AI is often introduced as a shortcut: more content, more insights, more efficiency. But for many organizations, especially those responsible for digital platforms, the initial excitement is quickly followed by unease. Who is in control of the output? What happens to sensitive data? And how do you ensure that automation doesn’t quietly erode quality, tone, or accountability?
The challenge is no longer whether AI is powerful. It clearly is. The challenge is whether it can be introduced without forcing organizations to surrender control in exchange for convenience.
Many platforms respond to the AI era by embedding predefined features directly into their products. Buttons appear, suggestions are generated, and workflows change overnight. While this can be impressive, it also removes an important layer of decision-making.
When treated as a finished feature rather than a capability, AI tends to dictate behavior instead of supporting it. In that case, editorial teams may find themselves adapting to the tool rather than the other way around. Over time, this can lead to a diluted brand voice, unclear accountability, and a widening gap between human intent and automated output.
We need to stop asking what AI can do and start considering how we actually want it to behave. That’s the only way to build an AI strategy that is sustainable and scalable in the long run.
Umbraco’s approach to AI begins with restraint. Rather than embedding AI directly into the CMS, the core system is intentionally kept free of AI features. This is not an omission, but a design choice.
AI is introduced through a separate governance layer that defines rules, boundaries, and behavior before any output is generated. Which models are allowed? How should tone and audience be interpreted? What data must never leave the system? These decisions are made upfront, not retrofitted later. This is what we call “AI in Umbraco” – a framework where you define the boundaries and the AI simply does the work.
Rather than shipping one large AI feature, Umbraco.AI acts as the plumbing for AI in Umbraco.
It gives teams central control over:
Which LLM providers are allowed
What LLM models are selected
Tone of voice and audience context
System instructions and constraints
Logging, testing, and debugging
Restrictions (what elements the LLM should never get access to)
In other words, this is not where AI acts. It’s where AI is defined.
Talking about “AI on your terms” is easy in theory. In practice, it only becomes meaningful once there is a concrete foundation that allows organizations to decide where, how, and to what extent AI is involved.
By separating governance from functionality, AI becomes something that is configured deliberately rather than enabled blindly.
When AI operates within clearly defined boundaries, its role changes. Instead of acting as an autonomous decision-maker, it becomes an assistant - one that works in context and under supervision.
This means AI can help with repetitive or time-consuming tasks, offer suggestions based on existing content, or surface insights that would otherwise require significant manual effort. But the final responsibility remains human. Editors still decide what is published. Strategists still define intent. AI supports the work; it does not replace the judgment behind it.
This shift from automation to assistance is subtle, but crucial. What follows is not a single feature, but a set of practical outcomes that build on that foundation.
With a governance layer in place, AI can be exposed through modular packages that solve specific problems rather than attempting to “be intelligent” everywhere.
One practical example is prompt‑based functionality. Instead of asking editors to write prompts manually or rely on generic chat tools, repeatable tasks such as generating summaries, refining copy, or suggesting metadata can be formalized. These prompts inherit tone, audience context, and guardrails from the governance layer.
Tasks that previously varied in quality depending on time, experience, or workload can now be supported in a predictable way, without removing editorial ownership.
Beyond isolated tasks, AI becomes more valuable when it understands where it is operating. A prompt like this can look at the current page and all the rest of the content around it, and take that into account when creating something such as a meta description, to make sure it knows what this metadata should involve. This is also where agents and copilots come into play.
Rather than producing content in isolation, these tools can analyze the surrounding context: the page structure, related content, and the broader site. Editors can ask for suggestions, improvements, or guidance, and receive responses grounded in the reality of the platform rather than generic advice.
Importantly, this interaction remains conversational and assistive. The AI can propose changes, but it does not publish, restructure, or override decisions on its own. Responsibility remains clearly human.
The most tangible shift happens when AI is allowed to work with actual user behavior. Through integration with analytics and engagement data, such as from Umbraco Engage, AI can move from speculative suggestions to evidence‑based recommendations.
This is where the Model Context Protocol (MCP) comes in. MCP is a secure connector that allows external AI tools to interact with Umbraco.
This enables scenarios such as identifying meaningful audience segments, interpreting performance trends, or suggesting experiments based on observed behavior. Instead of asking what might work, teams can explore what the data suggests could work better.
Without embedding any tools directly into the CMS. Without breaking governance. With the MCP Server, AI can now:
Identify dominant personas
Suggest segmentation strategies
Propose A/B tests
Recommend content or CTA changes
Adapt content dynamically based on journey stage
… all based on actual data from your CMS and visitor database.
Crucially, this data remains first-party and governed. Think of it like inviting AI to look at your data through a glass wall. It is invited to reason over what it sees, but it can never extract or externalize it. By using the Guardrail feature, you’re the one who decides what remains visible, ensuring that everything sensitive is anonymized or blocked from AI access entirely.
When insight and assistance are combined, the AI’s role shifts. It starts helping with the heavy lifting that usually requires significant manual effort. These can be tasks like proposing alternative content, outlining A/B tests, or highlighting where users are getting stuck in their journey.
What it does not do is act unilaterally. Each step still requires human approval, refinement, and intent. The system is designed to reduce effort, not responsibility. This distinction matters. Automation without accountability creates risk. Assistance with oversight creates leverage.
This approach points toward a more mature way to handle AI – one where intelligence is introduced gradually, governed explicitly, and evaluated continuously, rather than just turned on and left to its own devices.
Rather than promising transformation through automation alone, we focus on enabling better decisions, faster iteration, and more informed experimentation, so that organizations aren’t forced to compromise on ownership, tone, or trust.
In that sense, “AI on your terms” is not a slogan. It is the outcome of designing systems where control comes first, and capability follows.
Even here, the principle remains the same. Learning from your data shouldn't mean losing control of it. Data stays owned. Decisions stay accountable. AI operates as a collaborator that reasons within defined constraints, not as an opaque engine pulling unseen levers.
The future of AI in digital platforms is unlikely to be defined by the loudest features or the most aggressive automation. It will be defined by trust, clarity, and the ability to adopt new capabilities without destabilizing existing practices.
By treating AI as infrastructure rather than spectacle, Umbraco points toward a quieter but more durable future. A future where organizations can benefit from intelligence at scale, without giving up control over who they are, how they speak, or why they publish.
This approach turns AI into something modular and observable. A tool that is governed and extendable by design. But most importantly, it makes AI optional.
In the end, AI is most powerful not when it takes over, but when it works on your terms.
AI is part of any digital platform. But how it’s introduced matters just as much as what it can do. Where many platforms have rushed to add shiny AI buttons deep into the core of their products, we chose a more considered path. Not because we doubt AI’s potential, but because we believe deeply in the kind of product Umbraco is, and the people who use it.