Rethinking Qualitative Evidence in the Age of AI: Decentralizing Decision-Making in MEL

Organizations collect vast amounts of qualitative data from the field i.e. interviews, observations, reflections, FGDs. Yet, most MEL systems are designed to only sustain periodic upward reporting (to donors/stakeholders and leadership), or for storytelling as a communications add-on, rather than to support the daily decisions of field and program staff.

As AI tools enter our workflows, this gap matters more than ever. Unstructured qualitative data fed into AI often produces weak synthesis or outputs that lose the very context that makes the evidence meaningful.

Using an education case-study, we'll explore:

  1. Why qualitative data so often stays siloed, and what happens when it does

  2. How structuring qualitative evidence enables more reliable, responsible AI-assisted analysis

  3. When AI should support evaluation and when human judgment should lead

  4. How to make evidence visible across teams for decentralized learning


For MEL and program teams exploring the responsible use of AI for qualitative data analysis to support ongoing learning and decision-making, without losing rigor or context along the way.

In the webinar

Akshay Roongta, Co-Founder, Dots

Akshay Roongta has over a decade of experience working across WASH, public health, financial inclusion, agriculture, and education. He brings a deep understanding of how complex, ground-level realities can inform better decisions and systems. He believes that the most valuable insights often lie in lived experience — and that good technology should help uncover and act on those insights without losing their nuance.

Yashna Jhamb, Co-Founder, Dots

With a background in ethnographic research and systems thinking, Yashna Jhamb works at the intersection of design, data, and impact, helping teams make sense of complex realities through storytelling and structure. She is the co-founder of Dots, a SaaS platform built by Ooloi Labs that helps organizations collect, organize, and analyze qualitative data at scale.

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