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Bangladesh is one of the most climate-vulnerable countries in the world. Left unaddressed, climate change could cost the country's agricultural sector up to $7.7 billion per year — a crisis felt most acutely by the more than 45% of the population whose livelihoods depend on it. Bruno Galdos and José Pastor know that story well — not from Bangladesh, but from home.

As we move into the final use case of our #GenAI4GoodChallenge finalist spotlight series, we turn to Polisense AI — one of three teams building the Extreme Weather Early Warning System in Bangladesh.

Bruno and José come from Peru, a country with a long agricultural tradition repeatedly disrupted by El Niño events. What changed is the data. The tools to model the environment and act preventively now exist. What hasn't kept pace is translating that data into guidance that reaches the people who need it most before the damage is done. That gap is what brought them to the Challenge.

Polisense AI is working to build a generative AI tool that would draw on existing geospatial and climate data to generate actionable early warning guidance for farmers — delivered through accessible channels, in time to act.

If they get this right, it won't just protect harvests. It will demonstrate that the data the world already has, pointed in the right direction, is enough to change outcomes.

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#GenAI4GoodChallenge #GenAI4Good #AIforGood #IEEEHT #HumanitarianTech #TechForGood #ResponsibleAI #AI4Impact #SocialImpactTech #GlobalDevelopment #PublicInterestTech #DigitalPublicGoods #BeHealthyBeMobile #UNIATF

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In the Barind region of Bangladesh, the traditional signs farmers have relied on for generations to decide when to plant and when to irrigate are no longer reliable. The weather has become a guessing game—and in a drought-prone region where a single missed irrigation window can mean a total crop loss, that uncertainty carries real consequences.

This is the reality that drew Andri Pranolo and the Barind-Copilot team to the #GenAI4GoodChallenge—and the problem they're working to solve.

Satellite data exists. National datasets exist. But as the team puts it, that data is often trapped in technical formats that don't help farmers. By the time a traditional advisory reaches a rural area, the window to save a crop may have already closed.

Barind-Copilot is working to build a generative AI tool that would translate high-level climate data into simple, actionable guidance — delivered through WhatsApp and audio summaries, designed for fragmented connectivity and varying levels of digital literacy. The kind of tool that answers the question a farmer actually needs answered: "Should I irrigate this week?"

"Seeing a farmer make confident decisions about their crops amidst the unpredictability of the weather is what makes this work meaningful."

If they get this right, it won't just help farmers navigate an unpredictable season. It will give them back something climate change has taken: the confidence to plan ahead.

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#GenAI4GoodChallenge #GenAI4Good #AIforGood #IEEEHT #HumanitarianTech #TechForGood #ResponsibleAI #AI4Impact #SocialImpactTech #GlobalDevelopment #PublicInterestTech #DigitalPublicGoods #BeHealthyBeMobile #UNIATF

Smallholder farmers produce up to 80% of the food in Lesotho, yet nearly half the country's population still lives below the food poverty line. For Reitumetse, T'sepo, and the AgriConnect team, those two facts have always lived side by side— and the distance between them is precisely what they're trying to close.

Continuing the #GenAI4GoodChallenge finalist spotlight series, this week we turn to the third and final team building for the Agriculture use case in Lesotho.

Reitumetse Sehloho and T'sepo Thamae grew up in and work within the country they're building for. They've watched farmers lose crops to unpredictable weather, pest outbreaks, and the absence of timely guidance — losses that, as they put it, are often preventable. The difference between a successful harvest and a failed one frequently comes down to a single thing: access to the right information at the right time.

AgriConnect is working to build a generative AI tool designed for the realities farmers actually face: limited connectivity, low-cost devices, and varying levels of digital literacy. The tool would evolve based on farmer feedback, remaining relevant as conditions on the ground change.
"Technology should adapt to people, not expect people to adapt to technology."

If they get this right, it won't just strengthen harvests. It will demonstrate that the most meaningful technological progress is the kind built by the people closest to the problem, for the people who need it most.

Next week: the teams working on the Extreme Weather Early Warning System use case in Bangladesh. Stay tuned.

🔗 Link in bio to follow the series.

#GenAI4GoodChallenge #GenAI4Good #AIforGood #IEEEHT #HumanitarianTech #TechForGood #ResponsibleAI #AI4Impact #SocialImpactTech #GlobalDevelopment #PublicInterestTech #DigitalPublicGoods #BeHealthyBeMobile #UNIATF

"The stakes of getting AI-driven agricultural advice wrong are not abstract—they are life and death."

For Manali, Ayush, and Buvi, those words come from experience.

This week's #GenAI4GoodChallenge spotlight turns to Inko, the second of three teams building for the Agriculture use case in Lesotho.

While building a voice bot for farmers in Uttar Pradesh, India, the Inko team encountered something that reframed everything. Farmer suicide rates in the region were alarmingly high. A misidentified crop disease, a wrong remedy, an entire harvest lost—for a family with no alternative income, that loss can be catastrophic.

That experience shaped not just their technical standards, but their understanding of what this work is actually about. The urgency, as they put it, is not just technical. It is deeply human.

Inko is working to build a generative AI advisory tool for Lesotho that would give farmers access to timely, reliable guidance through low-tech channels like SMS and voice, designed for low-bandwidth environments and limited connectivity. Every response would be grounded in verified, structured data, with human oversight built into the workflow from the start.

"Trust in AI must be earned, not assumed."

If they get this right, it won't just change what farmers know. It will change what becomes possible for the families who depend on them.

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#GenAI4GoodChallenge #GenAI4Good #AIforGood #IEEEHT #HumanitarianTech #TechForGood #ResponsibleAI #AI4Impact #SocialImpactTech #GlobalDevelopment #PublicInterestTech #DigitalPublicGoods #BeHealthyBeMobile #UNIATF

"In agriculture, a single wrong piece of AI-generated advice can destroy months of labor and devastate a family's livelihood."

For Alice Dong, that's the north star that guides her team.

As part of the GenAI for Good Challenge, we've been asking each of the nine finalist teams what drew them to this work. This week: AgriPivot, led by Alice and Paul Hill, building for the Agriculture use case in Lesotho.

Alice is an agricultural Extension specialist in rural Colorado and an AI/ML engineer—two worlds she has spent her career trying to bring together.

The GenAI for Good Challenge gave her a unique chance to act on both.
What crystallized the urgency was a number: 1 to 4,000. One advisor for every 4,000 farmers in regions like Lesotho. Advisors are the human link between official knowledge and the field — and at that ratio, most farmers never get the guidance they need in time.

AgriPivot is working to build a generative AI tool that gives advisors fast access to accurate, vetted answers so they spend less time searching and more time in the field. Their system would be engineered to say "I don't know" rather than guess, and to always keep a human expert in the loop.

For AgriPivot, success wouldn't be a launch. It would be hearing an advisor in Lesotho say: "This tool saves us time and helps us give better advice on maize and beans."

If they get this right, their solution will give the people who serve farmers back something they've been running out of: time.

🔗 Link in bio to follow the series.

#GenAI4GoodChallenge #GenAI4Good #AIforGood #IEEEHT #HumanitarianTech #TechForGood #ResponsibleAI #AI4Impact #SocialImpactTech #GlobalDevelopment #PublicInterestTech #DigitalPublicGoods #BeHealthyBeMobile #UNIATF

Some teams arrive at a challenge through a defining personal moment. Young AI Leaders arrived through research, and what they found made the decision easy.

Peter, Jan, and the Young AI Leaders team are AI engineers who, when they looked at the Gambia use case, saw something both urgent and solvable: mobile phones and WhatsApp in nearly everyone's hands, yet people still struggling to get reliable guidance on diabetes and hypertension. The infrastructure for delivery already existed. What was missing was a tool built well enough to use it.

What actually excites them isn't building a prototype. It's building something that survives the real world—integrated into existing frameworks, designed to work within the fragmented infrastructure of local clinic workflows and government systems.

An isolated, shiny app, they write, is useless.

Their standard would be built on WHO protocols, established clinical guidelines, and strict data privacy best practices. Those standards exist for a reason, and their job is to adhere to them, not reinvent them.

And when they imagine success, they don't imagine a launch moment. They imagine the opposite.

"We'd be proud if the system has simply become boring, reliable infrastructure — quietly doing its job without breaking."

Deployment, they say, is day one. Not the finish line.

Next week, we shift to the teams working on the Agriculture use case in Lesotho. Stay tuned.

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#GenAI4GoodChallenge #GenAI4Good #AIforGood #IEEEHT #HumanitarianTech #TechForGood #ResponsibleAI #AI4Impact #SocialImpactTech #GlobalDevelopment #PublicInterestTech #DigitalPublicGoods #BeHealthyBeMobile #UNIATF

Continuing our spotlight series on the GenAI for Good Challenge finalists — this week, a team whose work starts with a question most AI developers never ask: what happens when the map is wrong?

For Madani Gadjigo, it's personal. Growing up between Senegal and France, he watched geographic distance create invisible barriers to healthcare. His background in geomatics gave him a language for the problem. The numbers gave him the urgency.

Hypertension rates in The Gambia have jumped from 30% to 47% in just over a decade. Many people don't know where to seek screening — not because the care doesn't exist, but because the data pointing to it can't be trusted. Facility registries with coordinates off by kilometers. Navigation systems built on outdated information. Building on top of that, as Han Kyul puts it, is like building a house on sand.

Together with Andy, Madani Gadjigo formed Innov8AI — a team working to develop a health navigation tool for The Gambia that treats the data layer as the product, not the prerequisite. Their approach would function as a system that learns: every interaction surfacing gaps, every gap driving improvement.

"Doing this right means health workers in The Gambia are still using the system because it's become essential operational infrastructure."

For Innov8AI, success wouldn't be a chatbot that launches. It would be an approach that gets replicated — in other countries, by teams who recognize that data quality is foundational, not a footnote.

Impact, they say, is measured in systems changed. Not just systems built.

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#GenAI4GoodChallenge #GenAI4Good #AIforGood #IEEEHT #HumanitarianTech #TechForGood #ResponsibleAI #AI4Impact #SocialImpactTech #GlobalDevelopment #PublicInterestTech #DigitalPublicGoods #BeHealthyBeMobile #UNIATF

"I knew his time was limited, but this was still too soon."

Those were the words of a grandmother after losing her husband at 64 to a disease that had been building quietly for years. Her granddaughter, Valentina, never forgot them. Today, she's building the tool that might have changed the outcome.

Avoidable outcomes are, by definition, preventable. That belief is what brought nine finalist teams to the GenAI for Good Challenge. Over the next nine weeks, we're sharing what drove them here.

We begin in The Gambia, with Amina Care.

David came searching for purpose—a belief that AI could serve as a lifeline before silence becomes a crisis. Kevin spent 15 years watching the same story repeat. Valentina carried her grandmother's words into this challenge. And Hrithik brought the conviction that tools like this should reach the people most often left behind.

Together, they built Amina Care, a team developing a generative AI tool for families in The Gambia, accessible through WhatsApp and voice in Mandinka and Wolof. It wouldn't diagnose or replace a doctor. It would just show up early, consistently, before the silence becomes irreversible.

"Doing this right looks like a quiet voice on WhatsApp that a grandmother in Brikama trusts because it speaks her language and knows its limits."

If they get this right, it could change what's possible for people across The Gambia, and far beyond.

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Deadline Approaching!Nominations for the 2025 IEEE SIGHT Volunteer of the Year and Group of the Year Awards are open through 16 March.

🏅Volunteer of the Year
Recognizes exceptional leadership, community partnership, sustainable solutions, and active engagement in the SIGHT mission.

🏆Group of the Year
Recognizes SIGHT Groups that implement impactful humanitarian technology projects, meet reporting requirements, and demonstrate strong, consistent engagement.

Deadline: 16 March 2026
Letters of endorsement required (2 for Volunteer nominations; 1 for Group nominations)

Submit a nomination to help highlight the SIGHT members contributing to sustainable development and community impact worldwide.

https://www.grantinterface.com/Home/Logon?urlkey=ieeecorporate

The 2025 IEEE SIGHT Volunteer of the Year and Group of the Year Award nominations are now open!

These awards celebrate the individuals and Groups who strengthen the SIGHT network, advance sustainable development, and make a meaningful impact in their local communities.

🏅 Volunteer of the Year
Recognizing exceptional leadership, community partnership, sustainable solutions, and active engagement in the SIGHT mission.

🏆 Group of the Year
Honoring SIGHT Groups that deliver impactful humanitarian technology projects, complete reporting requirements, and demonstrate strong, consistent engagement.

Deadline: 16 March 2026
Letters of endorsement required (2 for Volunteer nominations; 1 for Group nominations).

Apply now and help us spotlight the SIGHT members driving sustainable impact around the world!

The 2025 IEEE SIGHT Volunteer of the Year and Group of the Year Award nominations are now open!

These awards celebrate the individuals and Groups who strengthen the SIGHT network, advance sustainable development, and make a meaningful impact in their local communities.

🏅 Volunteer of the Year
Recognizing exceptional leadership, community partnership, sustainable solutions, and active engagement in the SIGHT mission.

🏆 Group of the Year
Honoring SIGHT Groups that deliver impactful humanitarian technology projects, complete reporting requirements, and demonstrate strong, consistent engagement.

Deadline: 16 March 2026
Letters of endorsement required (2 for Volunteer nominations; 1 for Group nominations).

Apply now and help us spotlight the SIGHT members driving sustainable impact around the world!