HR AI Governance to drive Strategy and Execution

The proliferation of AI platforms and solutions in the market, with overlapping capabilities, bring attention from CEOs and C-suite teams to define the path forward to oversight AI initiatives across the enterprise.

→ Bottom line: organizations are looking for balance between rapid innovation and how to scale AI in a responsible and compliance manner to manage risk, protect employee data and enterprise intellectual property.

Based on client experience, here is a 4-Tier Strategic Governance model. The framework below provides a high-level view of governance layers, participants, cadence and focus.

1. Enterprise Strategic (CEO-led, Quarterly)

→ Who: C-suite and Independent Ethics/Risk Advisors

→ Focus: Enterprise risk, big bets (>$1M), regulatory alignment

→ Example: Approving enterprise-wide upskilling program on AI fluency across the entire organization

2. HR Strategic (CHRO-led, Monthly)

→ Who: HR, Legal, Tech and Employee Council

→ Focus: Use case prioritization, policy guardrails ($100K-$1M)

→ Pro Tip: Involve employees early in the design to make them partners in change and transformation

3. Operational (Digital HR-led, Bi-Weekly)

→ Who: Process SMEs, Change, Data, Privacy and Ethics leads

→ Focus: Execution, risk monitoring (<$100K)

→ Pro Tip: Create “AI Impact Assessments” and business cases before deploying each use case

4. Tactical (Delivery Teams, Weekly)

→ Who: Designers, Builders, Testers and Change Managers

→ Focus: Rapid deployment and continuous feedback loops

→ Example: Execute product roadmaps and track value realization metrics for new AI initiatives

Your next steps:

1️⃣ Assess your current AI governance maturity

2️⃣ Map stakeholders to the 4-tier structure

3️⃣ Define decision rights and escalation paths

4️⃣ Establish success metrics for each tier

Final Thoughts:

Do you have clear answers to these questions?

✅Who approves new AI tools in HR?

✅How do you monitor for bias in automated decisions?

✅What’s your process when AI makes a mistake?

Lastly, keep in mind that every organization is different and unique. It’s critical to design a tailor-made governance framework that balances strategy, design, deployment, and monitoring of AI systems with fairness, explainability, privacy, and legal compliance in mind.

What’s your biggest AI governance challenge? Share in the comments

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Note: All views expressed in this article do not represent the opinions of any entity whatsoever with which I have been, am now, or will be affiliated. My opinions are my own.