How to Use GoHighLevel SEO AI Recommendations with Infinite Scroll

What GoHighLevel SEO AI Recommendations are designed to do

GoHighLevel SEO AI Recommendations present suggested SEO improvements as individual cards inside the platform. A card may describe an issue, identify where it occurs, or suggest an action related to page content, structure, or another on-page SEO consideration.

The recommendations are inputs to an SEO workflow, not a replacement for review. An AI suggestion can be useful while still requiring context from the page, the business objective, the target audience, and the wider site structure.

Infinite scroll changes how the list is loaded. It does not change the need for prioritization, ownership, validation, or documentation.

How infinite scroll works in the recommendations view

Infinite scroll removes the need to select a next page manually. The view starts with an initial group of recommendation cards. When you move toward the bottom of that group, GoHighLevel can load more cards and append them to the existing list.

1. Open the SEO AI Recommendations view for the relevant site, funnel, or page.
2. Review the initial set of recommendation cards.
3. Continue scrolling toward the bottom of the currently loaded results.
4. Wait for additional cards to appear if more recommendations are available.
5. Continue reviewing until the list ends or you have reached the scope of work you planned to handle.

This is a presentation and navigation pattern. It helps you move through a long result set, but it does not automatically create a project plan or confirm that every suggestion should be implemented.

Step-by-step process for reviewing GoHighLevel recommendations

1. Open the correct recommendations view

Sign in to GoHighLevel with the permissions required for the relevant website, funnel, or page. Navigate to the area where SEO AI Recommendations are displayed and confirm that you are reviewing the intended asset.

This first check matters when an account contains multiple websites, funnels, or pages. A recommendation can be reasonable for one page and inappropriate for another. Establish the scope before working through the list.

2. Read the first batch before scrolling continuously

Start with the cards already visible. Read the description, inspect the location or page context provided, and determine what type of change is being suggested. Avoid scrolling as quickly as possible simply to load the entire list.

Instead, use the first batch to identify patterns. Several cards may relate to similar page elements or indicate that a broader content or structure decision is needed. Grouping related suggestions can be more useful than treating every card as a separate task.

3. Give each recommendation a clear disposition

For each card, choose a simple outcome:

• Apply: The suggestion is relevant, understood, and safe to implement.
• Investigate: More context, technical review, or business input is needed.
• Defer: The suggestion may be useful, but another dependency or priority comes first.
• Dismiss: The suggestion is not relevant to the page or objective.

The exact tracking method can vary. You might use a task list, a project management workspace, or an internal change log. The important point is that the decision is visible rather than being held in memory.

4. Scroll to load the next group

Once you have reviewed the current group, continue scrolling toward the bottom of the recommendations list. When more results are available, the interface can load additional cards below the existing cards.

Give the interface time to load before assuming that the list has ended. If the view appears stalled, pause and check whether the browser, connection, or page state is affecting the result.

5. Repeat the review cycle

Use the same sequence for each newly loaded group: read, understand, decide, record, and then continue. This creates a repeatable operating rhythm instead of turning infinite scroll into an unstructured stream of suggestions.

When you apply a recommendation, record what changed and where. If the action affects page content, metadata, structure, or another setting, the note should make the completed work understandable to the next person who reviews the page.

A practical operating model for AI SEO suggestions

A simple review model can keep the feature connected to business work:

This sequence separates recommendation discovery from implementation. That distinction is useful because the person reviewing the cards may not be the person responsible for changing the page.

How to prioritize a long list of recommendations

Infinite scroll can make a large list feel manageable, but it can also encourage teams to work in display order. The first card is not necessarily the most important card. Prioritize according to the likely business effect, the confidence that the issue is real, and the effort or dependency involved.

• Start with clear issues: Review recommendations where the page context and proposed action are easy to understand.
• Protect important pages: Consider the role of the page in the site, funnel, or customer journey before changing it.
• Group related changes: Handle recommendations that belong to the same page element or content decision together.
• Separate analysis from editing: Do not make a rushed change simply because the card is available.
• Assign an owner: A recommendation without ownership is an observation, not a task.

Example: turning a recommendation list into controlled work

Imagine a marketing team reviewing AI recommendations for a service page. The first cards suggest several content and on-page improvements. Rather than editing the page immediately, the team groups the cards into content changes, structural questions, and items requiring technical review.

The content owner handles the clearly understood copy changes. A marketing lead investigates whether the suggested wording still reflects the service offer. A technical owner reviews the structural items. Recommendations that do not fit the page objective are marked for dismissal. The team then scrolls for more cards and repeats the same process.

This example does not depend on a particular project management tool. The important design choice is that the scrolling interface feeds a decision process instead of becoming the process itself.

Troubleshooting infinite scroll behavior

If additional cards do not load as expected, work through basic checks before assuming that recommendations are unavailable:

A stalled list does not necessarily mean that no further recommendations exist. It may reflect a temporary loading problem, a permissions issue, or the end of the available results. Treat the interface state as something to verify rather than infer.

Designing a better workflow around GoHighLevel SEO AI Recommendations

The feature is most useful when it sits inside an agreed process. Define who reviews recommendations, who approves changes, who implements them, and how completed work is checked. If the same person performs every step, the ownership still needs to be explicit so progress can be reported reliably.

Keep the tracking method proportionate. A small site may need only a focused checklist. A larger operation may need a structured workflow with statuses, owners, due dates, and links to the affected pages. The purpose is not to create administration for its own sake. The purpose is to prevent useful recommendations from becoming unowned or duplicated work.

If SEO recommendations are part of a wider CRM, operations, or automation environment, the surrounding process may need attention as well. ConsultEvo’s systems, CRM, automation and AI implementation services provide context for designing connected workflows. For teams already managing work in ClickUp, ClickUp workspace architecture and workflow consulting can help make ownership and status visible. The principle remains the same: clarify the process before adding more automation.

ConsultEvoClient Work: Automation, CRM and Operations SystemsExamples of connected systems designed around operational problems, data, automation and AI.→

GoHighLevel infinite scroll solves a navigation problem. Your operating process must solve the decision problem. When recommendations are reviewed in batches, assigned clearly, and validated after implementation, the feature becomes part of a dependable SEO workflow rather than just a convenient list.