Applied Agile AI Orchestration Capability Assessment

How does execution respond when strategic priorities shift mid-cycle?

Formal replanning and control Friction before adjustment Adaptive team decisions Fluid reprioritization aligned to outcomes

How are delivery outcomes monitored during active work?

Milestones only Periodic review Frequent feedback Continuous calibration

How much autonomy do teams have in evolving their way of working?

Minimal Limited adjustments Significant autonomy Full self-regulating capability

How visible is outcome alignment across teams?

Task-driven work Outcomes inconsistently reinforced Outcomes guide prioritization Outcomes shape execution decisions

How consistently does learning influence future execution cycles?

Rarely integrated Inconsistent improvements Learning informs cycles Learning reshapes patterns dynamically

How is AI positioned within operational workflows?

Automation tool Productivity assistant Integrated capability Decision-influencing partner

How clearly are AI limitations understood?

Poorly understood Surface-level awareness Clearly articulated Explicitly governed

How are AI outputs evaluated?

Informally Manual review Defined criteria Structured accountability

How consistently is AI aligned to business objectives?

Opportunistic usage Selective alignment Intentional mapping Systematically outcome-aligned

How clearly is AI assistance distinguished from AI authority?

Unclear distinction Recognized inconsistently Understood in decisions Explicitly structured

How interconnected are workflows across domains?

Siloed Partially connected Mostly integrated End-to-end awareness

How are downstream impacts considered?

Rarely assessed Occasionally reviewed Regularly evaluated Systematically modeled

How visible are dependencies?

Discovered reactively Identified in reviews Mapped and monitored Actively managed

How are feedback loops structured?

Ad hoc Reactive Intentional Continuous and measurable

How does decision-making account for long-term systemic impact?

Immediate focus Occasional consideration Frequent inclusion Strategically aligned

How are ethical implications addressed?

After incidents Periodic review Proactively discussed Embedded in design

How is trust calibrated when using AI?

Assumed Reviewed when needed Actively monitored Governed structurally

How are human impacts evaluated?

Rarely considered Informally considered Regularly assessed Central to decisions

How transparent are AI-influenced decisions?

Limited transparency Explained upon request Routinely documented Designed for explainability

How is accountability structured when AI influences outcomes?

Ambiguous responsibility Clarified after decisions Defined within roles Explicitly governed

How intentionally are AI prompts structured toward defined outcomes?

Exploratory interaction Task-oriented prompts Objective-aligned prompts Engineered for measurable delivery

How consistently are contextual constraints embedded in prompts?

Loosely defined Partially provided Intentionally structured Systematically embedded

How are AI-generated responses integrated into workflows?

Informally used Selectively applied Integrated into tasks Embedded in orchestrated cycles

How disciplined is prompt refinement over time?

Rarely refined Occasionally adjusted Iteratively improved Continuously optimized

How clearly is AI prompting treated as an orchestration capability?

Ad hoc usage Useful technique Recognized capability Structured orchestration discipline