HUMAN + AI SIGNALS
KIMBERLY MCCONECHY + KELLY TARRY
Phase One | 60 leader conversations

A year of experimenting. 60 leader conversations. More questions about what AI means for work.

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.

Independent, cross-industry practitioner research | 60 completed leader conversations | Findings are directional
How we got here

We started with the work.

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.

Then we got curious.

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 leader conversations. 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.

What stayed with us

The signals did not disappear. They got harder to ignore.

More than 40 leader conversations after our first share, the same three tensions continue to surface across industries and different stages of adoption: capability, leadership clarity and the boundaries leaders are drawing around human judgment.

01
The Capability Paradox
Producing an answer is getting easier. Knowing whether it is good still requires judgment, context and domain knowledge.“I think knowing what to put into the engine is just as important as knowing if what came out makes sense.”
02
The Leadership Translation Gap
Tools, policies and training can move faster than practical direction about what good AI use actually looks like at work.Access ≠ adoption ≠ transformation.
03
The Human Boundary
As AI capability expands, leaders continue to return to context, relationships, accountability and consequence.“You can't give accountability to AI.”
4.9
LEADERSHIP CLARITY / 10
A number we keep coming back to

Controls can advance without clarity.

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?

RESPONSIBLE ADOPTION 7.8 / 10    |    LEADERSHIP CLARITY 4.9 / 10
Voices from the research

The quotes are often what stay with us.

“You can't relieve oversight.”Anonymous research participant
“AI won't be able to look around a room and see fear in people's eyes.”Anonymous research participant
“I don't think you can outsource the part that needs to care about people and care about doing the right thing.”Anonymous research participant
“It allowed me to spend more thoughtful time analyzing the aggregate than spending the time aggregating.”Anonymous research participant
One click

Which signal feels most familiar where you work?

Thank you. That helps us understand what is resonating beyond the interviews.
What is happening in practice?

What does AI actually look like in your organization today?

Select all that apply.

Thank you. This gives us another directional view of how AI is showing up in organizations.
From signal to practice

We are less interested in simply identifying the signals. We want to understand what organizations do about them.

Research can tell us where tensions are emerging. Practice is where we find out what it actually takes to respond to them.

AI capability is bigger than technical capability.

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.

Eventually, the work itself has to be reconsidered.

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.

We are putting these questions into practice. Over the past year, we have been experimenting with AI-enabled workflows, supporting adoption, contributing to broader AI strategy and thinking more deliberately about the capabilities people and leaders will need as work changes. What we are learning is pushing us beyond the question of how people use AI and toward what needs to change around the technology for organizations to use it well.
A question we keep coming back to
Are we redesigning work, or simply doing the same work faster?

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.

What we are thinking about next

The first phase was about listening. The next is increasingly about application.

What happens when organizations move beyond AI access and experimentation? What capabilities do people and leaders need? What needs to change in the way work is designed? And what does HR need to rethink as a result?

We are continuing the research, challenging our assumptions and putting more of these questions into practice. We will share what we learn.

Research note: K2 Human + AI Signals is independent practitioner research. The Phase One sample is directional and weighted toward HR and People leaders. Index values shown here use external participant responses. Findings surface patterns, tensions and counterexamples and are not presented as statistically representative.