AI Governance: The Gap Between Having AI and Governing It

AI adoption is accelerating faster than any technology shift in corporate history. Generative AI tools went from novelty to necessity in under two years. Teams across every function — marketing, finance, HR, engineering, customer service — are using AI to draft, analyse, summarise, and automate. The productivity gains are real.

But there is a widening gap between organisations that use AI and organisations that govern it. And that gap is where the risk accumulates — quietly, until it doesn't.

The Problem No One Wants to Talk About

Most organisations cannot answer three basic questions about their AI use: What AI tools are being used? Where is sensitive data being processed? Who is accountable for AI-related decisions?

The reason is straightforward. AI adoption has been bottom-up, not top-down. Employees discovered that ChatGPT could draft proposals, Claude could summarise legal documents, and Copilot could accelerate code reviews. They adopted these tools because they made work easier — not because anyone in governance approved it.

The result is shadow AI: artificial intelligence used across the organisation without formal oversight, risk assessment, or data governance. A 2024 Salesforce study found that more than half of generative AI users at work were using unapproved tools.¹ Forrester research indicates that 60% of organisations lack a formal AI governance framework.²

This is not a technology problem. It is a governance problem. And it sits with the board, not the IT department.

Regulation Is Coming — Ready or Not

The regulatory landscape is shifting fast. The EU AI Act — the world's first comprehensive AI legislation — begins enforcement in August 2025, with full compliance obligations applying from August 2026. Penalties reach up to 7% of global annual turnover for the most serious violations.³

In the UK, the Department for Science, Innovation and Technology (DSIT) has established a principles-based framework — safety, transparency, fairness, accountability, contestability — and sector regulators are now embedding these into existing regulatory requirements. ISO/IEC 42001, the international standard for AI management systems, published in 2023, is gaining traction as the certifiable benchmark. And ISO 42005, providing guidance on AI impact assessments, is expected to follow.

For organisations operating across jurisdictions, the compliance surface area is expanding rapidly. Waiting for clarity is no longer a viable strategy. The organisations that act now will have governance frameworks in place when enforcement begins. Those that wait will be scrambling to catch up under regulatory pressure.

Why Traditional Approaches Fall Short

Traditional consulting-led governance reviews take 3-6 months and cost six figures. They produce excellent frameworks — but they arrive after the window for proactive action has often closed. For many organisations, especially mid-market and growth-stage companies, the budget and timeline simply do not fit.

Internal teams face different constraints. AI governance requires a blend of regulatory knowledge, risk management expertise, and technical understanding that few organisations have in-house. The IAPP reports growing demand for AI governance professionals, but supply lags far behind.⁴ Building internal capability is the right long-term play — but it does not solve the immediate gap.

What Governance Intelligence Actually Looks Like

Effective AI governance intelligence is not a 200-page policy document that sits on a shelf. It is board-ready, scored, evidence-based insight that answers the questions leadership actually needs answered:

• •What is our AI governance maturity relative to peers and regulatory expectations?
• •Where are the critical gaps — and which ones carry regulatory or reputational risk?
• •What is our exposure to third-party AI risk through vendors and partners?
• •What should we prioritise — and what is the roadmap to compliance readiness?

This is organisational governance — not model governance. Model governance (bias testing, drift monitoring, explainability) matters enormously for individual AI systems. But organisational governance addresses the broader question: does our organisation have the policies, processes, accountability structures, and oversight mechanisms to use AI responsibly at enterprise scale?

Where to Start

The most effective starting point is a baseline assessment. Not a 6-month consulting engagement — a rapid, structured diagnostic that tells you where you stand today across the dimensions that matter: strategy, risk, compliance, oversight, and operational maturity.

That is exactly what we built the AI Governance Pulse to deliver. Fifteen targeted questions, five governance dimensions, three minutes. Free, instant, no sign-up required. It will not solve your governance challenge — but it will tell you, in scored and shareable terms, where the gaps are and what to prioritise.

From there, deeper assessments build on the baseline — from readiness assessment to comprehensive risk intelligence. Each product adds depth, detail, and actionability. We call it a trust ladder: start with a free diagnostic, build confidence in the approach, then commission the depth of analysis your organisation needs.