Sowing the Seeds of Resilience: Can AI Empower Women in Agriculture?

The cacophony and hype around Artificial Intelligence (AI) seems to grow with every month. Whether being heralded as the key to exponential economic gains and human efficiency or the potential downfall of the species, AI is never far from the headlines. Whatever a person might feel about this emerging technology, it is now omnipresent and unavoidable. Could AI be the silver bullet to boost rural women’s agricultural income? No, because silver bullets don’t exist. Could it offer real value to rural women in agriculture? Yes – and here are four areas where we see action and potential.  

1. Access to information

AI-powered mobile apps can provide women with valuable information on best farming practices, weather forecasts, and market prices, helping them make informed decisions and improve their yields. Digital Green, in partnership with governments and community organizations, uses an AI-powered platform to boost the cost-effectiveness and efficiency of public extension systems (government-supported services that provide education and technical assistance to farmers and communities to improve agricultural practices). The platform can then quickly deliver this information in local languages (in women’s voices), enabling extension agents to easily provide contextually relevant and timely agriculture advice to local farmers to help improve their productivity and well-being. 

2. Credit and insurance risk assessment 

By analyzing data, assessing creditworthiness, and even supporting alternative credit scoring, AI can better facilitate access to microloans and insurance, enabling women to invest in better seeds, equipment, and agricultural inputs. Indian digital credit provider Lendingkart aims to improve entrepreneurs’ access to working capital to establish and expand small businesses through unsecured loans – a groundbreaking approach, considering women are less likely to have the assets to guarantee their loans beforehand. By vigilantly training their credit scoring model to remove bias, Lendingkart treats men and women equally, where other models often default to privilege men’s credit history.

Another example that supports financial inclusion is AI-enhanced microinsurance. AI can help design and deliver microinsurance products that protect women farmers against climate-related risks, such as crop failure due to extreme weather, offering affordable premiums and quick payouts. For instance, Pula uses AI to offer parametric insurance tailored to smallholder farmers (with a focus on women), helping them recover quickly from climate-related losses.

3. Precision agriculture 

By analyzing vast amounts of data collected from sensors, drones, and satellite imagery, AI can provide insights and recommendations for optimized farming practices in real-time. Amini, a Nairobi-based tech company, uses AI to better monitor, predict, and manage crop health and soil conditions across Africa, analyze soil samples to determine nutrient levels, and recommend appropriate crop choices and fertilization methods. This leads to healthier crops and better yields, improving farmer incomes. Given that women make up 80% of Kenya’s smallholder agricultural labor force, soil insights that better inform their farming can save them precious time and money. Another example is FarmBeats by Microsoft, an AI and Internet of Things (IoT) platform that uses sensors to provide real-time insights, enabling farmers to use data to help optimize their practices.

4. Service aggregation and organization 

By using machine learning to understand the underlying drivers of demand for tractor services, Accelerating Business to Empower Rural women in Agriculture (ABERA) cohort member Hello Tractor built a predictive demand model for tractor utilization in Kenya and Nigeria, enabling the company to accurately forecast usage throughout the year. Hello Tractor is then able to better predict demand and supply of tractor owners, proactively recruit tractor owners in locations where there is likely to be a shortfall, and plan informed decisions around growth.

However, for all the promise of AI, it is crucial to stay grounded in reality. In 2024, women are still less likely to have a mobile phone, mobile internet, or bank account, and have lower levels of digital literacy. In low-income countries, only 20% of women are using the internet. This is even more pronounced in rural areas. This reality limits women’s engagement with these life-enhancing technologies: both their ability to inform them, and use them to save time, money, and mental efforts. As one of ABERA’s Advisory Committee members, Nicoline de Haan, said –  if we want technology to improve rural women’s lives, before AI, we need to increase women’s digital inclusion. 

Furthermore, the use of AI, with all of its possibilities, requires a level of competence and trust in digital services. Physical interaction with real, live humans remains crucial in onboarding previously unconnected, less empowered women. While there is an argument that initiatives such as AI-powered chatbots can leapfrog digital literacy challenges, getting to the point of using a chatbot requires a degree of ability in itself – a lack of which could result in confusion and dissuasion to persevere with the technology.  

Beyond access, it is also important to note that AI is often designed in a way that excludes women – for new adopters or low-literacy people, some technologies can be complex to understand, training datasets used exclude low-income and vulnerable women, and algorithms are largely biased and do not take women's needs into consideration.  It is crucial to pay close attention to the gender data biases that have been built into this technology so that when AI is used it doesn’t exacerbate existing biases such as misrepresentation and stereotyping. The Such inbuilt prejudices can automatically result in women being excluded and further exacerbate issues of exclusion. 

Throughout several inclusive business analyses the ABERA team has undertaken to date, the same enablers emerge that support a climate-smart, gender-transformative business case, with women's groups and phygital approaches amongst them. For ABERA, this suggests that for low-income women, much of the time there may be a requisite baseline level of digital and financial literacy needed before they can benefit from these new technologies.