One of the common misconceptions about Monitoring and Evaluation (M&E) is that everything must be fixed from the start, indicators, methods, and data collection tools. In theory, this sounds ideal. In practice, anyone who has worked on development programmes knows that implementation rarely unfolds exactly as planned.
Communities relocate. Markets shift. A climate shock disrupts supply chains. Security conditions make field access impossible. These are not edge cases, they are recurring realities in development programming. And when they occur, rigid M&E systems do not just become inconvenient. They become disconnected from the programmes they are supposed to serve.
So the real question is not whether M&E should be flexible. It is: what kind of flexibility preserves credibility?
In my experience leading MERL functions across diverse programme contexts, flexibility in M&E is often misunderstood as the freedom to change indicators whenever circumstances feel difficult. That is not flexibility, that is inconsistency, and inconsistency weakens evidence. Real flexibility is structural. It means designing systems that can adapt how they measure without compromising what they measure. Rigidity is not rigor. The two are easily confused.
Consider a project that begins with in-person household surveys as its primary monitoring tool. If population mobility or security conditions make access unfeasible, the M&E team may need to shift to phone-based follow-ups or sentinel community monitoring. The measurement objective stays constant. The method evolves to serve it.
The same logic applies at the level of insight. Early monitoring often reveals that a programme is solving the wrong problem. A skills training intervention, for example, may discover that the binding constraint is not skills at all but market access. Holding rigidly to the original indicators would produce clean data about a problem that is no longer the most relevant one. Refining the monitoring framework to capture market participation and income pathways generates insight that actually supports programme management.
At Innovision Africa, we have had to navigate these kinds of challenges across projects in the agricultural, health, livelihoods, and financial sectors. What those experiences have reinforced for me is that the best MERL strategies do not treat disruption as a threat to the framework. They treat it as an opportunity to strengthen it.
In my view, the key is balance. Effective M&E systems maintain stable core indicators that anchor measurement throughout the programme lifecycle, while allowing monitoring approaches to evolve as implementation realities demand. Any adjustment should be clearly documented so that transparency and accountability are never in question. This reflects a broader shift in development practice: adaptive management is increasingly central to how effective programmes operate, with leading frameworks emphasising learning and responsiveness alongside measurement (Valters, Cummings & Nixon, 2016; USAID CLA Framework). Yet adoption remains uneven, and many systems are still designed for control rather than learning.
That gap is worth closing. M&E should not exist to confirm that a plan was followed. It should exist to understand whether change is actually happening, and why.
Flexibility, managed with discipline, is not a weakness in a monitoring system. It is often what keeps it credible.
At Innovision Africa, our M&E practice is built around this principle, structured enough to generate reliable evidence, adaptive enough to remain useful when conditions change.
Source: Bamberger et al. (2016); OECD (2019); Patton (2011); USAID (2018); Valters et al. (2016); World Bank (2017); Befani & Mayne (2014).
Author: Obadiah Okpokpo, MERL Lead, Innovision Consulting Africa
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