I feel like everyone is talking about adaptability in the public sector, do you? Talking about being flexible and adaptive these days seems common but also impossible in a sector where failure is unacceptable. Is there actually a way to do it? The common advice is to adapt, iterate solutions, and learn in the process. However, little is said about how to do that in practice and how to do it without losing rigor. This blog proposes a way to systematize lessons learned from interventions to accelerate learning and improve results, inspired by Vijay Govidarajan and Chris Trimble.
Learning with purpose
Public sector and development organizations are generally well-established entities that rely on routine and processes to function. These kinds of organizations expect certainty and assurance. Plans and budgets generally guide operations, and little deviation from plans is expected given the predictability of outcomes.
This certainty in the organization differs when thinking about individual projects, particularly those dealing with complex problems (I talk more about them here and here). In these types of problems, there isn’t a particular known solution. There is a hypothesis of what could happen if a certain solution is implemented and based on that, there is a plan to be executed.
Learning for results is then the process of turning speculative predictions regarding the execution of a plan into reliable ones. In other words, it’s about systematically understanding the gap between your plans and real outcomes so that your predictions can improve, and your results can be more accurate.
Rigorous learning
Adaptation and iteration can be perceived as a threat to accountability. The fact that one may depart from original plans poses the question of whether changes are compensating for poor execution to show acceptable results.
Accountability can be of three types:
1. Results: Measures whether the predicted outcome is achieved.
2. Actions: Monitored before achieving any result, measuring any deviation from the original plan.
3. Learning: Involves following a rigorous learning process. The rest of this blog gives you practical steps for this third type of accountability.
Step-by-Step Guide to Learning Accountability
1. Identify Your Theory of Change
A theory of change explains how inputs will translate into outputs and outcomes. A strong theory of change increases the probability of success. It generally relies on evidence to show that the intervention works for the expected purpose.
To put it simply, your theory of change should be able to answer two questions: what are you spending the money on (inputs and activities) and why (expected products and results). You can look at some examples here and here.
In uncertain scenarios, your theory of change is your hypothesis. The difference between a plan and a hypothesis is subtle but fundamental. When you have a plan, the goal is to execute it with fidelity. When you have a hypothesis, the objective is to test it and refine it as fast as possible.
2. Test Your Hypothesis
When testing a hypothesis, your assumptions are as important as data. However, because we tend to focus on hard facts, assumptions often don’t get the emphasis they deserve, even though they are essential for learning.
You cannot evaluate outcomes and lessons learned unless your hypotheses are clear. I like this guidance chart to help you evaluate which hypothesis evaluate first:
3. Compare Predictions and Outcomes to Assess Lessons Learned
In projects dealing with simple or complicated problems, predictions may be presumed correct. For complex problems, predictions must be presumed wrong. The goal is to bring predictions in line with outcomes, not the other way around. Be comfortable with the fact that predictions could be wrong to avoid defensiveness, which is toxic for learning.
“You have to create an environment where it is comfortable for people to tell you openly that things are not going well. Most problems are not because people screwed up; they are because there is something happening that nobody anticipated. You have to let people know that you are going to support them and help them figure out how to respond.” Brian Hall
Learning Accountability
Having learning accountability means:
1. Taking the plan seriously and executing it as anticipated.
2. Having a clear hypothesis of record—what you are spending your money on and why, what is known and unknown. Assess if it needs to be revised.
3. Ensuring everyone on the team understands the hypothesis of record and can differentiate the most critical ones, those that are highly uncertain and highly consequential.
Revise the hypothesis only when there is evidence of change and respond quickly to critical information.
Assess Your Results
• Are you succeeding or failing? What is going well or poorly?
• Is there a difference between predictions and outcomes? Why?
• Is there a problem of wrong predictions or poor assumptions?
• Is there a problem of low outcomes or poor execution?
Bottom line
Adopting a learning mindset means knowing the fastest way to assess if assumptions hold. When you can assess that, your predictions will improve and as a consequence your outcomes will improve too.
