In December 2025, Anthropic launched a large-scale public conversation on artificial intelligence - one that intentionally moved away from abstract speculation about risks and benefits, and toward the lived experiences of people already using AI in their daily lives.

Through an interview-based study conducted via Anthropic Interviewer (a specialized version of Claude), the organization collected perspectives from 80,508 participants across 159 countries and 70 languages, making it, by their own account, the largest and most multilingual qualitative study of its kind.

The goal of the research was ambitious: to understand what people hope for and fear about AI, not in theory, but grounded in practical interaction with the technology. What’s been largely missing in public discourse, Anthropic argues, is a shared vision of what “AI going well” actually looks like for individuals and society.

In this article, I approach Anthropic’s findings through a Business Analysis lens, applying core BA competencies to interpret individual aspirations as signals of organizational needs, systemic change, and value creation opportunities.

AI becomes a powerful accelerator for Emotional Intelligence (EI) when it’s used not as a replacement for human sensitivity, but as a mirror (how does AI -someone else - see me?), a simulator (what if I?), and a coach (how can AI help me to become better in EI?). It helps you observe patterns you normally miss, practice difficult interactions safely, and receive feedback that humans rarely give with such precision.

Emotional Intelligence is usually broken into four domains: self‑awareness, self‑management, social awareness, and relationship management. AI can support each one in different, practical ways.

Artificial Intelligence has moved from hype to necessity. Yet while organizations rush to adopt AI, many still struggle to deliver real value. AI projects fail more often than traditional digital initiatives - not because the technology is immature, but because managing AI requires a fundamentally different mindset, lifecycle, and governance model.

This article offers a clear, experience‑based guide to managing AI projects effectively, drawing on Business Analysis, Agile practices, Data Governance, and the CPMAI lifecycle.