Change actors of all stripes face a tension between the certainty needed to act at scale, and the uncertainty inherent to change at any level.

On the one hand: The world is full of injustices and problems, many of them systemic and impacting people’s lives across contexts. A moral imperative drives us to address these problems. Doing so at scale—in fact, doing anything at scale—depends on our ability to plan, prioritize, allocate resources, coordinate, and control. Whether within an organization or across a sector, these functions require us to make assumptions. They require some degree of certainty.

On the other hand: The world is complex, opaque, and never fully knowable. Most of our social goals—like justice, equality, development, freedom—are not levers we move directly, but rather are emergent properties of the interplay of policies, interests, structural constraints, and a whole lot of history. An action that we think will advance one of those goals may fail or backfire, for reasons that were impossible to know in advance, and perhaps only slightly clearer in retrospect. Some amount of uncertainty is inherent to social change.

The drive toward certainty shows up in systems and practices like strategic planning, budgeting, gantt charts, logframes, and outcome indicators. The inherent uncertainty appears as these tools often fail, and we find ourselves changing course.

The tension between these two often leads to well-meaning calls for more flexibility. Grantees want donors to be more flexible in budget lines, communities want NGOs to be more flexible in project goals, and everyone wants government to be more flexible in everything.

But working flexibly isn’t enough. What we need is to work adaptively. Both flexibility and adaptability acknowledge the uncertainty of progress and allow for changing course. The difference lies in how and why those course corrections take place.

Working adaptively means building in systems for probing and sense-making from the start (e.g. feedback loops, frequent monitoring, capacity for analysis/synthesis). Such systems proactively seek to learn and understand, rather than merely reacting after reality intrudes on the plans. Working adaptively also means having mechanisms in place to make those changes deliberately, through shifts in tactics, programs, resourcing, staffing, and more.

This cycle might operate at different rates depending on the work: daily, weekly, or monthly iterations and pivots for micro-changes; monthly or quarterly for tactical shifts; quarterly or annually for strategic shifts. More frequent if the context is shifting too.

This idea—that adaptation is more than flexibility—isn’t new. But without a clearer articulation of those systems (for probing/sense-making) and mechanisms (to make deliberate changes) it’s all a bit academic.

Fortunately, there have been a few efforts to make this more concrete. In fact, there have been many efforts: in a post co-authored with Alan Hudson of Global Integrity on Duncan Green’s From Poverty to Power blog, we tried to informally map a few of those efforts to promote more adaptive work in the aid, development, and governance sectors. The post (a few weeks old now) is here:

(The Monty Python joke was Duncan’s suggestion, because of course it was.)

Separately, I’ve been supporting a joint effort between Mercy Corps and the International Rescue Committee to share lessons and practices from six case studies of working adaptively in some challenging contexts: emergency relief in Syria and Niger, Ebola response efforts in Sierra Leone and Liberia, market systems development in Uganda, and health systems development in Myanmar. This report may be one of the best articulations to date of those systems and mechanisms that distinguish adaptation from mere flexibility. You can find it here:

More on these efforts, and others, as they evolve.

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