In today’s rapidly evolving technological landscape, artificial intelligence (AI) has emerged as a transformative force across sectors and business functions. The global AI market was valued at an estimated USD 184 billion in 2024 and is expected to triple by 2030, with AI now ubiquitous, generating significant value for companies and consumers alike.
However, the rapid adoption of AI brings significant concerns, from privacy and safety to the potential for systemic disruption if left unchecked. This is where AI governance becomes critical.
The global push for AI governance
AI governance is no longer a niche discussion within individual organisations. It has become a global priority. The AI Action Summit held in Paris in February 2025 marked a significant global multilateral effort to shape the future of AI governance. The summit produced a Statement on Inclusive and Sustainable Artificial Intelligence for People and the Planet, highlighting human-centric, ethical AI and the importance of bridging gaps between public and private initiatives.
Founding members, including Brazil, Chile, Finland, France, Germany, India, Kenya, Morocco, Nigeria and Slovenia, launched the Public Interest AI Platform and Incubator to foster sustainable development, including responsible energy usage. These talks unfolded against the backdrop of an intensifying global race for AI innovation.
What is AI governance?
AI governance goes beyond compliance with laws such as the EU AI Act. It represents a holistic approach to ensuring AI systems remain safe, ethical and trustworthy, including:
· Hard requirements (laws and regulations) for which non-compliance may result in penalties
· Soft requirements (ethical and societal expectations) such as labelling AI-generated content, which may not yet be mandated by law, but is critical for transparency
Done right, AI governance increases trust, mitigates reputational risk and fosters responsible innovation. It can also deliver business value in areas such as product quality, customer loyalty, and competitive differentiation.
Why AI governance is so challenging
AI governance sits at the intersection of rapid technological evolution, legal uncertainty, ethical debates and organisational complexity.
Challenges include:
· Uncertainty of risks: many AI risks, from misinformation to model manipulation, remain poorly understood
· Opacity in systems: general-purpose AI systems may make it harder to detect mistakes, making oversight more difficult
· Real-world failures: in 2024, Air Canada was held liable for its customer service chatbot misinforming a passenger about bereavement fares. This illustrates how poor governance can cause legal and reputational harm
· Rapid evolution: new developments such as “agentic AI” carry distinct risk profiles from more familiar chatbots and require tailored governance structures
· Competing priorities: governance goals may conflict, such as the need to collect sensitive data to train AI systems on inclusivity versus the need to protect individual privacy
Practical steps for organisations
While global regulation is still evolving, organisations cannot afford to wait. Effective governance starts with concrete measures:
· Understand and map AI usage: identify where and by whom AI systems are being used, their purpose, effectiveness, cost, stakeholder involvement and perception. Mapping across all business functions, including HR, client interactions and third-party platforms, provides a foundation for tailored governance and helps prioritise high-risk systems
· Set clear policies and guidelines: develop internal policies covering ethics, compliance, procurement, contracting, risk management and acceptable use. These should reflect organisational priorities and sector-specific risks. For example, marketing firms may place higher ethical weight on the accuracy of generative outputs, while law firms will emphasise confidentiality and access controls
· Procure responsibly: establish procurement policies to vet third-party AI providers and datasets. Follow best practices such as avoiding “black box” algorithms, checking for bias and ensuring contractual terms cover data ownership, liability and ongoing monitoring
· Conduct risk assessments: build on frameworks like NIST’s AI RMF or the MIT AI Risk Repo to evaluate risks at every stage of the AI lifecycle. Use structured methods from traditional red-amber-green scoring to adversarial testing to understand both individual and interacting risks
· Assign responsibilities: create dedicated oversight bodies, such as AI governance committees or embed AI responsibilities into existing risk and compliance teams. Ensure personnel have both technical and ethical expertise and involve stakeholders across disciplines
· Monitor and adapt: governance is not a one-off task. Regular audits, performance checks and adaptation to emerging risks are essential. Transparency-by-design approaches, explainability measures, and human oversight must be built in from the outset
· Train and raise awareness: ensure employees understand governance principles, ethical considerations and compliance requirements. A workforce equipped to recognise and respond to AI risks is a key enabler of responsible adoption
Looking ahead
AI governance is no longer a question of “if” or “when”; the answer is yes, and now. Global investment, regulatory activity and adoption rates are accelerating. Organisations that embed robust AI governance frameworks will not only reduce compliance, financial and reputational risks but also strengthen trust, brand reputation and competitiveness.
HewardMills supports organisations in navigating this complex global landscape. From building governance frameworks and conducting risk assessments to implementing monitoring systems and training programmes, we help organisations adopt AI responsibly and confidently
