AI-Enabled Consumer Engagement to Advance Family Planning

Before applying to this Grand Challenges request for proposals (RFP), applicants should familiarize themselves with the supporting documents, including the terms and conditions of the Gates Foundation, the Rules and Guidelines, Application Instructions, and Frequently Asked Questions (FAQs).

If you plan to apply to this RFP, we will host a dedicated webinar on August 4, 2026, 7:00-8:00 a.m. US PDT. The session will provide an overview of the RFP and include time for questions. To attend, please register and submit your questions in advance. A recording will be posted on the challenge page following the session for those unable to join live.

Background

Despite significant investment in family planning programs across sub-Saharan Africa, unmet need for contraception remains high. Women and girls face persistent barriers to accessing accurate, personalized information about family planning - barriers that clinic-based counseling alone cannot overcome at the scale and cost required to meaningfully shift contraceptive prevalence. Digital tools have expanded reach, but most existing approaches rely on one-way or rule-based messaging that cannot replicate the kind of responsive, personalized conversation that moves women from awareness to action.

Recent advances in large language models have changed what is possible. Artificial Intelligence (AI)-enabled consumer engagement tools can now hold nuanced, multi-turn conversations in local languages, respond to individual questions and concerns, and deliver personalized information at very low marginal cost across both public and private sector channels. Platforms with existing distribution - through telecoms, government health systems, community networks, and commercial channels - are beginning to deploy these tools at scale across the region, often serving broad women's health and consumer health needs that could extend to family planning.

What the field lacks is evidence. We do not yet know which AI-enabled engagement approaches work best to drive contraceptive uptake and continuation, which conversation features and content strategies make the difference, or how to evaluate interaction quality in ways that predict real outcomes. Without that understanding, investments in AI-enabled family planning tools cannot be optimized, replicated, or responsibly scaled. This challenge is designed to generate that evidence - and to build the shared analytic foundation the field needs to learn from it.

The Challenge

This RFP seeks to answer a core question for the family planning field: can AI-enabled direct-to-consumer engagement improve contraceptive uptake, continuation, and informed method choice at scale - and, if so, which approach works best?

This challenge is designed to generate practical evidence on what high-quality, AI-assisted family planning engagement looks like in the AI era. We are interested in understanding which AI-enabled tools are most effective to engage users and improve contraception uptake and continuation. More specifically, we are interested in understanding which tools and approaches (e.g., content personalization, rapid content scaling, automated customer journeys, etc.) work best for which types of users to directly engage them and improve uptake and which platforms/tools have demonstrated less success for this objective. We do not expect applicants to arrive with platforms already proven for contraception; we welcome consumer engagement approaches with a demonstrated track record in women's health or consumer health more broadly, and ask applicants to deploy, test, and assess how well they work for family planning.

The challenge will focus on women and girls of reproductive age in sub-Saharan Africa. Interventions can be delivered through public sector platforms, private sector channels, or hybrid models.

Applicants must be able to measure the quality and characteristics of the AI-enabled interaction, scale and usage, and the user outcomes that occur after the interaction. Successful proposals should demonstrate a credible pathway between AI-enabled engagement and family planning outcomes, while also contributing to learning that can inform the broader field. Applicants should be prepared to assess not only whether the intervention changed behavior, but how and why.

Objectives

The objectives of this challenge are to:

1. Generate evidence on impact and scale. Demonstrate whether and how AI-enabled direct-to-consumer engagement improves contraceptive uptake, method continuation, and/or informed method choice at scale.
2. Identify the features of effective engagement. Determine which interaction characteristics, content strategies, personalization approaches, prompts, follow-up models, or conversation patterns are associated with stronger user outcomes.
3. Build a shared evidence base for AI-enabled family planning engagement. Produce labeled conversation examples, interaction taxonomies, quality rubrics, safety, confidentiality, and guardrail protocols, and/or other analytic tools that can help the field evaluate and improve AI-enabled consumer engagement beyond a single program or platform.
4. Assess cost, feasibility, and added value. Document the cost, operational requirements, feasibility, and comparative value of AI-enabled engagement relative to alternative consumer engagement or demand-generation models, including the conditions under which AI-enabled approaches are likely to add meaningful value.

Proposals should be designed to produce both programmatic results and field-building insights. The strongest applications will combine a compelling intervention model with a rigorous approach to measurement, a clear plan for analyzing interaction quality, and a commitment to generating practical learning that can inform future investments in AI-enabled family planning tools.

Funding Level

We will consider proposals for awards of up to $500,000 USD for each project, with a grant term of up to 12 months. Application budgets should be commensurate with the scope of work proposed. Indirect costs may be included in the budget, up to a maximum of 15% of the total budget (subject to the Gates Foundation's indirect cost policy).

Eligibility Criteria

This initiative is open globally to nonprofit organizations, for-profit companies, international organizations, government agencies and academic institutions. Multi stakeholder collaborations are encouraged. Individuals and organizations classified as individuals for U.S. tax purposes are not eligible to receive an award from the foundation as part of this initiative.

We are looking for proposals that:

• Applicants should have existing women’s/ consumer health intervention cohorts in Nigeria, Ethiopia, the Democratic Republic of Congo, Tanzania, Senegal, Niger, Kenya, South Africa, Zambia or Côte d’Ivoire.
• Applicants should have an existing consumer engagement approach - in family planning, women's health, or consumer health more broadly - and an established user base or distribution relationship - characterized by engagement (e.g., monthly active users), not size alone - ideally in at least one target geography at the time of application
• The AI-enabled engagement approach itself can be new and innovative, but the grant period should be spent on deployment, learning, and evaluation, not on acquiring users or negotiating platform access
• Propose a credible evaluation design that measures the quality, safety, and confidentiality of the AI-enabled interaction and downstream user outcomes, including contraceptive uptake, method continuation, or informed method choice
• Have a clear theory of what effective engagement looks like and how interaction quality connects to behavior change - and a plan to test it
• Operate at sufficient scale to generate meaningful findings within the grant period
• Commit to producing outputs that contribute to the shared evidence base - including but not limited to labeled interaction examples, conversation quality rubrics, interaction taxonomies, or safety, confidentiality, and guardrail protocols
• Demonstrate that AI-generated content is grounded in vetted, locally relevant family planning information aligned with national MOH or WHO guidelines
• Are led by organizations with deep contextual knowledge of the target geography and population
• Define their target user and explain how relevant their existing user base is to family planning outcomes - particularly for solutions not originally focused on family planning
• Describe their user acquisition model, including key metrics, and their user retention efforts - recognizing that engagement quality and the human touchpoints before and after the digital interaction matter as much to family planning outcomes as user-base size

We will not fund proposals that:

• Rely on engagement metrics alone - sessions completed, messages sent, users registered - as primary evidence of effectiveness
• Lack an existing user base or distribution relationship in a target geography at the time of application
• Propose work outside of sub-Saharan Africa. Preference for solutions targeting Nigeria, Ethiopia, the Democratic Republic of Congo, Tanzania, Senegal, Niger, or Côte d'Ivoire
• Are primarily research-led without a real-world operational deployment component at scale