Those leading major programs of business or IT-enabled change tend to have a natural inclination to reduce variability. Put in a more traditional way; the reduction of options and uncertainty is often taken as a proxy for progress and clarity of the road to a successful change initiative.
Similar to traditional people Change Management concepts of, and techniques around managing resistance to change (general presumption that people are resistant to change/new ways of working), current management practices seek to avoid change - change is bad! Juxtopose this against the modern cliches that proclaim 'change is inevitable', 'the pace of change has never been greater', or 'change is the new norm' etc. and it would seem to suggest that traditional approaches are not in tune with today's reality.
A more realistic model to adapt/change quickly at scale encourages us to accept that we will need to adjust our plans and approaches as we undertake the change journey and learn. New options will present themselves - these are an opportunity to course corrrect or pivot. We should preserve those options and as more learning occurs they will naturally reduce to an appropriate, feasible and desirable set of opportunities. A focus on eliminating variability too soon perpetuates a risk avoidance culture wherein people can't gain experience by learning what works and what doesn't. Such a culture is also not condusive to drive continuous improvement or innovation.
Taking IT system builders as an example, other than a general understanding of system intent, they recognize that very little is known at rhe beginning of the project. If it was, they would have already built it. Traditional design practices tend to drive developers to quickly converge on a single option - a point in the potential solution spacwe - and then modify that design until it eventually meets teh system intent. This can be an effective approach, unless, of course, one picks the wrong starting point; then subsequent iterations to refine the solution can be time consuming and lead to suboptimal design. And the bigger, more technically innovative the system is, the higher the odds are that your starting point was not the optimal one.
A better approach is to cast a wider net initially, considering multiple design choices. thereafter, the continuous evaluation of economic and technial tradeoffs occurs - typically exhibited as objective evidence presented at integration learning points. The weaker options can then be elininated over time; and convergance on a final design occurs based on knowledge gained to that point..
This process leaves design options open as long as possible, converges as and when necessary, and producers more optimal technical and economic outcomes.
To learn more about assuming variabilty, the other 8 foundational principles of SAFe and how to apply the Scaled Agile Framework's proven knowledge base, lean, adaptive and product development flow principles in your enterprise context, wht not join us for our 2-day Leading SAFe course in February 2016, Vancouver BC. Click here for more details and book by 15 January 2016 to receive a $200 early-bird discount [code: ADAPT2016].