Examines how AI-mediated interfaces sit between people and systems, interpreting intent, presenting recommendations, and shaping real business decisions before execution.
What This Dimension Examines
We examine how AI-mediated interfaces translate human intent into system actions, and where that mediation introduces ambiguity, inconsistency, or hidden risk:
Decision Paths Created By AI-Mediated Interfaces
We trace how mediated inputs such as prompts, selections, or guided flows turn into downstream decisions like routing, eligibility, escalation, or next-best-action logic.
Consistency Between Intent, Interpretation & Action
We assess whether AI interpretations align with business logic and policy, or whether teams compensate with workarounds because mediated actions are unreliable or unclear.
Trust, Overrides & Informal Guardrails
We identify where AI-mediated outputs are trusted, where they are overridden, and where informal human guardrails replace formal decision logic.
Why This Matters
AI-mediated interfaces quietly reshape how decisions are made. When interpretation is inconsistent, accountability blurs, overrides increase, and execution drifts from intent without anyone explicitly choosing that outcome.
What Leadership Gains
Clarity On Mediated Decision Ownership
A clear view of which decisions are being shaped by AI-mediated interfaces, who owns those outcomes, and where responsibility is assumed rather than explicit.
Reduced Override Behavior & Execution Drift
Visibility into why teams bypass or adjust AI-mediated outputs, allowing leadership to address root causes of mistrust rather than adding more tooling or training.
