HUMAN + AI SIGNALS
KIMBERLY MCCONECHY + KELLY TARRY
Over the past year, we have been learning AI by using it: building tools and workflows, supporting adoption in our teams, and getting closer to the organizational questions that emerge when AI moves from experimentation into everyday work. That experience made us curious about what other leaders were seeing. So we started asking.
For the past year, we have been actively experimenting with AI in our own work. What began with learning how to use AI well quickly moved into building GPTs, tools and workflows, testing them in real work, supporting team adoption, and contributing to broader organizational AI strategy.
Along the way, we have learned that building the technology is often the easier part. The harder work is helping people participate in the change, connecting new workflows to how work actually gets done, building capability, and creating enough space to test, learn and adapt.
Our own experience gave us a point of view. We did not want it to become an echo chamber. We wanted to understand what other leaders were experiencing across different organizations, industries and stages of adoption. Where were they seeing opportunity? What was proving harder than expected? What was changing for people, leaders and work?
So we started listening. What began as a handful of conversations grew to 60. Those perspectives now sit alongside what we are learning through our own practice and what we are reading and hearing across current research, books, podcasts, and practitioner thinking.
Across industries and different stages of adoption, the conversations keep bringing us back to people: capability, leadership clarity and the boundaries leaders are drawing around human judgment.
Leadership Clarity remains the lowest external dimension in our Human + AI Index. Responsible Adoption is higher at 7.8. The pattern is not universal, but it raises a useful question: are tools, training and guardrails moving faster than practical leadership direction?
The deeper we get into implementation, the less we think successful adoption is simply about teaching people to use AI. Technical fluency matters. So do judgment, critical thinking, communication, adaptability, and the human capabilities leaders need to bring people through change.
Automating individual tasks is one thing. A bigger question is what happens when organizations begin reconsidering workflows, roles, capability, and how work gets designed around what humans and AI each do well.
AI can create capacity. What organizations choose to do with that capacity, and whether roles, workflows and expectations change with it, is becoming a much more interesting question.
Want us to follow up?
We are continuing to talk with leaders, challenge our assumptions, and test what we are learning through our own work. We are particularly interested in what leadership, capability and work design need to look like as AI becomes part of everyday work.
We are beginning to bring what we are learning into conversations with leadership teams, HR teams and organizations navigating what AI means for people and work.