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?

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.