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

60 conversations later.

We started by asking leaders how AI was changing work. More than 40 conversations since our first share, some signals have strengthened, others have become more complicated, and the questions are getting more interesting.

Independent, cross-industry practitioner research | 60 completed research interviews | 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 the technology well has grown into building tools and workflows used by our teams, strengthening the foundations that support adoption, and contributing to broader organizational AI strategy.

Along the way, we have learned that building the technology is often the easier part. Bringing people into the design, connecting new workflows to the way work actually gets done, building capability, and creating enough space to test and learn are just as important.

Then we got curious.

Our own experience left us with bigger questions. Were other leaders seeing the same things? Where was AI actually changing work? What was happening to capability, leadership, judgment, and the human parts of work as adoption increased?

So we started asking. Those 60 leader conversations 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

Three signals are getting harder to ignore.

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.

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 we are taking into practice

Two ideas are becoming more important to us.

Adoption has to be designed with people, not around them.

The workflow may be automated, but people still need to understand where it fits, help shape how it works, test it in context, and build confidence using it.

AI capability is bigger than technical capability.

We are leaning further into what people need to work effectively alongside AI, including both technical fluency and the human capabilities that become more important as technology takes on more of the work. Over the year ahead, we will be putting that thinking into practice through broader organizational capability work.

Our perspective is shaped by more than the interviews. Alongside 60 leader conversations, we are continually learning from current research, books, podcasts, and practitioner thinking, while experimenting with AI in our own day-to-day work and organizational environments.
One question we are carrying forward
Are organizations changing the 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.

Keep the conversation going

What would you like to see from us next?

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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.