AI models are helping prevent child malnutrition – and that has big implications for business, too.
A recent initiative in Kenya, led by Gavi in collaboration with IBM’s Data Science and AI Elite team and Action Against Hunger, demonstrates how Machine Learning models can be used not just to trace problems, but to anticipate them. A custom AI model was trained on diverse data sets such as rainfall, vegetation levels, staple food prices, and malnutrition screenings. This model can now forecast child malnutrition rates up to six months in advance, enabling healthcare and government responses before situations become critical.
This predictive approach has proven to be over 80% accurate—significantly enhancing response efficiency and ultimately driving better outcomes for vulnerable populations. The model is also holistic, combining environmental, economic, and health data streams to provide actionable foresight.
For businesses in the martech or customer-centric sectors, there are clear parallels. A similar predictive model, customized for marketing or sales environments, could anticipate when a customer may churn, when campaign fatigue may set in, or when a product may fall out of favor—months before it happens. Custom AI models that incorporate customer engagement data, external market signals, and buying behavior can empower teams to take preemptive action, increasing satisfaction and retention.
AI consultancies like HolistiCrm can apply such methodologies across business domains to enhance predictive precision, operational performance, and strategic agility. Investing in advanced Machine Learning models is not solely the realm of public health—it’s an essential strategy for any customer-facing business aiming for long-term stability and value.