RSTMH — the Royal Society of Tropical Medicine and Hygiene — is a UK-based global health charity with a 120-year history, working at the intersection of research, policy, and practice. With a small core team and a large international network of members and volunteers, the organisation operates with significant complexity across grants, publishing, membership, and governance.
The Challenge
RSTMH’s leadership recognised early that AI would have meaningful implications for how the organisation operates and delivers on its mission. Rather than reactively adopting tools, they wanted to take a deliberate, evidence-based approach.
Initial experimentation had already begun across the organisation, reflecting both curiosity and initiative. At the same time, many core processes involved significant repetitive manual effort each year. Alongside this, were valid concerns around data privacy, particularly the use of free AI tools with sensitive personal and academic data.
Against this backdrop, leadership reached a clear inflection point: how to move from scattered exploration to a coherent, board-ready strategy.
The opportunity was not to “introduce AI,” but to:
- Prioritise where AI could deliver real value
- Ensure alignment with strategic goals in global health and member services
- Establish clear governance around risk, cost, and data sensitivity
- Provide the Board with a credible, structured basis for decision-making
This was a question of confidence and clarity.
Our Approach
Vaul Labs designed and delivered a focused, strategic workshop with RSTMH’s senior leadership.
The session was structured around a simple but rigorous framework: evaluating potential use cases across impact, effort, risk, and cost.
Pre-work ensured discussions were grounded in real operational workflows, including grants, governance, research, and communications. During the session, we worked directly with these workflows to:
- Identify high-value, feasible AI opportunities
- Surface constraints around data, systems, and governance
- Force explicit trade-offs between competing ideas
This was a focused decision-making exercise, designed to produce actionable outputs that could be taken directly to the Board.
Scope & Constraints
The engagement was shaped by several important parameters:
- Data sensitivity: significant volumes of personal and academic data across grants, membership, and publishing
- Third-party systems: reliance on external platforms limiting direct AI integration
- Budget constraints: need for low-cost, high-impact solutions appropriate for a small charity
- Governance expectations: requirement for clear oversight on risk management, data handling and ethics
These were treated as design inputs, ensuring recommendations were practical and implementable.
Outcomes
“Like many organisations, we saw significant potential in AI but lacked a structured way to turn that into action. Our board needed an evidence-based view of where AI could add value, and where it could not. The Vaul Labs workshop gave us a clear, grounded view of how AI could support our organisation today, not just in theory, and helped us move from broad exploration to a focused, well-governed set of practical opportunities, assessing cost, risk and feasibility. The outcome was a clear AI governance policy and a prioritised set of AI opportunities, giving our board the confidence and clarity to make informed investment decisions.”
Tamar Ghosh, Chief Executive, RSTMH
From Exploration to Evidence-Based Prioritisation
- A shortlist of credible AI use cases identified, focused on areas such as governance preparation, desk research, and data handling.
- Low-value or high-risk ideas explicitly deprioritised.
- Shift from exploratory activity to evidence-based decision-making.
Board-Level Confidence and Governance
- Clear articulation of where AI adds value, and where it does not.
- Outputs structured for direct Board discussion, including impact, cost, and risk considerations.
- Reduced ambiguity around data privacy and acceptable use.
Strategic Alignment with Mission
- All opportunities mapped to RSTMH’s core priorities: sustainability, member value, and global health impact.
- Shared understanding across functions of what “good AI use” looks like in context.
- Reinforced focus on outcomes, not technology adoption for its own sake.
A Repeatable Framework for Future Decisions
- A consistent method for evaluating new AI ideas as they emerge.
- Practical governance principles to guide safe experimentation.
- A foundation for ongoing, structured adoption rather than one-off initiatives.
RSTMH’s approach reflects a principle we see consistently in successful AI adoption: treating AI as a strategic capability rather than a collection of tools. By establishing clear priorities, aligning leadership around shared objectives, and putting governance in place from the outset, they created the conditions for confident decision-making and focused execution. For organisations with complex responsibilities and lean teams, that clarity often becomes the biggest accelerator of meaningful, lasting impact.