Navigating AI Governance: An IT Perspective

I have been in I&T Governance for a decade now. During this time, I’ve seen how emerging technologies continually reshape the way organizations must think about risk, accountability, and strategic alignment. AI is no exception. When I review any AI governance article, I examine it through the lens of established governance principles, practical risk management, regulatory readiness, and the operational realities of integrating AI into enterprise environments.

1. Alignment with Organizational Governance Principles

From an IT governance standpoint, effective AI governance must align with established principles such as accountability, transparency, compliance, and risk management. My key focus is on whether the article acknowledges that AI is not a standalone technology it must be embedded into existing governance frameworks like COBIT, ISO/IEC 38500, NIST AI RMF, and data governance programs. If the article positions AI governance as something fundamentally separate from enterprise governance, that’s a conceptual weakness.

2. Risk-Based Approach

A mature AI governance model must be rooted in enterprise risk management. I look for whether the article properly addresses:

• Algorithmic risk

• Data quality and lineage risk

• Model drift

• Supply-chain risks (third-party models, APIs, cloud dependencies)

• Cybersecurity concerns are unique to AI systems

• Ethical and societal risks

An expert critique would stress that AI is not merely a technical risk domain, it is a strategic source of operational, reputational, and regulatory exposure.

3. Accountability & the “Human-in-the-Loop” Model

Strong governance requires clarity on roles and responsibilities, especially as AI systems become more autonomous. A good AI governance article should:

• Distinguish between model developers, data owners, business owners, and risk/compliance functions

• Emphasize human oversight, not human presence as a symbolic control

• Treat accountability as a non-delegable duty

If the article fails to specify who is responsible for AI outcomes, that is a significant governance gap.

4. Transparency, Explainability, and Documentation

From a governance perspective, transparency is not optional it is required for auditability, regulatory compliance, and stakeholder trust. I evaluate whether the article:

• Discusses model documentation and version control

• Mentions explainability requirements (XAI)

• Considers audit trails for training data, model changes, and decision outputs

Without these elements, governance cannot be demonstrated or assured.

5. Regulatory Preparedness

As an IT governance expert, I expect an article to show awareness of rapidly evolving global regulations:

• EU AI Act

• U.S. AI Executive Orders

• NIST AI RMF

• OECD AI Principles

• Sector-specific rules (finance, healthcare, public sector)

If the article underestimates regulatory obligations or treats them as future issues, that is a fundamental oversight.

6. Integration with Data Governance

AI governance must be built on sound data governance pillars:

• Data quality

• Data privacy

• Data lifecycle management

• Consent and purpose limitation

• Access controls

Any article that separates AI governance from data governance misses the single most important dependency.

7. Ethical Governance and Responsible AI

From a board-level governance view, I expect a balanced focus on:

• Fairness and bias mitigation

• Inclusivity

• Sustainability

• Societal impact

If ethics are treated as an afterthought or “nice to have,” the article lacks maturity.

8. Operationalization: Processes, Controls, and KPIs

A governance expert looks for practical mechanisms not just theory. I check whether the article discusses:

• AI model lifecycle management

• MLOps/AIOps integration

• Governance controls (gates, approvals, checkpoints)

• KPIs and KRIs for AI performance and risk

• Monitoring frameworks

Effective governance cannot be aspirational it must be operational.

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Summary of Your Expert Take

As an IT governance specialist, my primary critique of any AI governance article is whether it meaningfully integrates AI oversight into existing enterprise governance structures, addresses real-world risks, clarifies accountability, provides for transparency, aligns with regulatory frameworks, and proposes actionable processes rather than high-level principles. Good AI governance must not be visionary it must be executable.