Leadingagile Blog Why AI is the Latest Forcing Function for Transformation

Getting your teams, technology, and the underlying data encapsulated, pointing it at a business problem, and getting it to reliably deliver value props-that's the foundation for AI readiness.Video TranscriptMike CottmeyerWhen we think of an agile team, the way I've often described it, right, six to eight people that have clear direction from the business around a problem to solve, and they have ownership over whatever piece of the technology is. And that team is able to take the direction from the business and deliver something of value for feedback every two weeks. And it's really difficult in most of the companies that we work with is that the way the technology stacks or architected, they don't necessarily lend themselves to the team owning somethingThat they can deliver against, that a customer can touch and feel and use. So I've had this hypothesis for 20 years now at this point, if we can break down the technology architecture into composable chunks, technical term, right, composable chunks, and that technical component can have a team that's dedicated to owning it, and then that team is funded consistently over time in a way that the business is willing to pay for it, it sees the value prop of that team continuously iterating against that object. And then I can get that team and that technology object aligned with the business and get it to deliver in a reliable, predictable way, right? That's like what's been underneath what we've been trying to do around here for 15 years now. It's been in my head for probably almost 25.And so that was what kind of took us down this path of asking the question, because historically what LeadingAgile had done is we had taken and looked at organizations through a business capability lens because something that the company wants to pay for and putting teams around it and then feeding those teams work through our governance models and stuff. So my hypothesis is, is that if we can get teams and technology and the underlying data encapsulated pointed at business problems and get it to deliver those value props in a reliable, predictable way that somehow underpins AI readiness. So one of the clients that you're working with right now, we're going through a big kind of extraction modernization exercise. And as we extract and modernize the applications, we form teams around them, we tie them to value props, we encapsulate their data. How does that, since you're our AI data specialist on that account, how does that process of extraction modernization team alignment to organizational objectives, how does that help us exploit the AI use cases that this particular client wants to explore?Eric FlecherLet's say, if you look at organizations that exist today, especially large ones at scale, you could probably make a pretty strong argument. The way they're organized and structured goes down to the first principles of how humans have to organize and structure in order to keep predictability and dependability right in place. And those systems hierarchical or matrix based, whatever it may be, are organized based on the constraints that humans have in terms of the ability to know domains or certain areas of their subject matters or to their responsibilities or their ability just to purely network with others and maintain state and status across these teams. So these systems of people and management structure exists and emerges, and then it naturally would be the technology that supports these things organically grows inside of those structures. Now, that may have been fine up until a point where that AI line was crossed a number of years ago.And the reason why that shift change is so dramatic now and so loud in the marketplace is all of those decisions around your technology, around who owns the data that supports those technologies around the governance, around these systems to make your systems legal in certain regions and countries and states, et cetera. All these things emerged because of those systems being in place. Now we have the power of ai, the ability to take an expert in a space or a team of experts in a space and give them the collective knowledge and capability of society and work. It's now a very strong function, a forcing function to say, well, I need to be able to deliver in a certain business domain data from this team, a transactional system from that team. I need to plug into KPIs and metrics that the management team requires. Or maybe I'm going to reinvent new metrics that best fit my business for where I want to grow it to.And that slicing doesn't usually fit in the structures that have emerged organically over time. So what we're doing with my client now is what're doing is we're slicing off these business domains. We're understanding what needs to be true in that business domain from an executive at the top that wants to put a dollar of funding into the system for an initiative or a goal or a strategy to the factory floor at that needs to create that value. Being able to draw a string or a line to say, how do I measure the effectiveness of that system and how do I improve it to do all this work? There's a number of things that have to take place. We have to grab the playbook of an organizational transformation of delivery transformation. We have to think about how we plan and slice that work. But we also need to think about how do we re-architect the business and all of those key elements, those assets the business has so they're available and they don't break the business while you're making these changes.There might be a table or a database at my client that has rich data that I could use to create the next horizon of smart outcomes that they want in their business space. However, that data might have compliance burdens associated with it. There might be sensitive data that they don't want from one organization or one team inside that organization for the other teams to be able to get access to. But in order to be able to make these systems, I need to have access to that data. So as we go and organize around these domains, we're also going to encapsulate these systems and transform data into a product or a service. And when you package it like a product, you're also considering the things that need to happen around that data so that it can be used safely at scale.So that could be a la carte type experience for whoever or whatever initiative gets spun up after our initial work. So each time we take a slice, we encapsulate the things that are important, we productize it, we make it interfaceable, we make it easily consumable, and then we push those up into that service layer, and then we start again. And each time we do that, we encapsulate more, so we can add more velocity in the system and move faster and have more access to a wider variety of use cases. That is all in service of business objectives.