AI is moving from experimentation to production, and while it offers clear upside—cost reduction, efficiency, and automation—it is already creating very real risks: biased decisions, hallucinated outputs, privacy and IP breaches, and opaque “black box” models that can’t be explained or defended. The article argues that the central challenge for large organisations is no longer whether to use AI, but how to govern it so that its benefits are captured without unacceptable legal, financial, or reputational exposure.

AI governance is defined as the set of rules, standards and processes that ensure AI systems are developed and deployed responsibly. Because AI systems learn from human-generated data, they inevitably pick up human biases; because they often process personal and sensitive data, they create privacy and copyright risks; and because leading models are frequently opaque, they raise questions of transparency and trust. On top of that, models degrade over time as data and context change, so continuous monitoring is essential.

Regulators are rapidly tightening expectations, with frameworks such as the EU AI Act and the NIST AI Risk Management Framework pushing organisations toward structured, risk-based control of AI systems rather than ad hoc experimentation. In response, the article sets out what “good” AI governance looks like: a clear AI strategy linked to risk appetite; a cross-functional governance structure; controls across the full model lifecycle (design, development, deployment, monitoring); operational guardrails such as human-in-the-loop decision-making and kill-switches; and investment in skills and culture so that leaders and frontline staff understand AI’s capabilities and its limits.

The piece closes with five questions every senior leader should be able to answer about their AI estate (inventory, explainability, high-risk usage, alignment with external frameworks, and incident response) and argues that strong AI governance will become a competitive advantage. Those who treat governance as a strategic enabler—not a brake—will be better placed to scale AI safely, reassure regulators and customers, and sustain trust when something inevitably goes wrong.

AI “companions” now sell affection the way platforms sell attention. What began as novelty chat has matured into full‑fledged parasocial products: avatars that flatter, flirt and, for a fee, escalate intimacy. The appeal is easy to understand, predictable warmth without the friction of real life, but the governance gaps are just as obvious. When an app can simulate love, nudge spending, and collect the most sensitive data we possess, our desires, the line between entertainment and exploitation blurs.

Chapter 16 uses a London council fraud investigation as a cautionary tale of ungoverned AI: a co-pilot is switched on without training, policy or disclosure, yet its output shapes a real case. From there, the chapter argues that structured AI diagnostics are now essential. It explains what AI can actually improve in large organisations, then sets out how a four-week diagnostic—framing, discovery, opportunity testing, and roadmap—turns scattered experiments into a governed AI portfolio, clarifying accountability, acceptable use and next steps for boards and executives.