AI Governance

The framework of policies, standards, and processes that ensures data assets are managed consistently, securely, and in compliance with regulations throughout their lifecycle. Strong data governance increases the reliability and value of data as an intangible asset, directly supporting analytics, AI applications, and data monetisation strategies.

A framework for developing and deploying artificial intelligence systems that are fair, transparent, accountable, and aligned with human values and societal well-being. Responsible AI encompasses technical practices such as bias testing and model interpretability, alongside governance processes including ethical review boards, impact assessments, and stakeholder engagement.

The policies, procedures, and mechanisms established by an organisation to ensure the reliability of financial reporting, effectiveness of operations, and compliance with applicable laws and regulations. The COSO framework provides the most widely adopted internal controls standard, defining five components: control environment, risk assessment, control activities, information and communication, and monitoring.

Artificial intelligence systems designed to provide human-interpretable explanations of their decision-making processes and outputs. Explainability is increasingly required by regulators — particularly in financial services, healthcare, and criminal justice — and is a key differentiator for AI products seeking enterprise adoption in regulated industries.

A framework for evaluating a company's performance across environmental impact, social responsibility, and corporate governance practices. ESG factors are increasingly material to valuation, investor mandates, and regulatory compliance, and intersect with intangible asset categories such as reputation and organisational capital.

A set of practices combining machine learning, DevOps, and data engineering to standardise and streamline the end-to-end lifecycle of machine learning models, from development through deployment to monitoring. MLOps encompasses version control for models and data, automated testing, continuous integration and deployment, and model performance monitoring in production.

The branch of applied ethics concerned with the moral implications of designing, deploying, and using artificial intelligence systems. AI ethics addresses issues including fairness, transparency, privacy, accountability, and the societal impact of automation.

The processes, governance, policies, and technology used to ensure that an organisation's critical shared data entities — such as customers, products, suppliers, and accounts — are accurate, consistent, and controlled across all systems and business units. MDM creates a single trusted source of master data, reducing duplication, resolving conflicts, and enabling reliable reporting and analytics.

What AI governance frameworks should companies adopt?

Companies should adopt AI governance frameworks covering ethical principles, risk management, transparency, accountability, and compliance — drawing from standards like the EU AI Act, NIST AI RMF, and ISO/IEC 42001.

What is responsible AI and why does it affect enterprise value?

Responsible AI is the practice of developing and deploying AI systems that are fair, transparent, accountable, and privacy-preserving — it affects enterprise value by reducing regulatory risk and building stakeholder trust.

What does AI due diligence involve for investors?

AI due diligence evaluates a company's AI capabilities, data assets, model performance, technical debt, governance practices, talent dependency, and the defensibility of its AI competitive advantages.