1. Measuring AI Amnesty Success: Metrics and Frameworks for Boards
   Building on your recent amnesty concept and MIT’s 2025 research on pilot failures, explore how boards can define and track success metrics for amnesty programs (e.g., disclosure rates, value capture from hidden innovations, and cultural shifts). Reference emerging 2025 trends like EU AI Act enforcement deadlines, providing practical tools like dashboards and KPIs to help executives evaluate ROI without prescriptive designs, maintaining your data-driven, board-centric tone.

2. Agentic AI Autonomy: Governance Implications for Multi-Speed Adoption
   Extending your themes on AISA stages and agentic AI shifts (from Stack AI 2025), discuss how boards can govern increasingly autonomous AI agents in enterprises with varying maturity levels. Tie in current events like recent U.S. AI infrastructure investments (e.g., hyperscaler nuclear deals), offering strategic guidance on risk prioritization and CoE integration to balance innovation with the Six Concerns, appealing to executives navigating regulatory and ethical challenges.

3. From Shadow to Spotlight: Integrating Employee-Driven AI into Strategy
   Following your shadow AI narrative and value discovery patterns, examine how boards can formalize employee-led AI innovations post-amnesty, aligning with Well-Advised priorities and Five Pillars. Incorporate 2025 insights from reports like a16z’s CIO surveys on governance first-movers, providing actionable steps for creating innovation sandboxes while addressing concealment risks, positioning it as a trust-building evolution for senior leaders.

Yes, all three suggested article ideas incorporate natural links to your previously published work, as they build directly on themes, frameworks, and concepts from your blog compilation (e.g., governance priorities, AI adoption stages, and innovation strategies). These connections create continuity, positioning the new pieces as evolutions of your established thought leadership. Below, I outline the natural links for each, referencing specific elements from your past articles to show how they align thematically with your board-centric, data-driven approach for executives.

1. Measuring AI Amnesty Success: Metrics and Frameworks for Boards

This article would naturally link to several of your foundational pieces on AI governance and measurement:

• “AI is transforming governance: Six key Boardroom priorities” (implied in your compilation via recurring themes): The Six Concerns framework (Strategic Alignment, Ethical and Legal Responsibility, etc.) is directly referenced in the suggestion, allowing you to connect amnesty metrics (e.g., disclosure rates, risk reduction) to addressing these concerns, as in your discussions of board oversight in “Crossing the GenAI Divide” and “Why Boards Need to Watch the EU’s General-Purpose AI Code of Practice.”
• “The Complete AI Framework series” (including AISA stages and Well-Advised priorities): You could tie success metrics (e.g., value capture from innovations) to Well-Advised for measuring AI ROI, echoing your emphasis in “Crossing the GenAI Divide” on balanced value creation across pillars like Operational Excellence and Responsible Transformation.
• “Rethinking Business Cases in the Age of AI” (mentioned in the briefing note): Link amnesty outcomes to high-value opportunity identification, as in your value discovery patterns, reinforcing data-driven metrics from MIT research in your “Crossing the GenAI Divide” post.

These links maintain your pragmatic, solution-oriented tone, using amnesty as a practical extension of governance gaps you’ve previously highlighted.

2. Agentic AI Autonomy: Governance Implications for Multi-Speed Adoption

This suggestion aligns closely with your work on adoption maturity and infrastructure, offering seamless references:

• “AI Stages of Adoption (AISA) and multi-speed reality” (core to your Complete AI Framework series): The article’s focus on varying maturity levels directly extends AISA from posts like “Crossing the GenAI Divide” and “AI Sovereignty: A Board’s Guide,” where you discuss multi-speed adoption and governance for different functions.
• “Five Pillars capability areas and AI Centre of Excellence” (from “Crossing the GenAI Divide” and “Why Boards Need to Watch the EU’s General-Purpose AI Code of Practice”): Integrate CoE roadmap advice with agentic AI risks, linking to your emphasis on capability gaps and ethical guardrails in “UK AI Energy Constraints” (e.g., hyperscaler nuclear deals as current events tying to infrastructure sovereignty).
• “Six Concerns framework”: Reference this from “Board AI Governance Priorities” to frame autonomy risks, as in your “AI Sovereignty” article’s trilemma of trust, speed, and control.

This creates a forward-looking narrative, evolving your sovereignty and adoption themes to address 2025 trends like agentic shifts.

3. From Shadow to Spotlight: Integrating Employee-Driven AI into Strategy

This builds on your shadow AI discussions, with strong ties to innovation and cultural themes:

• “What Shadow AI Really Looks Like” and amnesty concepts (from the briefing note, but echoing your “Crossing the GenAI Divide” shadow AI economy): Directly extend concealment risks and value discovery to integration strategies, linking to your “Rethinking Business Cases” framework for employee-led pilots.
• “Well-Advised priorities and Five Pillars” (from “Crossing the GenAI Divide” and Complete AI Framework): Map employee innovations to these for strategic alignment, as in your “Safeguarding Innovation” concern from governance articles.
• “AI Centre of Excellence and innovation sandboxes” (from “Crossing the GenAI Divide” and “Practical Steps for Boards” in the briefing): Reference CoE recruitment and sandboxes as post-amnesty actions, tying to your “Increased innovation” value benefit in “Planning a cloud migration?” (broader transformation context).

Overall, these links reinforce your expertise in AI governance without forcing connections, appealing to your audience of executives by showing progression from diagnosis (e.g., shadow risks) to action (e.g., metrics and integration). If writing, aim for 1-2 explicit references per section to maintain flow.