EP 589: How the Future is Being Shaped by AI-Powered Autonomy

Topics Covered in This Episode:

1. AI-Powered Autonomy Shaping Future Work
2. Generative AI’s Impact on Business Transformation
3. Accenture Technology Vision 2025 Overview
4. Key Trends: Autonomy and Enterprise AI Adoption
5. Human Capability Expansion via AI Tools
6. Trust, Explainability, and Responsible AI Practices
7. Agentic AI Models and Productivity Shifts
8. Continuous Learning Loops in Workplace AI
9. AI-Powered Robotics and Multimodal Integration
10. Personalization and Brand Voice with AI Agents

Keywords:

AI powered autonomy, generative AI, large language models, future of work, automation, business transformation, Accenture, innovation centers, strategic visioning, co-creation, ecosystem partners, digital core, technology consultancy, technology reinvention, enterprise AI adoption, operational efficiency, Technology Vision 2025, AI trends, human-like capabilities, language barrier, technology acceleration, digital agents, digital transformation, customer interaction, trust in AI, responsible AI, data platform, knowledge graphs, AI-driven robotics, warehouse automation, personalization at scale, brand voice in AI, digital twin, agentic models, observability, traceability, explainability, continuous learning loop, employee upskilling, generative AI productivity, change management, value-driven outcomes, super agents, utility agents, orchestrator agents, AI partner, human agency, AI collaboration, AI model accuracy, enterprise adaptation, digital twin technology, business process automation, AI in branding, personalized AI assistants, AI-powered design tools, responsible data usage, AI-enabled content creation

Podcast Transcript

Jordan Wilson [00:00:16]:
For decades, the business world has slowly become more and more autonomous, but obviously generative AI and large language models have changed that conversation completely. What is capable now and in the future of work when it comes to AI powered technologies is almost unheard of and it feels sometimes unreal, right? How we're able to work today and how we're able to automate our work and think about even just autonomy in general. So that's a conversation I'm excited to have today on Everyday AI as we tackle how the future is being shaped by AI powered autonomy. I'm excited for this one. Hope you are too. Welcome to Everyday AI what's going on y'? All? My name is Jordan Wilson and I'm the host and Everyday AI, it's for you. This is your daily livestream podcast and free daily newsletter helping everyday business leaders like you and me not just keep up with what's happening in the world of AI, but how we can actually use all this information and expertise to get ahead to grow our companies and our career. So if that's what you're trying to do, you're in the right place.

Jordan Wilson [00:01:23]:
Welcome home starts here with the unedited, unscripted daily live streaming podcast. But if you want to take it to the next level, make sure you go to our website@your everydayai.com so there we're going to be recapping the highlights from today's interview and I'm excited for our guests as well as we're going to keep you up to date with everything else happening in the world of AI. So for all the latest and freshest AI news, it's going to be in the newsletter. All right, enough chit chat. I'm excited to have a great guest on today's show so we can talk about how the future of our work is being shaped by AI powered autonomy. Because like I said, I think the business world has been more and more automated over the past few decades. But when you combine traditional automation with what is capable today and moving forward with AI and large language models, the possibilities, well, they're pretty exciting. All right, so enough of me chit-chatting.

Jordan Wilson [00:02:17]:
I'm excited for our guests. So livestream audience, please help me welcome to the show Mary Hamilton, the managing director at Accenture, leading the connected innovation centers globally. Mary, thank you so much for joining the Everyday AI Show.

Mary Hamilton [00:02:29]:
Thanks for having me Jordan.

Jordan Wilson [00:02:31]:
All right, can you tell us a little bit about what you do at Accenture?

Mary Hamilton [00:02:34]:
Yeah, absolutely. So I have been part of our innovation organization for many, many years. And I lead our basically physical platform, our innovation centers around the world, they're connected as one network and it's really our global standard for how we engage our clients in a physical way. We bring them into our centers to do strategic visioning, design, co creating, learning, connecting them with ecosystem partners to help them drive continuous innovation and transformation.

Jordan Wilson [00:03:05]:
Yeah, and I'm sure that the overwhelming majority of our audience, especially here in the US is very aware of what Accenture does. But maybe for those that, I don't know, maybe have been living under a rock recently, can you tell a little bit about just the overall work that Accenture does and maybe even talk to us a little bit how that work has maybe changed over the past few years, kind of since this generative AI boom has really started to take over how we all think about work?

Mary Hamilton [00:03:33]:
Absolutely. So Accenture is extremely large company. You probably, you may not know, we're over 750,000 people employees and we are a global consultancy. We focus on everything from technology to operations, strategy, consulting, basically run the full gamut. And what's really exciting about how our business and work has changed over the last few years is we really are truly a unique partner to help drive reinvention at our clients. So if you think about Fortune 500 companies and the changes they need to go through and make in this new era, we have basically all the capabilities brought together with a focus on generative AI and AI data, the whole platform, digital core, et cetera, to help our clients go through that reinvention journey, really like solve the harvest problems that all of our clients have.

Jordan Wilson [00:04:34]:
And you know, I'd say let's just start with this right away. So we're always in our daily newsletter, we're always, you know, putting out when big companies such as Accenture do these very in depth studies on how AI is impacting work and impacting the future of work. So maybe could you even. We did share this one. I went back and looked earlier this year when you released the Accenture Technology Vision 2025 report. Could you tell us a little bit about what that report, number one, what is it and what were some of the key findings inside that report?

Mary Hamilton [00:05:09]:
Yeah, absolutely. So we do a technology vision report every year. We've been doing it. This happens to be 2025. We've been doing it for 25 years. It tends to be, it's a Accurate forecast of the trends that we see coming from both technology and business and what companies can expect. And you know, as I said, we've been pretty, we've done pretty well of a batting average of, you know, what, what comes to life this year. And normally it goes across all technology, but this year AI has really taken center stage.

Mary Hamilton [00:05:43]:
It's been the highlight and it's because we're hitting this inflection point of rapid acceleration where the technology is gaining more and more human like capabilities. Right. And that's through vision, language, reasoning and it's changing the game. It's fundamentally shifting how businesses are using that technology. And we believe that, you know, as we see this technology proliferate and accessibility, you know, it's the every, it's an everyday technology that people are using is going to drive new levels of autonomy in the business. It's going to change the way people do business. It's going to change the contract and the way people think about the work that they do within the enterprise and also how companies and brands interact with their customers and their clients. So it truly we see this as game changing.

Mary Hamilton [00:06:34]:
And so that's what's driven the broader theme of the tech vision. So we always have a broad theme and it really is this year around autonomy. And then we have within that four different trends that talk about, you know, specific areas that we think are changing significantly or will have an impact to businesses.

Jordan Wilson [00:06:54]:
And I do want to maybe dive a little bit more into those four trends identified in the report. But maybe first I kind of want to give people just the answer. Can we just skip to the end? So like when we look at what the future looks like and how the future is actually being shaped by AI powered autonomy, what would you say was your biggest findings specifically toward that?

Mary Hamilton [00:07:18]:
Yeah, you know, I would say it, it really is about how that autonomy is giving people skills that they never had before. And in, in turn people are using it in new ways that we never expected to be possible. And one of the things that has created that change and made it so game changing is that we've really, truly broken the language barrier. Right? We can now have a conversation with this technology. And that goes back to those human like capabilities that we've never seen with technology before. So that gives us the ability to, I mean, I think in the future it superpowers us, right? It gives us superpowers that we never had before. So the autonomy isn't just in the technology being able to go do things with, you know, limited human intervention. It's also giving us humans more autonomy.

Mary Hamilton [00:08:13]:
And I'll, you know, just take a simple example of something like Adobe Photoshop. Historically, you know, I had to be, you know, an expert to be able to edit a photo and add this in the background and take out that, you know, this thing. And I had to know how to use all those tools. Now I can just, you know, give a quick direction, you know, add this background, give me a photo with, you know, with this and this and, and it can do it. And suddenly I have the autonomy to design something that I was never able to do. So the autonomy for me I think it's really important that it's, it's going both ways and it's giving people the ability to have more capabilities and to supercharge what they do.

Jordan Wilson [00:08:55]:
So you know, I, I, I, I love your example there that you brought up because, you know, it just so happens, plus years ago when I start first started using Photoshop, right. I remember gaining some of that autonomy or agency, very manual, right. Like why I'm sure I spent dozens of hours doing a certain variation of what you just laid out, right? Like in something like Adobe Photoshop. So how does that change the future of work for a lot of people, you know, who have that domain expertise that maybe, you know, for your example, maybe they spent a decade or two decades really refining those skills that now you can kind of do with a couple clicks of a button. So how does that kind of change, you know, human domain expertise, you know, moving forward?

Mary Hamilton [00:09:48]:
Yeah, it, it, it does change the game. I see this technology as a partner, right? So it becomes a partner, a sidekick if you will, to help people up level to change the rules. You know, make no mistake, you know, we're not going to be doing the same kinds of things that we do. Work is changing and I think the companies that are going to be successful and the employees that are going to be successful are going to start thinking about how do we actually transform the outcomes that we're trying to achieve and to do that, use technology as a partner to achieve them. So you know, a day to day, you know, yes, I'm not going to be clicking through and using each of these tools, but I might be able to create more content more quickly and more in a more personalized way to deliver than I'd ever been able to do before. So it opens up new possibilities because I can do more and not have to spend the time on the, you know, the detailed clicking through, using the, the tool. Does that make sense?

Jordan Wilson [00:10:45]:
No. Yeah, absolutely. And you Know, I, I know there's not one perfect answer to this question because I think that this is what so many of us are trying to figure out, right as we're tackling, you know, like, you know, AI powered autonomy or, you know, human agency, you know, and I'm sure it's something as Accenture, one of the largest global consulting companies in the world, are constantly having to talk to businesses about all the time. But what are those steps? Right. So for me, even myself personally, you know, I have to manually unlearn how I've, you know, successfully done a certain task for maybe decades and that can be a very challenging and sometimes daunting endeavor. What are some of those best steps that leaders in the enterprise need to be making as we look at a future that is more shaped day to day by AI powered autonomy?

Mary Hamilton [00:11:39]:
Yeah. And add to that, the technology continues to evolve and change even as we're doing, as we're unlearning and creating this change. So it's not a one time set of steps. Right. It's a continual cycle of change. But I would say, you know, one lead with value. Right. Think about what is the outcome that you're trying to achieve? What is the value you're trying to achieve? Whether it's interacting with a customer or, you know, doing a day to day job, what are you ultimately trying to, to get to? Not the steps within it, but what are you trying to, to, to, to drive.

Mary Hamilton [00:12:16]:
And then think about, you know, how do you unlearn, how do you reinvent the ways that you're working? How do you, I think it's important to think carefully about the responsible use of this technology. Right. So, you know, how do you close the gap on some of the responsible, you know, biases and things that we've all seen in the past. And then the last piece I think is really around, well, for the enterprise and maybe for the individual, but for the enterprise it's about how do you make sure that we have the right data, the right digital core enabled to allow this technology to be used more effectively. So I think there's a lot of pieces to it. You know, it's the individual changing their process, but it's also enterprises taking that step back and saying what are the outcomes, what are the processes that we need to change? What is the, you know, the digital core? How do we address our data in a way that's going to make this technology successful? Because that leads to, and I'm sure we're going to talk about this, about the trust, right? And if you don't have the trust that these systems are going to work, it's all going to fall down. And those are kind of the core steps that I think are critical when we, you know, when we think about how to make this, these systems successful and scalable and usable for employees, for customers, et cetera.

Jordan Wilson [00:13:36]:
Yeah, and let's even just tackle that one right now. So when it comes to trust, how should we all be thinking about AI? Because, you know, you see these model updates. Updates, right. And as someone that has been covering AI every single day for almost three years, it's even hard for someone like me to keep up. Right? But the average, you know, decision maker is not spending 10 hours every single day keeping up with AI. So how do you find that sweet spot between taking advantage of the most innovative technology that you don't really have a choice but to adopt to. But at the same time, Most, you know, 99% of people can't even explain what a large language model is or how it works or what a tokenizer file is. Right.

Jordan Wilson [00:14:22]:
So how do you find that balance between kind of grasping today's and tomorrow's innovation with being the trust and explainability piece?

Mary Hamilton [00:14:31]:
Yeah, it's a great question. I think it all comes with building it in the small moments, right? That, that is at the core, every interaction someone has, they learn a little bit more about what the system will and won't do. And to your point, it continues to evolve and change. So we have to have those channels, we have to have those ways to continuously connect, to continuously educate folks on what it is, what it can do, what it can do next, what's coming, and continue to create that virtuous cycle around the learning, right? It learns, the more we use it, it learns and it becomes more, more efficient, more effective. You know, I know I, I talk to my AI assistants all the time and I tell it, I want less snark and I want, you know, more, more accuracy. And I want more, you know, I want it this way. And I'm constantly fine tuning how it's responding to me. Not just the answers itself, but the way in which it's having that communication so that I can consider it a trusted partner.

Mary Hamilton [00:15:35]:
And I think there are two pieces of the trust that we need to think about. One is, you know, really around this responsible AI, you know, as I mentioned, addressing things like the biases, et cetera. But the other part is around the accuracy, right? Is it predictable, is it consistent, is it traceable? Or do I understand how it got its answer. And that is all, you know, inextricably linked to autonomy. The more I trust them and the more trust something, the more I will allow it to do something on my behalf. So we're never going to get to that autonomy unless we trust the system to do things for us. And that that trust is really again built in those micro moments of, you know, did I ask it something, did it come back with the right answer? Did it come back with a, you know, answer that I can understand? Can I adjust the answer, can I adjust the way it's speaking to me? All of that's going to play into creating that autonomy.

Jordan Wilson [00:16:34]:
So, you know, I'm curious, you kind of mentioned a little bit of your own, you know, personal use of, of AI and you know, obviously accentures at the forefront. Can you give any examples of what that kind of observability or traceability or, you know, going and looking at an answer and saying, do I trust this? What does that actually look like? Right, Because I think for the most part a lot of people are just now trying to do more and be more productive and they're not necessarily going back in reverse engineering how a agentic model with tool use got from point A to point B. So what does that actually look like to start to develop that trust and transparency and traceability?

Mary Hamilton [00:17:19]:
Yeah. Let me take an example. So when we started our responsible AI practice, one of the things we did was not just outline what does it mean to be responsible and have good practices, have frameworks around it, but we actually built technology solutions to help ensure that the systems and the responses and the data are not compromised, that they're providing the right answers, that they're responsible answers and to flag when there are challenges or things that aren't working. So I mean, in my personal life, you know, when I'm, I'm using chat, I might, you know, I frequently call it on, you know, hey, that's not right. Because of this. Can you remember next time, you know, to double check or to cross check this. But the more we bring systems on board in the enterprise to help provide those cross checks and to ensure that the answers we're getting are correct, the better data that we have in place. Right.

Mary Hamilton [00:18:17]:
You know, we're, we're doing a ton on the data platform side from knowledge graphs to provide the ontology of, you know, how do these, what's the context around this data? What does it mean in this specific business process? Those are all going to create better answers on the enterprise side that start to build that trust. So for me, you know, it's really about, you know, how do you, how do you go into things with a lens that, you know, this, this new wave of technology and the new wave of everything we're seeing from, you know, social media to, you know, our own technology, we have to approach it with a lens of it. It's not trusted yet and until it's verified. Right. As opposed to, you know, how we used to operate, you know, we read something, you kind of trust it and then, you know, maybe you double check it to see we should operate on a basis of, I'm not sure it's trusted yet. Let me make sure that it's verified. And in the enterprise, providing those technology solutions, providing those double checks, whether it's human or technology to ensure that we are providing the right answer, I think is really critical.

Jordan Wilson [00:19:24]:
Yeah, I think that's a great call out there. I do want to rewind a little bit and talk a little bit more briefly about this new Accenture Technology Vision 2025 report. I know it did come out earlier this year and we will share. If you're listening on the podcast, make sure to check out today's newsletter. We will link to the full, I think 67 page report. But it seemed like there are four kind of big pillars from that report. Could you briefly walk us through what those findings were?

Mary Hamilton [00:19:55]:
I sure can. In case you don't have time to read the 67 pages, let me give you the quick download. So the first trend, and again these are all tied to AI and autonomy. The first trend we called the binary big bang and that is really thinking about the enterprise. And as generative AI is becoming central, central to, to try that again. As, as generative AI is becoming central to enterprise tech development costs are plummeting. There's lots of new systems everywhere. And those digital agents that we, we've been talking about, everyone's been talking about are gaining more and more autonomy and that is really transforming applications as we know them today.

Mary Hamilton [00:20:35]:
And again, it's that language barrier that I can come up with an idea and I can very quickly build out an application to test out that that idea. We're having a proliferation of applications within the enterprise that are going to be able to use these new generative AI systems. So that that's the binary big bang. The second one is called your face in the future and that's really about thinking about how brands are interfacing with their customers. When we're starting to see AI agents, personalized customer interactions at scale, brands have to be really careful that if everybody's using the same agents, that every brand doesn't start to have the same voice. And brands have to protect their unique voice. And how they can do that still through AI agents and personalization, but do it in a much more careful way so that their brand and personality comes through. And we talk about this if you wonder, but I'll share.

Mary Hamilton [00:21:36]:
You know, I've experimented with this a little bit myself. I have my own digital twin, you know, my own kind of personal brand. And getting her to, you know, have that personality and be, you know, like me has been something really interesting. And it's been a journey over the last couple of years as we've, we've evolved my, my digital twin. So something I've tried out in real life here. The third trend is around when large language models get their bodies. So this is really around how this is going to change the game for robotics. It gives the ability now to create more generalist robotics.

Mary Hamilton [00:22:15]:
It's bringing the 3D world, right, the multimodal aspect to robotics and really change the game about how we can not go through such painful programming of robotics, but really start to be intention driven about how we use these. And we're going to see a boom. We think we're going to see a tremendous boom around AI driven robotics. We've got a partnership with Kiam Group as a great example where they are using robots to perform warehouse tasks more seamlessly and partner and interact with warehouse staff to fulfill orders faster, more cost effectively, more safely. So it's a great example of bringing this stuff to life. And then the last trend is called the new learning loop. And that's really where we see this technology as one that it's a continuous cycle of learning. So as people learn from the technology, they will become more effective at using it.

Mary Hamilton [00:23:13]:
And as the technology learns from people, obviously we're going to have smarter and smarter systems. So it's really about enabling that continuous learning loop.

Jordan Wilson [00:23:20]:
And I, I love the last one especially because this is something that I personally talk so much about on this show is, you know, everyone's wondering like, okay, if these, you know, large language models are able to give us, you know, 60, 50 or even 40% more productivity, right, what should we be doing with that time? And I love the, the thought of the learning loop. And I always tell people, well, go through, if you're using a reasoning model, right, go through and look at it, summarize change of thought, see where it went wrong, where it didn't, and then run the same thing again and improve it and put in, you know, better inputs, you know, to try to get better outputs and learn how the model is working. So, you know, when we are looking at the future of work and we're talking about models that are now agentic by default, you know, we're talking about models that are getting very high in terms of, you know, kind of their, their benchmarks compared to the smartest humans in the world. How should we be, you know, keeping up with these models and, you know, being an active participant in that learning loop when they are changing so much?

Mary Hamilton [00:24:30]:
Yeah, 100%. It is a huge challenge. And I think especially in the enterprise, again, going back to trust. If folks don't understand, if employees don't understand how these things are working and how they're evolving and how they can use it, it's all going to fall apart very quickly. Right. There was a study done that a lot of employees are actually using generative AI but not telling their employers that they're using it because they're concerned about the consequences, you know, both, hey, well, if I'm using it, you know, could it take my job? But they're using it as a sidekick. They're using it to be more effective and more efficient and to do more. And so it's about that acknowledgement.

Mary Hamilton [00:25:12]:
You know, I'll share another example that we built an online course with Stanford called the Generative AI Scholars Program. And it's really about helping our clients sharpen their AI skills and knowledge so that we help employees up level. Because one of the things we found is that organizations that have that commitment to up level and continue help their help their employees continuously learn, have better trust and have better adoption, have better likelihood of driving this technology to scale than if they, you know, did it. You know, folks don't understand what they're trying to learn. So I agree with this. It's just a really important point that people need to continuously adapt and learn with this technology.

Jordan Wilson [00:26:01]:
All right, so Mary, we've covered a ton in today's conversation. But you know, as we wrap, maybe what is the one most actionable takeaway? Right? So if you pique someone's interest and they're like, yeah, like, I really do need to be looking at, you know, the future of the workplace and not the workplace of yesterday. What's your one most important takeaway for those people?

Mary Hamilton [00:26:24]:
I would say, I mean, I'm most excited about where we're going with Agentic. And it is something we're investing a huge amount of focus, time, energy, dollars, you know, driving this with our clients. So the takeaway for me is to start thinking about the outcomes of the work that you're doing. What are you trying to achieve? Kind of full circle back to where we started working. What is the value that you're trying to drive? And then how could generative AI start to take on parts of those roles? And if you, you know, if we think about the whole agentic landscape, right, there's, there's utility agents, there's super agents, there's orchestrator agents. And thinking about your role and job, maybe not in terms of agents, but how does it get done? How could you be more efficient? What more could you do if, if you start to pull in some of these, you know, autonomous capabilities that could help you do that. So for me, you know, that that's the takeaway. And I try to do that every day with my own, my own digital twin.

Mary Hamilton [00:27:30]:
What, what else should could she do? What more could I do? What other locations could I, you know, could I be doing other podcasts maybe? And what do I need to enable then to make that happen from a data from an agent platform standpoint?

Jordan Wilson [00:27:45]:
Great, great advice and a great way to wrap up today's show. Mary, thank you so much for taking time out of your day to join Everyday AI. We really appreciate it.

Mary Hamilton [00:27:54]:
Thanks for having me. This was fun.

Jordan Wilson [00:27:56]:
All right, and as a reminder, y', all, we, we are going to link to that report and a lot more that we didn't have the time to get to. So if you maybe miss something while you're out on your morning jog and you're like, what was that knowledge that Mary just dropped on my head? Don't worry, it's going to be in our newsletter. So if you haven't already, please go to your everydayai.com Sign up for the free D newsletter. Thank you for tuning in. Hope to see you back tomorrow and every day for more Everyday AI. Thanks, y'all.