“An organization’s ability to learn, and translate that learning into action rapidly, is the ultimate competitive advantage.” – Jack Welch
Evolution is ubiquitous. The old adage of “evolve or perish” holds true in business as in nature. Just as people and organizations evolve, so has change management including how it’s viewed and executed. Before we get into the details of delivery it’s useful to look back and understand how change execution has evolved. Slowly at first, and now, like everything else, at a much more rapid pace. Each generation built upon the previous with approach and centricity adjusting along the way.
Generations of Change
The historical way organizations drove change was quite simple. A leader said something to the effect “now we will do things this way” and people either got with the program or were let go. This approach went on for centuries from kings and emperors down to business leaders and managers. It was simple and direct; all be it crude. However, with the lack of employee mobility things like employee satisfaction were not that critical or even tracked. So, employees who wanted to continue to work simply did what they were told.
Come the mid-20th century science began to be brought to bear on the challenges of change. The primary focus was performance improvement and numerous change gurus emerged. Names such as John Kotter, Kurt Lewin, and Ronald Lippitt to name just three. These experts used modern (for the time) analytical methods from fields such as operations research, process reengineering and psychology to construct models of change. The intent was to create a step-by-step approach an organization could follow to successfully implement change. The models were useful in so much as they provided frameworks that could be employed, but many did not take into account the intricacies involved in practically changing large organizations nor did they provide much in the way of detail of how to tactically implement the proposed steps.
As the end of the 20th century approached and the Internet age began, technology began to emerge as a new change driver. The idea of “Digital Transformation” came into being and everyone began to hop on the bandwagon of technology driven change. In this case it was using technology to change how an organization works (i.e., put in a new system and let the built-in “best practices” drive an organizations new way of doing things). This worked to an extent, but since each business is different it was never a case of plug-and-play. Customizations were always required and could often be time consuming and quite expensive. In addition, the customizations would often be done in a way that would simply allow existing processes to be done faster generating efficiency, but not necessarily change. How many ERP system implementations were done with the vision of transforming an organization only to run way over time and budget with the organization “settling” for “just get it done”.
The early 21st century saw the introduction of agile methodologies. These were first applied to technology implementations moving us from the “big bang” approach of large system implementations to a rapid iteration of smaller steps. Over time this approach was adopted and applied to business change. Rather than a single, multi-year change effort such efforts were broken down into bit-sized chunks and executed with each building upon the other in rapid succession. The obvious advantage being results were seen faster, even if on a smaller scale. In addition, any missteps or delays in one aspect of the enterprise change could be decoupled allowing some parts of the change effort to continue moving forward while another part was paused or reconsidered.
A Look Ahead
At the risk of getting too far out over my skis, it’s not hard to see where change management is going next. The future of change management – call it 6.0 – will be predictive. This means business leadership will no longer necessarily be the driver of change, but rather data will. In this next generation 6.0 change model, Artificial Intelligence (AI) combined with greater data availability through the connected network of things (IoT) will begin to make anticipating the need for change more predictable. For example, it will become much straight forward to determine if your company’s performance is falling behind and in need of a focus on performance improvement before it becomes a disaster. Similarly, AI systems that can analyze multiple strategic scenarios and measure the probability of various outcomes will be able to preemptively alert management to potential risk. For example, a non-traditional competitor moves into a tangential market. Today, managers must guess if the player will continue to encroach and potentially become a direct competitor or if that was a move of convenience. Based on history AI systems will be able to predict the probability of the various options so leadership can better decide when and how to allocate resources. This world is still a way off but is coming as technology continues to advance and be applied to non-traditional business decision making such as transformation and change.