CMS modernization needs a content and measurement plan

A new CMS needs a content and measurement plan

A new content-management system, or CMS, can remove architectural constraints that make a brand’s content difficult for AI systems to read. Marketing teams then need to organize that content into answers those systems can find and use, and measure whether the answers appear. So the investment decision includes the platform, the content it manages and the operating practices around both.

That work often starts with content built for an earlier discovery model. Legacy platforms were designed to compete for links in search results, so useful information may be difficult for a large language model, or LLM, to interpret or retrieve. Modernization can address those constraints alongside technology debt, the accumulated cost of maintaining older technical choices. Its value depends on identifying which constraints the existing architecture creates.

Yet some brands keep optimizing for Google and publishing more content without knowing where they appear in LLM responses. These teams first need to establish their position and check whether useful answers are easy to retrieve. Diagnosis and content restructuring can begin before a migration, and the findings can help define what a replacement platform must support.

AI-mediated discovery makes content retrievability a commercial issue

The commercial risk begins when a potential buyer gets an answer without visiting a website. An estimate puts the share of U.S. Google searches ending without a site click at 68%. Within an AI-generated answer, citations, references or links to supporting material, determine which pages users can see. A brand may be absent from that decision if the system cannot interpret and use its content.

That absence matters especially when the user is already choosing a product. A question such as “what should I buy” asks for help with a purchase. Even relevant content can be excluded if the system cannot locate and understand its answer. Conventional search engine optimization, or SEO, therefore needs to be accompanied by work on content structure and machine readability.

The opportunity also extends to users who click through. Semrush conversion data describes visitors referred by LLMs as 4.4 times as valuable as visitors from organic search. Semrush has a commercial interest in marketers investing in search and AI-visibility measurement. The figure concerns the value of referred visitors; assessing a CMS investment requires a separate connection between platform changes, visibility, referrals and business outcomes.

Before any referral occurs, poor structure can also waste editorial effort. A team may already have produced useful information that an answer system struggles to extract. Organizing it more clearly gives existing work another chance to reach buyers. So the practical starting point is to examine the answers already available.

Build sections that answer one customer question independently

Existing content can cover the right subject while burying the useful answer in a long discussion. Teams should first locate answers to customer questions, then check whether they remain clear when separated from the surrounding page. This matters because passage-level retrieval allows an answer engine to select a section of a page for use in a response. The section becomes a unit of content design.

To make that unit useful, organize each relevant section around one customer question. Its wording should establish what is being answered and provide enough context for the answer to make sense independently. A broad page can address several needs, provided each section preserves its own meaning. If a passage depends on the page opening to explain what it refers to, that context needs to travel with the answer.

Once the answer is self-contained, the page’s technical structure can help systems find it. HTML, the markup that organizes a web page, signals headings and sections to bots, automated programs that visit and read pages. A modern CMS can support this organization through machine-readable architecture. Teams can use that capability to make useful passages easy to find and keep each question, answer and necessary context together.

Clear organization also helps a system interpret a passage as a coherent, trustworthy response. But a technically accessible page can still contain information that is difficult to use independently, so teams need to review both access and meaning. Existing passages are therefore a practical priority before commissioning more material. Restructuring them can recover value from earlier editorial work and establish requirements for future publishing.

Research gives teams a reason to examine those requirements closely. Princeton researchers found that content and structure changes alone could improve visibility in generative engines, systems that produce answers from retrieved information, by up to 40%. The finding gives teams a concrete reason to review answer organization before deciding where to start. Brands already gaining AI visibility are also described as applying sufficient rigor to content structure.

Teams can turn that rigor into a repeatable editorial check: identify the customer question, make the section answer it and confirm that the answer retains its meaning independently. Applying the check to new material extends the benefit beyond a few prominent pages. It also reduces the chance of continuing to publish content with the same readability weakness.

The resulting content can serve a wider set of discovery environments. These include standalone AI applications and voice assistants; chatbots in browsers, social media and workspaces; and specialized shopping assistants. Their retrieval and presentation methods differ, but a clear passage that makes sense independently gives multiple LLMs material they can use. Readable content can therefore support discovery across these environments.

Measure AI visibility before deciding what the CMS must change

With a clear content-design requirement, teams can use measurement to find where work is needed. Current AI visibility offers a useful signal about content structure, although other factors also affect whether a brand appears. Teams should establish that starting position and check whether their CMS or digital experience platform, or DXP, a broader set of tools for managing digital content and experiences, already offers AI content intelligence capabilities: tools that assess how content appears in AI answers and identify opportunities to improve it.

A useful assessment begins with the business context those tools need. The team defines its market, identifies competitors, specifies its brand voice and describes target audiences and customer personas, meaning profiles of the buyers it wants to reach. Those buyers’ needs then become prompts that express purchase intent. Together, these inputs define the questions and competitors against which the tool will assess the brand’s presence.

For a hypothetical kitchen-appliance seller, useful prompts could include “Where can I find a six-burner drop-in induction rangetop?” and “What’s the best side-by-side refrigerator with glass doors?” These specifications describe particular buying needs, so the prompts test whether the brand appears for products closely related to its offer. The assessment now has a direct connection to customer demand.

The intelligence tool then runs the prompts against LLMs and compares the brand’s appearances in responses and citations with those of selected competitors. In a hands-on test, an unnamed author entered brand context into a platform and found gaps in some answers, along with one competitor holding a substantial lead. The test shows how visibility assessment can identify where to investigate further. Findings can point teams toward existing answers that need improvement or questions that need new content.

Bot activity adds a different view of the same content. Tools can measure traffic from LLM bots to show how machines interact with a site and help identify where machine access or readability needs attention. Brand appearances and citations measure representation in answers, while the value of referred visitors connects subsequent visits to business activity. Keeping these measures distinct helps teams interpret changes without treating crawler traffic or mentions as referral revenue.

Repeated testing turns the initial assessment into a way to track progress. Teams can rerun selected prompts, compare their visibility with competitors over time and use tool recommendations to identify content gaps. New content follows a diagnosed need, while existing answers remain candidates for structural improvement. Keeping the prompts consistent also makes changes easier to interpret.

Those observations need a suitable time frame. Early citation movement often appears within four to 12 weeks, compared with the three to six months most SEO practitioners expect for ranking changes. Citation movement tracks visibility in AI answers, while ranking movement tracks a separate form of discovery. Teams can use these ranges to schedule observation while allowing for variation in when results appear.

Use the diagnosis to choose platform work and build measurement into operations

The assessment should define the changes needed before they become platform requirements. For a brand preparing to leave a legacy CMS, a targeted Answer Engine Optimization, or AEO, and Generative Engine Optimization, or GEO, audit can examine how well its content performs in AI-driven answer environments. The review should cover the technical foundation, content quality and current key performance indicators, or KPIs. Its immediate purpose is to identify short- and medium-term improvements that can begin before moving to a replacement platform.

An existing platform may already support the first stage of that work. If its CMS offers AI content intelligence, the team can activate it and supply the competitors, personas, prompts and broader brand context needed for diagnosis. The resulting observations can inform which content and architectural changes deserve investment. This lets the team assess its current position while evaluating the need for replacement.

Where replacement is already justified, implementation should enable intelligence capabilities alongside the content work. Architectural constraints may prevent teams from producing machine-readable content efficiently, so modernization can remove a real limit. The accompanying content plan should specify how editors will use those capabilities to create answers that make sense independently. That connects the platform’s structural improvements to an observable change in publishing practice.

Vendor activity makes existing and planned capabilities worth examining. Adobe acquired Semrush, Sitecore acquired Scrunch, and Optimizely announced a partnership with Conductor. These companies have a commercial interest in demand for AI-visibility capabilities because those capabilities can strengthen their platform offerings. The relationships show investment in the market, but buyers still need to examine how the tools fit their own content workflows.

That fit matters because AI visibility tools are not yet fully embedded across major CMS ecosystems. Teams need to assign responsibility for running assessments, interpreting results and turning gaps into content changes. Regular use brings the tools into publishing decisions. Access creates the opportunity, while an assigned process makes the work happen.

Once that process is established, the CMS and intelligence tools need to connect with the organization’s other systems. A shared dashboard should bring together content performance, AI visibility, conversions and spend-related KPIs so teams can compare progress with business activity while preserving each measure’s meaning. Continuous monitoring then helps teams choose the next improvement and shows how visibility changes after the work.

Keep new publishing from adding to the retrofit workload

The same monitoring process should also influence material still being created. Most current visibility tools assess content after publication, so teams often discover structural problems after editorial and production work is complete. Findings from published material can still guide the next piece: editors can apply what they have learned about customer questions, answer completeness and readable structure before another correction is needed.

Creation-time guidance could eventually make that feedback more immediate. A possible future workflow would suggest improvements while someone creates a blog post or video, helping the creator prepare it for LLM discovery before publication. That remains a prospective capability. Current publishing processes need an explicit step for applying the diagnostic tools already available.

Without that step, each new piece published without visibility insights and readable structure can add to the later remediation workload. The backlog can keep growing even after a company gains access to intelligence tools, because publishing practice determines how those tools affect new work. Before publication, the responsible editor should check the customer question, the answer’s independent meaning and the structure that makes it retrievable. This applies the existing diagnosis where another retrofit can still be avoided.

Key highlights

• Plan beyond the platform: CMS sponsors need a modernization plan that links architectural changes to content structure, publishing practices and measurable outcomes. An assessment of existing constraints helps define what a replacement platform must support.
• Treat AI discovery as a commercial channel: Marketing teams need to assess whether their content appears in answers to purchase-related questions. Track citations, referred visits and business outcomes separately to understand how visibility contributes to demand.
• Build self-contained answers: Content owners should organize each relevant section around one customer question, with enough context to make sense independently. Review existing material first to recover value from earlier editorial work.
• Establish a visibility baseline: Marketing and analytics teams should test buyer-focused prompts against LLMs and compare brand appearances and citations with competitors. Repeating consistent prompts helps identify content gaps and track progress.
• Let diagnosis shape CMS investment: Platform and content teams should use an audit of technical access, content quality and current performance to prioritize improvements before migration. Assign ownership for visibility tools and connect their findings to publishing decisions.
• Apply findings before publication: Editors should check each new piece for a clear customer question, a complete answer and retrievable structure. Using visibility findings during creation reduces the volume of content requiring later repair.