CARE Failing Forward

"Ok, fine. But we won't buy that." That's what farmers kept saying when presented with the tractors the best experts in the world said they needed. Jehiel Oliver from Hello Tractor talks about how learn to listen to the market first, and the experts later. That's especially true when you're dealing with farmers, who know that no matter how ambitious a startup founder you are, you still can't break the laws of physics. The only signal that matters is whether the actual user will adopt the solution in their environment. A product can be technically elegant and still be irrelevant. If you optimize for what prestigious outsiders recommend, you can burn years building something the market quietly refuses to buy. He also gives real lessons on what it takes to lead: start from humility. Don't force yourself to move faster than a market will scale because you think you can solve all the world's problems. Make space for your teams to admit failure by admitting failure yourself. Spend at least two days a week making sure your teams are on track, and find ways to incentivize success and creative solutions.

The smartest move in AI today is to evaluate what works before you scale on excitement alone. What happens when Low and Middle Income Countries start pulling investments away from traditional health, judicial, or extension systems in order to build AI solutions instead? It can have huge upsides, but if you're only testing the AI and not the impact for people, it could lead to "irreversible losses." Listen to Attaullah Abbasi from J-PAL talk about their new AI Evidence Playbook . AI is moving faster than the evidence can keep up, and that means people are often making decisions about if an AI "works" with engagement metrics like user adoption and message volume. Those can be dangerously misleading. The success of many AI tools drops dramatically when you release them into the wild, because if people don't have the bandwidth to use them well, or the system can't act on what the AI found, your investment isn't delivering real change. Atta talks about the difference between the speed of evidence, the speed of technology, and the speed of change. Your AI tool might evolve hundreds of times weeks, but it's still going to take a whole school year to see if kids passed the end of year reading exam. The evidence playbook gives you practical tools to understand what is working and what's not, and what methods will give you good enough information to move to the next phase of investment.

Cocoa income comes just twice a year, but families need stability every day. Carol Miloky (CARE) and Fatou Ndiaye (Mars) unpack what they learned from a decade of working with cocoa‑growing families. Savings groups were helping women build businesses, but without stronger household planning and clearer financial decision‑making, those gains didn’t always translate into lasting resilience. Over time, CARE and Mars saw a simple truth: stability at home shows up as stability in the supply chain. They explore how focusing on whole‑household financial visioning, helping families plan across the year, smooth income gaps, and make shared decisions, creates more predictable livelihoods and stronger supplier relationships. The result? Stronger enterprises, safer homes, and, as Fatou puts it, farmers who stay committed for the long term. Read more about this decade-long partnership here Women for Change - Empowering Women Cocoa Farmers - CARE

Amanda Larson and Aimee Mateo talk about building health technology in the Philippines . HEAL Hub is an app that helps local health workers stay up to date on training and the materials they need. But the health workers aren’t who you think they are: in many areas, health workers are on average 60 years old, and handing them a smart phone is not going to immediately translate into results, no matter how good the app is. Amy says the biggest change she would make is to start earlier designing WITH health workers, to understand what they need, what the Ministry of Health requires, and what kinds of examples will make a difference for them. Their biggest goal now is to figure out how the tool can integrate into the health system so it can last for the long haul.

Ask yourself, 'Did the system become stronger because we were there?’” That’s the biggest piece of advice Nelima Otipa and Nithya Ramanathan from Nexleaf Analytics have for you about trying to scale up technology. It’s not about building the perfect tech. It’s about seeing people as actors in their own systems. You need to adapt with governments, not sell to them. Inspired by their article on Scale that Lasts in Stanford Social Innovation Review (SSIR), we talk about why governments often prefer paper systems, how reliability is the key to trust, and what it takes to build a culture where you lead by understanding failure instead of ignoring it.

What happens when your brand new tool makes things worse for low performing entrepreneurs? Or restricts your most successful teachers so they can't unlock the full power of their skills? One of the most important questions you can ask about an AI tool is NOT, "is it working?" You really need to ask, " who is it working for ?" and " How did the change happen ?" Crystal Huang from IDinsight and Matthew Smith from IDRC talk about ways to evaluate AI tools, and how to think about if they really are creating the change you hoped for. Just like the internet didn't end poverty, neither will AI. What succeeds or fails will depend on how well we implement AI, and whether we can truly center how technology connects to humans and real life contexts. What do we do to take something that works in a controlled setting and help it survive the messiness of real-world implementation?

Robots that get stuck in the mud, a "successful" product a farmer will never use more than once, and a financial model clients can never pay for. Listen to Ankit Chandra and Ishani Lal talk about their article, Why AgTech Startups Fail , what inspired them, and what they learned. The core lesson is that we need to change what counts as success in AgTech. Success is not "does it work in the lab?" Success is, "Will this save the farmer time or money? Will it increase profit and lower risk?" Maybe most importantly, "will any farmer ever choose to use this more than once?"

Lindsey Moore was working in AI before most of us knew what it was, and she can tell you the most common mistakes to avoid. Ignoring context, building ever more precise models that provide terrible answers, and assuming that AI will replace smart strategy and human decision-making are three on the top of her list. If you're looking to do more with AI, she recommends you invest in learning good research methods, double-down on your data architecture, find ways to counteract bias, and stay skeptical. Developmetrics ' Large Language Model with was trained on 60 years of USAID documents, and taps into a wealth of expertise that doesn't exist anywhere else. It can tell you what has worked, and what hasn't, over decades of work in dozens of countries. Here's what it tells us: we often repeat the same failures over and over again. Why? Because failures are as much about organizations as they are about tactics. The newest widget won't solve an organizational culture that drives people away from spending time understanding the local context.

Eric Kaduru and Julia Arnold talk about why simply distributing phones doesn't help people--especially women--access the internet. After seeing free phones get broken, stolen, or cause men to punish women for owning phones, they needed a new plan. Instead, they talked about learning from HIV prevention campaigns in the 90s, demystifying something complex, and making learning accessible. Social norms are at least as important as technology. Soap operas , hip hop concerts, and talking to men are critical tools in opening up access to tech for women in Uganda, and worked better than free phones ever have.

CARE has a more than 30 year history with savings groups--starting from the lowest tech version you can possibly imagine: 25 women with a box and a notebook in Niger. Building on that, in 2013, we launched the process of building Chomoka--an app that would help women in savings groups manage their record keeping and connect to digital finance. Christian Pennotti talks about that journey, and why we finally passed the work over to Ensibuuko . Building a successful, scalable enterprise around digital tools was ultimately better put in the hands of a tech company that focuses on banking tech for people who can't access existing tools. What did we learn, and what are we doing now? Tune in to find out.

What happens when you try to build an AI tool that works for women entrepreneurs – and it totally flops? In this episode of Failing Forward, CARE’s Koheun Lee and Sarah Hewitt share the story of their ambitious attempt to create a women-centered GPT trained on real-world data from women entrepreneurs. Spoiler: it didn’t go as planned. But the failure revealed a lot. In this episode, Koheun and Sarah discuss: Why CARE built a custom GPT to fight bias, and what went wrong How even well-trained AI tools can reinforce stereotypes and exclude women What we learned about prompt design, user behavior, and the limits of scrappy innovation Why most users still defaulted to mainstream AI tools Actionable tips for using AI more intentionally, and with less bias What this "failure" taught us about building better tools and better teams Tune in for a candid conversation about tech, bias, and what it really means to learn in public. To learn more and join the conversation, visit the Women’s Entrepreneurship LinkedIn Community of Practice .

"If you've already built an elevator to the first floor, why not take it all the way to the top?" Vidhya Sriram talks about the journey of savings groups (also called VSLAs) at CARE, and what it took to think not just about scale, but also about the biggest benefits to women. VSLAs do build savings and income, but they can also do so much more. She talks about understanding what women themselves aspire to, not what we aspire for her. She also talks how savings groups can be a platform for women to build economies, change the support women get from the people around them, and thinking about building not just savings, but also equality that benefits the whole community. Check out the report here .

What happens when you don't see the results you hoped for in your project? If you're Dr. Nahla Abdel-Tawab from Population Council , you publish your results , learn from them, and try again. Some of the biggest barriers they faced were: assuming that private sector health solutions were the answer, asking workers the wrong questions about what they needed, and not understanding the context that garment factory workers in Egypt deal with when trying to access health care. Great examples of learning, admitting that it didn't work, and finding a better way to get results. You can see more about the project here .

What does it take for people and the planet to thrive? We have to show up. C.D. Glin , President, PepsiCo Foundation and Global Head of Social Impact, PepsiCo . After decades in social impact, government, and philanthropy, C.D. talks about some of his earliest lessons as a Peace Corps Volunteer in the new South Africa, and the inspiration of Nelson Mandela’s quote, “I never lose. I either win or learn.” Appreciative inquiry, meeting people where they are, and knowing you don’t have all the answers or some of the key lessons.

What happens if we stick with business as usual? We fail by default. C.D. Glin , President, PepsiCo Foundation and Global Head of Social Impact, PepsiCo, Inc talks about food systems are failing women, and what companies can do to correct for that. Thinking with a whole of company approach, beyond just philanthropy, is critical. Companies have to use their profits, their products, their procurement, their people, and their markets if we’re going to achieve #zerohunger. He talks about how COVID-19 was a wake up call to the visceral challenges in the global food system—like climate change and inequality—and how to turn a moment into momentum. He also talks about how projects like She Feeds the World can help address these challenges.

Tahira Nizari and Barnabas Mtelevu talk about what it took to overcome the challenges in the tea sector in Tanzania, and how assuming that smallholder women farmers could immediately join a global supply chain demanded new partnerships and new plans. How do you grow from an individual farmer to a business? Don't assume it will happen automatically. Just because you're a businesses doesn't mean it will work. On the other hand, you can't assume a development project is set up to meet market needs. A demonstration factory, joint ownership with women farmers, and getting global investment were part of the key in shifting to a more business mindset in the partnership between CARE and Kazi Yetu . It led to a 17% increase in tea prices and an 83% ROI. Learn more about how they did it here .

What happens when your consultation processes go off the rails? Lauren Beriont from The Emgergence Collective talks about how a lot of our feedback and co-creation processes face three major problems: 1) They assume a trust that does not exist between different stakeholders 2) They are centering the wrong actor--the donor or the most powerful group in the process--instead of focusing on the impact the world needs to see. 3) They are looking to validate a plan that is already in place (but maybe not on paper yet), not create a new plan How do we fix that? Have scary conversations. Talk about what's not working, what assumptions people holding, and tackle real power dynamics. Look at what impact donors expect, vs. the kind of impact people are actually seeing happen.

What's in a logo, and why does localization need to include participant-led logo design? Zinat Ara Afroze and Sairana Ahsan explore the logo competition to have frontline service providers design a logo for their own services. What did they learn? Only 9 people out of a potential 450 participated in the competition the first time around, and understanding why not, and what level of understanding it takes to draw a logo that sums up your job showcased how much more shared understanding there was to build. Zinat's recommendation for what you should do? Practice adaptive management and CLA . Check out their case study here .

Inspired by his recent blog post on From Poverty to Power , Duncan Green reflects on why it's important to learn from failure, and some of his own failure stories. "Think before you jump", and "be a reflectivist as well as an activist" are some of his key pieces of advice to people working in the sector. He's got stories about playing chess from the management bunker, evidence-based humility, and How Change Happens, the second edition paperback and Open Access that's coming out starting from August 16. Want to hear more stories from Duncan and the change makers he works with? Listen to his podcast: GELI Stories podcasts

"I wish I had known that my biggest source of learning would be my field colleagues. ...I believed in textbooks." Dr. Muhammad Musa reflects on 41 years of work in international development. His two biggest lessons are: learn from your frontline staff, and tell stories with impact. Some lessons he learned in the decades are to build a fearless climate--a climate of trust, where staff at all levels can learn from failure, and can take time to reflect. He also notes that moving from a project to a movement--one that changes a system--is the best way to create change. Technical solutions are not the only (or even the best) way to get the job done.