AI governance: What it is and why it matters

There’s no denying that artificial intelligence (AI) is a game-changing business tool. It’s embedded into operations across every industry, unlocking new efficiencies and transforming the way we work. 

In fact, in marketing alone, AI is already embedded deeply within content creation workflows: Over 90% of marketers now use generative AI (GenAI) tools weekly or more frequently, with over 50% saying it improved the quality of their work.

So, yes, AI is useful, but it isn’t perfect. AI development inevitably brings both risks and opportunities, which means brands need to be responsible about how they use it — just as they would with any other new technology — to sustain the positive impact.

We’re not talking about sci-fi robot takeovers here, but mishandling AI today could produce damaging outputs that are opposed to company objectives, leaving you trailing behind competitors, struggling to maintain compliance with AI regulations, and eroding trust with customers.

Long story short, it’s a problem brands need to take seriously, and that’s why we want to talk about AI governance.

In this post, we’ll explore the AI governance challenge: the risks of integrating AI tools, how effective governance helps mitigate them, and how a digital experience platform (DXP) like Contentful makes that process easier.

What is AI governance?

When we talk about governance, we usually mean big-picture management. In content marketing, it’s the standards and guardrails you put in place to align the content that you create and publish with your brand and business needs.

While AI technology — including large language models (LLMs), agentic chatbots, and other GenAI innovations — is relatively new, the principles behind its governance should be familiar. AI governance is about having the right policies, procedures, practices, and strategies to control how your organization integrates and uses AI tools within your tech stack.

The goal of AI governance is to ensure that brands can benefit from the speed and quality of AI outputs, and pursue internal AI development, while using the technology safely, ethically, and in alignment with business objectives and regulatory obligations.

Practically, AI governance requires brands to consider not only what kinds of AI tools they use, but how they use them, who uses them, and how that use is monitored and validated. 

With that in mind, an effective AI governance strategy should include:

Strategic governance: Applicable to high-level company policies, ethical principles, values, and compliance objectives. Highly relevant to C-suite employees, legal teams, and other leadership team members.

• Ethical standards for AI outputs that align with company and regulatory objectives.

• Risk and compliance controls to ensure the company doesn’t generate AI outputs that expose it to regulatory risk or that harm users.

Operational governance: Applicable to day-to-day tasks and concerns how AI tools intersect with teams and workflows. Relevant to developers, content creators, designers, marketers, and other content-adjacent team members. 

• Roles and responsibilities for AI tool use to ensure accountability and transparency.

• Documentation outlining how AI tools fit within existing workflows and how users should interact with them.

Why do we need AI governance?

In smaller organizations with small digital footprints and limited AI integration across workflows, creating a comprehensive AI governance framework may not require significant administrative or logistical effort.

That picture changes at the enterprise level, however, where digital ecosystems may span multiple brands and stretch across multiple borders and regulatory environments. Here, the risk of AI failure and misalignment is magnified — along with the potential negative effects.

But in both scenarios, AI governance isn’t about launching a heroic effort to prevent a single catastrophic threat or ethical failure. It’s about building a foundation for responsible, safe, and compliant adoption of AI technology over the long term.

Because, while your organization’s AI adoption may be limited or incremental right now, that will change as the technology is increasingly embedded in national and global business infrastructure. Getting your AI governance practices in place today means that your business and your teams will be able to make better decisions about AI in the future.

Let’s zoom in on some key reasons to establish strong AI governance.

Customer trust

Generative AI tools frequently hallucinate when used without effective guardrails or oversight, producing factually inaccurate, misleading, or incoherent outputs that damage customer trust and brand credibility.

Privacy

AI systems often require customers’ personal information to generate useful outputs. Organizations must be confident that their systems are using that information in accordance with privacy regulations and internal data handling rules, especially when compliance violations can result in significant financial and operational penalties. 

As part of that challenge, the governance strategy must account for the risk posed by different types of AI data. These include training data (used for custom LLMs), prompt data (the most common location of customer data), and output data (another potential location for customer data). 

Ownership

AI tools can be inscrutable; it’s often unclear how they generate outputs, and who is responsible for them. That ambiguity is unhelpful for internal team cohesion and communication. 

A lack of clarity over who should use AI tools, who sets the rules, and how outputs should be reviewed creates bottlenecks, slows remediation, and undermines efforts to create and optimize content experiences.

Compliance risk

The less your teams understand how AI tools work, the more likely they are to expose your company — even unwittingly — to regulatory risk. AI regulations, such as the EU AI Act, are evolving rapidly worldwide, especially around personal data protection. These regulations introduce obligations to maintain detailed documentation of AI use, to ensure transparency around AI decision-making, and to conduct risk assessments to explore the potential impact of AI integrations. 

AI regulations typically encourage organizations to acquire governance tools with specific features, such as audit logs and access controls, and these requirements can change as new threats and challenges emerge. Companies that don’t stay on top of the latest AI compliance obligations risk having the legal rug pulled from beneath them.

Inconsistent quality

Without appropriate oversight and structure, AI outputs can vary wildly in quality. Even when tools themselves are consistent, differences in how teams across brands or countries deploy and use them can cause variation.

Internal adoption

The success of AI tools depends heavily on the ability and attitude of the human team members operating them. If users don’t understand the technology, or don’t trust it, organizations are unlikely to see optimal impact. Likewise, teams won’t be open to adopting new AI tools in the future, leaving brands behind competitors with stronger, more reliable governance.

It’s important to remember that, while internal culture may be positively aligned with AI adoption, it could, under different circumstances, be aligned against it — a scenario which makes the governance challenge more difficult. 

What does good AI governance look like?

Now that we know the risks, what can your organization do to mitigate them? Effective AI governance requires firms to balance technology with human oversight, and understand that governance strategy should apply at all operational levels, wherever AI intersects with teams and workflows. 

Here’s how governance might factor into a hypothetical content workflow.

• During content ideation, human reviewers might be required to validate AI-generated content suggestions, checking accuracy and brand alignment. 

• During personalization, role-based permissions could control which content team members are able to customize AI outputs for certain audience segments. 

• Finally, a compliance control stage could be imposed to ensure AI outputs meet regulatory and privacy requirements.

If AI governance strategy is misaligned or inadequate, an organization increases the possibility of negative consequences. An unauthorized prompt making it into the personalization process, for example, would likely result in content experiences failing to engage their audiences — which would undermine campaigns, require remediation, and squander commercial opportunities.  

With those factors in mind, effective AI governance should involve these key pillars:

Written policies and procedures

Document your AI policies and procedures clearly and comprehensively in order to define how AI tools will be integrated and used day-to-day. These written policies serve as benchmarks to ensure consistent application of rules and practices across departments.

Produce two versions of your AI policies: one for human consumption, and one for the AI models and agents integrated into your digital ecosystem. The machine-readable versions will serve as guardrails to keep agentic tools aligned with business objectives.

For example, you could produce a style guide that exists as a prose document, which sets out brand voice, tone, and terminology directions for human team members. At the same time, you could input and encode this style guide in your AI content management tools, setting out formatting standards, constraints, and prompt templates.

Human review

AI governance is about protecting and promoting human interests and improving outcomes for your team members. That means governance strategies should include a human review stage to verify the quality of outputs.

In content marketing, this review should happen before publication to assess accuracy, tone, and corporate alignment. Adopting a risk-based approach here can be helpful: lower-risk content, such as internal comms or minor tweaks to product descriptions, could receive lighter review, while more sensitive content receives closer, more intense scrutiny. 

Remember: Ideally, AI tools minimize the need for human intervention but support and optimize its impact when it’s necessary.

Roles, responsibilities, permissions

Assign clear roles and responsibilities to team members who use or interact with AI tools to establish transparency and accountability. Leverage technology to apply relevant permissions and access in order to properly secure AI deployments across your ecosystem.

AI culture

Brands won’t be able to optimize human skill and expertise unless team members understand their AI technology and have the skills to use it within the governance parameters set by the organization. With that in mind, leadership should embed responsible AI use into company culture by communicating tech changes early, scheduling training sessions, and sharing knowledge.

Testing and monitoring

Given the pace of AI innovation and evolving regulations, building an AI governance framework can’t be a static, box-ticking exercise. It must reflect the brand’s unique business and risk environments, and be monitored and tested continuously to ensure it delivers on its objectives.

Robust monitoring also supports AI audits — which will become increasingly important as regulations evolve. Organizations that consistently document AI use, effectiveness, and impact will have valuable data available for compliance requirements and internal transformation projects.

How does Contentful create a foundation for responsible AI use?

Here’s how the Contentful DXP helps brands get their AI governance framework right. 

Structured content clarity

Generative AI works best when it understands the data it handles. A structured content model within Contentful supports this clarity by breaking every piece of content into its smallest components: headers, body text, metadata, images, and more.

That built-in structuring means that AI agents don’t have to guess what they’re looking at. They understand what each piece of data represents and can use that information to make decisions and generate outputs that are better aligned with the requirements of the governance strategy. 

And here’s a pro-tip for optimizing your governance strategy: Each content type in Contentful’s structured model includes a description field to document its purpose — and every individual field can have help text explaining how it should be used. This metadata guides editors and content creators, but it also serves as valuable structured context that AI Actions can read to produce more accurate, on-governance outputs.

AI Actions

AI Actions are automations built into the Contentful platform to help streamline content creation, alt text tagging, SEO, segmentation, personalization, and translation and localization. They represent an intersection between human users and generative AI engines: While there are a suite of templated AI Actions, teams can also create their own customized variants to meet specific content management needs. 

Working with AI Actions, teams can create new prompts that define AI outputs precisely, and input policy documents into the AI system — and do all that without having to leave Contentful. 

Essentially, AI Actions changes governance strategy from a static policy document into an ongoing, active process, making it easier for team members to control the impact of AI on content operations, and to apply the rules they set. 

Role-based permissions

Contentful includes a role-based permissions model that allows brands to define precisely who can use, configure or modify AI Actions. This separation of privilege ensures that AI tools deliver value, while preventing uncontrolled use.

The permissions can be layered to reflect seniority, with junior team members able to generate AI-assisted suggestions, and more senior team members able to customize AI Actions, or create prompts for new actions. 

Audit logs

Contentful’s audit logs document who has used AI Actions, when they used them, and what changes they made. This visibility allows brands to examine AI use, investigate and solve issues, demonstrate compliance to regulators, and optimize workflows.

Audit logs enhance the efficiency and impact of content operations, but also boost the confidence of team members, who can operate with the peace of mind that there’s a clear record in place for subsequent audits. Even better, these logs can help ensure alignment with internal policies and external regulations, such as the EU AI Act and similar legislation. 

Controlled API access

AI tools interact with content through an application programming interface (API) layer, pulling data in, generating outputs, and pushing data back out into the relevant systems as efficiently as possible.

That API-first architecture means every AI integration — whether a hosted LLM, an in-house model, or a partner service — goes through the same secure, well-documented path to interact with content. API-keys enable content owners to control which systems and services can access content data and what actions they’re permitted to take with it. This level of controlled API access reduces the risk of unintended data exposure to unsecured apps and users, unauthorized content changes, or AI tools operating beyond their intended scope. 

Wrapping up

Responsible AI governance isn't an exercise in setting rules for its own sake. The most effective AI governance frameworks reflect the unique risks and characteristics of their operational environments, and adapt as regulations evolve.

That means your AI governance strategy doesn’t need to be a “big bang” event that transforms your tech stack overnight. Instead, it should build safety and confidence over time. You could, for example, codify a single AI policy into your first AI Action, and then introduce it to a pilot team to gauge its effectiveness.

That incremental approach means that your brand will keep pace with AI development with fewer surprises. It will empower team members to become familiar with tools without becoming overwhelmed, to optimize their use within established guardrails, and to scale that process consistently as the brand integrates new innovations.

Not sure how to start that journey? Let us help you get your AI governance framework right from day one: Learn how we integrate AI natively in Contentful, browse the full range of AI Actions, or get in touch with our sales team to find out what’s next.

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