The Corporation of the Future: Agile, AI-Powered, and Human-Centered

Jerry: I'm Jerry Won. Welcome to Tomorrowist, where we explore the trend shaping the future of work. This week, we're imagining what the corporation of the future looks like and how to build it today. In an age of AI and constant disruption, forward-thinking, companies are beginning to rethink how they're organized, shifting from rigid hierarchies to more adaptive models.

Here to share his expertise in shaping the organizations of Tomorrow is Chris Mulh, global co-leader of operating model and org transformation at Alix Partners. Chris, welcome to Tomorrowist.

Chris: Hi, Jerry. Thanks. Great to be with you [00:01:00] today.

Jerry: I'm excited to talk about this. You know, we, we talk about, you know, on this show quite often and just in our, in our daily discourse about how technology and then all these things are impacting how we work. And, um, you know, we don't talk about this, right? Because we assume that corporation structures and how we, uh, work or how we are organized, um, is, is one of these things that we don't, um, see as, as, um, you know, uh, malleable as perhaps it can be so excited to learn, uh, through this conversation how we go.

Um. You know, in your role as, as a managing director at Alex Partners, you've worked with businesses in all types of different industries on transformation. Um, even our own research here at SHRM, uh, notices that, uh, for CHROs, uh, 30% of them, in fact, they ranked the org design and change management amongst their biggest focus for this year in 2025, which is the second highest priority.

And so we know leaders are thinking about it, um, and wanting to get ahead. And trying to do today what we can do best for tomorrow. Um, what are some of the most common pitfalls, uh, organizations face when they start to think about redesigning their structures for the modern [00:02:00] challenges?

Chris: Yeah, it's a great question, Jerry. I think there are three things that come to mind. I think one is culture. Uh, I think what happens is in these businesses. The businesses of today, the legacy businesses that aren't built AI native, I'll say you've got processes and rituals that have formed and, and given people successful careers.

And now we're talking about taking those and completely changing them and, and in a sense tearing them up and starting with completely different processes and rituals that have AI embedded. And so from a cultural perspective. It's a really hard thing to manage through, and so I think that's, that's a, a big one that we're seeing.

Second is leadership. I think from a leadership perspective, you've got these people who got in their seats the traditional way with the traditional processes [00:03:00] and, and a traditional mindset. And if they're not willing to break kind of those paradigms in, in, in the ways that they think and the way they operate.

Then the business isn't going to transform because everything starts with the executive team. And if they're not pushing this and, and driving it and building, uh, and rewarding it and building that into the fabric of the way that the business is, is running through the transformation stage, they're gonna fail.

And, and then lastly, hierarchies. And I think we'll probably talk a lot about this today. But old organizational structure with the kind of siloed pyramid structure, um, it, it's, it's rigid in nature. And when we're talking about bringing AI and technology into organizations and redesigning the operating models, when you look at a business, many of the core processes that each business, regardless of industry is, is.

[00:04:00] Is using to drive, you know, their, their sales, their customer interaction, uh, their r and d. These are, these are things that are cross-functional in nature. And if you've got these rigid siloed structures and you're trying to embed AI in processes that are cross-functional, it can get really bureaucratic quickly and bogged down the, the pace of change, which is the last thing you want.

Uh, you, you need this transformation to be nimble, agile, flexible, rapid testing, rapid failure, and, and you're not able to do that if you've got this cloud of bureaucracy sitting over the top of it with multiple levels of approval from multiple departments and heavy oversight and steering, uh, you know, you need to eliminate that and set up more of a network structure.

Which we'll talk about today because that's gonna be the unlock to, to get this to be as nimble and agile as [00:05:00] possible.

Jerry: Yeah. You know, I think based on your experience and what you've shared and, and again the conversations that we've had on this show and, and just, you know, a lot of business leaders are talking about how do you sort of. At least nudge away and, and to even, um, ask certain questions on, hey, we always say, we've always done it this way.

You know, why fix it if it's not broken? But also sort of trying to rethink of if we were to restart this organization today from scratch, would we still build it this way? Um, and also build it sort of in a future proof mindset. Knowing that the technology will continue to change and then change at a Quaker pace.

Um, let, let's talk about this organ, the idea of the organization of 2030, which from a future perspective, Chris, it's only five years away, but considering the pace of change, particularly AI and technological change that we've seen even in the last five months, um, we may not know what five years looks like.

And so share with us your experience and your expertise on, on what you think the organization of 2030 would look like.

Chris: Yeah, and I'll, I'll talk, it [00:06:00] could be 2030, maybe it's 2035, but certainly it's within the next 10 years. Uh, I think what's gonna happen is you, you're gonna see two main themes. Um, first is I think you're gonna see innovator led organizations. What do I mean by that? So let's think about a startup today.

You've got a vc, you startup, you're founder led. You develop a product that expands into multiple products, multiple geographies, and at some point, most businesses get too big for a founder and an innovator to run, and they have to hand over the keys of their business to someone who knows how to run a traditional business because they're just, they're built to innovate and, and, and they're not built to run this large bureaucratic organization.

And that's not, it's really not what they want to do, to be honest, in most cases. And what we think is gonna happen in the future is a lot of that bureaucratic work, if not all of it, is going to get automated. And so we [00:07:00] think companies are going to be able to have effectively, you know, software in a box, you're starting off your sales and maybe you've got one or two switches flipped where AI's helping you do a couple very basic.

Things. But then as that company expands and grows and starts to develop new products, new markets, you're flipping on more switches in that box. So it could be finance switches, it could be product development switches, it could be customer support switches, but the AI is, is able to take on a lot of that extra work.

So instead of hiring people, you're flipping switches and the AI's taking that on, right? And so what happens is as the business grows, the innovators. Don't have to give up control of their company. They can keep innovating and driving that, that engine, uh, because the rest of the business is, is AI automated.

And so we think the power will stay in the hands of the innovators a lot more than, than it is today. Um, and then the second thing, [00:08:00] which I was getting into a little bit with that answer is leverage. We think AI obviously is gonna provide tremendous leverage. You're gonna have 10, $20 billion companies with.

In the hundreds of employees, maybe in some cases less than a hundred employees. And, and we think that the, there will be, you know, depending on the industry, uh, every, every business is a bit different. But there in general, there will be sort of three core groups within businesses. So you'll have your innovation side of the business where you've got product managers.

Who are curating hypotheses and constraints informed by AG agentic, AI agent input. Uh, then you'll have designers direct that, direct the AI for to explore different concepts and, and, and for accessibility. And then you'll have engineers who are supervising the agent output, who are focusing on more of the system architecture, um, and the integration.

[00:09:00] And so those are sort of like traditional roles that exist today that are gonna change in the future. Then you're gonna have new roles. So you'll have things like AI product owners who are looking at the agent's behavior and guardrails and making sure those things are in check. Uh, you'll have model governance and safety leads who are making sure that, you know, the, the models are properly governed, that the safety constraints are being met, that are set by the overall business.

You'll have data product managers. And then you'll have platform engineers who do things, you know, make sure that the AI sort of tool chain and the machine learning operations are all working as they should. And, and so you'll have this rapid, continuous product updating that's driven by this ongoing experimentation, testing and customer feedback that's almost happening real time.

So you've got products out there in the market, they're interacting with customers, you're getting real time feedback, and your AI agents are able to, you know, recode iterate, you know, ship, ship, new bits of code and, and new [00:10:00] versions, uh, and new product updates like almost continually. And, and so that's, that's the innovation side of the business.

Then you'll have a big go to market engine, but that's gonna look very different than it does today. So you'll have agent AI developing marketing campaigns that are highly targeted and customer and customized because we have real time feedback on what customers want, how they're interacting with the product.

Um, you can do real time AB testing and the agent AI can adjust that. And so your, your feedback on the AB testing is almost instantaneous, and it's making adjustments to see, you know, how it's impacting sales and, and, uh, top of funnel. And then on, on the sales side, you're gonna, you're gonna have sales reps who become advisors who are really steering the ai.

And look, in many cases your customers are gonna be ai. And so your AI sales is gonna be selling to your AI [00:11:00] customers. And you're have humans sitting on top saying like, what's working, what's not? Uh, you know, and then at some point. Especially in B2B there, there's needs to be human interaction. So the sales role's not going away.

Um, the salespeople are gonna be curating insights that, that they're seeing based on product, uh, capabilities and customer needs. They're gonna be co-creating value cases with customers and they're really managing this multi-threaded stakeholder, uh, dynamic situation that's augmented by the AI like copilot that's sitting next to them.

Um, and so we think go to market's gonna change, but, but people are still gonna be very, very important in that process. And then if you think of more of like the g and a, the finance, HR, accounting, uh, legal roles, it, AI is gonna run most of that end-to-end. It's gonna be fully automated. You're still gonna have humans required to do strategy.

The governance and ethics as [00:12:00] we talked about earlier, and really the model break fix. So they're gonna be handling exceptions. What's the model doing right? Where's the model going wrong? How do we fix that? Uh, and their job is to drive stakeholder trust. So we've got all this AI that's running our, our g and a, like how do we make sure that the executive team and the board and the constituents are customers of what that the, the AI is doing are.

Are, um, accepting of the outcome and that it's driving the right results for the business. And so I think, we think that's how, that's how businesses could look in five to 10 years.

Jerry: I, I think it's, you know, certainly a more efficient way. If you look at sort of, you know, um, a, a metric that is often talked about is, is revenue per employee. Um, you know, and the way that you're describing it, and I think even the ways that we're observing it, right? As we see newer companies, you know, develop, um, with, with AI at its core, or even AI as a product itself.

Um. There's, there's a difference I think, in, in looking at these organizations that are being built from the ground up with AI infused into every part. And then for [00:13:00] most of our audience, um, you know, for what it's worth, it's not a good or a bad thing. Most of us work for large organizations, historic organizations, longer tenure organizations that already have, you know, um, not only systems, but lots of people already doing most of these things.

And so, um. Share us your thoughts on sort of the, the realities of the difficulties, challenges and opportunities for large, you know, um, older, more static organizations, sort of rolling back. A few things or innovating or to make themselves more nimble. Is that possible? Or, you know, how do you advise organizations that are large, complex, with a lot of humans already embedded into every bit of the process to get to that, you know, sort of the vision or the, uh, model of the 20 30, 20 35 organization that we just spoke about.

Chris: Yeah, I think, I think it starts with the strategy and so you need to take a step back and say, okay. What do we want AI to do [00:14:00] for us? And, and you. It could be you look at product develop, you know, let's take a software company, right? You might say, okay, well in our software company within product development.

These are our objectives. This is how we want to use the, the AI within services. This is how we want to use the ai. Same with go-to-market sa, same with with G and a. And so you, you wanna make sure that you've got the right goals across the right, uh, core process areas of the business or functional areas, if you will.

And then I think the key is once you know what your objectives are in each of those areas. What are the right use cases? So what are the, the processes that could be cross-functional, um, within those areas that we wanna start with because they make the biggest impact. There's the biggest, uh, you know, case for change, whether that change is getting a product in a customer's hands faster, whether that change is, is driving out cost, being [00:15:00] more efficient, making our customers happier, whatever it might be, and, and then take those use cases.

And prioritize them based on their ROI, um, and the, and, and how easy or hard they're going to be to implement. And I think if you, if you start there, um, and then you take one of those high impact use cases and you drive it home, you, you, you build it out, you innovate, you fail, you, you change it, you iterate, you succeed.

When you get that success on that, on that important use case, um, you're gonna start to build that buy-in in the organization. And the ability to do the other use cases that are sort of in the, in the plan is going to, is going to increase. People are gonna be more on board, they're gonna be more accepting, they're gonna be more excited, they're gonna wanna be a part of it and, and they're gonna help drive it ultimately.

And so that's kind of what we're seeing as the biggest unlock. The problem we see with a lot of companies is they're. They have the, we gotta get in the AI game and we've got [00:16:00] a bunch of people running around doing AI and we have no idea what the ROI is. We don't know what the projects are that are out there.

What are we working on? What should we expect from it? When should we expect that, you know, how do we need to change our operating model based on these, these projects being completed? Right? They're not planning for that. And so if you're thinking about those things upfront and you get that proper foundation laid, and then you build some wins.

We're seeing a lot of success in, in companies who are adapting that way. Even really, really large, you know, 50, 60, $70 billion companies.

Jerry: Let, let's talk about leadership. I, I think, you know, obviously leaders need to, um, be very mindful and sort of how they approach it. There are, you know, real implications to workforce, um, you know, retraining, reassignments and things like that. And so when we think about how AI impacts not only the organizations of the future, perhaps organizations of today, um, how does that change?

The leadership roles for executives [00:17:00] from traditional to what they need to be, uh, learning and adapting to, um, you know, not a lot of us, you know, in these roles. Um, learned AI growing up. Obviously it's all new and we're sort of learning it together. Um, and the nuance for executives at the C-suite in particular are how to infuse this not only into their day-to-day, but how they can leverage it, uh, to help their organizations move forward.

Chris: first thing I'll say is the executive team is gonna be more important in the future than, than it is today. Um, when you talk about the CEO. Uh, we imagine them being the conductor of the orchestra, right?

Where the instruments are, the ai, um, and the people are interacting with those instruments. So the CEO is gonna be studying the strategy, like we talked about with the executive team, and then really continually evaluating and challenging some of the scenarios that the agent AI is, is bringing and the proposals and, and, you know, saying, which of these are [00:18:00] right?

What's the direction we wanna move in? You know, breaking ties, uh, making sure that the, the AI is, is doing what it should be within the guardrails and policies that the executive team needs to set. Um, and, and so that's one of the main roles you're gonna have. Also, new roles, right? You're gonna have a chief AI officer.

A lot of companies already are standing those up, and especially the AI native companies. Just making sure that AI is performing as expected. It's operating ethically. Um, and then, and then when you start thinking about some of the other executive roles, like I said, they're probably gonna become even more important.

So your GC or your chief legal officer is gonna be critical for setting responsible AI policy. I mean, we've had so much conversation in the world about responsible ai, this GC or, or chief Legal Officer, that's, that's their job is to make sure that we've got, we've got that established and we're adhering to it.

Um, the [00:19:00] people and, and the models and then the CHRO. I mean, you can imagine, or the chief people officer, you've got, first of all, this massive transition, right? Where you've got, you're disrupting the culture, you're disrupting these traditional processes, rituals, jobs, and you're moving AI agents in. So jobs will go away, jobs will get created.

People need to change skillset, right? To be able to remain relevant in the workforce. And the CHRO is, needs to be at the helm of all of that. Not to mention, once the AI's up and running, they're now managing a blended workforce. So you've got your digital workforce and you've got your human workforce, and you've gotta make sure that those two are working together as as they should be.

Uh, and then you've got your CFO. So your CFO needs to make sure all the financials are calculated and reported correctly. Uh, it's making sure that the AI that's doing all of that work is doing it as expected, because at the end of the day, they still need to sign the, the, the financial statements and they're accountable for those.

And so that [00:20:00] if there are errors happening in the, in the AI that's underneath that, just as there might be today, with humans doing some of that work, they need to make sure that they're, they're. Checking for all those errors and mitigating against any problems. Uh, but then they're also leading the capital strategy.

They're driving investor trust and they're dealing with the risk governance of the model. Um, so all of those roles are critically important to the future. And, and, uh, you know, we see, uh, a skillset shift for those leaders as well. They're gonna have to be adaptable and, and be able to have some technical literacy, uh, that they might not have today.

Jerry: I think one of the things that many people, regardless of whether they're executives or even entry level folks are thinking about today, uh, as AI has changed fundamentally how we view and, and do work is how do I make myself irreplaceable or less replaceable? In a world where it seems like roles and functions that were once thought of as untouchable by AI or proving us wrong, um, what are some skills [00:21:00] or talents, tools, things, uh, can folks be thinking about learning, developing so that we become less replaceable and become more valuable in the organizations of the future?

Chris: Yeah, so I think first it's gonna be thinking and problem solving and, and for me, this is the most important. You already are seeing people who are using LLMs at work every day and then just type in the question, they take the answer and you know, they're sort of spitting out it's happening in schools that's happening all over the place, and they're not thinking about, does that answer make sense?

You know, is that really answering the question? Is it considering all the right variables, et cetera? And so we think that, like looking back at education, right? Engineering degrees or liberal arts degrees. Degrees that teach people how to think, how to take complex problems, break them down, solve them, you know, talk to people about them at a [00:22:00] foundational level.

Um, and, and, and really like drive execution and solution. That's critical. And so those are the types of. Educations that we think people are going to need to be successful in the new workforce. Uh, next it's less education related. It's more, um, human related. People are gonna need to be adaptable, flexible, and really have a desire to learn.

I think everyone's roles are gonna be changing rapidly over the next five to 10 years. If you're not saying, Hey, how do I get smarter? How, how do I get better? How do I use AI to help me? Um, while also being flexible and adaptable and understanding that the role I have today might be completely different than the one I have tomorrow.

I, I think people who aren't making that transition mentally are, aren't gonna be successful. right? I think EQ is another one, and it sounds weird. It's like, Hey, we're putting more, you know, AI and [00:23:00] automation. Like why would EQ be important?

One is that we, we were making this transition, right? And so during the transition, obviously EQ is important because you're dealing with people's egos, their careers, their lives, and, and trying to help them make this transition. And, and so that takes a lot of, of eq. Um, but once the agents are up and running, a lot of what humans are going to be doing, as we talked about earlier, is the oversight and interaction.

And, and their work's gonna center around conflict, conflict resolution, and rapid change. And so if you're not capable of understanding the conflict, dealing with it, like that's gonna be people's entire jobs. And so if you don't have high eq, all of these humans who are using AI agents need to interact with each other to be really successful as a business.

And if you're not able to do that with high emotional intelligence, uh, you're gonna have problems. [00:24:00] Um, and so that's, that's a third, and then I mentioned this one earlier, but data and technical literacy, I think everyone's gonna need to have some level of data and technical literacy to be successful.

Jerry: We talked about strategy earlier, and I think that's a fascinating one, right? And I think even as individuals as almost daily, there's a new AI tool with new versions. One saying, we're better for this and we're better for that and you should use us in the B2B AI world. The limits or, or the, uh. Options seem limitless in terms of new tools, new companies being developed, saying, I am your all in one best tool for innovation.

This is, uh, bombarding the inboxes. Of executives who are being pitched the next big thing. Um, as you mentioned, they have to be very careful in how they not only, uh, look at what they use, but uh, what programs they use, what partners to go along with, um, in an age where things are going to continue to evolve so quickly.

Um, what are some. You [00:25:00] know, uh, what, what's some good advice for folks as they consider what to go in on? How intense to go in on? I think there's some, you know, wise advice to sit back on the sidelines and observe before you jump into things. But FOMO kicks in and as you watch the headlines of yet another company using AI to get ahead, how do one, how do you balance that?

Um, you know, uh. Approach for executives. And then what are some tactical strategies to help them decipher and to decide, uh, what to go in on and what to wait and see.

Chris: I'll go back to what I was saying earlier. I think, and I'm not gonna belabor the points, but I think you start, you gotta start with the strategy. You have to say like, where do we want AI to interact? What, what do we want the objectives to be across different parts of our organization. And then you've gotta pick the right use cases to go after.

Um, so I, I already talked about that. The, the thing that I think, and it relates to the exact question you asked, [00:26:00] which is like, you've got basically inbox inboxes flooded with people trying to sell point solutions. Uh, when you go back to that idea of a chief AI officer, uh, or your chief technology officer.

I think it's very important to think about how do I bring, like I can't just go build 75 or 7,500 point solutions, right? With different software that doesn't talk to each other. And so how am I gonna architect the, this whole thing together? Uh, we, so one of the things that, that we've been doing a lot with clients lately is, is talking about the concept of.

Uh, a unified data layer, uh, or a, a, a data activation solution. And so the idea there is, you know, it's a, we think it's a thing of the past for companies to go on a 10 year ERP consolidation or, uh, this large data warehouse consolidation. [00:27:00] We've got technology out there like Palantir, like Snowflake, uh, even databricks, other things out there.

That can sit on top and connect to all the different systems that companies have today in their, in their disparate system landscape. And in that layer, which by the way in many cases is read, write, so Palantir for example, you can do read, write, um, you can build applications that are AI enabled. And you can link all of those systems and data together virtually, so you don't have to consolidate your systems, you don't have to consolidate your data when I need data from 17 different systems to make a decision.

I use that, that UDL or that DAS layer on top to go in, grab what I need and serve it up to me so that I can make the decision I need to make. You can create custom applications. Um, it's a really, really powerful tool that we're working with companies to, to implement. And [00:28:00] so I think as companies are exploring all of these point solutions, you've gotta be thinking like, what, what's my connectivity layer?

How do I bring all of this under one? Master orchestrator from an AI perspective that can ta tie everything together, um, and allow me to continue to iterate and, and develop new things, new capabilities, new applications as I, as I progress on my journey. And so that's a really, really important architectural, uh, consideration that, that companies need to be making.

Jerry: A along the same vein, um, give us your thoughts on, you know, too much, not enough governance. Um, the, you know, uh, yeah, that's like, give us your thoughts on that. Again, you want speed, but you don't want to be too quick and, and, you know, be, put yourself at risk of, of, you know, not thinking things through.

Chris: it e, exactly. I think this is one of the biggest challenges, like we talked about earlier. You've gotta be able to set your [00:29:00] objectives and your guardrails. And then set the teams up who are working on these solutions to run as quickly as possible. And so they need the psychological safety to be able to take risks.

They're gonna fail. We wanna celebrate and learn from those failures rapidly, iterate and improve. It, it, it all goes back to essentially like the agile mindset. What you don't want. Is, let's say you've got, uh, a, a product development process. You've got your, your engineers, your r and d, your product management, but you've also got your finance team involved, your marketing and your go-to market team involved.

And probably, you know, six or seven other traditional departments or functions that we have in a business today. You can't have, uh, you know. Executives from each of those areas as as a steering committee, and then representatives from each of those areas who are doing the work. But then they need to get approval from their leader for, for the piece that they're working on.

And then all the [00:30:00] leaders need to come together. No, it's not gonna work. You've gotta take the team that knows that process the best. You've gotta set some guardrails and objectives for what they're doing, and then you gotta let 'em run. And you've gotta do it in an agile way where they're coming back, running sprints.

Sharing what the output and outcomes are of, of what they're pulling together so that the, that steer committee can make sure that it's on track, it's achieving the, the results it needs to achieve. Um, but you can't get in the way of the progress and the speed, because if companies are doing that, these AI native startups that are coming after every industry right now, including consulting, they're gonna, they're going to eat your lunch because that's not how they're built.

They're built. How can I use AI to solve the problem with as few people as possible? And, and that's the way they're moving and they're trying to develop and iterate as quickly as possible. And they're using AI to do a lot of that coding, a lot of that testing, and a lot of that, uh, you know, even the market testing [00:31:00] and, and customer, uh, customer need testing.

So, um, you've gotta be nimble, you've gotta be agile, and you've gotta be quick.

Jerry: So for many of our audience members who are at that executive level, particularly CHROs, you know, they may be asked to, you know, recommend. AI tools or softwares or, or things you, in your role as a consulting agent, consulting firm, um, have that role too. Um, what are, what are some of the things that you do at Alex Partners to vet these tools and to get in the habit of always trying new things and, and put that into the context of giving our audience members advice to again, you know, have the good a, a good sort of lens through which, uh, you can decipher what you recommend, as you know, uh, tools that can be further implemented throughout a large organization.

Chris: Yeah. So a couple things. I'd say, uh, one, there's a place for the off the shelf tools for sure. There's no doubt. We have, we have an AI and data team, uh, but we also [00:32:00] have all of the rest of our consultants. Who are constantly sitting in on, you know, sessions where companies like Stack AI and Palantir and, and a bunch of different solutions are, are sharing with us, Hey, here's what we can do, this is what we're capable of, et cetera.

Um, and then we're, and then we're trying to use some of those at clients to see, you know, what works, what doesn't work, what we think is best. Um. But I also think companies need to sit back and say like, where do I wanna buy a software that does something versus where can I build something myself? So, so take Stack AI as an example, right?

That gives people who don't have the coding ability and skill, um, the ability to create applications with no code, right? And so where are there things where you can use a software and people who know the process but don't know how to code? Can, can build something themselves or, or with maybe the help of a [00:33:00] small development team really easily and quickly.

and it might be a better idea to do that than to go out and buy something off the shelf. Or where might it be best to just use like a, an easy LLM, you know, AI agent and, and kind of, you know, get that installed into a process or in, in a couple of the process steps to do what you need it to do. Uh, and so I think it's, it's really about.

A start with the use case. Figure out what you wanna do, and then look at the landscape of solutions. Do we think this is better to build ourselves? If we are, is it an application? Is it just using an LLM? Um, how do we embed that into the process? How do we need to go out and buy a software? Or there's a, there's a fourth option.

It's like, what if we hire, uh, a large business process outsourcer, like a Genpact, an Infosys, a Wipro. To, or an Accenture to come in and take that, to take that on and drive all the automation for us. You're gonna give up [00:34:00] a little bit of the control and a little bit of the, the cost savings, um, because they need to make their margins, but they're getting really, really good at these things.

And so that needs to be an option on the table as well. And so I think our advice is we're testing these things. We're, uh, we're piloting all four of those on a regular basis. We have teams who can develop. AI and, and, and code things and, and design things for clients. Uh, particularly if it's, if it's stuff that's like the lifeblood of the business and there's not really a software out there that can do it, and you've got, you know, proprietary ways of thinking and, and doing things, we can, we can code that.

Um, but, but at the end of the day, like our job is to make sure that companies understand the different options and are figuring out which one of those options is right. For that use case and then making sure that no matter which options they choose, all of that's orchestrated underneath an architecture that makes sense for them going [00:35:00] forward.

Jerry: You know, one, one thought I wanna share with our audience is, you know, uh, the, the goal of this conversation with Chris, uh, is, is not to overwhelm you. It is not to make you feel that you are, you know, uh, falling behind. Because certainly I think, you know, it is easy, uh, and understandable for a lot of folks to feel overwhelmed with the, with the amount of conversations around AI that we have.

Um, and then some of these changes, you know, are, will be fast. Some of these changes will be slow. Um, but. I think one thing that we can agree on is that change is necessary. Our, you know, our learning, our testing, our trying, and our implementation. And, and so leave us, uh, with, leave our audience with this final thought, Chris, or, or sort of a, a charge.

Um, give us one thing that our audience members can do today to better equip themselves, to better be prepared for the organization. Change the transformations that are gonna happen in a more connected AI world tomorrow.

Chris: Uh, the word I would use is explore. Get out there, get your hands on this technology, see what it's capable [00:36:00] of, see what it's not capable of. What are its limitations? Understand it, get used to working with it. And, and the people, the people I've seen most successful and the companies I've seen most successful are those that do that.

And, uh, I, I can't say enough. I remember sitting at a desk a year and a half ago thinking, I don't understand what this AI stuff is. What's this chat GPT thing? And I started playing around with it. And it's hard to explain before you start, uh, you know, using it and testing it and seeing what it's capable of and, and how fast it's improving.

It's hard to understand like, how to use it. Once you start using it, your mind just expands your, oh my gosh, you know, 80% of my day is spent doing things that I hate doing. Like it's, it's busy work, it's bureaucratic. Things like this can take all of that work away from me. And free me up to do all of the strategic thinking and, and, uh, the fun things that, that I want to do.

[00:37:00] And so I, I think, uh, the World Economic Forum, I'm gonna probably get the number one. They published something saying that like, the net impact to this isn't gonna be, the world loses, you know, hundreds of millions of jobs. It's like, it's gonna be a positive, I think it's like a $9 trillion impact on the economy and the number of net jobs is gonna go up.

What I think is gonna happen is we're gonna end up with, uh, businesses are gonna have fewer people, but we're gonna have so many more businesses. And you know, the, the number of companies that exist in the world, the number of products that exist in the world are going to go up by a factor of 10, maybe a factor of a hundred over the next five to 10 years.

And so, with the volume of businesses, and you're gonna be able to have that. 'cause AI is, is your leverage, right? With that volume of business, you're gonna have so many jobs in all of those companies that are requiring people to be able to interact with the ai, but they're gonna be able to do things that are a lot more fun, um, and, and exciting and [00:38:00] thoughtful than what they do today.

And so my parting thought is don't be scared. Embrace it, experiment with it, and ride it to the future where I think we're all gonna be a lot happier with the work we're doing day to day.

Jerry: Amazing. This has been a very, uh, educational and insightful and inspiring conversation. Uh, Chris, small partner, managing director at Alex Partners, thank you so much for joining us.

Chris: Thanks for having me, Jerry.

Jerry: That'll do it for this week's episode. See you next time on Tomorrowist.