Mission Log

UNICEF Innocenti’s Guidance on AI and Children  provides one of the most developed frameworks to date for understanding how artificial intelligence affects children’s rights, agency, and life chances. The document treats children not as abstract future users of technology, but as people whose lives are already shaped by algorithmic systems. Education, welfare, health, protection, migration, and content moderation systems increasingly rely on AI. The guidance recognizes this r

The World Food Programme’s AI governance framework offers a concrete example of how a large public-interest institution organizes responsibility around artificial intelligence. The document treats AI as part of the operational fabric of the organization. It addresses how systems influence logistics, targeting, analysis, and programme design. Governance is presented as an ongoing institutional function rather than a one-time approval step. This framing reflects the reality man

Across Europe, public opinion on AI is shifting in ways that matter for how power gets organized. According to EY's 2025 European AI Barometer, people increasingly see AI as economically useful but politically risky. They associate it with productivity and efficiency—and with surveillance, job loss, and disinformation. The data shows Europeans are not anti-technology. They are cautious about who controls it and how that control gets exercised. And beneath this ambivalence, so

The EU AI Act recognises that AI systems shape people’s lives, not just organisational efficiency. It treats AI deployment as a deliberate choice that carries responsibility for human impact. For high-risk uses, organisations are now expected to assess who is affected, what rights are at stake, and how harm is prevented before systems go live. Those that approach this moment with intention have an opportunity to build AI that serves people, earns trust, and stands the test of

Institutional fundraising requires precision. Success depends on preparation: identifying opportunities early, aligning teams effectively, and presenting proposals that match donor priorities. BD teams manage substantial operational work—maintaining opportunity trackers, developing presentations, adapting proposal language, conducting donor research, and coordinating internal processes. AI can handle routine tasks efficiently, freeing capacity for strategic work: relationship

nstitutional fundraising teams are under pressure. Budgets are tight. Proposal deadlines are nonstop. And competition for funding has never been fiercer. That’s why more NGOs are looking at data and automation, not just to save time, but to make smarter decisions about where to focus limited resources. Machine learning is one way to do that. It’s not futuristic or overly technical. In fact, many of the tools your team already uses are powered by machine learning, and the meth

NGO teams are not short on skill, purpose, or data. They’re short on time, staff, and energy. Every day, your team is responding to donor requests, coordinating with partners, drafting reports, updating project trackers, and trying to get a comms post out — all with one eye on the field and one eye on the budget. What’s broken isn't the mission. It’s the manual workflows. That’s where AI-powered automation using tools like Make.com , ChatGPT, and Claude can finally give you b