By Gbadebo Alabi

In a pointed challenge to development finance institutions and impact investors operating across Africa, Chidinma Lydia Ezenwa, a Digital Innovation and Technology Governance Expert whose work spans AI policy, innovation ecosystem development, and responsible technology deployment in emerging economies, says the absence of rigorous governance frameworks around innovation evaluation is quietly enabling unvalidated claims to drive capital allocation decisions with real consequences for the farmers and communities those investments are meant to serve. “When there is no independent verification layer between what an innovator reports and what a funder believes, you do not have a governance system,” Ezenwa said. “You have a trust system. And trust, without accountability structures, is not sufficient at scale.”

Ezenwa speaks from direct field experience. Over the course of her career evaluating agricultural and digital technology innovations across Nigeria, Kenya, Rwanda, Tanzania, Malawi, Ghana, and Côte d’Ivoire, she has conducted independent field verifications of AgriTech innovations at various stages of development, assessing whether protocols submitted on paper were actually being executed in the field, whether data collection was rigorous and complete, and whether the farmers described as end users had genuinely interacted with the technologies being evaluated. The patterns she encountered, she argues, are not anomalies specific to individual programs or organizations. They are structural, recurring features of how innovation evaluation operates across the emerging economy funding landscape. “The gap between a submitted protocol and what you find on the ground is not always a sign of bad faith,” she said. “But it is always a sign that funders need independent eyes before capital moves.”

Her concerns are both methodological and systemic. On the methodological side, Ezenwa has repeatedly encountered innovation protocols approved for field testing that contain significant gaps in enrollment criteria, randomization design, and control group construction. Teams frequently cannot clearly distinguish their test populations from their control populations at the time of independent evaluation, a foundational problem that makes any impact claim statistically unreliable. In multiple instances, she has found sample sizes in submitted protocols that bear little resemblance to what is actually being tested in the field, with variances going unexplained and unquestioned by the programs that approved the protocols in the first place. “If the data underpinning an impact claim is collected at a fraction of the scale described in the protocol, the claim does not hold,” she said. “And no one discovers that without a site visit.”
The problem, she argues, is compounded when evaluation frameworks reward presentation quality over field verification. Across the innovations she has assessed, Ezenwa notes a consistent pattern where teams with polished narratives, strong pitch materials, and well-written protocols are not always the teams with the most rigorous data or the strongest real-world results. “There is a version of due diligence that stops at the desk,” she said. “It reads the documents, scores the submission, and moves on. That version will consistently miss what matters.” She has equally encountered innovations with genuinely compelling real-world results, including agricultural technologies demonstrating measurable improvements in crop survival rates, post-harvest loss reduction, and farmer income, results that were verifiable precisely because independent evaluators visited the farms, interviewed the farmers, and cross-checked the data against what was physically observable. “The innovations that hold up under scrutiny are the ones where the story in the field matches the story in the documents,” she said. “That alignment is rarer than it should be.”
Her analysis connects directly to her broader work on responsible AI and digital innovation governance. As ecosystems across Africa increasingly integrate data-driven tools, machine learning applications, and AI-powered technologies into agricultural and development programs, the question of how those technologies are evaluated before deployment becomes a governance question with equity implications. Several of the innovations Ezenwa has evaluated deploy drones, computer vision algorithms, digital market platforms, and AI-assisted data collection tools in low-resource farming communities. In each case, the integrity of the innovation’s impact claim depends not just on the technology functioning as described, but on the evaluation framework being robust enough to surface the difference between a functioning prototype and a proven solution. “Responsible AI governance is not only about what a technology does,” she said. “It is about whether the systems surrounding that technology, the evaluation processes, the data standards, the accountability mechanisms, are strong enough to distinguish real impact from a well-constructed demonstration.”
The problem is further compounded in emerging economy contexts where evaluation capacity is unevenly distributed, logistical barriers to field verification are high, and the pressure to show results quickly causes programs to prioritize narrative over evidence. Ezenwa has observed this dynamic consistently. Teams with sophisticated pitch materials and well-written protocols are not reliably the teams with the strongest field results. Conversely, some of the most genuinely promising innovations she has encountered are being operated by teams whose documentation lags their actual progress, creating a risk that strong real-world performance is systematically undervalued relative to polished reporting. “A governance framework that rewards documentation quality over field evidence will misprice innovation,” she said. “That is a technology governance failure with direct equity consequences, because the communities depending on these solutions are the ones who bear the cost.”
Her prescriptions are both structural and institutional. She is calling on development finance institutions, multilateral programs, and impact investors deploying capital into African technology ecosystems to treat independent field verification not as an optional enhancement but as a baseline governance requirement. Protocols should be rigorously reviewed and locked before field evaluation begins. Innovators should operate under clearly defined accountability standards, with documented justification required for any deviation from approved methodology. Evaluation frameworks should differentiate between innovation stages, recognizing that applying identical assessment criteria to an early-stage proof of concept and an established growth-stage company produces category errors that distort both competitive outcomes and funding decisions. “Governance frameworks that do not account for stage of development are not neutral,” Ezenwa said. “They favor incumbent advantage over genuine innovation, and that is exactly the opposite of what development-oriented capital is meant to achieve.”
Beyond the structural, Ezenwa identifies a cultural dimension that she argues is equally central to responsible technology governance in emerging economies. Across her field evaluations, the innovators producing the most credible and durable results are consistently the ones most willing to surface limitations, flag protocol variances proactively, and engage critically with the evaluation process rather than manage it. “Responsible innovation culture and responsible governance culture reinforce each other,” she said. “When accountability structures are weak, founders optimize for the evaluation rather than for the outcome. When accountability structures are strong, the incentive shifts toward honesty, and honest data is the only kind that actually supports good decisions.”
As AI tools and data-driven technologies become more deeply embedded in agricultural value chains, financial systems, and public service delivery across emerging economies, Ezenwa believes the governance infrastructure surrounding those technologies must develop at the same pace. “We cannot have sophisticated technology deployed in low-resource contexts and governed by frameworks that would not pass scrutiny in higher-resource ones,” she said. “That asymmetry is itself a form of harm, and addressing it is precisely what responsible AI and technology governance in emerging economies means in practice.”
Her field work across multiple countries and dozens of independent site evaluations has produced a body of evidence that she believes should inform how programs across the development finance sector approach innovation accountability. “We have the tools to build evidence-driven governance frameworks for technology evaluation in emerging economies,” she said. “The question is whether the institutions with the power to require them are willing to do so. That decision determines whether responsible AI and innovation governance remains a principle or becomes a practice.”