Howto nail your AI governance
Will you help look at Capability Building? More like experiential learning—applying concepts in real-world scenarios while balancing (AI-saturated) results-driven efficiency with human sense-making.
This blog series delves deeper into the intersection of technology and human development, highlighting how you can build your resilient adaptiveness to thrive in this modern age.
🚀 Getting Started with AI:
AI governance is inherently complex. It requires balancing autonomy and control, managing risk and opportunity, and enabling multi-stakeholder input.
Below I assemble some tips, simple handson tools that help you:
Strategize & manage risks
Build trust and accountability
Respond to change and feedback
1. Set the Tone: Be Strategic, Not Just Experimental
AI is a powerful tool, but it’s not magic. Avoid getting swept up in the hype or distracted by shiny tools. Instead:
Nail your AI governance.
Start with Purpose: Ask, What are we trying to improve or transform?
Link AI efforts directly to strategic goals.Focus on Outcomes: Don't measure success by activity or tool adoption alone.
Instead, track real impact.Lead from the top and distribute decision-making.
Run & review risk screens before naive stacking tech debt. Build internal capabilities.
🧠 “Measure the outcomes that matter—not vanity metrics.”
2. Manage the Inevitable: Shadow AI is Already Here
Employees aren’t waiting for your AI governance strategy—they’re experimenting already. Strong to have a open & curious mindset; but that’s also risky.
Good to be in the fearless company of the dynamic duo of strategyteaming.
“Someone you trust has already submitted company data into an AI tool, copied the output, and pasted it into something business-critical.”
This phenomenon—Shadow AI—mirrors what happened with Shadow IT. It requires urgent attention.
🔒 Hands-on Advice:
Create a clear AI usage policy and educational campaign.
Provide approved tools and guardrails rather than banning everything.
Involve Legal, Risk, IT Security, and Business leaders early.
🎯 “Governance needs to balance enablement and protection.”
3. Design Success with a few Balanced Scorecards
Borrow relevant result-oriented lessons from agile and digital transformation practices.
I frequently build upon sources like Team Topologies. Patterns offers a robust lens to implement distributed, effective AI governance, focusing on clarity, boundaries, and sustainable team interactions. It prevents centralized bottlenecks while keeping risks in check, making it ideal for fast-moving, AI-adopting enterprises.
🌟 Bonus: Extend the Team API to AI Governance
Use Team API documents (from Team Topologies) to clarify:
Which teams own AI use cases
What models or tools they use
How compliance and ethics are handled
Who to call for help
I also like the approach of BVSSH.
Use a Balanced Scorecard tailored to AI to track system health and stakeholder value, not just outputs.
👉 Use this as a live dashboard—not a one-off slide.
4. Build a Solid Data Baseline
You can’t improve what you don’t measure. And if you measure the wrong things, you’ll optimize the wrong behaviors.
Audit current processes to establish baselines.
Track adoption metrics, but pair them with value realization and impact tracking.
Set up a feedback loop: continuous improvement over one-shot evaluation.
🛠️ Governance tools to consider:
AI audit & inventory checklists
Prompt engineering guidelines
Use dashboards with business KPIs
5. Embed AI in the Flow of Work
AI is most valuable when it helps people do better work, not more work. Look for natural friction points.
Practical Use Cases:
Summarizing customer feedback to improve product direction (Value)
Surfacing blockers in project delivery (Sooner)
Suggesting decision support options based on OKRs (Better)
Reducing repetitive compliance checks (Safer)
Drafting personalized customer interactions (Happier)
👥 Empower cross-functional teams to explore and experiment within boundaries.
6. Lead with Transparency and Curiosity
We’re still early in the AI journey. Leaders should model curiosity, experimentation, and reflection, while also being clear on expectations and safety.
Share lessons learned—successes and failures.
Avoid top-down mandates; instead, co-create the AI journey with employees.
Open the space for constructive conversations about fears and hopes.
7. Don’t Wait to Get Risk-Ready
Before your AI use scales, have a plan to handle:
Bias & Fairness
Data Leakage
Compliance (GDPR, ISO, NIS2)
Model Oversight
Risk-awareness is not a blocker—it’s an enabler of sustainable innovation.
8. Review Agreements
Make all AI governance agreements explicit and temporary—subject to regular review.
Avoid overly rigid or long-term rules.
Allow learnings from pilot teams to flow back into governance.
📆 “Let’s review our AI ethics agreements every 3 months as usage evolves.”
9. Helping & Peer Development
Encourage peer learning and internal coaching:
Seniors help teams avoid AI pitfalls.
Ethics team shares learnings from use case audits.
Peer feedback circles to improve prompt design or AI-supported decisions.
👥 Enables distributed capability-building.
🧭 Your AI Opportunity Starter Kit
You can use this scorecard to evaluate your workflow or process.
This scorecard is meant to help you figure out how to govern the AI solution fit for your business.
Curious to learn about your specific context.
How can we translate these practical governance principles to your experience?
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