---
id: "action-design-human-ai-decision-systems"
type: "action-item"
source_timestamps: ["§ The Evolved Framework", "¶9"]
tags: ["ai-governance", "system-design"]
related: ["concept-human-ai-decision-architecture", "concept-analyst-to-integrator-evolved"]
speakers: ["Michael D. Watkins"]
action: "Design decision architectures that explicitly dictate which inputs receive algorithmic treatment and which require human judgment."
outcome: "Maintains accountability for recommendations emerging from opaque AI systems."
sources: ["reskilling"]
sourceVaultSlug: "hbr-seg-reskilling"
originDay: 10
articleStem: "hbr-nm-100-3-forces-manager-to-leader"
sourceUrl: "https://hbr.org/2026/06/3-forces-are-redefining-the-transition-from-manager-to-leader"
sourceTitle: "3 Forces Are Redefining the Transition from Manager to Leader"
---
# Design Human-AI Decision Architectures

**Action:** Design decision architectures that explicitly dictate which inputs receive algorithmic treatment and which require human judgment.

**Outcome:** Maintains accountability for recommendations emerging from opaque AI systems.

Leaders must stop trying to personally synthesize all organizational data. Instead, they should focus on building and governing systems that integrate AI analysis with human oversight, ensuring clear accountability for the final decisions. This is the operational form of the [[concept-analyst-to-integrator-evolved]] transition and produces a [[concept-human-ai-decision-architecture]]. The accountability mechanism itself remains an [[question-ai-accountability-d10|open question]].