Practical AI Governance

AI is increasingly positioned as a transformative force for local government, promising improved efficiency and innovative solutions to operational challenges. However, its adoption is not without risks. Ethical considerations, regulatory compliance, risk management, and the need for transparent oversight all demand careful attention.

To address these complexities, organisations are developing AI governance frameworks, yet at the same time a concerning trend is emerging as technology companies lobby for a reduction in regulation. For instance, major technology companies are sceptical about being able to comply with the EU AI Act and the European Union, despite leading on AI regulation, continues to face pressure to relax key provisions.

Implications for public sector organisations

Any shift away from strict governance at a national level, or from the tech sector, will present significant risks for public sector organisations:

• Increased exposure to unregulated AI solutions, where vendors prioritise rapid deployment over fairness and accountability.
• Ethical risks and data privacy concerns, which could lead to public backlash and legal challenges.
• Heightened compliance burdens, as local authorities are left to navigate AI risks without clear national guidance.
• Erosion of public trust, as citizens become more sceptical of AI-driven decisions in critical public services.
• Operational disruptions, where poorly governed AI systems fail to integrate with existing infrastructure.

AI governance is an enabler, not a barrier

While the pace of progress is important, responsible AI governance cannot be sacrificed. Public sector organisations must take a proactive stance, ensuring that governance remains a priority despite shifting national and industry trends.

Far from hindering innovation, AI governance provides the foundation for responsible and effective deployment. By implementing structured governance frameworks with clear roles, risk management strategies, and measurable KPIs, local authorities can harness AI’s full potential while safeguarding public trust and making the right investment decisions.

Balancing technical rigour with accessibility

Ensuring that AI Governance frameworks are both rigorous and accessible is challenging. Governance must address critical issues such as data quality, ethical use, risk assessment, sustainability and operational resilience while remaining understandable to non-technical stakeholders. For public sector organisations, this requires:

• Concise, high-level briefings for policymakers, ensuring they have the necessary insights to make informed decisions and align AI initiatives with strategic objectives.
• Public engagement through consultations and transparency reports, ensuring accountability.
• Strong ethical oversight and accountability structures to prevent harm and bias.
• Detailed implementation guidelines and checklists to ensure that project teams follow best practices and address both governance and resilience challenges, such as system reliability, adaptability, and continuity in case of failures or disruptions.

A practical AI governance structure

Effective AI governance goes beyond high-level policies. It requires clear operational oversight supported by appropriate structures, well defined roles and responsibilities. To ensure AI governance is both strategic and operationally effective, public- sector organisations can adopt the Deciders, Advisors, Recommenders, Execution Stakeholders (DARE) model.

• Deciders: Shape the long-term AI strategy and ensure that AI adoption aligns with an organisation’s broader public service goals. Their decisions set the boundaries, policies, and ethical guidelines that govern how AI can and should be used. Without Deciders, AI adoption risks becoming fragmented, reactive, or misaligned with broader organisational goals. 
• Advisors: Ensure that AI governance is informed by legal, ethical, and risk considerations. They provide expertise on how AI policies should be structured to prevent bias, discrimination, and unintended harm. Without Advisors, AI systems may be implemented without adequate scrutiny, leading to issues such as bias in decision-making, lack of transparency, or regulatory non-compliance. Advisors help ensure AI is deployed in a way that is not only effective but also responsible and legally sound.
• Recommenders: Bridge the gap between strategic goals and technical feasibility. They evaluate AI solutions, assess implementation challenges, and propose solutions that align an organisation’s goals and resources. Without Recommenders, AI adoption may be technically unrealistic, poorly executed, or misaligned with organisational needs. They ensure AI solutions are not just innovative, but also practical, scalable, and aligned with operational capabilities.
• Execution Stakeholders: Turn AI policies and recommendations into real-world applications. They ensure AI is deployed, monitored, and continuously improved. Without Execution Stakeholders, AI initiatives risk failing at the implementation stage due to lack of resources, operational barriers, or unforeseen technical challenges. Their role ensures AI remains functional, adaptable, and beneficial to both the council and the public.

This structured approach ensures that AI governance is not siloed or disconnected from real-world needs. Each role plays a critical part in balancing innovation with responsibility and contributes to ensuring clarity, and effective decision-making.

AI governance is not just about risk management, it is about ensuring AI delivers public value while maintaining trust and accountability. By taking a structured, proactive approach, public sector organisations can deploy AI responsibly, balancing innovation with robust oversight.

Ade Bamigboye will be speaking at next months Think AI for Government sharing more of his insight into the local authorities journey into AI. Register today.