How culture shapes what AI adoption delivers, and how to see it in time to act. Written for the coaches and change management professionals who lead the human side of AI implementation.
By Alexis Halkovic, Ph.D., ACC, Co-CEO, CultureCampOctober 27, 2026
AI results depend on two inputs: technical readiness and cultural readiness. Most organizations measure only the first.
When results stall, the operations dashboard can't say which input fell short, so leaders buy more technology to fix what is often a culture problem.
Measure culture with the same discipline as technology: a team-level baseline before the first use case, short recurring check-ins during it, and progress reported next to the technical and operational metrics.
6%of organizations see significant financial return from AI. Nearly three-quarters of them redesigned their workflows.McKinsey, 2026
80% vs. 37%say AI improved their own productivity, versus those reporting any impact on earnings.McKinsey, 2026
41%of employees feel comfortable taking risks without fear.Seramount, 2026
60% vs. 45%of employers think their teams are open about AI use, versus employees who keep their use private.Slingshot, 2025
Executive summary
Organizations judge AI transformation by its operational results, but the forces that shape those results sit upstream of operations. Operational results are the product of two inputs: technical readiness and cultural readiness. Both are necessary, and neither is enough on its own.
Most organizations measure the technical input closely, tracking deployments, licenses, integrations and usage. They rarely measure the cultural input as part of the transformation itself: whether people understand the change, see leaders model it, feel safe to experiment, and are rewarded for working differently.
That imbalance creates a diagnosis problem. When operational results fall short, leaders can't tell which input fell short, so they focus on the one they can see and invest in more technology to solve what is often a culture problem.
Three arguments
Culture is a co-equal layer of AI transformation, alongside the technical and operational layers, rather than a soft complement to the real work.
Operational metrics cannot diagnose themselves. Without a separate signal for each input, anyone trying to locate the problem in a stalled initiative is guessing.
The cultural input can be measured with the same discipline as the technical one: a baseline before implementation, a recurring signal during it, and evidence leadership can act on.
The stalled-initiative diagnostic from the paper. Only one quadrant is ready to scale, and each of the other three calls for a different response.
The P&L will always be the final scoreboard, but it shouldn't be the first place leaders learn that the culture wasn't ready.
What's in the full paper
The model: two inputs, one output, one lagging result
The seven dimensions of cultural readiness, with the evidence and warning signs for each
Three patterns that never show up on a usage report: shadow adoption, trust in output, and identity and relevance
The nine signals to measure, and what each looks like when it's weak
Where dashboards, project tracking, change frameworks, pulse surveys and individual assessments stop
Recommendations for coaches and change professionals, and what to ask of the executives you advise
About the author
Alexis Halkovic, Ph.D., ACC, is Co-CEO of CultureCamp. She is a social psychologist and a people and systems coach who guides values-driven founders through periods of existential change. Her work connects how individuals experience change with how teams and organizations absorb it.