Evidence from 200+ programs sat unanalyzed — teams had the expertise, not the capacity. Country teams now lead their own analysis.
A global humanitarian organization running more than 200 programs across 35+ countries, with a monitoring, evaluation, and learning (MEL) function responsible for building analytical frameworks, measuring outcomes, and synthesizing evidence across the entire portfolio.
Synthesizing insight from large bodies of evidence across the portfolio was largely out of reach, not because teams lacked expertise but because they...
“In ours, it is different: AI lets us do things we simply could not do before or were never going to be resourced to do—even though we knew they mattered. Synthesizing and drawing insight from large bodies of evidence and learning used to be out of reach for us in most cases. Claude puts it within reach, and it does it at the speed our work moves at, which on the humanitarian side is fast. That is what lets an organization like ours take a real step toward being a learning organization rather than just aspiring to one.”
Global humanitarian organization delivering disaster relief and development aid.
Anthropic is a technology company specializing in artificial intelligence and machine learning solutions.
Mercy Corps's Research synthesis is part of this use case:
Related implementations across industries and use cases
Data lived in paper registers; monthly snapshots meant crises surfaced too late. Staff now run offline dashboards, no engineers or internet.
Feedback in 30+ languages piled up, safeguarding reports sometimes misgraded. Claude translates and proposes grades; staff own the call.
Manual survey tagging kept staff from the field. Now, workers use Copilot to analyze donor feedback and build tracking apps in days.
Manually parsing thousands of documents slowed analysts. Now, a custom AI research engine synthesizes traceable market insights in hours.
Managers spent hours synthesizing reports. AI now drafts briefings 90% faster, saving sales teams an hour daily.
Each funding partner took 12 hours to vet. A Copilot agent now scans their news and websites, handing fundraisers a risk summary to review.
Scared patients chased cures through forums no one had vetted. Sage answers only from the Foundation's reviewed library — or declines.
Scaling AI access across a 100,000-employee workforce risked system overloads. An auto-scaling platform launched with zero downtime.
Energy market research that took weeks now closes same-day. Staff freed from chasing information to make the calls that matter.
Evidence from 200+ programs sat unanalyzed — teams had the expertise, not the capacity. Country teams now lead their own analysis.
A global humanitarian organization running more than 200 programs across 35+ countries, with a monitoring, evaluation, and learning (MEL) function responsible for building analytical frameworks, measuring outcomes, and synthesizing evidence across the entire portfolio.
Synthesizing insight from large bodies of evidence across the portfolio was largely out of reach, not because teams lacked expertise but because they...
“In ours, it is different: AI lets us do things we simply could not do before or were never going to be resourced to do—even though we knew they mattered. Synthesizing and drawing insight from large bodies of evidence and learning used to be out of reach for us in most cases. Claude puts it within reach, and it does it at the speed our work moves at, which on the humanitarian side is fast. That is what lets an organization like ours take a real step toward being a learning organization rather than just aspiring to one.”
Global humanitarian organization delivering disaster relief and development aid.
Anthropic is a technology company specializing in artificial intelligence and machine learning solutions.
Mercy Corps's Research synthesis is part of this use case:
Related implementations across industries and use cases
Data lived in paper registers; monthly snapshots meant crises surfaced too late. Staff now run offline dashboards, no engineers or internet.
Feedback in 30+ languages piled up, safeguarding reports sometimes misgraded. Claude translates and proposes grades; staff own the call.
Manual survey tagging kept staff from the field. Now, workers use Copilot to analyze donor feedback and build tracking apps in days.
Manually parsing thousands of documents slowed analysts. Now, a custom AI research engine synthesizes traceable market insights in hours.
Managers spent hours synthesizing reports. AI now drafts briefings 90% faster, saving sales teams an hour daily.
Each funding partner took 12 hours to vet. A Copilot agent now scans their news and websites, handing fundraisers a risk summary to review.
Scared patients chased cures through forums no one had vetted. Sage answers only from the Foundation's reviewed library — or declines.
Scaling AI access across a 100,000-employee workforce risked system overloads. An auto-scaling platform launched with zero downtime.
Energy market research that took weeks now closes same-day. Staff freed from chasing information to make the calls that matter.