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CARE Failing Forward: Daily Summaries Delivered
by Emily Janoch
CARE staff and other guests around the world talk about experiences we learn from failure, ways to create safe space to talk about failure, and how we use that to get better at our work.
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The most recent episodes — sign up to get AI-powered summaries of each one.
- 4 days ago32 min
Teaching AI to Say “I Don’t Know” and other tips for evidence, policy, and high stakes decisions
What if you could spend a hundred billion dollars on something that works? Why isn't that happening already? What if the best option to change people’s lives is something you don’t already know about? Linxi Wang of the World Bank Group’s Impact AI team shares what it takes to build AI for evidence-informed policymaking. From learning through setbacks to evaluating accuracy, handling uncertainty, and helping AI acknowledge when evidence is missing, the conversation explores why trustworthy tools require ongoing work—not just a promising prototype. “Each policy decision has the possibility of impact downstream like thousands, if not millions, of people’s lives.” The conversation covers the risks of high-stakes AI, how people use AI-generated insights, and why success should be measured by better decisions and improved lives.
- Sep 12, 202631 min
A Solution Without a Platform
What happens when you build a brilliant solution that solves problems years ahead of it's time, but you have no platform? It fails. You can't get it to the people who could use it. Chris Noble talks about how lessons from how tech startups fail shape what we should be doing in international development and social good today. He calls it the "Wheel of Failure." The biggest piece of advice he'd give his younger self? "Don't flinch. Spend more time talking to the people. In every moment of discomfort or fear... I wanna fight or flee. And I do both in pretty much equal, measure. But because we're humans, there's a third alternative to fight and flee, which is listen. …. Don't force it. Take a minute. Talk to the people, listen to them a little bit. And then see if you can find your way forward." He also talks about relationships over transactions, how trust is the best thing we can build, and why you should have Voltaire and Winnie the Pooh as philosophers you listen to.
- Aug 26, 202629 min
Ok, fine. But we won't buy that.
"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.
- Aug 12, 202625 min
Don't scale the excitement; evaluate what's working.
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.
- Jul 29, 202628 min
Why family stability matters for your chocolate
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
- Jul 15, 202610 min
Start By Listening
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.
- Jun 17, 202633 min
Plan for when it breaks
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.
- May 11, 202632 min
Who is it working for? The messy realities of AI in practice.
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?
About CARE Failing Forward
CARE staff and other guests around the world talk about experiences we learn from failure, ways to create safe space to talk about failure, and how we use that to get better at our work.
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