation with co-pilot and ask them about certain aspects of the meeting, it's impressive how rich that information is. So another area that employees are often seeing benefit is with their one note kind of notebook. So they might have years of bits and pieces of information, meeting notes etc. Stored there, and so they're now using co-pilot to interrogate that and pull out thematically certain trends or topics that they just wouldn't have the headspace to uncover on their own.

Speaker 3: 29:38

I've been writing a journal for the last 15 years, every day using day one. It just freaked me out. If I ran that through AI, I'd probably find out how my whole mood and everything changed over my time in London. That would be amazing.

Speaker 2: 29:51

I've got a tip for you, though, for your podcast. So you're using Otter and doing that sort of stuff. One of the other things we're starting to see is just language translation. So you've got your podcast. You've got the stream of data there. You can push it through AI tools and have it in four, five, ten different languages instantly.

Speaker 2: 30:10

The models will get better, but we've been playing around with some of that right now. So we've got a very lovely Scottish chat with a lovely Scottish accent, and spoke about whatever he was speaking about. We put it through one of the tools, got it in German in his accent still, and actually because it was a video even done the lip syncing essentially Stuff that you would only see in the Hollywood movies. Now it's like one click away. But, the gimmick aside, you're dealing with the German team, the French team, the Spanish team, let's say, european languages, for now. You can now interact with them in a way that you couldn't do before. You don't always have to speak English. Once the models get better and we'll have some of the Asian languages on there as well imagine what that will unlock. So I think that there is quite a lot going on that will be super impactful, and AI isn't just about productivity and those hard measures. But that soft measure of just having your podcast in Spanish, what would that do? That would just open up a whole new audience.

Speaker 3: 31:09

Hadn't thought about that, but the technology just makes this so much easier, doesn't it, julia? I read recently an article you wrote that GNAI is not a product you can buy, implement and adopt once. It's a concept, a paradigm, almost a complete new way of thinking. So what should companies be doing now to prepare their workforce for using GNAI and also the impacts on talent of GNAI?

Speaker 4: 31:29

The overarching thing that we talk about in the employee adoption side is that this isn't a once and done training exercise, for example.

Speaker 4: 31:38

So you have to really think about it being the journey, and I think where we've seen the best success in organizations is where they have an honest dialogue with employees.

Speaker 4: 31:48

There's a lot of uncertainty, there's some fear, there's some distrust, and so I think having that honesty and having a two-way dialogue is very important. I think, rather than thinking about training, I think it's better to think about having your employees with agency. So the power for them to pick up the tools, to use the tools, to input into their development is very, very important. So this concept of co-creation or experimentation, allowing them to have a voice, is a very important part of the journey that I think. With the speed at which we've adopted new technologies, some organizations have skipped over and now they're going back to that. So they're seeing that they do need to revisit how we bring employees along that journey. But it's not through an announcement, a training program. It's through constant reinforcement and constant ability to give them the power to make their own choices around what they get involved with, how they might reskill, how they might experiment, etc.

Speaker 3: 32:52

So that's a good point. For many, the impact of GNAI is so deeply personal and often invoking concerns about job security, skill relevance and careers. What can be done to address these issues with employees and consumers?

Speaker 4: 33:03

So I've worked in workforce planning for a long time. I have a very optimistic view on AI and the impact on workforces at a macro level, you know. So I see the biggest issues facing us is actually that we don't have enough people.

Speaker 4: 33:18

You know, when you factor in aging workforces, the need that we're going to have even in social care and the care sector, and the amount of people coming through the system, we don't have enough.

Speaker 4: 33:29

So I see AI as being a bit of a saviour to augment employees. So at that macro level, I'm quite optimistic and I don't see, in the short term, massive disruption in terms of people not having jobs. But what I do see, and what I think we do need to be quite concerned with, is the skill gap. So those that have the time to experiment, those that have the confidence and the aptitude to just get their hands dirty and get in this, it's going to open up amazing opportunities, amazing roles and amazing work. For those that don't have that opportunity, don't have the confidence, don't have the job at the moment that gives them the time to do this, they could be left behind and I think that skill gap is probably the thing I think is the most concerning from an employee perspective. So I think there's a real role for employers to have which allows the space and time for employees to re-skill in this area.

Speaker 3: 34:27

One thing I talk a lot about is the need for critical thinking If AI is going to do some of the heavy lifting. I don't now need to summarise the meeting, because a tool does it for me. Talk to me about how important critical thinking is, not just now, but also in our education system. Should we be teaching more about critical thinking at that early stage?

Speaker 4: 34:43

I've been quite interested for a number of years on the multi-disciplinary education system that's coming through. So, rather than specialising even in disciplines like law or engineering, where you really look at an issue every semester and you look at and you tackle that issue from all those different perspectives and I really think that's going to be the future, when AI allows you and gives you that foundational base knowledge so fast I think the role left for humans is to make the connections, to apply critical thinking, to have enough technical knowledge to be dangerous, but I think it's all those applications of things, even from our introductions, that both of us have quite a multi-disciplinary background and that's to our advantage. So I think, as we work into this new world, absolutely these human skills are fundamental and the sooner that we start to bring them into the education system, the better.

Speaker 3: 35:49

So final question on the future of work. We were talking for a number of years about the future of work being about remote working and distributed working and those sort of things. What's the future of work under AI look like?

Speaker 4: 35:58

It's early days to understand, but I do think that there's a huge optimistic benefit of, and we talk about productivity, but I mean in its broadest sense, so it can be unlocking time to be more creative can be a version of productivity. So for me, I think the future of work is really resonates around. How do we get rid of a lot of the non-value-adding work that we do? So if you talk to any office knowledge worker at the moment, they never get to do their job. There's so much internal process, internal meetings, the meeting about the meeting, and we've been talking about it for a number of years.

Speaker 4: 36:51

You add the digital debt from all the different chats and things like that, and it's really hard actually to carve out your time to do your actual job. So I'm very optimistic that these tools will help us go back to that. So it can take out a lot of the noise of our jobs, it can take out a lot of the process and it really allows us to go back to how do I as an individual and how does my team add value? And I think part of this journey actually is reflecting on how do I as an individual and me as a team, what is the value that we create. So that way, if AI can do some of the basics, where is it that I refocus my time? And whether that's done remotely or in short weeks. I think all of those different aspects are now probably going to be on the table, but I do think at its heart, it's really about a clear understanding of value and focusing on how you achieve that.

Speaker 3: 37:51

And Dashain. What's the future of work under AI look like for you?

Speaker 2: 37:53

There's a couple of points I like to make, really on the future of work. Under AI it could get very easy to work remotely, it could get very easy to be super productive, but actually that human connection, that actually being human a bit, interacting with your colleagues, your friends, family, that becomes just as important. I think you can be as productive as you like, but that spark of creativity just sometimes comes when you have a conversation, when you interact with someone. So I think that that's going to be quite important and offices might change to be more like creative hubs, more than sort of where you sit for eight hours sort of tapping away at the screen. That might be one thing, but it's also the mindset of organizations that need to change, and one of the earlier questions you asked about you know sort of what does it look like for the workspace as well? I think the future of work is going to change into two little things. We are going to have to have a mindset where we are used to constant evolution, with periodic moments of revolution. So that's how it's going to work, right. So you're always changing, always adapting, always taking on something new and embracing that, and then, every now and then, you have to throughout your operating model and do something completely different and not be afraid to do that, and companies that do that will progress and flourish and actually be attractive places for people to work.

Speaker 2: 39:18

And then the other piece is just that mindset of the individual I value and I'm looking for people in my team. I'm not looking for the best technologist I've ever seen, or the best X or the best Y. I'm looking for the best problem solver. I really don't care what discipline they have. I want them to look at something you talked about, creative thinking, but it's like how do you solve that problem? What's the methodology you use in your head? I haven't found an AI that can do that just yet. Maybe it'll come and I hope it will, but right now I value problem solving and we don't teach enough of that at school at the moment.

Speaker 3: 39:51

So, darshan, your title is Emerging Technologies Leader. What can we expect beyond AI? What's emerging that we may not have heard about in the media?

Speaker 2: 39:59

Some of it's in the media already, but you cannot ignore what Apple are doing any minute now. So it's out in the US already, but when Vision Pro takes over globally, that's fundamentally going to change the way that we interact with technology. I always think of it as we've got our phones in front of us, all of us. You can't see it on the podcast, but we've all got our phones in front of us just about, and we're always looking down. When the Vision Pro and other technologies like that, we're all going to lift our heads up. That's the first thing. We're going to look forward, not down.

Speaker 2: 40:30

I think that that's going to be a fundamental change, and I think some of the technologies behind the scenes and we've got a whole series of things called the essential eight go look it up. It's quite good. It's the resurgence of some of these technologies that will kind of in the background, they'll be super charged with AI, iot, blockchain, web3.0, everyone stopped talking about that, but that could be a thing as well. Those types of technologies are coming up and then out on the further radar, just different ways of working. Like him or not, elon Musk has a lot of business out there and one of his, the Neuralink business is kind of the next big thing, I think. So I'm going to struggle with the word, but Neurothropic computing is probably something that might be coming up, probably start off with the sort of diversity, inclusion sort of areas, but actually it'll become mainstream over time.

Speaker 3: 41:21

On the Vision Pro. I'm glad it's version one, because the iPhone version one was quite limited. The iPhone 15 has a lot of features. I'm hoping that we're not going to meet in a meeting where everyone has these big ski goggles on. I'm hoping it's going to be some sort of thin film layer contact lens we can look through so as I'm looking at you, I'm seeing everything as well. I think that's what it's got to get to, because putting that barrier in front of it, there are some amazing memes of people on the subway driving with this thing on. It's funny.

Speaker 2: 41:46

The technology will get better, the hardware will get better and faster, etc. You know, we've seen a matter of a launch, the collaboration with Ray-Ban, for example. So that does look like a normal pair of glasses, and we're going from artificial reality through to sort of augmented reality, through to sort of mixed reality, and once these things do get smaller and easier to use, I'm pretty sure we'll see a version of that in the near future. But yes, you're right, right now they do look quite intimidating, don't they? But we'll see what happens.

Speaker 3: 42:18

So technology is evolving just so quickly. How do you both stay informed and continuously updated, and how do your skills update to remain the forefront of this rapidly involving field?

Speaker 2: 42:27

I do two things. Number one I read a lot. I have my blocked out times for actually catching up on stuff. Julie, you mentioned that we don't always get to do our job. I actually block out time to do my job and I think that that's quite important. But I also learn from the people around me clients, conferences. I think the more I absorb, the better I understand what I need to do and then I can give back. Julia, how do you stay up to date?

Speaker 4: 42:52

Yeah, similarly, I pay a lot of attention as to what my 15 year old nephew is doing Would be my first phase. And yeah, I try to do a lot of reading. I find it hard to find the time, so it's the conversations. It's having as many conversations as possible and hearing the practical reality of what's going on is how I do it.

Speaker 3: 43:13

I'm lucky. I probably speak to 20 or 30 leaders a year just for the podcast, and so I'm learning so much. Today, I've learned so much as well, so thank you for that. We're almost out of time. We're up to my favorite part of the show, the quickfire round, where we learn more about our guests. So for both of you iPhone or Android, iphone, android Window or aisle Window I'm loving this In the room or in the metaverse.

Speaker 4: 43:34

In the room.

Speaker 2: 43:34

In the room.

Speaker 3: 43:35

Good to hear For both of you. I wish that AI could do all of my form filling.

Speaker 2: 43:40

Yes form filling. Oh my gosh. Yes, definitely.

Speaker 3: 43:42

The app you both use on your phone Spotify.

Speaker 2: 43:45

Email.

Speaker 3: 43:45

Julia, your biggest hope for this year. Next Happiness, Dushent. The best advice you've ever received Be yourself, Julia. What are you reading at the moment?

Speaker 4: 43:53

Iron Flame. It's a dystopian future.

Speaker 2: 43:57

Book of choice. Actually, I don't really have a book of choice. I have Tin Tin, which I'm reading to my daughter. I love Tin Tin. Going through the whole series of Tin Tins with my daughter.

Speaker 3: 44:05

What's your favorite Tin Tin episode or story?

Speaker 2: 44:07

The one at the moment is the one where they're going to space, that seems to be.

Speaker 3: 44:10

yeah, I can picture it. The rocket, the rocket one. I love that. That's the one. The rocket Tin Tin. Yeah, for both of you. How do you want to be remembered, for kindness?

Speaker 2: 44:16

That's a nice legacy to have.

Speaker 3: 44:18

Now, as this is the actionable Futures podcast for both of you, what three actionable things should our audience do today to prepare for a world of enterprise grade AI?

Speaker 4: 44:27

Educate themselves, experiment. So don't get stuck in planning mode and listen.

Speaker 2: 44:34

A mindset shift. Don't be afraid of change and constant change That'd be the first thing that I would say and embrace new ideas and new thinking wherever they come from. And read the Essential Eight from the PWC website I have read it.

Speaker 3: 44:47

It's fantastic. A fantastic discussion. How can we find out more about each of you and your work?

Speaker 4: 44:51

Probably best for me is LinkedIn these days, so we're trying to publish as much of our thinking there and also on the PWC website.

Speaker 2: 45:00

LinkedIn from a work perspective. And if you're really that board, go onto Instagram. You can see me posting about nature most of the time.

Speaker 3: 45:08

Thank you both so much for your time today.

Speaker 1: 45:10

Thank you, you're welcome, thank you. Thank you for listening to the actionable Futures podcast. You can find all of our previous shows at actionablefuturistcom and if you like what you've heard on the show, please consider subscribing via your favorite podcast app so you never miss an episode. You can find out more about Andrew and how he helps corporates navigate a disruptive digital world with keynote speeches and C-suite workshops delivered in person or virtually at actionablefuturistcom. Until next time, this has been the actionable futurist podcast.