Voice AI Helps Evaluators Listen to Communities at Scale
How Evaluators Can Bring Voice AI Into Their Daily Work
Lessons from Sattva Consulting’s Voice AI deployments, combined with an EvalCommunity perspective on turning experimentation into a practical, responsible M&E workflow.
M&E
Community feedback
Responsible AI
Don’t start with “Where can we use AI?” Start with:
“Where would a well-designed voice conversation help us collect, verify or learn from evidence better?”
What Sattva’s experience tells us
Sattva Consulting tested Voice AI across impact assessments, monitoring and verification exercises. Across its pilots,
Sattva reports more than 9,000 calls and more than 1,000 completed conversations, with completion rates ranging from about 10% to 65% depending on the programme and deployment approach.
In one skilling programme, 6,000 candidates were contacted, around 4,000 answered and 806 completed the full survey.
The exercise helped verify training completion, job offers, placements and retention across 20 training partners.
Sattva also tested Marathi, Kannada, Tamil and Telugu deployments.
calls across pilots
completed conversations
full surveys in one skilling deployment
From a pilot to a daily M&E workflow
EvalCommunity’s view is that Voice AI should not become a separate “AI project”. It can be treated as another method within
planning, data collection, monitoring, verification, analysis, reporting and learning. The evaluator remains responsible for the method and the evidence.
Decide where voice adds value
Start with the evaluation or monitoring need—not the technology.
Good candidates: beneficiary feedback, tracer studies, outcome verification, monitoring check-ins and post-training follow-up.
Understand who you need to reach
- Preferred language and communication needs
- Programme familiarity and ability to recall the experience
- Likely availability and motivation to respond
- Accessibility, literacy and connectivity considerations
Sattva’s scholarship and apprenticeship pilots showed how different programme relationships can produce very different completion rates.
Rewrite questions for the ear
- Keep spoken questions short and clear.
- Break complex questions into smaller parts.
- Add guided follow-ups for important open-ended answers.
- Use skip logic to avoid irrelevant questions.
- Separate structured questions from questions that need conversation.
Help the system understand the programme
- Programme purpose, target population and activities
- Relevant terminology and questionnaire logic
- What the agent must never invent or promise
- When a conversation should be transferred to a person
Sattva found that adding programme context helped the agent handle unexpected or non-standard questions more effectively.
Listen to real conversations
Run a small pilot before contacting the full sample.
Check: response timing, interruptions, pauses, accents, languages, context, important-answer capture and drop-offs.
Be transparent about AI
- Explain the purpose of the call.
- Clearly identify the AI voice.
- Obtain appropriate consent and provide an opt-out.
- Explain how data and recordings are handled.
Treat Voice AI like a fieldwork operation
Track answer rate, completion rate, drop-off points, language performance, unexpected questions and escalations while data collection is happening.
Build human quality assurance into the workflow
- Review a sample of transcripts or recordings.
- Check important details and contradictory responses.
- Triangulate critical information with administrative or other data.
- Document recurring AI errors and improve the workflow.
In Sattva’s scholarship pilot, the AI captured a family land-sale reference but missed the respondent’s mention of personal savings—an example of why human QA matters.
Move from calls to decisions
- Aggregate structured answers into monitoring indicators.
- Analyse open-ended responses for recurring themes.
- Compare patterns across locations, groups or partners.
- Flag unexpected outcomes and implementation problems.
- Bring findings into programme management and learning.
Use what you hear to improve the next round
Review what worked, where people dropped out, what the AI misunderstood, which groups were harder to reach and what findings changed programme decisions.
What this can look like in an evaluator’s week
| M&E activity | Possible Voice AI use | Human role |
|---|---|---|
| Routine monitoring | Short participant check-ins | Define indicators, review patterns and act |
| Outcome verification | Training, employment, placement or retention checks | Triangulate and investigate discrepancies |
| Evaluation | Structured surveys plus selected open-ended questions | Design methods, interpret evidence and assess limitations |
| Learning | Regular conversations about barriers and experience | Turn findings into programme adaptations |
| Reporting | Participant voice that adds context to quantitative findings | Judge credibility and communicate the evidence |
When Voice AI may not be the right choice
A human conversation may be preferable when the topic is highly sensitive, trust needs to be built carefully,
the respondent needs substantial assistance, or the evaluation depends on deep, nuanced qualitative inquiry.
The evaluator remains accountable
Voice AI can conduct the conversation, but it does not become responsible for the evaluation.
The evaluator remains responsible for methods, consent, data protection, quality assurance, interpretation and decisions.
Build your first Voice AI use case
Imagine you manage M&E for a vocational training programme with 10,000 participants.
The programme wants to understand completion, employment, six-month retention and barriers to employment.
Design the workflow:
- What is the exact M&E question?
- Who should be contacted?
- Which questions should be conversational?
- Where should follow-up prompts be used?
- What should be verified against administrative records?
- What human QA is needed?
- What happens if the AI misunderstands an answer?
- What consent and privacy safeguards are required?
- How would findings change programme decisions?
The EvalCommunity view
Sattva’s experience suggests that Voice AI can help organisations collect feedback faster, verify outcomes at scale
and experiment with multilingual community conversations. It also shows why adoption should be iterative: the first version will rarely be the best version.
For evaluators, the opportunity is to make Voice AI part of normal M&E practice—not to create an “AI evaluation” separate from evaluation.
Use it where it improves reach, speed or continuity; keep human judgment where interpretation, trust, safeguarding and methodological decisions matter most.
Prerit Shukla, What We Learned: Deploying Voice AI Across 1,000+ Community Conversations,
Sattva Consulting, August 31, 2026.
Read the original Sattva Consulting article →
This case study is an independent analysis and learning resource prepared by EvalCommunity Academy.
EvalCommunity does not claim ownership of the original Sattva Consulting content, data, methodology or case study.
The analysis and practical guidance are intended for educational purposes and reflect EvalCommunity’s interpretation
of the source material for an M&E and development audience. Readers should consult the original source for complete
context, methodology and limitations. Recommendations should be adapted to the specific programme, data-protection
requirements, ethical standards and professional judgment of the organisation using them.
Practical learning for evaluators, M&E teams and international development professionals.
