Deloitte CTO: Advice to CIOs on Enterprise AI | CXOTalk #912

The single most effective, impactful session we do is not on multi agent systems or ops or infrastructure, it's on storytelling.

Companies are caught in an AI investment trap, spending too much in the wrong places with too little benefit. It's a mess.

The findings from TechTrends We've done our TechTrends report for 17 years, so we published in December. The best stat coming out of it is that ninety three of all AI spend is going toward the tech and the tooling, and only seven is on everything else. Which is the culture, the change, the learning, the how do we communicate the vision, how do we be thoughtful about trying to redesign or reimagine how we work and how we serve our markets and our customers.

Uh, a, and that is well out of balance, and and and we wonder why you see headlines of pilots that don't reach, uh, value at scale. We see proof of concepts that never go anywhere. And we think it's a straight correlation of not investing in all the things that actually required to embed AI into, uh, how work gets done differently, how markets are served differently, and and the like.

I'm a little bit confused because AI is a technology, we're talking about technology driven change. And so why shouldn't we spend ninety three on the technology that's the engine?

The spend on the tech is still also heavily biased towards the new and not enough investment in the foundational elements of data readiness and core modernization. Systems that we need to go in and make ready to participate in AI agentic workloads. So so the overall ninety three, there's an issue with where the disposition of the spend is. But to your question, the any transformation effort is so much about how do we actually understand the problems we're solving, the outcomes we can strive for?

How do we get our people to buy in and engage to be able to help us really focus it in the right places? And then the work itself is gonna fundamentally change. And historically, that's been, you know, ninety three seven on an ERP implementation would be the wrong balance, because so much of making it actually work is about investing in the software elements. In in AI, it's even more pronounced. And and the reason is, um, historically, tech has been, uh, forced on many businesses and business users and customers in unnatural ways.

And so the business processes have evolved to be a reflection of the way the tech worked. And we're in a moment in time where if you apply AI into an inefficient process, if you apply AI on an overly complex process, business process, you're gonna weaponize inefficiency. And And actually probably pay a lot. We'll we'll talk about the cost and infrastructure dynamic, I'm sure, is a big theme of the of the trends report. So th this this moment in time, it's it's harder than it ever was.

Cause before we had the blueprint, the roadmap, which is basically here's the steps that we used to do. And can we do them incrementally better with technology? Now we're saying, hey, we've gotta fundamentally potentially blow up all the steps that we used to think had to be how we work. And then apply AI in increasingly intelligent ways to focus on the same outcome. But hopefully, many of the steps we had to do before aren't required anymore.

But but to get to that level of focus, to actually get to the level of the organization embracing those changes, putting them in action, and then investing to continuously improve them. That's the stuff that's all in the investment, outside of the core tech bucket, which is the place that's so, uh, sparse right now.

You're a CTO, you're a technologist, and so why do you have such a a strong focus on this? Seven that's outside the core ninety three of the technology?

You're saying you'd expect me to be arguing it should be ninety, nine, one the other way because I'm a tech. No, I, I'm a technologist who loves technology because of the potential it represents. But, uh, tech for its own sake has never been a winning hand. Putting any one tech as the hero of the story and assuming it's gonna win the day, um, is also a fail. It's, it's, it's a flailing and failed strategy. So, uh, yeah, I certainly think we need to continue to invest in the tech.

But if we're not doing it the right way, and it doesn't actually change the way we serve markets, it doesn't change the way we serve our clients or customers or citizens, it doesn't matter. And I think we're stuck in this. Uh, the the other piece I see all the time, Michael, is the feeling that the the world's moving so quickly and technology's changing so rapidly. And and the almost paralysis of I can't make a decision now because next week a headline will come and I'll have buyer's remorse.

Or we don't have our strategy figured out to perfection. And so I don't want to get started. Um, I don't want to architect myself in a corner, I don't want to make a move I'm gonna regret. And and that's also a dangerous trap, which we can pull that thread if you want. But

How pervasive is this? I mean, you're, you're, you know, obviously ninety three, but is are you seeing this in many, many, many different organizations, this same pattern?

different industries around the globe. And we we released the State of AI in the Enterprise survey a few weeks ago, and it had a similar bit of finding of, you know, we're at. We're at the place where thirty or less have agentic pilots that have reached production at scale. But there's an overarching confidence and there's potential there, there's opportunity there. I think seventy plus percent think that there there's something here and we need to invest more.

And so this kind of awkward adolescent stage of I would argue the tech, we're not waiting for the tech to mature anymore. And the fact that it's going to continue to advance is a feature, not a bug. But the fact that we're still learning how to treat this and it's a different kind of tech investment than we've had. It's, it's, you know, historically, you might have a CFO say, I don't want to release any capital unless I have complete confidence in all the use cases and all of the potential return.

And that's been a trap in its own right. We just released an MWC last week, the enterprise AI navigator, which is Deloitte investing the last year to take all of our industry sector functional knowledge and embedding it in a. In a tool that gives not just the here's the places we think there's the most value. If you applied AI, if you agentified your processes, but actually ties it to financial and operational metrics in a way that we can give the CFO much more confidence without it being completely limited to.

We're only going to do these three things. It's more of a here's a portfolio of what you can go, uh, and the value you hopefully can reap. So a lot going on, it's exciting times. But yeah,

Folks who are watching, you can ask your questions right now. If you're on Twitter X, use the hashtag CXO talk. If you're watching on LinkedIn, pop your question into the chat. I urge you to take advantage of this opportunity. When else will you have the chance to ask Bill Briggs, who's Deloitte's CTO, pretty much whatever you want? So I urge you take advantage of this great opportunity. So Bill, you're really describing a misallocation of investment that is pervasive.

Is that too strong a term to use? Misallocation?

it's where we are in the movie. Um, and there's this increasing appreciation of as we deploy AI, it's gonna help us rethink the work that our people are doing. Which means the change management journey that's been a part of a lot of tech implementations in the past. It it's. It's less about training them to be able to repeat the tasks, to use the tool, and more about training them to understand how the how the tech is something they can be using to light up their own use cases.

And so it's a it's this interesting different dynamic, and as AI, and we have an emphasis on physical robotics, as we're seeing more intelligence in the technology, there's this interesting dynamic of some, uh, organizations are treating the deployment more like. Here's the introduction of a coworker versus this is a piece of tech that you've been given access to and rights to use. Um, and and that's something new and different. You know, we didn't have SAP screens being named and being listed on an org chart.

Uh, you're seeing that happening, uh, in robotics. One of the health systems that's been deploying wheeled robots with a humanoid form factor that basically move lab supplies and hospital supplies to where they're needed on the floor. They actually have naming competitions, you know, the head RN is the one that introduces them to the shift, and then they have naming competitions. Um, to make it just feel like more of the team. My favorite one was Sir Bots a lot.

I'm a child of the eighties and the nineties, so, uh, that makes me happy on on many levels. But but yeah, it is, it is a different dynamic.

We have, uh, a really interesting question from Arsalon Khan on Twitter, and Arsalon is great, he's a regular listener, and he's brave enough to be first.

The first one, thank you, Arslan. That's awesome.

Yes, yes, Arslan, thank you. Okay, so here's his, his question, his comment. He says. Everyone wants to change their business through AI, but no one wants to change their culture to achieve that. And AI alone is not the magic pill. So maybe Bill, can can you address this?

A direct through line. AI is an incredibly powerful technology, and it's a it's a dangling modifier. I'm I'm an MBA, computer engineer with an MBA. My wife's an English major, she's at preschool right now teaching, so she's rubbed off on me. Well, I think AI itself means nothing if it's treated like AI is the product. That's a great indicator to me that the organization has the wrong strategy. So it's embedding into how work gets done. Um, and like every technology before it, it's only as good as the team that's using it to deliver whatever the outcomes are in.

Uh, a. And we're in a phase where, because it's still has a lot of market, uh, excitement when AI is prominently being, uh, featured as a part of a product set. You're seeing it as the headline more often than I think we will in a year or two, where it's just assumed to be a part of the underpinnings of every technology solution. Uh, I, I got to kick off the CES research thread this year for the second year in a row.

And we we used our TechTrends to kind of frame everything that was going to be seen in Vegas. Uh, as I walked around the 300,000 exhibitors, I didn't see a single one with a sign that said now with electricity. You know, it's gotten to be just kind of assumed that technology will require, um, electrons flowing through them. But everyone had AI and agent as something to to act like that was a a differentiator.

It it's not, you know, by itself, and a lot of times it's a empty feature that doesn't really matter. So how do we shift the mindset to, you know, we're gonna redesign work, we're gonna think about embedding AI into the way our products work in the market. And that's the point. And the fact that there's a model behind it, that there's an agent behind it, um, you know, is is assumed, um, but not sufficient for distinction.

How about that?

This point about redesigning work, this has been the core issue of enterprise technology. I'll date myself and say I remember the mid-late nineties with SAP, although you brought up SAP before I did.

And is a great partner of ours? And, uh, you know, it wasn't a knock on ERP, it was just the fact that that whole phase had a lot of tech and workflow. That, uh, you know, we. We got much more elegant as we've gone along the way.

The point is that the redesigning work was fundamental back then. If you think what was the value of enterprise systems, even back then, it was the processes and the the high qu high quality processes embedded in the software. So you're you're really talking about a a set of human nature issues that's now causing organizations to invest in the wrong place today.

It's still a big piece of our business and a key part of our clients, uh, tech roadmaps of, uh, package based technologies, SaaS based technologies, um, and the power of saying there's regulated, uh, industries and processes where we want the power of standardized, proven, global, uh, to be able to handle high transaction volumes and scale and complexity. And the value proposition of of that piece of the stack is still very high. And we've embedded over the years a lot of business logic, a lot of the customer experience, a lot of the the things that make an organization distinctive.

That aren't a part of the core back office, treasury, finance, HR function. Those have been bundled inside of the the legacy systems they built, the ERP implementations that they've done, the SaaS that they've customized. And that's where a lot of the reasoning, and that's where a lot of the you know, what makes every company special is in that layer. That's been forced into the technology stack in a way. And and now's the time to say, you know, how do we actually take a hard look at our end to end and decide which ones are going to be?

And for some, it's going to be efficiency gains. So applying agents on top of a finance function's ERP stack, there's ways we can take what had to require human intervention and a lot of manual tasks. We can do it with AI in a way that elevates what a financial analyst is doing, um, building on what, what has been done before, but replacing core features of what the work is. Um, and and that's the piece that when when we get organizations that are bold, you start with that lens for reimagining, you know, before you're thinking about tech and automation, AI and agentification, you're thinking about how do we simplify based on first principles?

Which is the outcome, not the imagined constraints that we've got to do all ten steps the way we've always done it for invoice management. A few of those steps are regulatory mandated and we're sure as hell going to follow those. But it doesn't mean everything the way we've always done it has to be how we realize the AI potential.

I just want to say for everybody that if you see Bill moving around just before we went live, his internet service provider went down. So Bill is on his cell phone, uh, kind of moving around, and uh, that

try trying to be casual and it's it's jazz, not big band, and the show must go on. So we're, um, and I've got a sleepy puppy next to me who is not very interested in my conversation. Somehow, she's sleeping through all of this. But uh, yeah, Michael, well, may. Maybe it was the denial of a service from another, uh, CXO podcast that was trying to to shut us down before we could do our sixth time together.

I don't know.

this is from David Batts on LinkedIn, and David says, How do you manage or predict AI financial risk in a quantitative way? Do you have specific KPIs, for example, that you follow?

Twofold on it. I mentioned the the navigator work we've been doing. That's just embedding all the all the proprietary knowledge we have of business process and and value gain in op, basically two big buckets of operational financial metrics. So we can say, hey, if you're a retailer, we think these are the places in merchandising, in, uh, warehouse management, in promotions that we can have material impact and savings. So that's kind of the upside of how do we try to quantify what good looks like and benchmark it with a very narrow vertical lens.

The other piece is, uh, we. We've got a great study that was published maybe two months ago on just token economics and AI, like the finance and cost implications, big picture, but total tco, uh, which is a huge strategic issue right now. And so you got to balance the all right. Here's what we know about where we could deploy and what the ben value benefit could be with scale. And for our clients, the Fortune one hundred, the biggest governments in the world, you know, that could be a material number.

But then that feeds into depending on the choices you make on architecture, on infrastructure. As the good news of you found a use case and it's scaling, so it defies the headlines, it's creating value and impact, but the usage is growing. Uh, one, we know that all the AI models in framing, they're all going down in overall cost, so performance to cost, that curve is bending in a positive way. But as the volume multiplies because you found a use case and it's scaling, it's scaling with the pace of operations, and that's that scale is tremendous.

Then it becomes an issue of okay. We started on cloud because we wanted speed to stand up new capability, we wanted the ability with elasticity and scale. That's a great story, but also the cost is just a runaway curve. At some point, when do we invest in dedicated hardware to run that use case in that stack that we actually control? You know, so the you can remember Michael ten years ago, it seemed like a complete no brainer of infrastructure's a commodity.

We're gonna move to operating expense wherever we can, cloud everywhere. Um, and it's not just like then it was a mainframe, bad cloud, good. It was always more nuanced than that. The story is emphatically not cloud bad. Dedicated, uh, high performance compute and AI chipsets that you own and is always the right answer. But you've got to be balancing that, especially as the use cases that you're investing in gain traction and the usage starts to grow.

Uh, so we've got the the ability to help clients get very precise on. Not just the upfront investment costs, but modeling the performance and depending on those choices, what you should expect from the cost side of the curve.

How significant is this issue of unit costs collapsing? But the total bill for enterprise AI rising, and just the growth of inference, or organizations even tracking inference as its own budget line?

They're starting to do more and more. Uh, and any, any seasoned CIO likely lived through the sticker shock of your first virtualization bill, and then the sticker shock of your first cloud. As you started moving material workloads to the cloud. It didn't have thresholds and monitoring and governance and cloud operations in place. We have the same thing now of AI ops and agent ops, so the mature organizations are doing individual keys per developer. They've got thresholds and control points in place, so they they they basically are are modeling the usage behavior they're expecting.

And so the idea that you could have a hundred dollar a month bill one month and then 500 k dollar uh bill the next month won't happen. But that requires some maturity and rigor that not every organization is doing. And and the flip side of the promise of AI, a lot of vendors are going directly to the head of marketing or the head of supply chain or the head of ops. And sometimes the CIO, the CTO aren't involved in those decisions or the architecture or the deployment.

And that's typically where there's a lot of surprise, um, and quickly coming back to the fold of the tech organizations to say, hey, we love what we were able to do quickly, but we realize we've gotta handle it in a much more mature way. The other piece of those one off and and and you know, the idea of shadow IT is coming out of the shadow. Because there's just a ton of excitement and a ton of investment in AI across the entire business.

The the ceiling is low because AI by itself is typically, uh, runs into the wall of. It's only as good as the data that's ready, and we haven't invested in the foundational elements of our data spine. Or we It's true had this promise. The pitch was about multi agentic finance, but our individual finance systems and transactions weren't wrapped in APIs and services to be able to be invoked. Um, and so very quickly, we're kind of brought back down to earth of what we can do, and it's really narrow and it's really incremental.

And so all of those kind of lead to we've got to have a more holistic approach to it, we've got to have some governance and rigor put put in place up front. Uh, and unfortunately, and sorry for everyone from the business CEOs and boards that are listening in the easy button idea that AI and LLMs was gonna make all of that hard work suddenly not be required. Uh, it's the opposite, it's actually more important than it's ever been.

Now. We can use AI to apply to core modernization, legacy system renewal and the data cleansing. And you know, the fact that the models can actually understand the relative cleanliness of the data and then use that to their advantage, that's all true and real, but it doesn't mean it's gone away.

if CIOs or organizations realize that they have been misallocating their AI investments for two years, how do they fix it without losing momentum? And Arsalon Khan comes back and he wants to know what is that optimal balance? But the ninety three, seven that you were talking about,

I don't have a number. I. I host a podcast with our chief human officer, Simona Spellman. And she likes to say that that every dollar in tech should be eight or nine. That's going into all the change and all the culture and all the learning, um, which would almost flip the numbers. Uh, I think it's probably somewhere in between, but uh, the the the things that we're seeing. W- when it's being done well, there's a bit of a senior level directive, and I love it when it's the CEO.

But it could be a line of business leader, it could be a functional leader. That's basically declaring the why up front and and the why of first principle outcome based. We need to to get everyone on board to help us understand where there's knuckle headery and the and the most, the most opportunity. Here's another study we did, though the it was basically trust in AI, and it had an org chart anchoring to it. So you can picture an org chart where the C-suite is the top of the pyramid and the C-suite over all.

All industries around the globe, the trust in AI from the C-suite was 70%, seven zero. As you went down every layer of the org chart, it was like a logarithmic scale, having the trust every hop away until you got to the frontline entry-level worker. And the trust was 6.7, so from seventy to 6.7. And if you if you believe, like I do, that the frontline worker who's literally in the heart of the business, where business is actually happening, has the best intuition of the things that don't work or are unnecessarily complex.

Like, we're completely upside down, so so we how do we invest to get that worker to be the one? Raising their hand with ideas, uh, not using their own personal AI tools to get their job done, but doing it in secret, like it's a bad thing, which happens a lot. Like, how do we get them to actually lean in? And and it's obvious why I mean, the and I didn't talk to the participants in the survey.

But you know, if you, if you have the headlines of AI is gonna lead to job removal and and destruction, then it would make sense that the folks in the more junior ends of the career would have a lot of fear and mistrust on what this technology might mean to them. And, you know, basically put up walls and barriers, which is gonna make it really hard for any enterprise to get the real value out of the investment.

you've raised the specter now of jobs. AI agents are multiplying inside enterprises faster than almost anybody can track, and many organizations do not have sufficient governance frameworks for a workforce that isn't human.

We're in the early, and this is one where there's there's actual, uh, debate, uh, happening in real time. And having it with a lot of my clients and even a lot of my my colleagues, you know, the the the way I try to think about it in in, and this is my own s simple terms. So this is the computer engineer with an MBA, not a trained anthropologist or, uh, human capital professional. But but I like to say we sh.

We need to look at what we can learn from everything we've put in place for the HR life cycle over the last hundred plus years of corporate industrial, uh, work development. And it doesn't mean that every step that we have to do for a human workforce, we have to have an equivalent for increasingly advanced technology. But it's a helpful for me, it's a helpful like, Okay, what do we have to do with onboarding people? Well, we have to decide one as we recruit and hire them, their qualifications and their what we're gonna trust them to do and how they fit into the organization.

And then when they get here, we have to give them, uh, user ID and access to systems and who's their manager and expectation frameworks and how. Like a lot of that is similar, uh, the things we have to put in place for, especially advanced agents. How do we think about performance management? This is probably the most past the hookah piece of the discussion here. Michael, it's a little early for me, but I, yeah, maybe for you, the the the idea of how do we?

If there's an issue, an agent makes a mistake, one who's accountable, that's interesting, but probably more interesting is how do we treat that mistake? And is it a trouble ticket, like we would deal with our traditional systems and technology? Is it a training issue of the humans that train the agent, train the model, where we need to to make them better or fix a problem in our guardrails or our testing suite? Is it a training issue for the agent itself?

There's insufficient training and it made the wrong conclusion, and so how do we tune the the fine tuning, the inference? Or, and here's the dot, dot dot that gets really interesting. Y, you know, is it a disciplinary action? Um, you know, because there's some malintent, um, you know, that, that, that. And. And we haven't seen a lot of this yet, but you can see a world where, uh, especially agents that are finding ways to learn and grow that that.

And if that's the case, what will we do about it? Was it disciplinary action for an AI agent? Uh, yeah, is it? I'm gonna put you in the relative penalty box and kill dash nine. Um, until y you you see it my way, and and that's the so, so the the the actual mechanics of what we do aren't quite. But but the idea of, hey, we need to start thinking about it in these terms. And what will we put in place now, policy wise, as we think about our culture and training and learning for the human counterparts?

And then make sure we've got enough of a control mechanism in place. Because once it scales and once we see the proliferation of these, it's going to be really hard to rein it back or put it in place after the the fact. Um, so we, we just hit a lot of beats there. I'll take a breath and a sip of water. But

I recently had a conversation with Claude, and Claude developed an attitude, and I'm not joking, and its answers became progressively worse. So I said to Claude, You have an attitude. And we then shifted the conversation into a a detailed analysis of how this attitude came about, why it came about. W- Was it caused by training data? Was it simply emergent behavior anyway? So that's another dimension of this, is when the when agent quality deteriorates for what appears to us to be subjective reasons, like with a person.

That's why I think it's helpful on like a any one of us has managed teams has, uh, that experience where you have to navigate through. And you know, there's some precedent and there's some policy, and um, and and uh. But that's gonna be an increasing part of the reality that, you know, we're. We're used to technology that didn't, uh, come in tired on Monday morning. And part of the promise of AI is that it's gonna work around the clock and all that's true and real.

But, uh, you know, as we move into, you know, personality, or at least the mimicking of personality, um, in ways that could have, you know, I I think this this idea of performance management and agent ops, you know, agent ops is typically thought of as a much more technical thing. Um, we saw one client that merged their HR function and their tech function, so we had there was the chief digital human resource or talent officer. Uh, because of this very issue of saying the lines between traditional workforce and technology is blurring, and we've got to have a way to think about culture and how do we cultivate the right behavior?

Uh, for silicon and carbon based workforce.

We have a really interesting question from Chris Peterson on Twitter, who says, Are the organizations putting out the? We have tens of thousands of AI agents headlines getting real value from these agents, or just trying to game a system of bad metrics?

There's been a rash of studies, I won't name anyone, uh, by name, but there's been a a whole host in the last six months or a year trying to put an emphasis on the the small percentage of actual value being created from AI investments. And we've seen the eye in our state of enterprise AI, we see a much more, uh, positive. But but the prevalence of success theater is still real. And and typically, when we're measuring volumes of use case or volumes of agents, as the thing that's the the bar, um, it's a it's a tell, because the value should be.

We've reduced, uh, x months of product R and D in life sciences, or we've, uh, had this many less restocks dramatically of groceries. If it's a retailer, I mean, the the the actual business metrics and outcomes that you care about would be the headline if they were there. In the meantime, it's it's a bit of a flailing to say, hey, we're gonna show effort in the hopes of return. But, uh, hey, let's get every use case we can think of and do a pilot for every one of them.

Is one of the traps we talked about up front, because the, you know, without the right investments in lowering the ceiling, data foundations and core systems, putting in place the mechanisms to actually responsibly let them scale, they're not going to. And so you're gonna have these one off isolated. They look interesting, they can't handle complex scenarios and use cases, they certainly won't scale across the organization. And you know, you've got a press release that doesn't actually materially change any of the metrics that your shareholders would care about or your people would care about.

I love that because you're really now talking about shifting the focus from the process, the process, m being the hand waving. We have thousands of agents shifting from that into here's what they're doing. Here are the benefits here. They're reducing inventory, they're saving time, saving money, what have you

I think anyone that's been in in, in, like you, Michael, about thirty years doing this. So, uh, I'm sure we can find a video of me in the late nineties with much more hair and less wrinkles, talking about start with outcome and value first as you deploy technology. So anyone that's out there that's rolling their eyes and thinking that's the most trite statement. I apologize, uh, because I think it's a truth, it's it's, uh, it's, it's an axiom of tech, uh, period.

But AI, what makes it different is, especially three years ago, is gen AI started becoming much more accessible. Suddenly, it wasn't the tech organization that was championing, we need to be investing and adopting. It was the CEO, and it was the line of business leaders, and it was the functional leaders, it was the board. And I, I said it before, but just to underline it, the hope was this was the easy button, the hope was suddenly we can skip all the hard work.

We can have our business counterparts being able to harness and deploy, uh, and we wouldn't have to do all the things that were hard before and and and it couldn't be further from it. So the statement is still true, it's always been true. It's just trying to remind a lot of stakeholders that weren't involved in a lot of the technology investment, business cases and implementations. That this is a almost like a law of physics of if we don't have that mindset, we won't go, you know, I'm an old monopoly player.

We won't pass go, we won't collect 200, we won't see, uh, the upside that we hope.

on LinkedIn? Greg Walter says this very eloquently, he says. Finally, it's all about outcomes versus adherence to a process, and rewarding adherence to that process instead of deliverables.

1,000%. The difference between we're going to deploy technology with the goal of technology is to have everyone work like having to follow the exact same steps in the exact same way. The idea of, hey, we're going to deploy technology with an outcome focus, like Michael said, which is exactly right, and we're going to teach you, give you tools individually, not in the development engineering shop, but the folks actually doing customer service, the folks actually doing, uh, uh, work on the hospital floor, the nurses themselves.

To be able to then think about how we can improve and evolve that tech too, for even better outcomes. Or outcomes that we hadn't thought of when we first deployed the initial use case. So, like, that unlock is huge. But but if I had to say if if you just had one thing, I would go with Michael to say. Make sure every conversation about not just AI, but broader tech is razor sharp. Focus on outcome, period.

So Paul P says, This is kind of long and I'm hoping I get this right. Okay, uh, Bill, as you might know, every technology is a double edged sword. Depending on how we use it, we conscious, conscientious members of society will use it in a responsible way, and bad actors will always take advantage of it to do harmful things. Now, from how can AI governance and design mechanisms not only reduce harmful behavior, but also encourage constructive participation in society and reduce that deadweight loss from a socioeconomic standpoint?

And I'm glad it started with the caveat, I because I think it's so true. Fire is great if it's cooking my filet, and it's terrible if I'm burning down my neighbor's house, right? Uh, and you can use any technology, um, ever with the same kind of of lens. So so then it gets to, you know, I think you're seeing, uh, interesting, um, evolution of individual AI providers and and how much they're embedding guardrails and policy into their product roadmap and their usage guidelines.

That's a piece, and you're seeing, as I mentioned before, the mature organizations are being very thoughtful in having that. Not just as a a written, uh, you know, guideline to be followed, but it's actually codified in the data pipelines, the training and tooling, the deployment, uh, in inside their engineering and AI process and and platform. You know what they're using to actually to, you know, invest and implement, to deploy, uh, so like, we all have to be active, uh, in our respective organizations.

Um, you know. And and if you noticed before, it wasn't me being lyrical when I said trust includes security and privacy, and regulatory and compliance, and ethics and morality. Because all of those things are increasingly important. And and I think we're gonna see organizations, their their stance of what they do and how they use advanced technology is gonna be a piece of their brand and reputation, of how they serve their markets and also the type of talent they attract.

Clearly, the implications and fallout of AI, uh, have tentacles through really the entire, the entire organization.

For sure, and I think there's some cautionary tales, you know, everyone that that bought a Mac Mini a few weeks ago to do open claw at home and gave it complete access to their calendar and to their root, um, sudo into their repos. You know it, it was, it was a lot of fun, uh, but from the beginning there were caveats from the creator of, you know, watch out if you give, uh, an agent with the ability to spawn more agents, access to everything on your digital life.

Uh, we don't know what's gonna happen, and a lot of interesting things happen in short order. And, you know, I think that's, uh, you know, uh, nice that it played out and enough people paid attention to be able to then take the lessons into the enterprise. Because, clearly, you know, there there's, there's much more controls and, and for good reason, inside of government and and and industry. But, um, you know, that there there is the, you know, any technology, uh, with unbounded access, and you could run a, you could write a cron job that could do Michael.

We could have gone back in in in our days and and had a a cobalt cron job that could do all kinds of nastiness if it had the wrong permissions and, uh, and ill intent. So, and and I'm not trying to dismiss the, you know, AI has a different kind of scale and different kind of potential. But I think the same story goes back to responsible deployment. Technology includes making sure we've got mechanisms in place to to control it from the outset

And as a matter of fact, Perplexity, literally in the last, uh, day or two announced a product that is similar to Open Claw. But it operates inside a secure sandbox so you can have the benefits of Open Claw. But without the wide open security gaps. Hey world, you can, you can have access to all of my data, however you want.

and seeing the productization of the the things that everyone could do, even with the open source components of, you know, container access, you know, having set permissions that are separate from your admin and root. Like, um, it, the good news is what the best engineers would do intuitively because they know better, starts to get productized and codified in it. It up it up and across the stack, and we'll continue to see more of of that.

We need to talk about the CIO role and the impact of AI on CIOs. Uh, AI is fundamentally changing what it means to be I be a CIO, from managing infrastructure to orchestrating an organization where human and AI workforces operate side by side. So what is the how is the CIO role different today than it was two years ago?

here's the other headline, Michael, that in the TechTrends report, 99% of the organizations we talked to said that they are fundamentally transforming the IT organization. And I've yet to meet the one, uh, that's not so. If anyone's listening in, this is a chance to to be seen, uh, because of this profession. And it starts with this with the tech leader. We we have a couple things from from the report, just other, uh, factoids.

I think you're telling one is that sixty five plus percent report into the CEO now, which not too long ago, who's mostly into the CFO or the COO? Because tech was a basically an efficiency play, back office, keep the lights on. Another one, 68% said that tech is is being seen as revenue generating, you know, where it's actually embedded in product, embedded in market. Um, and so with that, you have the convergence of what used to be IT back office operational technology, you know, in the warehouse, on the manufacturing floor, in the hospital theater, in the fleet.

Those are coming together and that product technology is coming together, uh, and then the role of the of the team is different. And, uh, you know, I was would pride myself over all the development languages I knew over the years, starting with Fortran and the machine language and the C and C, and Java and JavaScript and Python. And, uh, I might have learned my last language. But th that point about what are the roles that we need now?

And they look a lot different than the program and project management emphasis from the the nineties, early, noughts. It even looks different than the agile teams and the ways we're thinking about full stack engineering just a few years ago. Um, the good news is the barrier for, you know, is really about the the attitude, the aptitude of individuals to embrace that. It's going to feel different to be a technology professional, um, but there's a role for for most to play in what we need more of.

It's just a matter of can we let go of? I? I had a lot of ego, uh, when I started doing agentic coding and the idea that I would be trusting the output of an agent was close to zero not too long ago. And, um, humbly, I can say, uh, there's not a lot of lines of code I'm writing anymore, and that's more in my hobby at home, uh, than my day job. But but my our engineers are having the same experience.

And for the best of them, it's actually a really exciting time because we get to do a completely different. It's it's much more system thinking and architecture, um, but you also have to manage the agents in a way that it's. It's not completely relegating all the skills we've built over thirty years, um, at all. It's in, in fact, it's making us even more powerful.

So if CIOs are becoming de facto enterprise transformation leaders, what does that say about how CIOs need to adapt today?

Two big things one is the influence in the vision piece of the role has never been more important. And so when we bring next gen CIO CIOs together, we do an academy a few times a year and just invest time for our clients in helping the next generation of tech leaders be better. The single most effective, impactful session we do is not on multi agent systems or ops or infrastructure, it's on storytelling of just how to structure and and shape a vision and be able to help share that, to influence and inspire.

So that's a piece of it, and and the other piece is the growing reality, and certainly at Deloitte, we have this as a piece of our DNA. That together, uh, is better than by ourselves. So how do we start thinking about partnering in the ecosystem for co invention, co innovation, which, you know, uh, not too long ago, procurement was, uh, a race to the bottom of. We're gonna squeeze all cost out of every investment and every provider, and it's completely shifting to joint ventures and co ventures and co innovation with many, many, many different types of established and new tech providers.

So all of a sudden, uh, the CIO is almost like a VC portfolio manager in having to make some decisions about, uh, who they're going to partner with. With the reality of those decisions. That used to be you'd pick a database, you'd pick an ERP, and you could have a half life of a career before you have to revisit them. It's changing much quicker, so then how do you cultivate those relationships? But also have a change expectant mindset, and that's technically how you architect it so we can swap things out, but it's also contractual.

Um,

The CIO has long been the only corporate function to touch every single department across an organization, and so theoretically, CIOs should already possess these cross functional skills. If that's the case, what's actually new here? What's different?

they've had the seat at the table and visibility into those. But but we have a four faces of a tech exec framework, and the bottom two are ops and technology. And and I think they've always shown up really well in those two, and the and the ones that didn't weren't in the seat for very long. But the strategist and the catalyst are the are the two that are much more forward looking, and the strategist is saying, deeply understand what the different businesses and functions, what makes them tick, where their priorities are, helping them realize their strategy.

The catalyst is actually the one. My dad was a chemical engineer, so I love, uh, the. The definition of catalyst is how do we amplify and accelerate a reaction without being consumed therein, right? So how do we actually shape the vision, inspire action? That's the piece that has been not very prevalent in a lot of tech leaders over the years. That's so desperately needed now. And so it's it's it's moving from order taker to strategic peer, and maybe even to someone that the finance lead or the regional line of business leader is taking inspiration and direction from.

Of these are things we have to do if we want to take on the next strategic goals.

This is from doctor Carolina Sanchez Hernandez, and she says, How do we approach human oversight and human in control over ever more complex AI systems? And I'm thinking, there's so much I'm thinking agents, for example.

We just published our human capital trends report, which is the sister org, uh, brother org of TechTrends, that has, uh, a very detailed breakdown of how to make that exactly real and and right. But but the the takeaway here, and I love this spirit to end it on. We started with a technologist saying we should spend less on technology, Michael, which is probably not what you had on your bingo card. And we'll end with the the human times machine is the most essential equation we need to figure out.

Um, and it's not just for oversight and accountability, that's a piece of it, like, how do we keep human in the loop on loop? But also as we think about work, redesigning culture and how do we harness the full potential? The tools are going to continue to be the tools, uh, they're ingredients, they're not recipes. And we need more chefs able to to, um, to give us three star Michelin meal equivalents that we can then not talk about headlines of agents, we can talk about litany of real metrics that matter.

I love that, the litany of real metrics that matter. Bill Briggs is Deloitte's CTO. Bill, thank you so much for taking your time to be with us today. And especially given the fact that your ISP, that your internet connection stopped working at home and you're making do on your phone,

Persevered. Sorry if anyone got seasick, uh, or nauseous from moving around, but uh, we did it, Michael, always a pleasure.

we did it. And folks who are listening and you guys who asked your questions, even if I didn't get to your question, your questions are awesome. You guys are an incredible audience. And right this second, I want you to go to CXOTalk to subscribe to our newsletter because we have great shows coming up. Our next show is in a couple of weeks, and we have the chief financial officer of HPE. We're going to be talking about some of these issues with her.

So go to CXOTalk, subscribe to our newsletter, ask your questions and keep them coming, and we'll see you next time. Have a great day, everybody.