Understand where the transformation is working — and what is getting in the way.
Adaptive AI-supported surveys and AI voice interviews, run against the context of the Applied SAFe® operating model, to separate adoption problems from operating-model problems.
The signals show that teams struggle. They rarely explain why.
Transformation leaders can usually see adoption metrics, delivery activity, survey scores, and retrospective output. Those signals often show that teams are struggling, but they rarely explain the underlying reason.
This service combines the context of the Applied SAFe® operating model with adaptive AI-supported surveys and AI voice interviews. It listens across selected roles, teams, and ARTs, identifies recurring patterns, and connects the findings to the transformation questions, practices, roles, tailoring decisions, and organizational constraints behind them.
Create an evidence-backed view of transformation readiness, adoption friction, and resistance across the organization — and distinguish adoption problems from operating-model problems.
The questions leadership cannot answer from dashboards.
- Where is the transformation gaining traction?
- Where are teams struggling or working around the new process?
- Which roles or responsibilities remain unclear?
- Which practices lack confidence or support?
- Are local constraints preventing adoption?
- Is tailoring appropriate for the context?
- Are legacy practices conflicting with the target operating model?
- Are leadership and teams aligned on the intended way of working?
- Is the issue communication and adoption, or does the process itself need to change?
Five steps from diagnostic to action.
Define the diagnostic
Agree the transformation questions, target populations, organizational scope, and decisions the assessment needs to support.
Establish the operating-model context
Use relevant context from the transformation so findings can be connected back to something changeable.
RolesPracticesProcess changesART structureTailoring decisionsIntended ways of workingOrganizational constraintsTransformation objectivesListen across the transformation
Adaptive AI-supported surveys identify broad patterns; targeted AI voice interviews investigate important areas in greater depth.
The interview can adapt toask follow-up questions · request concrete examples · clarify inconsistent answers · investigate workarounds · compare experiences across roles, teams, or ARTs · explore the reasons behind resistance or friction
Synthesize findings and root causes
Analyse recurring patterns and distinguish symptoms from likely underlying causes.
RolesPracticesProcess elementsTailoring decisionsOrganizational constraintsCommunication & enablement needsTurn findings into action
Translate the findings into prioritized transformation actions.
Improve communicationTargeted trainingClarify responsibilityChange tailoringRemove process frictionReconcile conflicting practicesRedesign an operating-model elementFollow-up assessment
“Teams are resisting SAFe®” was the wrong diagnosis.
- Assumption
Leadership believes PI Planning is struggling because teams are resisting SAFe®.
- What the interviews show
Across several ARTs, teams are working around the process because dependencies cannot be resolved under the current role model.
- Resulting action
Not another communication campaign. Review the responsibility and dependency-resolution model.
- Transformation readiness overview
- Adoption and resistance patterns
- Findings by role, team, ART, or cohort
- Evidence-backed root causes
- Operating-model findings
- Communication and enablement findings
- Prioritized transformation actions
- Recommended follow-up diagnostics
- Organizational operating-model context
- Roles and practices
- Tailoring context
- Adaptive AI surveys
- AI voice interviews
- AI-assisted synthesis
- Root-cause analysis
- Improvement recommendations
Find out what is really slowing the transformation down.
Use AI-assisted surveys and voice interviews to understand the transformation from the perspective of the people experiencing it, and connect the findings to the operating model that can be changed.