Hi, guys. In this video, I want to talk about when agile thinking becomes a powerful tool in the complex working environments many people face. To start off, I want to make a distinction between defined and empirical processes. Defined processes follow an exact set of stages and every part of the stage needs to be completed before you can move on. An example of this is waterfall project management. Empirical processes start with a hypothesis, tested and observed results, understand the results, and adapt the process to improve. 

Agile processes are always empirical and this is especially well reflected in agile frameworks like Scrum that emphasize iterative and incremental work with retrospectives built in, so teams and stakeholders can reflect on the work being done. I'll come back to defined and empirical processes in a bit. But next up, I want to introduce you to the Stacey's Process Complexity Model. This model was developed by Ralph D. Stacey and helps to illustrate issues around decision making in organizations. The vertical axis represent agreement with the horizontal axis representing certainty. As you move up the vertical axis, there is less agreement. And as you move right along the horizontal axis, there is less certainty. 

With me so far? The zone within the graph that is both close to agreement and certainty is the simple zone. In this space, everyone understands what the problems are and agree on how they need to go about overcoming them. This is where business as usual sits and there is no need to use an agile framework to start a project or something like that. Although, of course, you can still use an agile mindset here. If there isn't certainty on what the issues are, but there is an agreement on how to deal with it or vice versa, we enter into the complicated zone. This is where a lot of projects sit and any project management method, be it waterfall or agile, can be used here. As we push further out away from both certainty and agreement, we move into the complex space. Here, there is a lot of disagreement about what the issues are or how to deal with them. Agile frameworks like Scrum are great here as they use empirical process control to shorten the feedback loops and react more quickly to a changing situation. 

Finally, at the extreme range of certainty and agreement there is only anarchy and chaos. Here, there is very little knowledge of what's going on with almost no agreement on how to deal with anything. In this zone, frameworks like Lean Startup can be helpful as they shorten feedback loops even more by embracing the idea of minimum viable product, MVP. This allows teams to do the smallest amount of work possible to get feedback and then adjust. So, let's summarize them. In an ideal world, everything would be simple. But it's safe to say that most of the issues organizations are facing are complicated, complex, or even chaotic. If you find yourself in the simple zone, both defined and empirical processes should work just fine. After all, everyone knows what the issues are and what needs to be done to address them. However, the further you push away from the simple zone the more defined processes start to struggle. Defined processes need every part of a stage to be finished and signed off before the work can continue. This limits teams who need to deal with complicated or complex decision making. 

However, empirical processes like Scrum framework really come into their own here. Teams can react quickly to the challenging situations they find themselves in. As they have the trust of the organizations to self-organize, they can complete work in a way that the environment demands. Agile thinking is especially powerful in the chaotic sector. Teams can focus on creating an MVP and failing fast by building, measuring, and learning. Defined processes are only really good at dealing with work in the simple zone, and to a lesser degree in the complicated zones. They become a blocker as decision making becomes complex and are very difficult to use in the chaotic zone. Agile thinking on the other hand, can be used regardless of how far from agreement and certainty teams are because they are empirical by their very nature.