Many of the exciting challenges in the practical application of Business Agility today come down to the topic of adaptive problem-solving in complex environments. That often turns out to be much easier said than done, particularly because there are many different interpretations of what that simple topic may even mean and how to apply the topic effectively. This blog post presents my point of view; a point of view that has been distilled through my experience in leading over a dozen transformations that have produced a consistent 10x increase in baseline outcomes for businesses as full Business Agility adoption occurs.
As a quick definition of terms in this context… “Adoption” means that the principles and practices are becoming part of the behavioral habit patterns of an organization. “Adaptive” means that the relevant patterns may change over time. “Problem solving” means driving toward a particular business outcome. “Complex” means that the perceived expectations of the system behavior change based upon the particular subset of characteristics or data points chosen and/or based upon the particular external or internal conditions at a given point in time. “Environments” means engaging the full organizational system dynamics beyond the particular individual and team-level dynamics.
Applying the combination of these terms and definitions means that we can systematically address how things change in many ways for businesses, some of which can be expected and some which are inherently disruptive. Embracing these terms and definitions also allows us to address how some facts for businesses, that were once believed to be true and valid, can rapidly become no longer true; this seems to defy logic and to defy past patterns of success, yet it is the reality of many businesses today.
The practical importance of this topic emerges in how the corresponding disruption becomes a positive or negative outcome for your given business; an outcome that depends on the patterns you recognize and the response patterns you apply. How the particular set of patterns are recognized, and the corresponding response pattern dynamics, are the execution dynamics within the process of adaptive problem-solving in complex environments. Let’s look at what this means and what we can do about it.
The first step in the adaptive problem-solving process is to be able to evaluate the type of problem you are solving. Some problems are as simple as they seem; for these, best practices can be applied. Some problems have a lot of complicated or extremely intricate parts or calculations, each of which can be separated and re-combined in a linear fashion (e.g., lego blocks); for these, analysis and deductive synthesis can be applied. Some problems, however, have a completely different nature; we’ll call this type of problem “complex”. If the problem is truly complex, then mathematically it falls into the category of nonlinear systems, and nonlinear systems do not behave like linear systems in all cases.
Why that’s important is because we are taught, and in practice typically work with either linear systems or linear approximations of nonlinear systems; i.e., we think and work in a linear fashion. We’ve been fine with this in the past. However, we’ll soon get in trouble, if not already, if we continue down this linear path. The reason for this is with nonlinear systems the linear approximations work only in a rather limited and small space; when we expand to the full system space across an organization or across a business market or across time, it doesn’t work consistently anymore.
A classic example of a linear system belief is that the “Earth is flat”. Think back to when this claim was initially made. People could look out and see that the Earth appeared flat, and as a result they believed it was in fact flat; i.e., the Earth was a linear system that stayed the same beyond the horizon they could see at the time, which made the horizon the “end” of the Earth. Additionally, they could further validate their belief by operating locally as if the earth was in fact flat. However, there was an error in their belief; beyond the original horizon they could see, the original approximation falls off the edge.
Let’s apply this to adaptive problem solving, by looking at where the linear and nonlinear distinction has already been proven to be relevant. In the past the distinction of linear and nonlinear was primarily of interest in theoretical or academic applications, unless the field was directly related to nonlinear systems; e.g., mathematics or science or circumnavigation of the globe.
In the realm of business, however, the linear approximations were sufficient and typically held for the lifespan of the thought leaders and practitioners; linear systems were naturally their model of choice. Plus, a linear system is much easier to see and understand and apply, especially compared to nonlinear systems thinking, so the linear view became the dominant perspective for business, leadership, and communication… and it worked for many years…
That’s no longer true!
The reason it’s no longer true is the direct influence of technology. Along those lines, we may hear people state that “technology is changing faster and faster”. Yet it’s more than that. It’s the use of technology that is changing faster and faster; even faster than the technology itself. This ties back to businesses. Combining the two, the use of technology and business, is what creates a nonlinear system for business in today’s world. That’s why business leaders and organizations need to adjust their models in order to compete in a sustainable way. Initially this may seem like uncharted territory for business, yet we can draw on the disciplines of mathematics and science to help us chart the course; and we have.
We can dive deeper into the details and implications of the linear/nonlinear topic in a future blog. For now, let’s close with three significant observations:
- It’s no longer true that business can operate, in a sustainable way, using only the linear approximations and models that have worked in the past. The business community must embrace nonlinear systems modeling, thinking, and practices.
- Solutions for nonlinear systems depend heavily on the initial conditions, so where you’re starting becomes very important. The importance of initial conditions is amplified when the linear approximations are being made, since the approximations often mask the initial conditions or context prior to the approximation. In business agility transformations, this emerges as the “preconditions” that are typically masked in today’s frameworks and practices; i.e., the original initial conditions and context in effect before the approximations of the transformation are applied.
- Adoption of Business Agility behaviors, producing new business outcomes, does not materialize consistently without embracing an underlying nonlinear model; as a result, the adoption actually occurs and progresses as an organization is guided through a series of local approximations and adjustments, and simultaneously deals with the nonlinearities as necessary.
In the end game, the adoption is sustained through setting up an applicable ecosystem for Business Agility; an ecosystem that embodies the underlying nonlinear system itself. We won’t go into the actual math today, but suffice it to say that there is an extensive body of knowledge in the mathematics and science behind linear and nonlinear systems. It is appropriate to note here that for our work in Business Agility and Agile Organizational Leadership, the relevant initial conditions, approximations, and adjustments are manifested through the non-linear system dynamics embodied in the 7 Dimensions of Business Agility. Thus, we do have an adaptive problem-solving model for addressing these challenges in today’s complex business environment.