The conversation about AI in the social impact sector is all about data — how we can use AI to produce it faster, analyse it better, and generate new insights. But data, however good, is only valuable if it’s used; if it leads to decisions and action. And anyone who works at the intersection of strategy, evidence, and learning knows that this hinges on deeply human practices.
AI is transforming how we work. It can analyse vast datasets in seconds, surface patterns we might miss, and produce summaries in minutes. At Itad, like many others, we’re learning fast how to harness these tools to deliver quicker and sharper insights for our clients.
But it’s important to be clear-eyed about what AI can’t do when it comes to strategy, evaluation, and learning. Two elements stand out: building relationships and interpreting nuance.
Building relationships
Our work rarely boils down to simply delivering answers to client’s questions. It’s also about creating the conditions where people with diverse perspectives can engage with evidence, debate it, make sense of it, and ultimately, act on it. This requires us to build trust through listening to stakeholders needs, understanding their motivations, histories, and constraints. It means reading the room: sensing hidden tensions, knowing when to push or when to ease off, and framing messages so they land without triggering resistance. These are deeply human skills that help make evidence stick.
Interpreting nuance
Equally, our work requires judgement in interpreting evidence in context. Data is rarely neutral. It can be incomplete, contradictory, or shaped by political and cultural dynamics. Humans can navigate these complexities, spot when findings may be misleading or sensitive and bring lived experience to guide what matters most. AI can find patterns; it can’t weigh their significance in messy, real world situations where there’s often no single ‘right’ answer, rather just our best judgement on how to move forward.
A partnership with AI
For me, the vision of how we leverage AI in strategy, evaluation and learning, is one where we use it to handle the heavy lifting of data gathering, cleaning and initial analysis, so that we humans can double down on the relational and interpretive skills that turn evidence into impact: relationship-building, values judgement, and storytelling. In a world where AI use is more commonplace, it’s these human capabilities that will increasingly define our value as social impact advisers.
