Designing an AI-Supported Coaching System for Managers

Context

Organizations expect managers to coach consistently, equitably, and in alignment with performance standards—yet most managers operate without real-time support, shared language, or clear guardrails. As a result, coaching quality varies widely, development signals become inconsistent, and bias risk increases, particularly under time pressure.

Traditional training and playbooks help, but they do not scale day-to-day decision-making or reinforce standards at the moment coaching occurs.

Design Goal

The goal was to design a coaching system that:

• Preserves manager judgment and individual management style
• Enforces evidence-based, rubric-aligned standards
• Clearly separates development coaching from performance risk conversations
• Provides a more equitable and consistent employee experience
• Scales best practices without scripting or automating leadership

This system needed to support managers—not replace them.

My Role

I designed the end-to-end coaching architecture, including coaching mode classification logic, non-negotiable guardrails and evidence thresholds, output structure and language controls, reasoning transparency and validation loops, and manager flexibility with explicit justification requirements.

My focus was on system integrity, decision quality, and ethical application at scale.

System Architecture (High-Level)

The coaching system operates as a structured decision framework rather than a generative free-for-all.

Core components include:

• Validated Inputs: Job description, performance rubric, observed behavior, and impact
• Coaching Mode Classification: Reinforce, clarify, develop, course-correct, or reposition
• Evidence Thresholds: Minimum standards by coaching scenario
• Guardrails: Constraints that block bias, effort-only narratives, and ambiguity
• Structured Outputs: Consistent coaching format aligned to best practices
• Dual Outputs: Thinking version (manager-facing) and share-ready version (employee-facing)
• Final Quality Check: Alignment, clarity, sustainability, and tone validation

This architecture ensures consistency without eliminating judgment.

Coaching System Architecture (placeholder — replace with final diagram)

Equity & Ethics by Design

Equity was embedded structurally, not retroactively.

The system actively blocks effort-only praise or critique, prohibits personality judgments and peer comparisons, requires observable evidence before advice is given, separates development conversations from performance risk, and flags protected-class references and inappropriate data.

This approach reduces bias risk by design and increases fairness in how expectations are communicated and reinforced.

Why AI Was the Right Tool

AI was used not to automate managerial judgment, but to enforce discipline and consistency. Specifically, it enables real-time application of best practices, transparent reasoning behind recommendations, consistent structure under time constraints, and scalable reinforcement of organizational standards.

The value lies in how AI is constrained, not in its generative capability.

Outcomes & Value

The resulting system raises the baseline quality of coaching across managers, improves consistency and clarity of expectations, reduces bias risk and ambiguity, and preserves human leadership while scaling best practices.

It demonstrates how thoughtful system design can improve both performance and employee experience.

What This Demonstrates About My Work

• Systems thinking applied to complex talent challenges
• Ability to operationalize values and equity through design
• Comfort creating guardrails, not just guidance
• Strategic use of technology to reinforce leadership standards