AI Governance

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.

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 sequential stages of human testing required before a new drug or medical device can receive regulatory approval. Phase I assesses safety in a small group, Phase II evaluates efficacy and dosing, Phase III confirms effectiveness in large populations, and Phase IV involves post-market surveillance.

The configuration of financial, legal, and operational terms governing a merger, acquisition, or investment transaction. Deal structure encompasses the mix of cash and equity consideration, earn-out arrangements, escrow provisions, representations and warranties, indemnification mechanisms, and governance rights.

The policies, procedures, and controls organisations implement to ensure they do not engage in prohibited transactions with sanctioned countries, entities, or individuals. Sanctions regimes are administered by bodies including OFAC (US), OFSI (UK), and the EU Council, and violations can result in severe criminal penalties, asset freezes, and reputational damage.

The EU regulatory framework for insurance and reinsurance companies, establishing risk-based capital requirements, governance standards, and supervisory reporting obligations. Solvency II uses a three-pillar structure: quantitative requirements (Pillar 1), governance and risk management (Pillar 2), and disclosure and transparency (Pillar 3).

AI Integration Costs: The Hidden Expenses of AI Adoption

AI licensing fees represent only 20-30% of total AI adoption costs. The remaining 70-80% — data preparation, integration engineering, change management, and ongoing maintenance — are the hidden expenses that derail AI budgets and timelines.

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.

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.