In an era of shrinking aid budgets and broken assumptions, the development sector can no longer afford to evaluate after the fact. The future belongs to organizations that learn in real time.
The global development sector is facing its most significant reckoning in a generation. Aid budgets have been slashed. Flagship donors have retreated. The unspoken assumption that funding would always grow—that there would always be another cycle, another grant, another project extension—has collapsed. In its place is a harder, more honest question: what actually works, and how do we know fast enough to do something about it?
This is not merely a financial crisis. It is an epistemological one. For decades, the dominant approach to monitoring and evaluation in development has been built around a model of accountability—proving to a donor, at the end of a project, that predetermined results were achieved. It is a model designed for a world of stable funding and predictable change. That world no longer exists.
What is needed now is a fundamentally different relationship between evidence and action. Not evaluation as verdict, delivered at the close of a program. But learning as practice — embedded, continuous, and close enough to implementation to actually change what happens on the ground.
Conventional M&E approaches are structured around predefined indicators, measured at midline and endline. They are powerful tools for accountability and attribution: did the program achieve what it promised? But in complex development environments—where change is shaped by institutions, power dynamics, relationships, and continuously shifting contexts—this is often the wrong question asked at the wrong time.
The gap is not merely methodological. It is strategic. In an environment of shrinking resources, programs cannot afford to discover at Year 3 what was already apparent in Month 6. Decisions constrained not by lack of data, but by a lack of understanding of how change is actually unfolding—that is the silent driver of ineffectiveness in development work today.
The problem in most development programs is not a shortage of data. It is that the data arrive too late, answer the wrong questions, and sit too far from the people who need to act on it.
Development programs operate in complex systems. Whether it is a poverty reduction program, a skill development program, or a program meant to empower women through awareness—none of these follow neat, linear pathways. Outcomes emerge from the interaction of institutions, relationships, incentives, and context in ways that no Theory of Change can fully anticipate in advance.
Conventional experimental approaches have genuine value for certain questions. But they are designed for a relatively small set of variables, in relatively controlled conditions, over relatively long time horizons. They are not designed to answer the questions that program teams and donors most urgently need answered: Why are outcomes stalling in this particular district? What assumptions in our design are no longer holding? What is changing in the policy environment that we haven’t accounted for?
We need complexity-aware approaches to address these questions. In practice, this means examining the mechanisms through which change is happening—or not—tracing plausible contribution pathways, surfacing behavioral and relational shifts that conventional indicators would miss entirely.
The global funding crisis has made learning-based M&E not a nice-to-have, but a strategic necessity. A common misconception is that learning is a luxury—something that organizations invest in when they have the time and money to reflect. The opposite is true. Adaptive learning becomes more important, not less, when funding is tight or uncertain. The disruptions linked to major donor withdrawals have made this impossible to ignore.
When budgets were generous, organizations could absorb the cost of slow feedback loops. A program that ran for five years and produced its main evaluation findings at Year 4 could still generate learning that shaped the next cycle. But in a world where funding is contracting, programs are shorter, and every dollar is under scrutiny, that luxury is gone. Organizations that cannot demonstrate how they are learning and adapting will struggle to secure the funding that remains. Adaptive learning enables teams to identify problems early and redirect resources toward what is actually delivering results. In a constrained environment, this is not an abstraction—it is the difference between a program that survives and one that does not. The goal, simply put, is to waste less and learn faster.
Funding cuts do not reduce the need for adaptive learning. They expose why it was always essential — and why organizations that treated it as optional are now the most exposed to risk.
At the same time, the questions that funders now most want answered are precisely the questions that conventional M&E is worst at answering. Not just “did this work?”—but “under what conditions does this work, and how do we make it work better, faster, with less?” These are questions that matter for both donors and program implementers. Adaptive learning provides the framework to test and refine these answers systematically—making experimentation a managed process of discovery rather than a series of blind shifts driven by necessity.
There is also a deeper issue of ownership. When external funding declines, programs that depend heavily on top-down donor structures are the most vulnerable. Adaptive learning—by its very nature—requires close engagement with local actors: program teams, community organizations, government counterparts, and the people that programs are designed to serve. This proximity is not just methodologically sound; it builds the kind of local ownership and institutional capacity that makes programs resilient even when external resources contract. A program that has genuinely embedded learning into its culture does not collapse when a donor withdraws. It adapts.
There is one final risk worth naming directly. In today’s resource-tight environment, relying solely on experimental evaluations can leave programs evidence-rich but action-poor—rich in data about what happened, but poor in the real-time understanding needed to act differently. This is where adaptive learning comes in handy—the ability to adjust strategies, scale down, or pivot without collapsing the entire program.
It is not about abandoning plans. It is about having the evidence infrastructure to know when plans need to change, and the learning culture to act on that knowledge.
Farah Muneer works at the Monitoring and Evaluation for Learning and Adaptation (MELA) Initiative at BIGD, which works as a learning partner generating timely evidence to strengthen evidence-informed decision-making.
Illustration generated using Google Gemini
