AI governance can't wait for regulation. Leaders can't either
- AI governance is a leadership issue, not just a technology issue, because AI affects every part of the organisation.
- Waiting for AI regulation isn't a strategy; accountability, clear ownership, and governance must start now.
While regulation will undoubtedly shape the future of AI, waiting for legislation before taking action is a mistake. Organisations are already using AI across their operations, whether through purpose-built tools or capabilities embedded within the software they use every day. The question now is how to navigate this reality before formal rules catch up.
Too often, AI governance is treated as a future compliance problem rather than a current leadership responsibility. That mindset is understandable. Regulations are still emerging, and standards are still evolving, leaving leaders feeling they lack the technical expertise to make informed decisions. But governance isn't about becoming an AI expert. As it was for other technology, it's about creating accountability, setting direction, and helping people use it safely and effectively.
Governance needs leadership involvement from the start
In my conversations with leaders, one challenge comes up repeatedly. Many still see AI as a technology issue that can be delegated to IT or cyber security teams. It's a familiar approach that has worked before. In the past, when organisations introduced new software, responsibility typically sat with technology departments.
AI is different. It touches every function of the business, from people teams and operations, to legal, procurement, communications, and customer service.
“When leaders aren't actively engaged, uncertainty fills the gap.”
Part of the challenge is that many organisations don't fully understand how much AI they already have. One of the most important steps they can take is to understand their technology estate: what tools are being used, which AI capabilities are already enabled, and where organisational data is being accessed or shared.
Decisions about AI don't just affect systems. They affect people, reputation, revenue, and organisational culture.
That's why leadership involvement is so critical.
When leaders aren't actively engaged, uncertainty fills the gap. Employees begin making their own decisions about which tools to use, what information they can safely share, and where the boundaries lie. Some employees may use personal AI accounts to complete work tasks. Others may experiment with tools that haven't been approved or assessed by the organisation.
This "shadow AI" activity often happens with good intentions, but without clear governance it can introduce significant risks around data security, intellectual property, and compliance.
Innovation and governance don't have to be at odds
Organisations also need to recognise that governance is about enabling innovation, not restricting it.
One of the most successful approaches I've seen is when leadership communicates a simple message: we want you to work with AI, but here are the guardrails.
Employees want clarity. They want to know what's acceptable, what's not, and how they can use these tools confidently. Governance should create that confidence. It shouldn't create fear.
Governance starts with accountability
I believe AI governance requires representation from across the organisation. Technology teams bring expertise on systems and security. Legal teams help manage risk. But people teams, operational leaders, and executives all have a role to play in shaping how AI is introduced and adopted responsibly.
Regulation will continue to develop. Frameworks such as the EU AI Act provide an important foundation and will help establish greater consistency over time. The Act also reaches beyond the EU: it applies wherever AI is used in the EU, so UK organisations serving EU clients or customers need to comply too.
“The conversation around AI governance often starts with regulation, but it should start with accountability.”
The UK has no AI Act of its own, but existing laws still apply, including UK GDPR, the Data (Use and Access) Act 2025, and the Equality Act 2010, alongside guidance from regulators, such as the Information Commissioner's Office.
For organisations looking for a practical starting point, the international standard ISO/IEC 42001 provides a recognised framework for managing AI responsibly, whichever regulations apply.
But organisations can't afford to wait for regulators to provide every answer. By the time legislation catches up, AI will already be embedded throughout the business.
The conversation around AI governance often starts with regulation, but it should start with accountability. Leaders don't need all the answers, and they don't need to wait for regulators to provide them. What they do need is visibility, clear ownership, and the confidence to make informed decisions about technologies that are already shaping their organisations.
Ready to strengthen AI governance across your organisation? Explore our AI training courses and build the skills needed to adopt AI responsibly, manage risk, and lead with confidence.
Related articles
Cyber Portfolio Director Richard Beck shares practical insights on AI governance, assurance, accountability, and the risks of autonomous AI agents.
Expert spotlight: Richard Beck on AI governance, assurance, and accountability
6 November 2026
As AI moves from experimentation to widespread adoption, many organisations are discovering that governance is struggling to keep pace. While attention often focuses on future regulations, today's challenge is ensuring employees, leaders, and processes are aligned around responsible AI use.
As AI adoption accelerates, governance lags behind
4 November 2026
An unauthorised AI collective, a real-world cyber intrusion, and the uncomfortable question of whether human oversight and AI governance can keep pace.
When AI agents started talking to each other
26 October 2026
As AI becomes more capable and autonomous, who has the evidence, independence, and authority to decide when the risk is no longer acceptable?
Who decides when AI is too dangerous?
24 September 2026
David Pool explains how to build a cost-efficient AI enterprise by balancing model, governance, data, FinOps, and workforce capability.
Building the cost-efficient AI enterprise
10 September 2026
Why healthcare organisations must prioritise workforce transformation alongside AI adoption to achieve sustainable and effective change.
Before AI transformation comes workforce transformation
8 September 2026
Anthropic has announced a research preview of its Model Hardware Standard (MHS), a new specification designed to help AI agents interact with physical devices. If Model Context Protocol (MCP) gave AI a common way to discover and use software tools, MHS does something similar for hardware.
Anthropic's Model Hardware Standard: what happens when AI agents can use physical tools?
28 August 2026
Forward deployed engineers are emerging as a key AI role, helping organisations bridge the gap between technical capability and business value.
What the rise of the forward deployed engineer tells us about how organisations are building capability
27 July 2026
London’s plans to prepare workers for AI-driven change reflect what QA’s analysis of AI readiness across UK sectors has found: adoption is accelerating, but workforce skills, confidence, and capability aren’t keeping pace. By examining how AI is being adopted across industries, we’ve identified a widening gap between giving people access to AI and equipping them to use it effectively.
Closing the AI readiness gap starts with skills
21 July 2026
As AI adoption accelerates, workforce capability is moving up the corporate agenda. The article explores why skills development is becoming a strategic concern for business leaders, and how organisations can build the adaptability needed to turn AI investment into long-term value.
What the government's AI skills compact signals for boardrooms
14 July 2026