Former Accenture Tech CEO Reveals Top AI Mistakes to Avoid | CXOTalk #895
CXOTalk · 26 Sep 2025
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Transcript: https://podcasts.opennash.com/shows/cxotalk/former-accenture-tech-ceo-reveals-top-ai-mistakes-to-avoid-cxotalk-895-1cb76e/ (via OpenNash Podcasts, machine-generated)

Paul Daugherty has had an incredible career. At Accenture, he was Group CEO for Technology. He was their Chief Technology and Innovation Officer. He's written 2 books, he's advised many startups, sits on boards, and is now AI Advisory Chair at the large private equity firm TPG. Today, on CXOTalk number 895, we take an AI reality check with Paul to examine what works, what fails, and what's next. I'm your host, Michael Krigsman. Let's get into it.

I spent a lotta years at Accenture. I was there for 38 years until I retired last year, and it was an amazing vantage point from which to see the transformation, transformational power of computerization and digital technology and everything that happened over those 4 decades. And to put it in perspective, you know, in a way that some of you will appreciate, the first production system I wrote was in was in punch cards on a mainframe. So it's, uh, it's quite an arc from there to where we've gotten to today.

And, um, and, uh, I've always been passionate about technology, and, uh, in my role at Accenture, it was always about leading what's next and leading us into what's next. So, I'm still an advisor to Accenture. Um, I still do some work there. Uh, I took on a role, as you said, with TPG, which has been really exciting. TPG is a great firm. A lot of the partners are based in in the Bay Area at the heart of technology, and they've been a very tech-forward PE firms with, uh, you know, large PE firm, $260 billion, um, of, uh, of assets across a lot of different platforms, and it gives them a way to invest, uh, in big companies, the large companies

that are impacting the direction of technology, as well as through some of their platforms, in involved in the the leaders of tomorrow. And it's been a it's been great to, you know, come on board and work with TPG, and we can talk a little bit more about my role there.

Where are we today? What's the state of AI? Are we in a bubble? Is all of this hype real? What's going on?

This isn't a bubble, it's real, like, in terms of the real transformational, uh, power of the technology and what's already happening is real. We're just at the early stages of it, which is where you see some of these bubbly things happening. So, let me talk about, you know, a little bit about, um, you know, kinda what's real and, may you know, what's hype, what's real, and what's missing. I think what's you know, one example of hype, I think, is around AGI, artificial general intelligence.

And one of the problems with AGI is there's not really an agreed-upon clear definition of it. But generally speaking, people talk about AGI in the sense of, uh, general-purpose AI that can exceed the capability of anything a human can do. And I think we're far, far, far from that. Uh, and, uh, there's a there's a group of people and a group of eminent people that I respect that claim we're we're either there or pretty close to it.

I I'm with a different camp of people who who would, uh, disagree with that. I think, you know, what we have today are really amazing technology that approximate a lot of the things that people can do, but that are far from, uh, real AGI, as I would think about in terms of human capability. Mustafa Suleiman, uh, who you many of you may know from DeepMind and then Inflection, now leading AI at Microsoft, uh, Mustafa Suleiman has b- has had a series of posts that you should go look at, uh, if you haven't, the recent posts about consciousness and the the risk of assuming, uh, of giving a AI agency of things like AGI and consciousness because it can

lead to very bad human outcomes by misinterpreting what the AI really does. And I think Mustafa explains it really well, so I'd encourage you to read his posts and this, but I think that's a a bubbly aspect and lots of people like talking about AGI. Companies are claiming they're already AGI, but I I really don't believe that's, uh, that's the case. We've had AI around for 70 years. The the learning branch of AI, uh, with the transformer technology and large language models is real, and it's amazing.

If we just froze AI right now, no more innovation, no more products, freeze it where it is right now with large language models, I think you'd have 5, if not 10 years of business adoption of what the technology can do today, uh, much less the a massive, amazing arc that that it's on.

If we were to freeze it today, that implies that companies have seen benefits, they're getting benefits. Just, what are the what are those benefits?

Generally speaking, there's benefits in the obvious areas you see, like coding and software engineering. A lot of advances being made there, a lot of great tools there. In, uh, customer service types of applications and marketing applications, um, again, can go into detail. One organization I'm working with that had, uh, 30% increase in productivity and 60% increase in net promoter score and satisfaction by introducing new forms of of generative AI into their customer service organization, uh, and then in in different, um, in different, uh, industry kind of functions and and, uh, and such that we can kind of unpack a little bit.

So so there's real, you know, real opportunity in in there in there already. But just circling back, maybe I just wanna finish one thought on the on the what's missing from AI, because it's really important to where things are right now. I think everybody's a little too centered just on the large language models and this scaling law around large language models. And I think to get the next level to get to anything approaching AGI and to get to the next level of business improvement, we really need to focus on other forms of AI.

And there's a lot of things out there, large quantitative models, physical, uh, or sorry, liquid AI, um, a number of other things that are that are getting more at symbolic AI. Is Yann LeCun has been talking a lot about this, and I think the combination of the amazing things large language models can do and bringing the that learning branch of AI together with the symbolic AI techniques is what's really gonna propel us to, you know, to kind of a next, you know, next level of what we can do in business and next level of what, uh, AI itself can do.

What about the impact on organizations? Uh, you wrote a book, and a year ago, pulled out and sections from it that appeared on the cover of Harvard Business Review. And and you said this, most business functions and more than 40% of all US work activity can be augmented, automated, or reinvented with gen AI. So, as you look back a year later, how does that prediction stand up?

Yeah, it was, uh, Embracing Gen AI at Work was the, uh, was the article on Harvard Business Review. Um, and, uh, yeah, that came out of r- research, as you said, from our book and 1,500 organizations, lots of lots of, uh, research that we did, and I would a year later, we were low. The 40% was was low. I think we we we significantly understated it. We've done some additional research, and, you know, you can you can peg it above above 50% now in terms and that's looking at work hours that people expend, you know, that all of us do, that can be impacted by or that will be impacted by by generative AI.

Where we are t so the coverage, so to speak, the surface area that it impacts, as, you know, is is increasing or the potential is increasing. The actual, what's happening is still pretty light, you know? So let's just be be real about this today. In terms of, you know, adoption, we're still in the early stages, um, from some other research that, uh, that we've done. The, um, we would we would take a different cut and say that about 15% of organizations have made significant, you know, 1, 5 have made significant pro- some significant progress on scaling AI somewhere in the organization.

15%. Another roughly a third of organizations, so the balance of, you know, to get you to 50% are close on the heels and, you know, kind of doing things, working working to scale. And about 50% are still more in the kind of experimentation, trying things stage, and maybe individual use cases and parts of the organization, those types of things. So there's so y- yeah, there's over 50% of the work can be affected, but that's that's not happening today.

That's the potential, and organizations are working to get there. And I think one of the myths about generative AI now is I think you hear a lot of the tech industry talking about it, and it's like there's an easy button, just buy this and implement it, and hit the easy button, and you're you transform your business for generative AI. That's not the case. This is really hard, probably harder than the pre the technologies that come before.

This is harder than cloud and the cloud transformation because it's invasive in your organization, it's changing processes, it's changing roles of people, it's reeducating people and transforming your organization. You had to do some of that always, of course, with past technologies, but that's this is taking it to another level with AI, which is which means it's, uh, we got a lot of work ahead of us to get there.

Paul, you use the term invasive. What is it about AI that requires this invasiveness on organizations as companies adopt it?

There's 2 dimensions of it. One of which is the invasive the technology, uh, is invasive, and organizations are starting out with legacy systems, with technology debt that's been talked about a lot for years. So I won't go into that in detail. But, you know, the average organization still spends, you know, 50% to 2/3 of their budget keeping lights on on legacy rather than new capability. It's much higher than that in a lot of organizations. And that creates it's not just in that creates an investment challenge, but it also creates a real anchor and roadblock to to getting new capability introduced.

And AI is significantly different. You have a lot of work to do at the data level, uh, if your data fabric and, uh, getting that in in shape. You have a lot of work to do at your architecture and application layer, at your middleware layers, and some of that technology's immature. Uh, we can t dive more into this if you or your audience would like, but we we lack categories of products right now. There is not really, you know, good mature agentic middleware out there for organizations to tap into, and that's still an evolving product category.

So, on that technology dimension, that's hard work. The second dimension that makes it hard is the process dimension, because the the unlike cloud, which a lot of the benefits of cloud were technical, I can move to, uh, elastic capability and external data centers and things and reduce a lot of costs, and yes, there was business impact, but, you know, you got a lot of benefits. To get the benefit from AI, it's it's going invasively into processes and changing the way people work, and that's hard to do.

There's change management issues, there's organizational issues often where things cut across silos, you know? For example, wor- um, working with the organization, which, uh, that's looking at AI and customer service, but they realize the big benefit for them isn't just in customer service. It's integrating across product management and product innovations that they can really dramatically shorten this the the, uh, the cycle time between getting product, you know, customer feedback and product innovation out there. And that's really hard to compress those 2 functions into 1 and make it all work the way they want to with AI.

And that's an example of the invasiveness on that process side. And then you can get into cultural change and everything else that's part of it, but those are at least a couple of the the dimensions that make it difficult.

So, according to what you're saying, and I don't wanna put words in your mouth, AI is a forcing function for relatively broad organizational change with tentacles and implications that extend in a lot of directions. Is that an accurate way of saying re-saying what you just said?

Your AI strategy is kinda your business strategy, and, yeah, there's the big debate in organizations, do I you know, they wanna do AI. Do they need an AI strategy? I'd argue generally speaking, what you're really up what you're really doing is thinking about your business strategy with AI as a significant enabler, along with whatever other things are in the mix that you're dealing with, the, you know, macroeconomic issues and everything else that's going on. Um, so, yeah, so so you have to get in in in into the, you know, what what's happening in the business.

Now, there's things you can do more easily. You can roll you get some additional incremental productivity by rolling out Copilot tools and doing a variety of things. So, yeah, you can do that, but to really get some of the bigger benefits and look at the impact and how you can get there faster than your competitors and some of these broad, uh, organizational and process transformation, that that takes more work.

This type of change, it sounds like you're describing ERP and business process transformation from, you know, 25 years ago. How are we different today?

Client-server technology is what propelled business process reengineering, the famous Michael Hammer book and everything that some of the audience will remember from the 90s, and that the message there was similar, that you can't you can't create the superhighways of tomorrow by paving the dirt paths of of yesterday. And that that's similar to the message that I was just saying. I think it's just broader. You look at what cl- you look at the surface area of an organization that client-server impacted, and was quite low, whereas AI is impacting pretty much everything that that an organization can do.

So I think it's it is a similar message, um, in in a similar need to step back and rethink about it. And that's why I call it, rather than reengineering, I call it reimagining. You really have to step back and look at the art of the possible because you don't just have your human workers and customers in the mix. You have you have your your a your AI agents, and you have AI customers and partners that, you know, that are in that are gonna be part of your interaction that you're thinking about.

So, it does re really require this kind of reimagine reimagination capability.

Subscribe to the CXOTalk newsletter right now. Go to CXOTalk dot com because we have live shows, and they are we have amazing, really, genuinely amazing guests. So subscribe to the CXOTalk newsletter. Do it now. We have a question, uh, from LinkedIn, uh, on LinkedIn from Larry Larry Rubinoff. It's a very specific question. He says, What do you see as some of the key AI apps for the utilities industry?

You know, utilities is an interesting industry because you've got services, customer service and billing and things like that. You've got, uh, manufacturing like cap- process manufacturing like capability generation, trans- transmission, distribution, et cetera. You've got construction type of capability. So, utilities is kind of a microcosm maybe of, uh, of a little bit of everything that you have across other industries. So, I think there's clearly opportunities in that customer service area, um, but, like, we're seeing across industries, and I I know that's that's an area that, uh, I've done some work in, and I know that, uh, a lot of utilities have have done, uh, have done work in, and so that's that's an, I think, a clear area of opportunity,

uh, in the mix. Um, in the, uh, in the kind of more the operations, uh, side, there's been some really interesting work and innovative work, um, that some utilities have done around using AI for things like, um, wildfire prevention, you know, in the the West Coast of the US. in particular, where fires have been a cause of major outages and and system issues. They're using AI in a couple ways. One is to detect fires more quickly using AI analysis of satellite imagery and all sorts of things you can get.

But they're also doing, uh, one utility in particular is, uh, with my working with my former company, uh, is do is doing some work to look at how do how do you equip workers to go into very hazardous wildfire environments to main to, um, to preserve, you know, protect the the infrastructure, which is very difficult to do. So, um, a lot of imp- implications like that, uh, in utilities as well. There's some interesting things going on in the in emissions management for utilities to to help utilities, uh, uh, monitor emissions.

For example, things like, uh, meth- you know, tracking methane leaks or track track tra- tracking methane burn off in different ways to allow, uh, utilities to operate more sustainab- sustainably. So, I think there's a lot of use cases in utilities, uh, because, again, it it kinda pulls in a lot of these use cases that happen, uh, across the across the other other industries. Utilities just is a challenging environment sometimes because of the rate-based model and the way capital's allocated, it's sometimes hard to invest, you know, in those opportunities, but I think there are a lot of lot of opportunities.

We have another question, and this is from Joy Tuhan, and she says, Paul, it was great working with you at Accenture. What are some KPIs and metrics that organizations should use to show AI is creating real business value? It's a really important question.

If AI isn't moving your the performance metrics on, like, the page 1 scorecard of the company that they track, whatever they they're in an organization, they're gonna track, um, all sorts of, you know, key metrics to their business from, um, from, uh, you know, day services outstanding to accounts receivable, all also to all sorts of productivity measurements and manufacturing and other things. And, um, I think if if AI isn't making if it an impact on those things, I don't I don't think you're looking at AI right in terms of, uh, the impact it can have.

So, I think it's a matter of looking at the KPIs that are critical in your business and understanding which of those can be dramatically impacted by AI. In in my Accenture role, what we did to pick up throughout the Andriod's question, you know, we're looking at, uh, with AI even before generative AI, with just AI, you know, classical AI, s so to speak, before, we were looking at our, for example, our software development processes and looking at how do we improve the efficiency and productivity of those every year, and we were improving them about 10% a year every year, uh, and we would we'd measure that, you know, t to make sure we got those those kinds of outcomes.

So, I think that's I think the key is not necessarily a whole new set of outcomes, but to be very intentional about, um, the AI projects that you're pursuing and be able to tie those to the, you know, the page 1 metrics that you manage as a as a company. Of course, you're gonna have other metrics for your AI programs, are the programs working, are they successful in delivering value and everything, but that's, uh, I think that's key.

The one other element of that that I was gonna mention, but, you know, maybe in some other context, but that is critical is, um, is I think organizations also need to avoid AI use case itis. There's too many organizations that I walk into that very proudly show me 100 use cases of AI across the whole org, and they got so many, they're so excited, they got so many. I'm like, you know, pick 7, pick 5, pick a number, but focus and pick a few and and really tie them to something that that makes a difference in your company, make them happen.

I I don't think it's victory to have a lot of use cases. I think it's victory to have you, you know, a small number of use cases that make a difference so you can, you know, show the value, show the impact on your company, and then move to do more.

I have to reinforce that comment. There are so many folks who descri- who who have chopped up the organization, evaluated the organization, and identified all of these use cases. But, as you said, identifying use cases and putting people to work in so many different areas does not necessarily help you reach the finish line.

I think that's right. I think you can solve small problems and miss the bigger problem. There was one, uh, large consumer goods organization that I worked with. We worked on AI in the sales arena, and the initial use cases were around how do we get sales leads better f- to make a sales rep more effective and all sorts of things. It turns out the bigger use cases were thing the bigger, uh, productivity improvements, I should say, were cutting across use cases.

So, how do you combine a salesperson's productivity with things like invent- inventory availability and s and, um, and delivery times so that you could cut across that and and get things to customers faster? Like, and there's a whole different way of, you know, way of thinking, how can you move product to customers faster rather than just making an individual salesperson more effectively? And you'll miss opportunities for those bigger reimagining, you know, if you just get caught up in use cases.

This is from Bavana Bhagat, and Bavana says, Is the talent transformation with AI for real, or will it be another RPA?

AI risks being another RPA if deployed the wrong way. Um, I I think RPA, you know, RPA was we kinda had this problem of being a little fragile. You implemented use the RPA technology or robotic process automation for those who aren't familiar with it, which is being able to stitch together and automate processes across different systems. Um, it's very powerful technology, does a lot of great stuff, but, um, I I think it hit it hit limits in terms of what it could do and was wasn't flexible to adapt to changes on in the underlying systems on a in generally speaking.

I think the I think the AI could be subject to the same limitations if not deployed very well. So, I think the uh, I think the architecture, uh, of how you deploy AI is super important. The way you insulate from changes in underlying foundation models is super important. And I think, you know, architects are back. For those of you listening, if you're enterprise architects, we need lot more enterprise architects, you know, to to shape these AI solutions, uh, going forward that really understand all these things because it's super important and otherwise, you can end up with brittle AI systems as well.

Now, Bavani, in your question, you said talent transformation. I t I do think so, I'm not sure exactly what you meant by the talent transformation related to RPA and and AI, but, uh, there is a there is a, um, you know, I think, uh, a broad kinda reskilling of people, you know, develop software engineers, developers, architects, et cetera, to learn AI, maybe more so and f- well, certainly more so than people needed to to, uh, advance their skills with the RPA era.

So, I think that is part of the journey ahead, too.

Sasha in Namdar says, uh, he's from Accenture. He says, What are your thoughts on the future of the consulting business going forward?

Services, generally tech services, you know, let's just call include consulting, systems integration, and outsourcing, all that stuff together, tech services, generally, I think is, um, is is, um, uh, is is hugely impacted by everything happening with generative AI, and it's probably the biggest impact for many, you know, more of an impact than any of the previous technology changes. And what that means is it's that, you know, this the industry's at the center of some of the changes that are happening, which means it's both a threat and an opportunity.

It's a threat because I think the existing way that consulting happens, uh, the existing ways that systems integration and outsourcing happens aren't gonna be the ways to do it and are already not the ways to do it in the gen AI world. So, I think the challenge for any company, for Accenture, for, you know, any company in the industry, is how to be first and ahead in transforming to the new processes. So, on the consulting side, rather than being having s smarter people and good knowledge capital and walking in with a better presentation to the CFO's office, that was yesterday.

You know, tomorrow, what you need, you need to walk in with a foundation model with pre-trained on the client's data and in the CFO's office, be remodeling their business dynamically in the first meeting. That and that's a that's a big transformation on a lot of levels, and that's emblematic of what we what needs to happen in the industry and what what what organizations like Accenture, I know, are working on.

What does that do to the consulting industry when you have a lot of people whose primary skill was listening and now they have to, of course, still listen, but there there are so many more dimensions, and the talent pipeline has not necessarily prepared them for that.

This stuff is hard, as I've been emphasizing a lot as I've gone through. C- clients need help in doing this. So, I think there's a big role for services organizations to help companies do this faster, better, more eff- effectively. So, I think there's a big need for this for services to do that. Um, but they need different shape to it. So, an example, you need more forward deployed engineers, you know, in the business to more quickly drive change for the client.

Um, so it's a it's a matter of transitioning the talent, which I think, you know, companies have been, you know, Accenture, as an example, have been very good at tran- transitioning and and retooling talent. It's about, um, rethinking the workforce, the the degrees of leveraging pyramids and things like that will change, you know, depending on the type of work. As you look at this going forward, it's about, uh, s but moving services to software, so what of the services that are provided, where are there opportunities to platform platformize, if that's a word, or transition to services, kind of the Palantir model in that way.

And so, I think those are some of the those are some of the changes in organizationally, talent-wise, you know, technology-wise, Michael.

And this is from Ebru Bayer, who says, There are still organizations out there who haven't done cloud transformation. They're lacking data quality and governance. Do you see any prerequisites that organizations should focus on before jumping into defining an AI strategy or executing that strategy?

Those are impediments, but you can't wait. Like like to so it's it can't be sequential where I'm gonna do my cloud and data, and then eventually I'll get to AI. So, uh, there's a there's some companies that have decided that. There's some company that I've worked with who have looked at it and said, Man, I'm I'm in such bad shape on data, I'm just gonna do that, and then I'll I'll deal with AI once I'm done with that.

That's that's a a r- a small minority, though. I think most realize, Okay, I got a lot of work to do on the data side, but I gotta get some elements of AI working. I gotta make progress, uh, on my journey, and they're they're melding the two together. So, I think I think it's you gotta look at your specific circumstances. Those are big impediments. Many companies, I'd say most companies have some variety of the challenges, uh, that you mentioned, Ebru, um, but it's a matter of figuring out how do you parallel track it and and manage that in an effective way.

Let's talk about adoption, success, and failure. What can go wrong? We have all heard about a recent MIT study that said 95% of geni- gen AI projects fail. Now, there were some issues with that study. Maybe you could say it was talking mostly about proofs of concept. Nonetheless, when it comes to gen AI, what does failure mean? What causes failure? How do we even recognize it?

It came at a time in the market news and when people wanted to seize on it and used the message that 95% of projects were failing, like the headline, uh, in different ways. So, I think it was unfortunate that it got the traction in the way it did because I think that's kind of a misleading conclusion. Um, but, uh, and I don't think 95% of generative generative AI projects are failing. That's very I have a surface areas of hundreds of AI projects that I see, and it it's nowhere near 95% failure.

That generally speaking, people are making progress in different, you know, in different ways.

And what is failure? What do what how do we define failure? And then

I mean, the way I would define failure is you don't achieve the business objectives you set out to achieve, which gets into one reason for failure is sometimes they d they don't come they don't there's no clear North Star of what they're trying to achieve or no clear metrics from a business perspective. So, that's, um, so, that that's how I define, uh, define failure. There's other, I guess you could define it other ways, but I think that's that's one way to see.

I've seen people t develop great individual use cases that they couldn't scale. I'd say that's an element of failure too, because they realized that that, you know, they they developed it to, uh, great POC on a small scale, but they realized they had they were missing the data or whatever they d might have needed to, you know, to go scale it more. So, um, I guess there's 5 things that I that I find that, um, you know, companies that are successful are focusing on, um, and I've been talking about these for a while, so these aren't necessarily new if you've heard me talk the last few months, but but these are there's 5 consistent things that I've been seeing.

One is the value point that I just mentioned, Michael, you know, back to the definition. The there you you need to focus on the value, have a business case, tie it to metrics, understand what that business case is, and in in value generally will mean really looking across the use case items and looking at things that, you know, reimagining the process that are doing something significant. So, it's this value piece is important. Sounds obvious, why wouldn't everybody do that?

But, you know, not everybody does. A lot of people are starting in different ways. The second point, um, the of that, uh, you know, reason for success or, you know, obstacle, um, is is what I call the digital core and data. And, uh, and that was the at the heart of the question we just answered, but sometimes companies don't have their their digital core, the systems in place. If you're not on SAP's S4, for example, their new, uh, generation of SAP, you don't get access to their AI.

So, you gotta you gotta get your digital core in place and upgrade your SAP to the latest version to get access to the AI as an example. So, I think getting your digital core in place and getting the data in place is key, and and companies are generally speaking behind on the data piece, but that's, you know, that's that can cause that can cause failure or limit the the effectiveness. The third piece is talent and under investing in the people.

And this is the talent to do AI. Like, you have people who can do the AI, but it's more more broadly the talent across the workforce. Are you training the workforce with what they need to do? I think there's there's some best-in-class examples of companies that are doing a great job of educating their entire workforce on AI and what they're doing, what their objectives are, and involving their workforce bottom up. And these tend to be enlightened leaders who who kinda get it, eh, in you know, bottom up, you know, in in, uh, incorporating their workforce.

Uh, great things that companies like like Maersk, like Walmart, I think, are examples of companies doing this, that, um, that, uh, you know, that are really building the talent base they need going forward and the learning systems they need. The 4th point is responsible AI, uh, and really putting the guardrails around it so you know you're not getting into trouble as you do AI. The leaders leading companies, I think, are doing that well. And the 5th point is viewing it as an ongoing multi-year tr- change programs.

You're building the the the capacity for techno- technological and process and organizational change into your approach rather than viewing it as getting one use case or one project, uh, right. So, those are the characteristics that I see of companies that are that are doing well.

Let's talk about private equity and venture capital. Uh, just give us some insight into your role at TPG as AI advisory chair.

TPG, large private equity firm, $260, you know, billion in assets, um, and they s span all these different platforms. It's not just, you know, um, buyout, you know, uh, capital. It's growth they have growth capital, they have credit, they have real estate, they have, uh, impact funds, their, uh, climate fund and and others that are focused on impact investing, and it's it's really an amazing optu- you know, amazing opportunity to combine across those. And the reason they brought me onto the team is it's a very tech-forward team.

They they really understand it. They've been investing they've been very successful investors in a lot of domains, including technology for many years, uh, but they really wanted to look at, you know, how techno- you know, where technology is creating opportunities and how to accelerate some of the opportunities that they see. So, their role as AI advisory chair is to help with the with the overall strategy and investment themes and, uh, work across those different, uh, capabilities that I just discussed, looking at where there's opportunities, uh, helping, you know, uh, on deals that they're doing, uh, you know, new deals that they're doing, and, um, also work on pulling together, you know, uh, expanding the network, pulling in other, uh, expertise

on AI, building other connections in AI that'll help them, you know, do be be more even more effective, uh, going forward. And it's, uh, again, been a lot of fun, and given their s scale, they can have impact, uh, you know, kind of on a, uh, impact on on a big way, which has been very, uh, very exciting to see. So, again, that combined with the VC angle has been a a lot of fun to see it from both perspectives.

As you're looking at startups and investments, of course, the traditional evaluation points have been the product-market fit, the team, and so forth. How does AI now fit into investment analysis and decision-making?

It's a strategic thing. I'll go through this first. There's a strategic thing you have to look at that's a little different. And I'll I'll talk about, you know, technology-related companies first, but but it's not just technology, but this, I mean, phrase cautious in technology terms. I think there's, like, 5 ways that AI can in impact a technology company. First, it can be no impact. A, you know, so AI doesn't impact it at all. That's number 1.

R- rarely the case, but it could be the case, uh, that that that's the that that's the case. The second is AI enhances, you know, the what what the c- the the SaaS model or whatever the company does today. And look at Workday as an example. You know, the new agentic they just had some new announcements at, uh, Rising this week, and AI enhancing, you know, the the capability of w- of what what they do. I think that's that's a second level or second example.

The third level is the AI is becomes more important than the s underlying SaaS product or whatever itself. You know, the AI is the focus. I'd argue that when you look at GitHub, when you look at maybe ServiceNow now, when you look at the future of Salesforce, the AI, you know, becomes the defining capability around the product. The next level would be, uh, AI goes even further and commoditizes the underlying technology. Look at, uh, you know, Klarna and the way the way this the CEO CEO of Klarna has talked about the impact of generative AI and how he works with different enterprise software companies as an example, uh, maybe Zendesk, you know, others, you know, that that fall into this,

you know, kinda fell into this, uh, this pattern. And then the 5th level is is AI just just compresses the whole spend in the category because you don't need the the the software. Uh, Chegg might be a good example of this. Chegg, the learning company, who, when the generative AI models came out, people flocked to, you know, for free or whatever, for very low cost to generative AI models, and, uh, and Chegg lost a lot of its, you know, a lot of its, uh, population there.

So it goes that's kind of a little bit of a framework to look at. I think you have to look at that from an from an investing perspective, really look at that and understand, not just today, but if you look at the the move of the markets space in the next, uh, years, what the what the impact is.

We recently had the CEO of Zendesk on CXOTalk discussing their AI transformation. So, i if anybody's interested in that, we had an in-depth discussion. So, just go to CXOTalk dot com to check it out. All right, let's take another question very quickly now cause we're we're just running out of time. And this is from Sufyan Ben Sabur, and he says, What is next after AI, beyond AGI? And really quickly, please, Paul.

The th- things to look at beyond what we currently consider is is AI and AGI. I think look at physical AI. I know you said beyond AI, but not enough not many people are focused on physical AI, which is where really Elon's going with with X and XAI and everything he's doing, and where World Labs and organizations like that are going. So, think physical AI and AI meets the real world as one thing. Uh, look at quantum, uh, still general purpose quantum, still number of years off, but some specific impacts of quantum happening fast.

And related to this world real world blending thing is robotics and the the massive advances in robotics and these new physical world models that are coming, helping us make, you know, we we'll I'll I'll predict on the show right here. We're gonna have a a a moment probably bigger than the ChatGPT moment around physical AI and robotics and what they can do. That's coming relatively soon. Y- you know, maybe even bigger bigger to us in in realization than, you know, again, the ChatGPT moment.

How soon?

I think that's within a couple years, you know, in terms of really seeing these physi- physical models and, you know, the what they can do, you know, say 2 2 w- maybe 2 years, you know, sur- certainly about 5 years.

Stephen Redmond on LinkedIn says, How can organizations get ahead on organizing and cleaning their data while not losing momentum on AI adoption?

Really think about your business case, cause what I see time and time again at organizations is people coming up with great data plans, uh, and data strategies, and it gets shot down by management, whatever, because there's not a business case tied to it. So, I think you need to think about how to link your AI benefits or whatever benefits you have, an ERP transformation or whatever, with with getting your data state advanced and link that link things together so that your data transition your data modernization doesn't get cut off and shut down, which is what continually has been happening to companies for the last decade or more.

Rajat Kumar says, What will be the future of EMR, electronic medical record applications using AI? Are some of the major players integrating a AI into their apps, and what kind of integrations will that? Basically, the future of electronic medical records.

You know, if you look at the recent announcements from Epic at their event about a month ago, those of you that are in this space, they made a broad set of announcements and a really an expansion of what you think of as EMR. They're expanding Epic's role well beyond what you thought of as EMR to get into these care provision activities where AI will really have big impact. So, I think if if Epic's v- vision of the future is right, you'll see, you know, the EMR providers critical play a critical role in automating a lot of aspects of patient care, revenue cycle management, and and the like.

There's other views of the world which say that'll build built up around the EMRs, and, you know, question will be which which vision plays out.

Arselon Khan says, uh, responding to your comments earlier about enterprise architect architecture, he is a recovering enterprise architect. He says, All jobs will be affected by AI. Are there any that are still untouchable by AI?

Physical skills, you know, that that people do, and you go out and do things in the in the real world. I think those will, t take a lot longer for AI to, you know, AI to impact. I think anyth- anything where you're dealing with knowledge and information, um, it it'll it'll have impact. That's there there's we have a lot of data. I can put give you a link to send around, uh, based on different labor categories of where gener- generative AI will automate versus augment, and where there's no impact.

I can send that out maybe as a follow-up, Michael. I'll give you the link, uh, and that does it by industry as well as function.

We need to talk about responsible and ethical AI. Everybody, every technology company says, We are r- doing responsible AI. What's the reality behind that today?

Less than 20%, and that's probably a generous number, of organizations are really doing responsible AI the way I would characterize the need for it. The, um, to do responsible AI, right, I can't get into each one of these, but you need 3 things. You need principles. Everybody's got that. You need I wanna be f- be fairness, fairness, privacy, security, explainability, et cetera. So, everybody agrees on the principles. Most organizations have that. The second thing you need is processes.

How do you do it and make sure that you're doing AI responsibly? Uh, is it baked into your software engineering practices, et cetera? Most aren't doing that. And the third thing you need are tools and measurement. Do you understand every AI use case across your organization? Do you know what the risk level of each use case is, and do you know what's being done to mitigate any any, you know, bad outcomes, you know, from, uh, irresponsible outcomes, so to speak?

Uh, most organizations aren't doing that. So, um, so you need to we need to get beyond the principles and lip service to responsible AI to implementing, you know, really, uh, rigorous, industrialized, responsible AI. And like I said, less than 20%, probably a lot less are are really at that stage.

That was a harsh commentary.

It's true. I've been preaching on this, so to speak, for, uh, about 8 or 8 years now, involved a lot of the organizations that have been, uh, advocating for responsible AI, and it was preaching totally into the wilderness with 0% listening for a long time. Um, so I think the 20% I view as an accomplishment. You know, we gotta we got, uh, more seriously, but I think too many organizations don't really understand that it's a business issue because the biggest the biggest, uh, risk of AI is eroding trust.

If AI is used, deepfakes, whatever, think about all the different ways AI can tarnish your brand. Um, those companies that really develop trust in the way that they're using AI are gonna have a competitive advantage over other companies. This is about business and strategic value of your brand. This isn't about a feel, you know, a feel-good thing. I think too many people still think of responsible AI as a feel-good thing rather than something that's strategic and essential to your competitive survival.

What's your view of government regulation of AI?

I think it just needs to be balanced. I think we need a government regulation that encourages innovation, innovation you know, l- leading innovation, um, and and pro- and it has, uh, the right focus on preventing the real harm, deep fake and some of the real, you know, obvious harm that are around it. So that's that's a lot more to say on that, but that's the one sentence.

And one sentence, what technologies are you watching now?

One new thing I'm watching that I think is in the underappreciated, undervalued category, or really under thought about category maybe now, is crypto. There's there's a lot of people thinking about crypto, but it tends to be the crypto converted that are thinking about crypto. But crypto as a general-purpose technology for tokenization, not just of financial assets, but of, uh, you know, other things, data, manufacturing, et cetera, um, I think is is poised with, uh, recent steps the US has taken to really legitimize in a lot of ways, um, to really have a much more profound impact on businesses and everything we do than than the non-crypto community is aware of right now.

What is your trajectory for the next steps in your amazing career?

I'm gonna keep doing all the fun things I'm doing now because this is, like this is kinda what I love to do. This this arc of technology has been kinda my career and my my life, and, uh, wanna stay close to what's happening. Uh, beyond that's more more time with my family, which, uh, love to do, and I'm having a great time with that and, uh, hopefully getting a little bit better at guitar, which has been my retirement project since I left Accenture, trying to, uh, gear up and spend more time on guitar.

But all this exciting technology stuff is taking time away.

I did notice the guitar in the background.

Yeah, I'm getting pitched on a lot of startups that that can help me learn music faster. That's that's that's where I'm hopeful.

And with that, we're out of time. A huge thank you to Paul Daugherty. He is currently AI advisory chair at private equity firm TPG. He's a advisor to Accenture. He's doing a lot of other things. Paul, thank you so much for taking your time to be with us.

It's always fun, Michael. Thank you, and thanks to your audience. It's an amazing audience. That's what makes this so fun in addition to all the work you put into it, and, uh, love the questions. So, thanks to all who are tuning in.

Yes, for the folks who are watching, uh, there's so much information that has been packed in today. By Monday, the edited video we'll do some light editing, will be up on our site, and then next week we'll create a summary. Watch this video if you're interested in any of the topics that we've discussed. The replay will be there. You can take your time and you can unpack it. And right now, subscribe to the CXOTalk newsletter.

Go to CXOTalk dot com. We really have amazing shows coming up. We'll see you next time. Thanks so much, everybody. Thanks to Paul Daugherty, and have a great day.