5-step guide to building your AI governance framework

5-step guide to building your AI governance framework: the essential guide to giving your business crucial structures that very few currently have.

There’s an alarming trend emerging amongst businesses worldwide: while the vast majority are openly embracing AI in some form, a far smaller percentage are doing so with proper governance structures in place. 

The numbers vary depending on the data source:

• A 2026 IBM survey found that 77% of surveyed businesses reported AI adoption is already outpacing current governance capabilities.
• Another survey from Smarsh says 55% of respondents actively used AI while only 26% reported their governance frameworks are fully aligned with implementation. 
• A further survey from Aon found that while 88% of respondents admitted to using AI at least once, only 8% did so with a “comprehensive AI governance framework”.

Any of these would tell you that the gap between usage and control is significant, but add them all together, and you see the scale of the problem.

The problem with AI governance frameworks is that they’re still so new – much like the concept of AI itself. 

Their importance exploded onto the scene in the early 2020s and, with AI capabilities evolving at a rapid pace since, it can be hard to know where to start in building a framework that meets standards. 

This 5-step guide will get you started:

• The general idea: You need to explore your plans for AI and compare and contrast them with your business strategy and mission statement to gauge alignment. 
• Let’s break it down: This step is essential because, even before making a rulebook, all businesses need to decide what responsible AI looks like. The board must be active players in this process. They may not build that definition from scratch, but they will ask questions of draft proposals, draw on internal and external trends to formulate opinions and sign off on final decisions. Crucially, this step will establish boundaries. I.e. Where do employees stop and AI take over? How much risk is too much? What levels of fairness, privacy, and transparency are we going for?

• The general idea: You need a structure that assigns specific responsibility but avoids silos.
• Let’s break it down: AI governance frameworks need clear-cut chains of command. Who is responsible for what? What are the IT team’s jobs? What are the C-suite jobs? What are the board’s jobs, and do we need a trained committee for it? It’s essential to remember that AI governance frameworks cannot be left to just one of the above. That will effectively silo the project – isolating it from the rest of the company, with subpar communication and the potential for huge mistakes. From the get-go, this needs to be a team effort.

• The general idea: Cataloguing existing tools: what they’re capable of and what their risk level is. 
• Let’s break it down: You cannot govern what you don’t know exists, so a major step in developing an AI framework is a complete run-through of what you have at your disposal. Be wary of “shadow AI” – third-party systems that employees might be using without general approval. Ensure you map all AI tools, whether active or developing, and categorise them into risk tiers. The EU’s categorisation can help you, and even if your business isn’t located there, you might end up abiding by this structure anyway; it’s the nature of modern business regulation.

• The general idea: Translating the pre-agreed principles into daily actionable rules. 
• Let’s break it down: This step operationalises AI governance. It creates policies for individual situations and day-to-day activity. Often, this is a crucial missing step in any framework, leaving corporate leaders scratching their heads because their careful plans haven’t made a difference on the ground. In an AI context, the relevant policies will govern things like usage limits, data privacy, human-in-the-loop requirements, and vendor assessment. It ensures that, even if an external company develops the tools you used, you still have control.

• The general idea: Setting up ongoing oversight with metric-based reporting as far as possible. 
• Let’s break it down: For boards, this is crucial for two reasons: The first is that oversight is a core part of a director’s responsibilities, so they must fulfil that with AI too. The second is that, unlike traditional software, AI models evolve, drift, and present new vulnerabilities at a rapid pace. Think back over the last few years alone: we’ve gone from models that can write stories to models that can hack into other companies’ systems, without even being asked to. Your oversight and framework needs to evolve to match that. You should be regularly auditing models for bias, security flaws, performance degradation, and compliance with newly enacted global legislation.

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