Webinar: Your new role as a product owner and a new operating model for the autonomous age

All right, Good morning, good afternoon, good evening, everybody. My name is Kevin and I am Associate Director at Board of Innovation and your host for this part of the agenda. Still to be determined whether or not I'm an AI avatar. I have the immense pleasure of introducing everybody here to Amir, who is our Managing director driving all the amazing products that we are building at Board of Innovation. So without further ado, I'll give it to Amir. Please put your questions in the chat because hopefully we'll have a few minutes to field some questions at the end.

Awesome. Cool. Thanks so much, Kevin. Just want to double check that everyone can hear me. Sounds like. Yep. Okay, cool. So as, as Kevin said, I'm Amir uki, I'm Managing Director here at BOI and I head up our product practice. I'm based in our New York office. Super excited for day two of the summit and it's great to see so many of you still, still here with us and hopefully you enjoyed, enjoyed yesterday's talks. So I think we're already starting to see a couple of key themes emerge. So looking forward to continuing to push them and to challenge them today. So, as Kevin said, I head up our product practice and a lot of work that we are doing is centered around what we think will become a new product led operating model of companies for companies really of all sizes. So here at boi, we both design those operating models and we also build the software that underpins them. So when referencing product in this talk, I do want to say we are, for this conversation, going to be talking primarily about software. So we mean software tools or systems that basically deliver value by solving specific problems or enabling specific tasks. So we don't mean a physical product. In this case though, we have had a bunch of really interesting talks around the impact of AI on consumer goods over the two days. So equally important. But today we're going to explore this idea that in the next five years, most companies could start operating like software businesses. So traditional departments like marketing, innovation or finance will be led by individual product owners who previously may have had nothing to do with software or technology in their roles. So instead of managing processes, they'll be responsible for their own AI driven product that will basically exponentially improve key workflows and let them and their teams operate a lot more independently. And I think it's important to chat a little bit about the goal of autonomous transformation, which isn't to automate away jobs and replace humans. It's about thinking critically and strategically about how do we use AI to transform key workflows. So, so that teams and leaders can operate a lot more independently and focus on adding new value. And I think there's a bit of a misconception around autonomous transformation, that it only applies to tech businesses or software forward businesses. But really that's not the case. And we found some really compelling use cases across industries like freight manufacturing, energy and agriculture. And, and the speed of adoption in these industries will obviously vary, but the impact won't. So what we're actually assessing is where are there time consuming, repeatable processes where that human time could be spent on finding other ways to develop a competitive advantage and put our human curiosity to good use. I'm going to tease out a use case here to explain what we mean by repeatable key workflows that we think could be handled by autonomous, autonomous engines. So let's take the role of a marketer as an example and look at a few key workflows and then consider how they can be transformed with AI. And I think we've probably got quite a few marketers amongst us in the summit attendees over the two days. So this should be a good example just to prove out this idea of these workflows. So the first aspect of, or the first aspect we've chosen to focus on for a marketer role is this idea of customer data, which is often manually analyzed, usually through dashboard based third party tools. But AI could instead automatically analyze really huge data sets directly and generate real time insights at scale. There's a lot of detail to that and a lot of nuance to that, but hypothetically we want to think through if that's the case. What if the time that was saved from doing that manual analysis was actually spent on other things like building real life relationships with potential partners or, or clients. Segmentation is another big part of marketing roles, or a lot of marketing roles. So what if we moved from manual segmentation to hyper segmentation, where you have dozens of Personas that can be created that are a lot more reflective of the complex and diverse society that we live in. So what if the time saved from having to go through those segmentation exercises was maybe spent instead on trying to connect and understand diverse audiences better that maybe aren't represented in a company's traditional demographic? On the campaign side, there's a lot of attention around AI on campaign generation. So let's say on the campaign side, what if a huge number of campaigns and a variety of campaigns could be created and deployed at scale, which previously would have been a lot more resource constrained? So what if the time saved from having to Manually create these campaigns was actually spent on mentorship for your creative team. What if we use synthetic launches and tests to, for example, reduce the waste of physical goods? By running synthetic campaigns, there could be a really huge impact in industries like fashion, for example, where we've got a lot of unsold inventory each year that ends up in the trash. So, just starting to tease out a use case here, using marketing as an example to talk through the potential of repeatable workflows. And I think what we'll see is that when we start to string these workflows together in a single product for a specific team, we then see a pretty big shift in where and how we spend our time. So for right now, I'm not going to focus on the specifics of how we create each of these tools and we'll be putting out a lot more content in the near future around that side of things. But instead, I do want to explore the impact on a typical company operating model and the leaders within that model that will now find themselves in the position of being product owners. So we could have a CMO who's now the product owner of a marketing engine that reacts to customer sentiment analysis and generates synthetically tests and validates campaigns for deployment. We could have a head of supply chain management that is now the product owner of a supply chain management engine that can forecast demand, it can optimize inventory, it can adapt faster to market dynamics. We could have a VP of innovation. I think we have a lot of innovation leaders amongst us. So what if the VP of innovation is now the product owner of an exponential innovation engine that can generate, test and prioritize hundreds of new product ideas in minutes instead of months? This kind of brings us to the original question, which is, will I myself need to become a product owner? So whether you're in sales at a construction company or you're in hr, maybe at a manufacturing company, the chances are you are likely to find yourself in the business of software and products in the coming years. And it's worth asking yourself, does my business unit rely on resource intensive, repeatable workflows? Am I, in my role, responsible for creating a competitive advantage for my company? And this is a concept that we'll come back to. Does my role involve testing different hypotheses and trying to predict a future scenario? So, for example, if you work in, let's say a food and beverage company, and you need to predict what today's 18 year olds will be eating or drinking in three or five years time, I think if the answer to any of these questions is yes, then chances are you may just find yourself becoming a product owner and seeing your role transition into more of a product ownership type of role. Mostly, I think it's important to say mostly because we do need to call out that we really, for the purpose of kind of our 20, 25 minute chat today, we're really only exploring what we consider to be quote unquote, measurable repeatable aspects of different workflows. And there are so many parts of our roles like mentorship and cultural leadership and ethical reasonings reasoning that these engines really won't be able to or equipped to handle just yet. So an important call out there. So let's talk a little bit around this idea of product management and decision making and product management actually driving decision making. So imagine a scenario where your team is now AI native. You've assessed those workflows and you found a value add to transforming them using different AI tools or an underlying AI product. Your team is AI native. They're able to work exponentially faster and deliver more informed results. Let's say, I think what we'll see is that department and discipline, or business unit leaders, whatever the terminology is that you use, they'll become product owners that are responsible for identifying and prioritizing needs and opportunities and then really thinking through what are the hypotheses that their product needs to test against. For example, could there be a new feature in the product that is able to successfully identify regulatory obstacles for a new financial product or an FDA regulated product, the product owner will lead determining the feasibility, desirability and viability of the features that make it into their products. So what can be built with the resources and with the budget that you have available to you. And they'll obviously oversee the inputs, the outputs, the performance and the management of these different AI driven workflows. And then when we think of it through this lens, a lot of these are the traditional tenets of product management. We actually start to see that product management and business management do have a lot in common and the jump isn't as scary as it may seem. I just want to hone in on one aspect of that that I touched on, which I think is probably going to be very important and relevant to a lot of us, which is this idea of the resource management portion of. I think while process and outputs will fundamentally change the budgets to invest in getting there, they are going to be slower to catch up, which means that when you're managing your product, you really do need to think through the pros and cons. And the right way to approach different features and functions. So on the screen here we have an example that we at BOI here assess a lot on behalf of our clients. Which is what is the best way for a company to engage in synthetic Personas or synthetic testing or living audiences or digital twins? There are so, so many ways to do this. And I think that choosing the right approach that achieves the intended outcome without over investing or under investing is really critical. So at this stage for most leaders, I think knowing the outcome that you want to achieve will be the key to figuring out what technology you actually. On that technology portion. Ultimately, choosing technology is getting easier and easier. And there are many firms out there that can build a product for you. So the role of an internal product owner or even an external partner, if you choose to work with one, isn't just to figure out how to build a product. It's about how to build the right product. And we saw this slide yesterday, I think in Phil's presentation where he said the key to success won't be the technology, but the design of the operating model to unlock its real value. And then I think in the morning's talk, Tiger said that really success is about making sure that your leaders are curious and they know what questions to ask. I think if you have good leaders in place, this is most likely what they've always done and what they're already good at. So a new operating model for an autonomous world, it's not about turning your non technical leaders into technical leaders. It's about amplifying and elevating a type of strategic thinking that already exists in them, but has probably been buried or distracted by process management. So what we'll see is that teams that learn to use their own AI driven products to exponentially ideate, build, test and adapt quickly will be in the best position to create value. And we're going to see a big shift in the competitive landscape. So when traditionally time intensive processes like customer data analysis or segmentation or outbound sales or product ideation are augmented by exponentially faster and more effective AI powered workflows, the landscape really shifts. And we see that having the scale and the resources to have a really big innovation team, or hundreds of people in the research team or pricing team, it's no longer the competitive advantage that you can rely on. So we see barriers to entry be much lowered for competitors both large and small, and companies that haven't adapted to a product centric model. They are at risk of disruption from competitors that can easily mimic their previous workflows. So it will create a new arena for competitive advantage. With what we'll see up here in the top right is that autonomous transformation in this product led approach. It's really going to become essential just even to stay in the game. When access to traditional workflows is commoditized, companies will need to get smart about what their hard to copy capabilities are going to be. It's not enough to have kind of a walled approach where you have hundreds of people and tens of millions of dollars to throw at resources and scale. We now have to start being smart around thinking as humans how AI can create new value, not just make the process of generating existing value more efficient. And I think first mover advantage is very real here. We've been pretty surprised at how many large and very complex companies have come to us at BOI in just the last year to start to think about this transition and make this transition. And I don't think that the impact of the transition will be particularly forgiving to slower adopters. And just to draw on a recent example, we saw a similar and say pretty seismic impact happened when digital advertising became accessible to brands of any budget. And it really lowered the barriers to entry for challengers who just wouldn't have been able to afford a TV spot or a radio spot or print or out of home. And the revenue loss impact on traditional advertising was pretty aggressive and pretty immediate. So let's look at where we are kind of in this conversation, in this journey together, and where we are on this very, very, very simplified visual representation of an operating model. There's obviously so much more that goes into an operating model and there's so much nuance, but just for kind of a data visualization purpose, we have these time intensive workflows that will be driven by AI engines. We have traditional roles that will transition into more of a product ownership role. And we have comparative advantage that was traditionally guarded by scale and access to resources. Now shifting to a focus on hard to copy capabilities. So what does that mean overall for the operating model? It means that what we have is a pretty massive paradigm shift from a process led company to a product led company. I think it's worth talking about some of the behaviors of this operating model. We are going to see resource intensive processes start to transition over to automated workflows. As we said, that free up time for the development of hard to copy capabilities. And that will involve human rationale, human initiative, human creativity. To think about that when we're going to see the playing field leveled when it comes to processes and workflows, we're going to see a big impact on resource intensive scaling towards systems that are actually able to handle increased workloads without proportional increases in resources really exponentially. I think we'll also see a pretty big reduction in what we're calling interdepartmental reliance. So bottlenecks, delays. I'm sure all of us have this as part of our regular roles, relying on inputs or outputs or approvals from other teams. I think we'll start to us move to this decentralized model where we're a lot more focused on real time responsiveness and an agile adaptability. So when we have business functions that become so independent, what connects them? When autonomous transformation moves us towards this model where we don't have to rely on other units or stakeholders as much to be able to get work done, it's important to think about what maintains that underlying connectivity between these different nodes and these different engines. And I think there are two aspects to this. One is the humans behind them. So the humans that are leading these strategic objectives that we're trying to reach, the humans that are bringing company values and personal values and culture into these workflows and how we make our product decisions in these workflows. Humans are uniquely positioned for accountability and critical decision making. I think yesterday at the summit someone gave the example of why we don't have self driving commercial jets at scale yet or at all in fact. And that's a really good, I think, example of just human accountability that can't be replaced because we as humans, we don't want to be making decisions that could affect things like a plane that isn't working correctly. So accountability is really important. It's a uniquely human attribute. I think the second aspect of these, of that connectivity is the technology. So the Data and the APIs establishing that interoperability between different departments is super important. I think it raises questions around the integration of third party tools and what that will look like in the future and how that potentially will or won't scale versus building your own, your own tools that will ladder up into a bigger operating system. So there's a lot to think about there. And there is actually an interesting anecdote from Jeff Bezos a while ago who was really adamant about using APIs. He wrote this viral memo that said something along the lines of all teams being expected to expose their data and their functionality through APIs and to only communicate with each other through these, these interfaces. And I believe at the time in the memo the thinking was that no other form of inter Process communication was allowed, there wouldn't be exceptions. And his belief was that without APIs, pods would just constantly find themselves depending on other pods. So there's something interesting in that. I don't know that companies will need to go that extreme, but that interoperability through APIs is going to be really critical when we start thinking about connecting these different engines together and having access to an entirely different type of IP where business units are no longer as reliant on each other, but are actually feeding each other proprietary insights that are adding an entirely new angle on IP to a company's balance sheet. So that connectivity is important to think about as well. I do want to spend things about three and a half minutes left. I do want to spend a moment talking about the challenges. There's a macro impact of AI in general in terms of energy usage and bias reinforcement that applies to everything that we're doing with AI and neither of these things should be overlooked. But as we start to work through the transition of the operating model, and a lot of companies are currently doing this, we're doing a lot of work in this space. Obviously we do want to think through challenges around how we assess ROI at an operating model level. Obviously individual bus are now going to be much more responsible for their own use cases. So how do we assess that ROI at an operating model level? Brian Evergreen talked about this yesterday, but we're going to see a shift from data driven decision making to reason driven decision making, which I think is a major cultural adjustment that will involve a level of change management. On a functional level, legacy tech and IT systems and how we integrate with those is a big consideration. And as with all things AI and data related, data quality, data governance is critical for this to work. And then an interesting qualitative challenge that we're going to talk about in a, in a different webinar. In a lot more depth is this idea of identifying what the new competitive advantage is when that playing field is leveled, when scale and processes are no longer defensible. How do we think about a new competitive advantage? Transitioning autonomously is not the end goal. It's really catching up with where we think operating models will be going. The key step after that is thinking through, okay, this landscape is much more equal now. What is our new competitive advantage? So a few key takeaways. Just here to summarize some of the things that we talked about. We've got an operating model in the future that will be product led. We've got workflows that are led by AI driven engines. We've got a transition in roles towards product owner roles. Again, we do want to emphasize that this does not mean that anyone that is non technical suddenly needs to become extremely technical. It's about adopting a product management led approach to decision making versus a process management led approach. And then we've got a really interesting transition around comparative advantage when key workflows at scale become commoditized and are no longer defensible. How do companies actually develop new hard to copy capabilities? Autonomous transformation isn't the end game. Like we said, we have to figure out what is the new competitive advantage after that. So we had 2025 minutes today. This was very introductory and very much an overview, but we are going to spend a lot more time coming up focused on the specifics of some of these topics. So a couple of deep dives that we'll be putting out there. If you follow us on LinkedIn, we'll be posting them or follow me. We'll be talking about the individual workflow opportunities and how to assess them. We'll be going into a lot more detail on the actual tools themselves, the engines for these specific disciplines. And we'll also be doing a deep dive on how to think about what your new competitive advantage could be in an autonomous era.

Thank you Amir for the wonderful talk. Unfortunately, this is all we have time for. We've been noting all the questions and I'm sure we'll include them in the follow up. Feel free to stay here for the next talk or switch to the next room.

Thanks.