Preparing your Business for an AI-Driven Future

This is part three of our webinar on AI and its transformative role in business. Following the discussions on AI disruption (part one) and the challenges of AI adoption (part two), this third part focuses on preparing organizations for an AI-driven future.

Hear from:  

• Phil Le-Brun, Enterprise Strategist at Amazon Web Services, Speaker  
• Mary Purk, Executive Director and Co-Founder of AI at The Wharton School, Speaker 
• Andrew Simmons, Retail and CPG Practice Lead at Wavicle Data Solutions, Moderator

In this conversation, Phil and Mary share their insights on technology readiness, the importance of data infrastructure, and the critical role of leadership in creating a culture of learning and adaptability. This discussion captures their guidance for organizations looking to thrive in an AI-powered world.  

Watch the complete webinar here or keep scrolling to read a transcript of the discussion between Phil, Mary, and Andrew.  

Andrew: Phil, we’ve talked a lot about the people, culture, and process sides of AI so far. I’d be interested in your comments based on Mary’s last statement. But turning a little bit towards the technology side, one high-level question that I often hear is, “How ready is my technology stack for this advent of AI? I’m buckled up. Let’s go. Can you assess me? Can you make recommendations?” What are your thoughts on an organization’s readiness for AI technology?                    

Phil: The metaphor or analogy I use with organizations is that electricity is pretty important to most organizations, but you don’t find most organizations building their own power stations. The reason I say that is that we’ve got the cloud now, and the cloud isn’t just someone else’s data center; it’s a toolkit of hundreds of services that enable you to experiment cost-effectively and quickly across your organization.  

So, to me, number one is: Have you got your customer-facing, citizen-facing workloads in the cloud to enable you to have that agility to experiment? It’s the same with data. These days, buying a data center and then lots of expensive licenses—I don’t care what your problem is, the answer is this license we bought five years ago. That’s ridiculous.  

We’re hamstringing employees, and in fact, your data tells us that if employees feel like they can’t progress in their jobs, they can’t actually solve problems. Organizations are losing the very people they need: their top talent, the most creative folks, and the folks with data science backgrounds or deep insights into the business. They’re the ones leaving first, so you get into the technology stack which gives you the most flexibility to sort your data out. I think we’ve hammered on that. There’s no reason not to have your data in a secure location accessible to everyone.  

Yes, we can talk about data lakes and data meshes and how you go about that, but fundamentally, you know the goal needs to be ubiquitous data access within an organization. Then, you can start modernizing how you actually treat technology. Some of that could be taken from your legacy technology, those mainframes that have been around for years, which are the boat anchors of many industries.   

There are three ways of modernizing a mainframe: you rewrite it, which is always a lot of work; you strangle it, take bits off, and that’s more practical. Unfortunately, the most prevalent pattern is to leave it to the next leader. Let’s not be that leader, who leaves it to the next leader. Let’s start some of those really hard problems, modernizing some core systems that give you the ability to experiment and scale good ideas quickly with your customers.  

Andrew: Thanks, Phil. Mary, do you have anything to add before we move into some closing thoughts?  

Mary: I would like to make a statement in that we’ve said this part should be easy. It was change management leadership, but the technology piece that Phil’s talking about, yeah, the data needs to be accessible, and everything is so important. You know that there is accessibility, but it’s a tall order to really understand where your data is within these models, and then if you have these APIs, where the data is going from one model to another area, and it might be outside the organization, it will be very important for your organization to understand and challenge AWS, Microsoft, and Open AI; where does this data go? If I have an API, is it exposed to other risks? So, accessibility is super key, and I agree with that.  

But you need to know the risks of where the data’s going and how it’s connected through your different APIs. Like Phil said, you have a hundred different options. Do you know where it is now within the cloud or these data lakes? But you just need to know when to connect with your other vendors; you’re the parent in the room; you’re asking, “Tell me where it goes.” Many of these people are going to say, “It’s okay; it’s all contained, it was trained on data six months ago.” 

 We’ll then make an update, and then you’ll have your enterprise stuff, which is staying here, and it’s super easy. Well, it’s not super clean. You need to know and just continue to ask those hard questions. So, I would like to make just that statement within the technology aspect. That part’s not easy.  

Andrew: Thanks, Mary. Phil, before we wrap up, we’d love to get your thoughts on what Mary just said.  

Phil: 100% I think this goes back to some of that leadership education. What is the cloud? There is this perception that if data is in my own data center, which I manage, it’s more secure than the public cloud. It’s not now. There are certain things you need to do.  

As a customer, you are accountable for ensuring that your data is encrypted and that you manage access controls and such. But again, it starts with some of that education.  

The same goes for generative AI. Our approach is quite simple: Bring the foundational model to your secure environment and use your data there. No one but you has access to that. None of your training goes to anyone else. But really being clear about how to do this, why it’s different, and what you need to be thinking about as a board and a C-suite team is absolutely critical.  

Andrew: Well said. As we look to wrap up here, on behalf of our listening audience, I’d really like to thank Phil and Mary for your time and insights. It’s been insightful and hopefully impactful for our listeners. Mary, if we could start with you for just one kind of closing thought; and Phil, I’ll come around and ask you the same question.   

We’ve dived pretty deep into the areas of AI disruption, as the title of our conversation today. As we’re looking to turn the page into 2025, Mary, are there any particular near-term trends that you foresee that you’d like leaders and other industry participants to be prepared for?  

Mary: I think they need to be prepared for more change. It’s not going to slow down. Also, two things: One, the unstructured data is going to continue to increase, just like one hundredfold. Know what unstructured data you potentially have that’s going to be valuable to you and how you’re going to ingest that. Again, this is all related to data.  

It’s how you will communicate with not only your customers but also your employees, not just through text or emails but also through voice. It can be video, and it will be multi-dimensional. That’s going to be how we communicate. So, know that that is coming, and that’s what’s going to be out there.  

So, it’s more of a message to companies because that’s all going to be out there, and we’re going to have agents who are going to help us, which is going to be really helpful. But plan for that because it’s going to be multimodal. It’s going to be a lot of unstructured data, which we’re not super sure exactly how to capture efficiently but know that that’s coming. Then, how you’re going to use that to be a transformative company that survives out in the wild.  

Andrew: Exciting. Thanks, Mary. Phil?  

Phil: I agree with everything Mary said. We are at the start of the transformation, not the end. So, there will be more change to come, but it’s exciting. We’re going to do things we can’t even imagine at the moment, but one of you made the points earlier on about how all boats should rise. There’s a societal implication here that everyone can benefit from this. I’ll pick up on one aspect of that: we recently did a survey across Europe. We know there’s a €3.2 trillion opportunity in Europe alone, and the same is true for the US, Asia Pacific, and the like.  

From digital technology to AI, ML, and generative AI, the biggest challenge far outstripping everything, including regulatory concerns, is skill sets. We have to invest in people. Those organizations that understand that their people are going to stay if they feel invested, if they can experiment, and if they can pick up the skills, are the ones that are going to do some exceptional things.  

So, I think we need to start thinking about our organizations as places of learning. We should not send someone on a training course for a week when there’s time but build that learning into how people do their jobs day-to-day because that’s going to give them the tools to do some exceptional, unimaginable things for those organizations.  

Mary: Can I double down? I agree with that. But it is also part of the technologists’ job to ask the technologists what they need. Companies don’t need any more shiny new models. They need help with their skill sets and improving those skill sets. It’s like a conversation between both the companies and the technologists providing some of that content that’s necessary to invest in those skill sets.  

Andrew: Absolutely. For everyone tuning in, I hope you find yourself as excited and grateful as I am to be sitting here with this outlook on the world of AI disruption. Phil and Mary, thank you so much for your insights to help us navigate and take advantage of that opportunity. Thank you both very much for your time and for your insights, and we’ll talk to you all very soon.  

Phil: Thanks, Andrew.  

Mary: Thank you.  

As AI continues to evolve, preparing for an AI-driven future demands more than just adopting new technologies—it requires modernizing data systems, fostering continuous learning, and addressing workforce skill gaps. Phil and Mary emphasize the importance of agility, strategic planning, and accountability in leveraging AI to drive innovation and value. Organizations that embrace these principles will not only adapt to change but lead it. 

With this final installment, our webinar concludes, offering a roadmap for organizations to navigate the challenges and opportunities of the AI era effectively. You can also read more of our AI thought leadership here.