Evaluating AI Capabilities in a CMS

AI has become the most talked-about feature in the CMS market. Every vendor now claims some form of AI capability. But the depth, usefulness, and governance of those capabilities varies enormously.

For most enterprise teams, the challenge isn't whether to adopt AI but how to evaluate it honestly. Which AI capabilities actually reduce cost and increase speed? Which create new risks? And which are marketing labels disguising features that have existed for years?

This section provides a practical framework for evaluating AI in a CMS, designed to help you cut through the noise and focus on what matters for your organisation.

Why AI Deserves Its Own Evaluation

AI isn't just another feature on a vendor checklist. It touches data governance, developer workflows, content quality, compliance, and long-term architecture. Evaluating it requires a different lens than evaluating, say, a visual editor or a workflow engine.

The reason is simple: AI introduces a new kind of dependency. When a CMS uses AI to process your content, generate outputs, or assist development, you need to understand where data flows, who controls the models, and what happens when AI produces incorrect or inappropriate results.

If you treat AI as just another row in a feature matrix, you'll miss the risks that matter most.

The Three Tiers of CMS AI

Not every organisation needs the most advanced AI capabilities. Understanding the three tiers helps you match vendor offerings to your actual maturity and needs.

Tier 1: Content-Facing AI

This is the most common tier. The CMS provides built-in tools for:

• Generating or rewriting text (headlines, descriptions, summaries)
• Suggesting SEO improvements
• Auto-tagging images and assets
• Translating or localising content

Who it's for: Marketing-led teams that want to accelerate editorial output without heavy developer involvement.

What to watch for: Is the AI model-agnostic, or are you locked into a specific provider? Does your content leave your environment during processing? Is there an audit trail for AI-generated content?

Honest assessment: Tier 1 capabilities are widely available and increasingly commoditised. They provide real productivity gains, but they are unlikely to be a competitive differentiator. If your primary need is faster content production, most modern CMS platforms will serve you well here.

Tier 2: Integration-Layer AI

The CMS connects to external AI services through APIs, webhooks, or event-driven pipelines. Teams can:

• Route content through custom AI workflows (e.g., compliance checking, sentiment analysis)
• Bring their own models (self-hosted LLMs, fine-tuned classifiers)
• Trigger AI processing from lifecycle events (e.g., auto-summarise on publish)

Who it's for: Organisations with existing AI infrastructure or data science teams that want the CMS to participate in broader AI pipelines.

What to watch for: How open are the integration points? Can you connect to any model, or only vendor-approved services? How is authentication handled? What latency does the integration introduce?

Honest assessment: Tier 2 requires more technical maturity but offers significantly more flexibility. It's the right level for organisations that already have AI capabilities and want their CMS to work within that ecosystem rather than replace it.

Tier 3: Platform-Programmable AI

The CMS is architecturally designed so that developers and AI coding agents can extend the platform itself. This means:

• AI agents can scaffold new extensions, content models, and API resolvers
• The platform's codebase uses familiar, well-structured patterns (e.g., TypeScript, GraphQL) that AI tools can reason about effectively
• Lifecycle hooks, plugin systems, and typed interfaces give AI-generated code clear boundaries and predictable behaviour
• Developers review and refine AI output rather than writing everything from scratch

Who it's for: Engineering-led organisations that want to maximise developer velocity and treat the CMS as a programmable foundation rather than a fixed product.

What to watch for: Does the platform genuinely support AI-assisted development, or is this a marketing claim? Ask for demonstrations. Have a developer try using an AI coding agent during the pilot phase. Measure time-to-build and code quality.

Honest assessment: Tier 3 is the most powerful model but requires the most maturity. It demands developers who can review and validate AI-generated code, governance processes for what AI is allowed to modify, and a culture that treats AI as an accelerator, not an autopilot. For organisations with strong engineering teams, it can dramatically reduce time-to-build for custom features. For organisations without that foundation, it can create more risk than value.

Matching Tiers to Your Organisation

Most enterprises will benefit from a combination. You might use Tier 1 for daily editorial work, Tier 2 for connecting to your existing AI infrastructure, and Tier 3 for building custom platform capabilities. The key is to evaluate each tier separately rather than accepting a vendor's blanket "AI-powered" claim.

Questions to Ask Every Vendor

When evaluating AI capabilities during your Agile RFP or proof cycle, use this checklist:

1. Model independence: Is the AI model-agnostic, or are we locked to one provider? Can we bring our own models?

2. Data residency: Does content data leave our cloud environment during AI processing? Where is AI computation performed?

3. Training data: Is our content used to train or fine-tune third-party AI models? Can we opt out completely?

4. Scope of AI: Can AI tools generate platform extensions and code, or only content? What are the boundaries?

5. Governance: What review workflows exist for AI-generated outputs? Are there audit trails?

6. Optionality: Is AI optional? Can we disable it entirely without affecting core CMS functionality?

7. Transparency: Can we see what the AI is doing, what data it accesses, and what outputs it produces? Are there logs?

8. Cost: How is AI usage priced? Per request, per seat, per volume? Are there usage caps that could affect operations?

Any vendor that cannot answer these questions clearly and specifically should be treated with caution, regardless of how impressive their demo looks.

AI Governance and Risk

AI introduces new categories of risk that traditional CMS evaluations don't cover.

Data exposure: When content is processed by external AI models, it may be stored, logged, or used for model improvement by third parties. For regulated industries (healthcare, finance, government), this can violate compliance requirements.

Quality control: AI-generated content and code can be confident and wrong. Without review workflows, AI outputs can introduce errors, bias, or inconsistencies that are difficult to detect at scale.

Over-reliance: Teams that lean too heavily on AI without maintaining human expertise risk losing the ability to evaluate AI outputs critically. AI should augment capability, not replace judgment.

Vendor dependency: If your AI capabilities are tightly coupled to a vendor's proprietary AI layer, switching providers means losing those capabilities entirely. Favour platforms where AI is an extension, not a foundation.

To manage these risks, build an AI governance framework alongside your CMS evaluation:

• Define which content types AI can generate or modify, and which require human authorship.
• Establish review processes for AI-generated code before it reaches production.
• Require that AI processing happens within your cloud perimeter for sensitive content.
• Set clear escalation paths for when AI produces incorrect or harmful outputs.
• Review AI governance quarterly as capabilities and regulations evolve.

Evaluating AI During the Agile RFP

If you followed the Agile RFP model from Section 4, you already have a structure for evidence-based evaluation. AI capabilities should be tested within that same framework, not evaluated separately through slide decks.

During the Readiness Sprint, include AI as a defined scenario. For example:

• A developer uses an AI coding agent to create a new content model extension.
• An editor uses AI to generate localised content variants.
• A compliance team reviews the data flow of an AI-assisted workflow.

During the Experience Round, observe how each vendor's AI features perform in practice:

• Does the AI produce useful output on the first attempt, or does it require extensive correction?
• How transparent is the AI's behaviour? Can you see what data it accessed and what it generated?
• Does the vendor acknowledge AI limitations honestly, or do they present it as infallible?

During the Proof Cycle, measure AI impact quantitatively:

• Time to scaffold a new extension with AI assistance versus without.
• Editor satisfaction with AI-generated content suggestions.
• Number of AI-generated outputs that required human correction.
• Any data governance incidents or concerns that arose during the pilot.

These measurements give you real evidence rather than vendor promises. They also reveal whether your own organisation is ready to use AI effectively, which is just as important as whether the platform supports it.

Key Takeaway

AI is a multiplier, not a magic feature. The right platform makes AI useful; the wrong one makes it risky.

The enterprises that will get the most from AI in their CMS are those that evaluate capability, governance, and organisational fit together, not those that chase the most impressive demo.

Don't ask "does this CMS have AI?" Ask "does this CMS let us use AI safely, effectively, and on our terms?" The answer to that question will separate genuine innovation from marketing noise.