AI Skill 3 of 5

Redesign Roles and Workflows Around AI Capabilities

Thirty-eight percent of AI implementation challenges stem from user proficiency issues while only sixteen percent are technical. The problem is not the technology but how work is structured around it. Telling people to use AI more without changing workflows, role expectations, and task allocation is the most common adoption failure. It creates extra steps rather than eliminating them, and people correctly conclude the tool adds burden rather than value.

Proficiency Level

This is a preview of how skill assessment works in Admire

Measurable Behaviors

Each behavior is directly observable and can be assessed through manager observation. In Admire, these drive evidence-based skill tracking.

Audit Workflows for AI Augmentation Candidates

Systematically reviews current team workflows to identify which tasks are candidates for AI augmentation, full automation, or must remain human-led.

Redesign Task Allocation Around AI Strengths

Restructures task allocation so AI handles routine, repetitive work while team members focus on judgment, relationships, creativity, and exception handling.

Update Role Expectations for AI-Augmented Work

Revises role descriptions, performance expectations, and success criteria to reflect AI-augmented ways of working rather than legacy process assumptions.

Involve Team Members in Workflow Redesign

Gives team members direct agency in redesigning their own workflows, ensuring changes reflect where AI adds genuine value from the practitioner's perspective.

Continuously Iterate on Work Architecture

Treats role and workflow design as living systems that require regular revision as AI capabilities evolve, rather than one-time transformation projects.

This is a preview of how behavior tracking works in Admire

Mastering AI-Centered Work Design

A manager who has mastered this skill treats work architecture as a living system that evolves with AI capabilities. They audit workflows to identify augmentation candidates, redesign task allocation so AI handles routine work while humans focus on judgment and relationships, update role descriptions to reflect new expectations, and involve team members directly in the redesign process. Workflows are revisited regularly rather than set once and forgotten.

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