Exploring AI's Impact on the Circular Economy with Matthew Bunce

Revolutionizing Sustainability with AI: A Conversation with Matthew Bunce

In this episode of The Human Code, host Don Finley welcomes Matthew Bunce, an expert in AI and digital transformation, to discuss the role of AI in sustainability and smarter decision-making. The conversation covers Matthew's journey and his passion for the circular economy, the significance of AI in reversing logistics, and the challenges and opportunities in AI adoption. They delve into how generative AI and large language models (LLMs) can improve supply chains, reduce carbon emissions, and assist human creativity. Throughout the episode, the importance of responsible AI use and the future job market impacted by AI are emphasized, offering insightful perspectives for both tech enthusiasts and businesses.

00:00 Introduction to The Human Code 

00:49 Guest Introduction: Matthew Bunce 

01:52 Matthew's Journey into AI and Sustainability 

03:11 AI's Role in the Circular Economy 

06:03 Generative AI and Future Job Markets 

14:06 AI in Decision Making and Automation 

27:03 Responsible AI Implementation 

28:24 Conclusion and Sponsor Message

Sponsored by FINdustries
Hosted by Don Finley

Transcript

Don Finley: 0:00

Welcome to The Human Code, the podcast where technology meets humanity, and the future is shaped by the leaders and innovators of today. I'm your host, Don Finley, inviting you on a journey through the fascinating world of tech, leadership, and personal growth. Here, we delve into the stories of visionary minds, Who are not only driving technological advancement, but also embodying the personal journeys and insights that inspire us all. Each episode, we explore the intersections where human ingenuity meets the cutting edge of technology, unpacking the experiences, challenges, and triumphs that define our era. So, whether you are a tech enthusiast, an inspiring entrepreneur, or simply curious about the human narratives behind the digital revolution, you're in the right place. Welcome to The Human Code. In today's episode, we are thrilled to have Matthew bonds with us, a seasoned strategist in AI and digital transformation. With a background spanning decades in engineering and problem solving. Matthew brings a unique perspective to the role of AI in driving sustainability and enabling smarter. Decision-making. Tune in as we explore how AI is revolutionizing sustainability through the circular economy and responsible resource management. The power of feedback loops and AI driven decision-making and how they're transforming complex industries. Y a reasonable approach to AI adoption is essential for longterm success and ethical impact. Prepare for an enlightening conversation with Matthew buns on how AI can shape a more sustainable, efficient and humored centered future. All right, I'm here with, A new friend. I gotta say, we have a ton in common. We were doing the pre show and Matthew, it's a pleasure to have you here. Matthew Bunce is just a rather interesting individual who has a deep experience in AI and looking forward to having this conversation. So to kick it off, Matthew, what is like the one thing that got you interested in the intersection of humanity and technology?

Matthew Bunce: 2:01

Sure. Yes. And thanks, Don. It's a pleasure to be here. I would say that what got me interested in, technology. and humanity and their intersection is my work in my career. I've been a passionate engineer since I was a young boy, pulled my toys apart. I've been through all the engineering schooling, built robots and g codes and worked for companies that built robots. McKesson Automated Healthcare. And, later in my career, I worked in a hospital and one of those robots got onto the elevator with me to deliver drugs to, different, inventory cores within the hospital system. So what's interested me is how AI and robots and their convergence with connected devices can help humanity because I'm also, have certification in sustainability and the circular economy with Cambridge. And interested in responsible use of AI and making the, the world a better place to live.

Don Finley: 3:01

That's incredible. diving into this. it's always fun because there's always 15 questions that come about from the introduction and I'm like, all right, how do I want to sequence this going forward? let's touch on the circular economy first. I'm very curious as far as how you see AI playing into, our economy and then additionally, like how really the circular economy that you mentioned as that concept is really important to understanding our cycle of life inside of our economy.

Matthew Bunce: 3:29

Yeah. So the UN has put out 17 sustainability development goals and, they're all, noble pursuits. some of them are educational. I'm going to focus on the ones that are environmental. The three top things that we can do to save the planet are, reduce carbon emissions. from our, carbon footprint from the energy we use and produce. and then reduce carbon emissions from the herds of cattle that we feed from. And then also, the ocean current is slowing down. And that's what's causing climate change, perhaps because the Pacific garbage patch is, there's plastic in the ocean four times the size of California that is slowing the ocean currents down. So anyhow, to back it up to how AI can help. is we need to make more decisions, better decisions. We decisions are being made more rapidly and at a velocity, that we are not able to keep up with. And a lot of those are reverse logistics decisions, reverse logistics, tying it back into the circular economy is when we design things. Um, we also have to design our supply chains to return the, what's been consumed. and reclaim it and enter it back into the supply chain. So in one of my articles, I estimated that 10 times as many logistics decisions need to be made, to effectively, reclaim all of our materials so that they don't wind up in landfills and float out into the Pacific ocean and slow the currents. And there's also a lot of, other, innovative strategic decisions that can be made as well. concerning, our carbon emissions reduction, our energy consumption and the best way to produce energy and use it responsibly.

Don Finley: 5:17

I do like this angle that basically AI is going to need to help us in the decision making process. And then additionally, like our supply chains have gotten intensely more complicated over the last, let's say, 50, 100 years than they ever have been. and I can, Also share from being a small business owner and working in enterprise as well, that if you needed in any organization to support the capacity for 10 times the amount of decisions to be made, that effectively most businesses would just be grinded to a halt. But, what you're talking about is incredibly important. It's the world we live on. And also as the economy expands, we have to be more. aligned with what our second and third order decisions have as far as the impact goes. how do you see basically either generative AI LLMs or other forms of AI, assisting in this and then allowing the human to make those decisions or be part of that decision making process.

Matthew Bunce: 6:15

Yes. good question. I see, a lot of rapid advances happening with generative AI, particularly, uh, LLMs, large language models, and, The reason I think this, that it's going to accelerate things is because, agents are going to be created that are AI experts in specific fields. And then the LLM will allow these agents to talk to each other. and that's what the rapid acceleration, what will, achieve is higher orders of intelligence. and also combining this with connected devices, So if we had connected devices, at supply chains, reclaiming centers, or connected devices, sensors in landfills or in the Pacific Ocean monitoring, the garbage situation there. Better decisions would be able to be made because these LLMs could talk to each other to find opportunities to improve recyclability. improve the utilization of the reverse logistics centers, trucks, empty capacity on trucks. and these decisions can now be made in real time, Because of the connection to, the sensors. Yeah, so I think this will start to happen more and more rapidly as more devices are connected and come online and as more, agents are created, more generative AI LLM agents are created in disciplines, which is kind of what, We were discussing a little bit before the call, you said that, I think jobs and careers are going to become more individualistic. As you can imagine, there would be a lot more, expertise and it would be partitioned in different areas, geographical or functional, or, the expertise could be divided, to these, LLMs and generative AI technologies.

Don Finley: 8:12

And I think that's one of those areas where like the jobs that we all hold are somewhat created by our own individual nature. And like I came to this concept when I was thinking about the startup space. And basically what's required in order for a company to hit 10 million in revenue, And if you look at the numbers back in the eighties, it was something like you needed 40 to 60 people right around 50 people in order to hit that 10 million. Spot and this is with inflation and everything else, But then you move towards the early 2000s and The push towards cloud, and probably the late side of the early 2000s But like 2010 ish the push towards cloud software as a service and then additionally like a professionalized services as well Became a little bit more adept and so you could get away with adding or getting to 10 million dollars in revenue as a You Um, but with AI, I think we're actually at a point where you can start getting in, organizations of one and Sam Altman was even on stage a couple months ago talking about how he's, he has a side bet with other CEOs as far as when they're going to see, a new product. the first single person billion dollar valuation company. And that will be partially what AI is capable of making. but the key thing as far as the individualization of jobs is that now that you could create a company that has one person in it that delivers a billion dollars worth of value to the market, we'll likely end up seeing You know, I have a passion for X, Y, and Z, But I am not an effective marketer, So AI could come in and help that. Or if there's, I have abstract art, finding the people in the community who are also like interested in abstract art, but are interested in it from the perspective of making that value exchange. I think AI could help to, be the oil. In allowing the human to create what is in their soul's creation. And this is getting really off, but I'm hoping, and actually we're working on solutions to drive closer to that experience of, you have a purpose. You have something that like you are really passionate about creating. Let's utilize AI to help you get there, not to replace you in that function, but that like you are participating in that space. And I really appreciate how you've come at this from another angle of, there is always an automation. a replacement of some jobs, but a creation of different ones to help fulfill that. where do you see some of these jobs coming in?

Matthew Bunce: 10:54

Yeah, good point. I think you're referencing an article I posted about. Two jobs created for everyone lost as we automate. This is a concept that, Professor Emeritus, Dr. Badanda taught to us at University of Pittsburgh. And essentially it comes from, new markets that develop. as a job is automated in, autonomous vehicles, So truck driver delivery, something like this. well, potentially jobs are created because engineers are now going to put sensors into the street signs so that the cars can read the street signs, algorithms are going to be developed to help the traffic situations, or as I explained earlier, to monitor the utilization of trucks or empty trucks. there's been, cases where we've tried to understand how dangerous this is, How dangerous it is to humanity. What if the driverless vehicle hits someone? Who's to blame? that needs, new laws. That needs, thinking around, the governance of, those laws, administration of those laws. What laws need to be written? How are they going to be evaluated? It needs, regulations and standards. probably accounting standards that need to be developed for, the impact investment of AI and the responsible use of AI in the driverless vehicle, systems and supply chains. So there's a lot, the job market is only limited to our imagination on these things. There's a lot of thinking to be done. And as you put it, AI automates jobs when they were well defined in manual, in tedious. They do not automate jobs that require lots of creativity. And in, in a lot of those cases, AI is used to augment, human decision making. So that's how I see, jobs being created, as they are lost to the economy, to, to AI, there's additional jobs that will enter the economy as well.

Don Finley: 12:57

And I gotta say, I find it inspiring to think about the new jobs that could be created. do you know what the number one job that kids want to be today?

Matthew Bunce: 13:05

No, tell me YouTube

Don Finley: 13:06

YouTube creator.

Matthew Bunce: 13:08

creator. Yeah. It

Don Finley: 13:09

You and I never would have had that as a concept. I wanted to be a Disney cartoonist when I was younger, those kind of things, it's a nice refreshing reminder that the world is evolving, it changes, and that even as this technology is coming out, if we do it in a responsible way, that we will, we'll end up having much more, than we do, at this moment. And it just, the thought came by of, if a king from the 15th century came to your home, they would be really impressed with how well your house is, Like that you have running water, that there's air conditioning and heating, all these things. And then just the amount of stuff that we have in our homes. like I can see a picture in the background of your place and there's just all of these things that would just absolutely impress. And I'm really excited to be, in shock and awe for what the next hundred years could possibly take us, when it comes to where we'll take AI. we're seeing like decision making processes being assisted. Like that augmented intelligence, that ability to take away some of the tediousness. But I also know that like on an individual basis, I'll use it for brainstorming. And even while it's not, expanding the envelope of human knowledge in its creativity, It's offering enough of a backstop or relation to my conversation so that I can be more creative myself or help to deliver that. do you think we run the risk of changing the job front too fast?

Matthew Bunce: 14:41

I think that is a risk, and, yeah, that gets into the whole field and discipline of change management. what is too fast and how do we step into things? to drive the most value. it's something that we think about a lot. and, in particular, in my career, working with Fortune 500, Fortune 50 companies, that change management function and the incremental change of, digital transformation, how to step into it. And. Also achieve value as you're stepping into it so that the value matches the investment closely is something that we, consider in our projects and our initiatives very frequently. And I think what I found is that you do have to create, dimensions of change management. For different regions of the world or different lines of business or, different business units or, different capabilities, that you would deploy to different functions. And it's. It's almost, like a Rubik's Cube or something, the dimensions that are there and, strong projects and strong change management allows you to move in the different dimensions, and design, and develop technology, that is configurable, that can be agile and move in different directions, I think.

Don Finley: 16:06

So on the case of change management, I think. that's incredibly important from the standpoint of individual organizations. I can't do anything in any business without understanding like the impact that it's going to have. And it's just part of being a good steward of an organization is to have that, those controls in place that as you're implementing something, you're able to understand where it goes and can the team actually adopt it? Or are you causing more harm than good in some capacity? From the standpoint of we're discussing all these things in the current paradigm that we have of the educational environment, of the work environment, of capitalism itself. but we're getting to a point where intelligence is effectively dropping to a cost of zero, we're riding that curve down, which will allow for greater decisions to be made, as well as hopefully, finding those areas where human creativity can be part of That loop. I'd be really interested to hear your ideas as far as are we hitting the limits of where capitalism can take us? and is there a more larger scale type of impact that this will have on our society?

Matthew Bunce: 17:12

Yeah, good question. you did reference, something that came to mind the production function, won a Nobel prize in economics by Douglas and Cobb. And essentially it measures the output of an economy, based on two factors. And those two factors were capital, the investment of for capital equipment, in resources, which are people and what you're explaining is a new, a new variable to that function, which is technology somewhere. Technology came in and is, Interacting with resources, the cost of intelligence, as you put it, going to zero, increasing the economic output of the resources and capital that we put in, what are the limits to it? I don't know. I think it's an interesting question that you're asking. And, certainly, we focus at Aera Technology is on the decision. We apply AI to the decision. And we do that in a couple of different areas. We apply it in the collection of data and keeping data real time. Because in order to make decisions, you have to have the most current information, the up to date information. you have to have intelligence. You have to have some brain power, some mathematical function that you're using to evaluate that information, to, choose an option or an action. of your decision. And then you have to have processes where you're going to close the loop on that decision, take the action, and then monitor the result, and then assess your, the value you've achieved. over time and potentially learn from what your decision was and improve the value over time. So I think without question, technology and AI is helping companies to achieve greater economic output, simply because. Humans cannot keep up with the pace of the decisions that need to be made in the volume of decisions that need to be made. We kind need these digital analysts, these digital workers to do pieces of work for us, to keep information current and relevant and in real time, to apply all the advanced data science, that we need to do to make our decisions, and also to close the loop into the real world. And make sure that our actions we've taken are being implemented and measured.

Don Finley: 19:50

it's fascinating to kind of run through these things because there's Just so much impact that it's having on the world and the opportunity of it all But I love that at Aerotech you're actually taking this and helping to make decisions So you're setting up and helping companies make decisions and using AI to make decisions. the platform itself is making decisions.

Matthew Bunce: 20:14

yeah. what we do at Aera Technology is we apply, artificial intelligence to the decision making process. we do that in four different areas, right? We have, data collection, data harmonization. And we have a pillar for data. We have a pillar for intelligence. We have a pillar for automation. that, robotics, desktop automation, RPA, all of the automation, and then also an engagement pillar. to, engage a user, to make decisions with the machine and then evaluate those decisions over time to, improve upon them. Ultimately, we aim to automate the decision making process, so at first we have the human using the machine in the loop. making better decisions from the analytics that, and the augmentation that data science produces. And then we, Aim to put humans on the loop, guiding the machines to make decisions and ultimately take the humans out of the loop where the confidence is very high and the risk is very low. And those are things that we monitor and have, that produce confidence scores and predictions on what value will be achieved. And, and that's something that we monitor throughout the life cycle of the decision. and then, improve the user's decision making ability. And also, at times there's low confidence and even in the low confidence, that's very valuable to have as Because if there's low confidence in the recommendations or decisions that are made, then we want to pull those apart and apply more data science. Maybe there's patterns. What are the patterns we're detecting that are, making those decisions low confidence? what's different than the high confidence decisions? Maybe it's a certain area of the world, Or maybe, the data comes in and it's not a normal distribution, it's a non parametric distribution, or maybe it's a new product or new line of business or something like this. There's something different about it that is making the confidence score a little bit lower that it's going to achieve the value it, that is predicted. and therefore it needs further analysis perhaps. So there's a number of things you can do with this. the era technology decision intelligence platform. And it's the full spectrum of analytics, data science, augmenting your decisions with data science and fully automating decisions. But we're very responsible in our approach and methodology to doing so. And not only do we engage the users. But we also engage the developers. And Aera Technology is a glass box. the, super users, or the developer community, as we hand over the keys to the car to the end user, as you explained, we also hand over the, the mechanic guide. And, and allow you to produce your own car, build your own car. so we have a self service model. You get to see all of the information, all of the data come in. You get, Oh, one of the things that you mentioned that's important is the processing and cleaning of data, right? you get to see the data come in, how it's pre processed and cleaned and This is a large portion of what, data scientists do, Is they spend a lot of time cleaning the data, preparing it for data science models. so that part is visible, to the developer community. And, they can see the inputs to their models, have the ability to fine tune the models, they see the outputs in how they enter this automation process, and then also the engagement of, the user now takes digital. analyst, this digital worker, this digital data scientist, and interacts with the results. And that gets monitored as well throughout that whole pillar. So now that we've interacted, I disagree with the, machine's recommendation of what I should do. Now we monitor that over time. Who was right? Was the business analyst, right? The user of the technology, right? Or was the machine? And to what extent? There's also a measure of goodness, or wrong? And to what extent? And those are difficult things to measure because you get into a world, another author that we could put in here is Judea Pearl in his book of why you get into a system of. Measuring things that didn't happen, but could have happened, right? And, that's important to the learning equation as well. And, in responsible use of, decision intelligence, artificial intelligence, and applying it towards decisions.

Don Finley: 25:01

And I think you're hitting on a key idea here. For anybody that's looking to implement AI in their business or really implement any process, you need those feedback loops. And then, you know what I'm really appreciating what you're offering here as a, it has the framework built into it to track those decisions.

Matthew Bunce: 25:21

thing on loops to interject the, important about the feedback loops, a framework we can think of, or we implement is called systemic thinking and systemic feedback loops, systemic decision making, which has three parts. You're referring to the third part, which is closed loop thinking, Which is we've made a decision. We're closing the loop and determining what actually happened, what did we think we would achieve and what was actually achieved? That's the closed loop part of the, systemic decision making. The second part dynamic things change over time. So how do you accommodate that with your. Data processing with your, your data science modeling with your processes. How are you thinking about things could potentially change over time? And how are you testing the edge cases and corner cases to make sure you have a robust system design and decision making framework? And the third is Persona driven perspectives. So people make decisions in different ways and also, people evaluate decisions in different ways. There's different parts of businesses that need different things from a decision, So there could be a, conflicting goals within an organization. the sales team always wants to increase sales. Launch lots of new products. The operations team wants things well defined and they want to, improve the margins of their existing products, So these are some different goals that need to be evaluated in designs. So these are, different perspectives that get taken into consideration in a systemic, design for decision making. in

Don Finley: 27:03

That was a great interjection on like feedback loops And on that, I got to say, Matthew, it's been a pleasure talking to you. The one last question that I have for you is how do you take everything that you're doing? and what is your recommendation to people who are a just getting involved or looking at how their AI strategy needs to be implemented?

Matthew Bunce: 27:24

But,

Don Finley: 27:25

Not yet.

Matthew Bunce: 27:25

AI is like fire. it's very useful, but it's potentially dangerous. so, what I advocate is responsible use of AI. And I think that it needs to be well thought out. there needs to be more investment, more jobs created around the responsible use of AI, thinking on how to implement it responsibly, where to use it. what regulations and code should be put into place. what internal processes, documentation should be used. and I would say that you do need to get started. It's something that, just as fire is here, it's a reality. We're using it. there's no way to make fire go away. Same with AI. It's here and it's making a difference. And, I think, incremental steps towards using it and using it responsibly and thinking about how to use it responsibly and documenting this, are going to make a big difference in our economy moving forward.

Don Finley: 28:24

Thank you. Thank you for tuning into The Human Code, sponsored by FINdustries, where we harness AI to elevate your business. By improving operational efficiency and accelerating growth, we turn opportunities into reality. Let FINdustries be your guide to AI mastery, making success inevitable. Explore how at FINdustries. co.