Despite high adoption rates, most organizations see no ROI from AI because they neglect the human elements of feeling, thinking, and working with the technology.
A case study of a customer success team shows how automating core tasks without a human-first approach can lead to disengagement and a loss of connection to the work.
Employee feelings about AI, ranging from excitement to fear and anxiety, directly shape their behaviors and the success of AI implementation projects.
Research shows that the sequence of human-AI interaction matters, as AI can reduce brain engagement and create homogeneous thinking that stifles innovation.
Teams can regain ownership and engagement by implementing a "human take first" principle, defining team norms with "keep it up/cut it out" behaviors, and using anonymous feedback rounds.
A QA organization's struggle to scale AI highlights common pitfalls like fragmented tooling, team attachment to specific solutions, and a lack of shared success metrics.
The CLICK framework provides five essential pillars for building self-sufficient teams: clear purpose, linking connections, integrated work, collaborative decisions, and knowledge sharing.
The Stanford Human Agency Scale (H1-H5) helps teams intentionally decide the appropriate level of automation for different tasks, preventing unintentional drift towards over-automation.
As AI handles information processing, uniquely human skills like judgment, building trust, empathy, and critical thinking become the most critical assets for future-ready teams.
Leaders can guide their teams by applying the CLICK framework, the Human Agency Scale, and defining team norms to ensure AI is a partner, not a replacement for human judgment.
Jobs that call for the skills explored in this talk.