Quantifying the "goodness" of an AI-generated summary is analogous to measuring the impact of a peacebuilding initiative. Both require moving beyond simple quantitative data (clicks, meetings held) to define and measure complex, ineffable outcomes by focusing on the qualitative "so what."
Treating AI risk management as a final step before launch leads to failure and loss of customer trust. Instead, it must be an integrated, continuous process throughout the entire AI development pipeline, from conception to deployment and iteration, to be effective.
Navigating technological upheaval requires the same crisis management skills as operating in a conflict zone: rapid pivoting, complex scenario planning, and aligning stakeholders (like donors or investors) around a new, high-risk strategy. The core challenges are surprisingly similar.
When civil war made the government an unsuitable partner, an NGO successfully pivoted by collaborating with the church—the community's new trusted institution. In a tech disruption, companies must similarly identify and partner with new entities that hold customer trust.
An effective Human-in-the-Loop (HITL) system isn't a one-size-fits-all "edit" button. It should be designed as a core differentiator for power users, like a Head of Research who wants deep control, while remaining optional for users like a Product Manager who prioritize speed.