This webinar dives deep into the profound impact of AI and what it truly means for businesses. Our speakers explore how AI disruption is reshaping industries, moving beyond efficiency improvements to redefining business models and solving complex problems.
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In the first section, these AI experts drill down into the realities of how AI is disrupting businesses and how that disruption impacts the way businesses operate, including taking a look at the trajectory AI has taken as it redefines the way organizations operate and the changes to different industries.
Watch the full webinar here or keep scrolling to read a transcript of the discussion between Phil, Mary, and Andrew.
Andrew: Hello, everyone. My name is Andrew Simmons, and it is my pleasure to welcome you to a webinar hosted by Wavicle Data Solutions titled “The Disruption of AI: Challenges and Preparation for the Future,” I will introduce our esteemed guests in just a moment.
First, I’ll help set the table for what you’re about to hear. If you’re tuning into this, you’re probably already aware that AI is rapidly transforming industries and lives both professionally and personally. The path forward in terms of AI is not exactly clear nor without its challenges.
Today, we’re going to bring together Phil Le-Brun, Enterprise Strategist at Amazon Web Services and former CIO, who is a leading expert in cloud and AI technology, and Mary Purk, AI analytics advisor and co-founder of AI at the Wharton School.
As I mentioned, my name is Andrew Simmons. I’ll be moderating today’s discussion. I lead the retail and CPG practice at Wavicle Data Solutions, which focuses on data, analytics, and AI as part of our interdisciplinary practice.
Today’s discussion will focus on the disruptive power of AI challenges, the likely challenges organizations will face, and, hopefully, practical strategies for preparing to embrace and capitalize on today’s and tomorrow’s AI innovations. Whether it’s technical complexities or ensuring ethical deployment, today’s conversation will hopefully offer all listeners actionable insights so you can consider your organization’s participation in an AI-driven world.
Our first topic today is AI disruption and what it means for businesses.
Phil, as you and Mary know, AI has emerged as a powerful force in the business landscape for at least the last decade. But in the past few years, it’s become a business imperative across industries. As an expert with experience across a broad swath of industries, Phil, I will start with you. How do you see AI disrupting the current business landscape, and how does that disruption impact the way organizations operate?
Phil: Interesting question. Disruption happens because we’re often asleep at the wheel. We don’t see what’s right in front of us, and there’s this phenomenon called Amara’s Law, which says we overestimate technology in the short term and underestimate it in the long term. So you go through this cycle, which we see with generative AI and throughout history, where many people have done proof of concepts, and some have been disappointed. It hasn’t fundamentally revolutionized their business. But those who’ve kept at it are starting to see interesting results.
I hear from many organizations that their initial focus was on efficiency: How do I take more calls to my call center? How do I maximize my developers’ time? How do I generate more lines of code? How do I do sales more efficiently? The efficiency is great. But if you’re saving time and money, you ultimately have to redirect it to something. We typically call it “disruption,” but in reality, it is about driving your business.
We’re starting to see signs of that. People are rethinking how travel is done, for instance. If you visit a travel website today, you’ll often see lots of slider bars and checkboxes, which is the most unnatural experience when booking a holiday. All you really want to do is say, “My name is Phil, I am married, and I like a beach holiday.” Literally, describe what you want, and from that, the travel company will do the heavy lifting.
This isn’t even just about the commercial sector. We see the same in the public sector, too. It’s really interesting working with organizations like Swindon Borough Council. They’re using generative AI to actually make a plethora of government regulations and benefits accessible to folks with learning disabilities by taking very complex information and distilling it down in a way that’s digestible. NatWest is doing something similar to help protect wealth, for instance.
So, I think AI plays a particularly powerful role here in getting rid of some of the undifferentiated heavy lifting and then hiding the complexity of an organization’s operation from a customer to make it much easier to do business with them. At Amazon, we’ve been using machine learning for 25 years now to do exactly that: not to replace humans but to replace those things that machines are much more capable of doing to free up humans to do the things we are uniquely qualified to do.
Andrew: That really resonates with me, Phil. I’m just touching on something that you said there. When I talk to leaders today, a common phrase is, “What am I doing with AI?” As you indicate, AI is not a monolith. It offers a variety of benefits. I’d be interested in your thoughts on how leaders can consider some of the incremental offloading of operational balances while also considering some of those big-ticket innovations and disruptive things. How do you see leaders doing both, walking and chewing gum at the same time?
Phil: It’s actually a virtuous cycle. So, the less time you spend managing your internal operations, bureaucracy, paperwork, meetings, and PowerPoints, the more time and energy you can have to focus on those areas of innovation.
We talk about disruption as if it has to be a big overnight event, even though we know that’s not how it works. The reality is that this is about falling in love with a customer’s problem and then allowing people to experiment with their way there.
My advice is quite simple. Look inside your operation and calculate what we’d call your bureaucratic mass index (the amount of money spent on things we’ve always done, but no one can tell you why we’ve always done it) and the amount of time spent waiting for decisions six levels above you when you could have taken it quite competently and faster yourself.
So, walking and chewing gum is an interesting metaphor, but I much prefer the idea of shifting your focus from one area, the internal machinations of your business, to what really matters, which is your customers, citizens, patients, or students.
Andrew: Makes sense. And Mary, I’m just touching on some things that Phil indicated. Obviously, there are historical parallels to navigating an innovation cycle with either technology or process improvements.
We’ve seen those cycles move before. From your vantage point, Mary, in what ways has AI echoed history, particularly in the history of business, and in what ways do you think that it is new and forging new ground?
Mary: Well, I think Phil talked about, you know, the history of how some companies approach technology. Like, okay, here’s a new way in which we’re going to do business, and we have to do it because our competitors are doing it.
There’s a lot of following. Like what’s happening in our industry? What are the consultants telling us? What are technologies telling us? We’ve got to move everything to the cloud, we’re all doing that. There’s a lot of following, which is fine if you’re a fast follower. And I think that is occurring right now, but at a much faster pace because of trial.
So, people, companies, or entities can do that, but they will potentially have big voids within their industries, and potentially, those companies might not necessarily exist in a couple of years if they do it at that same pace because this technology – I mean, exponential is not even a good enough word to describe this technology – it’s transformative. In the truest form, it’s transformative.
Two key points that are different are that it’s accessible, and this technology allows for accessibility for all employees and departments to participate in that transformation, which is key. So, accessibility, and, for the first time, everyone doesn’t need to be a data scientist.
You don’t need to know how to code; you need to know logic. You need to be curious, disciplined, and focused on the right questions. This is something that Phil was basically saying: falling in love with the problem.
I’ve been doing a lot of these different conferences, and finally, even the technologists who are selling the goods are finally saying what we in academics or we in leadership have been saying: You need to focus on the question. What are we trying to solve? To Phil’s point: are you pushing more papers? I mean, you know what Asana does? It organizes everything. I mean, what does Salesforce do? It organizes everything for you. I mean, this is organization, upon organization, upon organization. That’s good because maybe we’ve been disorganized and weren’t efficient enough, and you can focus on the efficiencies, as Phil mentioned.
But he also mentioned in a big way, which I really believe is where companies are going to have to be, the innovation part. So, get your efficiencies correct. Use this technology to be more efficient, have better security, and have a better relationship with your customers. But if you don’t use this to innovate, you might not be around in the next three to five or seven years. It could be really quick. So that’s what is significant.
The last thing I’ll say about innovation and efficiency is that leaders must lean in and understand that they have to be leaders, not only of where we are going but of broad vision. They have to be able to communicate to their employees the trust of how they will manage their journeys within the company with this technology and clearly state, “We’re all adults here, and your job might change. Still, I’m going to give you some options, and you’re going to have to learn new skills, and you will probably have to keep learning new skills, and that’s our culture. And if you want that culture, I’ll support you, and I’m going to give you the tools for that, and I’m going to lean in, and I’m going to be smart about AI, too. I’m not just going to rely on my chiefs. I’m going to do it, too.”
Lead by example. So that was a lot, but those are the differences to me. Yes, there’ll be some similar things, but these two things will be different.
Andrew: Mary and Phil, when you’re talking to leaders about to embark on this journey, you know, obviously, as you indicated, there’s a large cultural piece to it: preparing your people and looking at your organization.
What would your advice be to leaders – you mentioned the trend over the last 25 years for organization that’s deeply embedded in some organizations – where AI might challenge the deep-seated need for some of those things, whether it’s unstructured data, where we need to be less rigorous, where knowledge management becomes generative, where AI can provide some answers across knowledge management databases, et cetera.
How are you talking to leaders about how to take their teams culturally and maybe even emotionally through this disruptive journey?
Mary: It’s good that you mentioned emotionally because some people are scared, and some people are excited. So, I think it’s good that you recognize that.
In terms of how to guide them through the journey, the leadership team, including the board, needs to be well connected to where AI will take their company.
Again, first, you have to ask where we will use these tools to solve our problems or innovate. So, you have to have that vision before talking to the customers. If you’re a leader and you’re not exactly sure, then you have to be vulnerable, and you need to talk to your team and board to understand whether you need outside help to guide you and achieve this mission of where we’re going and then ask how will this technology help us and then look outside in your competition. How is this technology helping our competitors? How will we differentiate and, as Phil mentioned, transform at that point?
Then, you can go to your employees and say, “This is our roadmap, and this is how we’re going to get there. I don’t have it all laid out exactly, but if you’re a part of this vision, then you need to contribute.” I always tell leaders that they need to say to their employees, “At this point in time, I advise you not to wait to be asked to do something with this technology and participate but to ask to participate.”
Andrew: That makes total sense.
Mary: That message has a double meaning. It basically says to these leaders and their teams, “All of us can contribute, and I want you to be part of this. It’s not going to be this, you know, top-down approach.” And to me, that will foster innovation and provide permission for these employees to realize we can all be part of this and use AI.
It is not just going to be in IT; they’re not just going to send down all these different acronyms that I have to learn and all these new systems. But you know, I think you have to get the vision first and then you can tell your employees how we’re going to manage through this and somewhat, if you would like, democratize how that innovation’s going to occur.
Phil: I 100% agree, Mary. I’d almost take one step back and say, even before you start the conversation about how we’re going to use generative AI, what business are we in? When we talk about business disruption and serving our customers better, what does that even mean, agnostic of the technology? I’m always surprised, working with senior-level leadership teams, how little time they spend together talking about what makes them special and getting that foundation right. So then, they can look at how to apply the technology.
The other thing I find curious is that leaders often talk about what we want our employees to do. I think it starts, and you implied this, Mary, it starts with us as leaders. Have we learned what AI, ML, deep learning, and generative AI are? Have we learned how to use data to make decisions, and are we role-modeling?
The reality is that over one-third of employees today are using generative AI to help with their job, whether it’s sanctioned or not, because, as you rightly pointed out, this is a bit like a calculator. Everyone has access to it. In fact, it’s even easier to get ahold of generative AI than it was to go and buy a calculator back in the day. So people want to use it. This is creating that environment, as you talk about.
I think it’s so essential that leaders role model the learning behavior as well as be specific about what great looks like when they start to apply technology or start to solve real problems for their organization.
Mary: Into what he said, what does great look like? That’s why leadership is so important at this point in time, and they are so lucky. For me, the success of what we do, not only within the United States but in the world, with this technology, is the leaders. I’m not just talking about the CEOs. I’m talking about the whole C-suite and board members. In my opinion, they all have a responsibility to not only their business but also to their employees and to society.
They are so lucky, Phil and I are so lucky, that we are in this position of influence. You have to be the best coach at this point in time, and you can really be satisfied with what you’re going to be doing with it, but you can change people’s lives. Not only your employees, but within society.
I think it’s really important that board members and such really evaluate the team that they have in place at these companies because leadership is just so important. This is a moment in time – I agree it’s longer term for how we’re going to adapt to it, you made a really good analogy at the beginning, Phil – but in terms of leadership and what people do with it, these next 18 to 24 months are key for leaders.
It was key for all the technologists, and they still have a lot of airwaves. But right now, it’s leaders and what they do with it.
Andrew: It does feel like that, for sure.
This is just the beginning of a detailed discussion about AI’s role in business transformation. Stay tuned for part two of this three-part series, where the speakers discuss more about the challenges organizations face in adopting AI and how they can overcome them. You can also read more of our AI thought leadership here.