So over the last few weeks I have been out and about speaking to prospective new clients on their AI projects. Interestingly, it was good to hear that most organisations I spoke to have already started their AI journey, every organisation is on a different journey, and at different stages. They have invested in tools, run pilots, and in some cases deployed live solutions. The energy is real, the enthusiasm is genuine, and the results are often impressive in those early stages. But when I ask them about their governance framework, the conversation usually gets quieter, some providing ‘woolly’ responses on what hey consider to be the Governance requirements.
Silence tells me something important.
We are at a critical inflection point with artificial intelligence.
The technology is moving faster than most organisations can absorb, and the gap between what AI can do and what organisations have actually thought through is widening. Governance is how you close that gap. Not governance as a box-ticking exercise, not a policy document that lives in a SharePoint folder nobody reads, but real, lived, operational governance that shapes how AI is built, deployed, and monitored inside your organisation.
This matters more than most people realise, and I want to explain why.
I am sorry Governance is a dry subject, it is not as glamourous as playing with technology and creating new solutions, but, it is an important, no critical, element of the solution that needs to be completed successfully so that the shiny new solution is allowed to do what you want it to do.
The Problem With Moving Fast
Speed is genuinely valuable in transformation work. Getting something into the hands of users quickly, learning from it, iterating, that is the right approach. But speed without guardrails creates a specific kind of problem that is very hard to unpick later. I have seen it happen. Teams build AI-powered workflows, automate decisions, integrate with core systems, and then six months later nobody can clearly explain what the AI is actually doing, who owns it, or what happens when it gets something wrong.
At that point, governance becomes a forensic exercise rather than a design principle, and forensic exercises are expensive, slow, and often politically painful.
The organisations that are getting this right are the ones that treat governance as a design input, not an afterthought. They ask the governance questions before they write the first line of code or configure the first workflow. We call it the ‘Alignment and Readiness’ Stage.
What Good Governance Actually Looks Like
Let me be specific, because governance can feel abstract until you ground it in practice.
When you are implementing an AI solution, particularly one built on a large language model service like Anthropic’s Claude, one of the most popular AI engines, currently, there are several dimensions of governance that need to be active from day one.
The first is ownership. Who in your organisation is accountable for this AI system? Not who built it, not who is using it, but who is responsible for its outputs and its behaviour over time. This person or team needs to exist before you go live, not after something goes wrong.
The second is transparency. Can you explain to a sceptical stakeholder, a regulator, or a concerned employee what this AI does and how it makes decisions? With Claude-based implementations this is actually more tractable than with some other AI approaches, because the model is designed with interpretability and safety in mind. But the transparency still needs to be documented and communicated by your team. The tool does not do that for you.
The third is data stewardship. What data is going into your AI, where does it come from, who has access to it, and crucially, what are you sending to the model provider’s API? These questions matter for compliance, for security, and for trust. Anthropic’s data handling policies for Claude are clear and well documented, but your organisation still needs to have made an active decision about what data is appropriate to use and under what conditions.
The fourth is monitoring. AI systems drift. Their performance changes as the inputs change, as user behaviour changes, as the world changes. A governance framework that does not include ongoing monitoring and review is not really a governance framework. It is a launch plan.
Where Transformation Delivery Fits
This is where the work Bushey Transformation Delivery does become directly relevant. Governance is not a technology problem. It is an organisational problem that technology creates. And solving organisational problems requires a structured, human-centred approach to transformation.
What we have found, working with organisations across different sectors and maturity levels, is that the governance conversation needs to happen at three levels simultaneously. There is the strategic level, where executive sponsors and board members need to understand what they are accountable for when AI is operating inside their organisation. There is the operational level, where the teams building and running AI solutions need clear frameworks, checkpoints, and escalation paths. And there is the user level, where the people working alongside AI every day need to understand what it can and cannot do, and feel safe raising concerns when something does not look right.
When all three levels are engaged, governance stops being a burden and starts being an enabler. It gives your transformation confidence. It makes your AI investments more defensible. And it creates the kind of organisational trust that allows you to move faster, not slower, because people know the guardrails are there.
No Problem Claude will fix it!
If you are using or considering Anthropic’s Claude as part of your AI implementation, there is genuinely good news on the governance front. Anthropic has built safety and responsible deployment into the core of how Claude is designed. The Constitutional AI approach, the focus on honesty and harm avoidance, the detailed usage policies, these are not just marketing. They represent a real commitment to building AI that organisations can actually govern.
But here is the thing. Even the most responsibly built AI tool requires responsible deployment. Claude will not write your governance framework for you. It will not decide which use cases are appropriate for your regulatory context. It will not define your escalation process or train your staff on AI literacy. That is your work to do, and it is work worth doing well. Oh!
A Practical Starting Point
If you are midway through an AI implementation and realise your governance foundations are not where they need to be, do not wait for a problem to force the issue. Start with ownership and transparency. Get clear on who is accountable and make sure you can explain what your AI is doing to someone who did not build it. From there, build out the monitoring and data stewardship pieces.
It is rarely too late to retrofit governance into an AI initiative. But it is always cheaper, faster, and less painful to build it in from the start.
The organisations that will get the most long-term value from AI are not necessarily the ones who moved fastest in 2024 or 2025. They are the ones who built something they can stand behind, explain, and improve over time.
That starts with governance.
Not Sure Where You Stand? There Is a Way to Find Out
If any of this has landed and you are sitting with a quiet feeling that your governance foundations might not be as solid as they should be, you are not alone. Most organisations we speak to have started well but have gaps they have not yet mapped. Sometimes those gaps are small and fixable quickly. Sometimes they run deeper than expected.
That is exactly why we developed the AI Governance Spine Assessment. It is structured around our proprietary Bushey AI Governance Spine™ framework, which identifies 17 governance elements and applies 52 key governance assessments across your AI implementation. The elements span everything from purpose, scope, and guardrails through to risk management, data governance, model governance, security, vendor oversight, and incident and assurance. Together they give you a complete picture of where your governance is working, where it has gaps, and what needs attention.
The assessment is not a pass or fail exercise. It is designed to give you clarity, not a report card. You come away knowing what your actual governance position is, what your highest priority actions are, and how to sequence the work in a way that is proportionate to your risk exposure. For organisations using Claude or any other AI platform, it also flags the governance considerations that are specific to working with third-party model providers, which is a layer that many governance reviews overlook entirely.
If you would like to understand your governance position before your next AI initiative moves further forward or simply want a second opinion on what you have already built, we would be happy to have that conversation.
AI Governance is the critical but often overlooked foundation of any successful AI transformation, requiring organisations to establish clear ownership, transparency, data stewardship, and ongoing monitoring before problems force the issue rather than after.
Bushey Transformation Delivery’s AI Governance Spine™ provides a structured path forward, assessing 17 governance elements across 52 key assessments to give organisations a clear, honest picture of where they stand and what needs to change.
Bushey provides independent governance and assurance for technology transformation. Through structured oversight and disciplined programme control, we ensure outcomes are achieved with clarity, accountability, and confidence, supported by specialist capability across change, project leadership, AI, Cyber, Data Centre, and M&A services. Our focus is on aligning transformation to business objectives, applying proven frameworks, and enabling secure, resilient, and future-ready environments.
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