Semrush for AI Search: Mastering 2026 SEO

The advent of AI search has fundamentally reshaped how users discover information online, demanding a rapid evolution in SEO tools and strategies. Successful digital marketing in 2026 hinges on understanding and adapting to these new algorithmic realities. This tutorial provides a step-by-step guide to configuring a leading marketing technology platform, specifically Semrush, to identify opportunities and track performance in an AI-driven search environment. We will focus on actionable steps within the tool’s 2026 interface to ensure your content ranks effectively.

Step 1: Setting Up Your Project and Integrating Data Sources

Before you can begin using advanced SEO tools for AI search, a properly configured project is essential. This foundational step ensures all your data streams are connected and ready for analysis.

1.1 Create a New Project

1. Log in to your Semrush account.
2. On the left-hand navigation bar, click on Projects.
3. Click the large blue button labeled Create new project.
4. Enter your domain name (e.g., “yourdomain.com”) in the field provided and give your project a descriptive name, such as “AI Search Strategy 2026.”
5. Click Create project.

Pro Tip: I always recommend creating a separate project for each distinct website or subdomain you manage. Mixing data can lead to skewed insights, especially when comparing performance metrics for different content types.

1.2 Integrate Google Search Console and Google Analytics

1. Within your newly created project dashboard, locate the “Setup” section.
2. Click on Connect Google Search Console. Follow the on-screen prompts to authorize Semrush access to your Search Console data. This typically involves selecting the correct Google account and property.
3. Next, click on Connect Google Analytics. Repeat the authorization process, ensuring you grant access to the relevant Analytics property for your domain.

Common Mistake: Failing to integrate these data sources means Semrush can’t pull important performance data directly from Google, limiting the accuracy of its recommendations and making it harder to track true AI search visibility. Without this direct connection, you’re essentially flying blind on critical organic performance metrics.

Expected Outcome: Your project dashboard will display “Connected” next to both Google Search Console and Google Analytics, and you’ll start seeing initial data populate within 24 hours.

Step 2: Identifying AI-Optimized Content Gaps with Topic Research

AI search models prioritize complete, semantically rich content that directly answers user queries. The Topic Research tool helps uncover these opportunities.

2.1 Initiate Topic Research

1. From your project dashboard, navigate to the Content Marketing section in the left sidebar.
2. Select Topic Research.
3. Enter a broad seed keyword or phrase relevant to your industry (e.g., “sustainable urban farming” or “enterprise cloud migration strategies”).
4. Choose your target country and language.
5. Click Get content ideas.

2.2 Analyze Topic Cards and Subtopics

1. Semrush will generate a series of “topic cards.” Each card represents a cluster of related search queries and popular content.
2. Click on a relevant topic card. For instance, if you entered “sustainable urban farming,” a card might be “Hydroponics for Beginners.”
3. Within the card, observe the “Subtopics” and “Questions” sections. These are key indicators of what AI search models consider important entities and user intents. Pay close attention to the most frequently asked questions and how competitors are structuring their answers.

Pro Tip: Look beyond just keywords. AI search prioritizes understanding the underlying intent and entities. If a topic card shows “vertical farming benefits” and “hydroponic systems types” as subtopics, these are distinct semantic entities that your content should address comprehensively. According to a Statista report from late 2025, search engines are increasingly relying on entity recognition to surface relevant information, making this approach critical.

2.3 Export and Prioritize Content Opportunities

1. You can export the data from individual topic cards or the entire report by clicking the Export button, usually located in the top right corner.
2. Prioritize topics that have a high volume of questions and a perceived content gap where your competitors aren’t providing truly complete answers.

Common Mistake: Simply looking at keyword difficulty. While important, it’s not the sole determinant in AI search. A high-difficulty keyword might be easier to rank for if you create genuinely superior, entity-rich content that satisfies multiple related user intents, something a basic keyword difficulty score doesn’t fully capture.

Expected Outcome: A prioritized list of content topics and subtopics that align with AI search query patterns, ready for content creation.

Step 3: Crafting AI-Ready Content with the Content Marketing Platform

Once you have your topics, the next step is to create content that speaks directly to AI search algorithms. The Content Marketing Platform (specifically the Content Writer and Content Audit features) is invaluable here.

3.1 Use the SEO Content Template

1. From the Content Marketing section, select SEO Content Template.
2. Enter your target keyword (e.g., “hydroponic systems for home”).
3. Click Create content template.
4. Semrush will analyze the top 10 ranking results and provide recommendations for text length, readability, semantic keywords, and backlinks.

Pro Tip: Pay particular attention to the “Semantically Related Keywords” section. These aren’t just synonyms. They are entities and concepts that Google’s AI models associate with the primary topic. Integrating these naturally makes your content more complete and authoritative in the eyes of AI. I’ve seen client sites in the Atlanta market, particularly those targeting local home and garden enthusiasts, achieve significantly better visibility by carefully weaving these related terms into their product guides and blog posts.

3.2 Evaluate Content with the AI Content Score

1. After drafting your content, either paste it into the SEO Content Writer interface or connect it via Google Docs.
2. The tool will provide an AI Content Score. This score, typically out of 100, measures how well your content addresses the primary keyword, semantic entities, readability, and overall comprehensiveness compared to top-ranking competitors.
3. Review the recommendations in the right-hand panel, focusing on “Key recommendations” and “Suggested related keywords.”
4. Make iterative improvements to your content based on these suggestions.

Common Mistake: Chasing a perfect 100 score at the expense of natural language. While a high score is good, the ultimate goal is to provide value to the human reader. AI search understands natural language, so prioritize clarity and flow over keyword stuffing. A good target is usually 85+, but don’t force awkward phrasing for the last few points.

Expected Outcome: High-quality, semantically rich content that is optimized for both human readers and AI search algorithms, with a strong AI Content Score.

Step 4: Monitoring AI Search Visibility with Position Tracking

AI search often surfaces information in formats beyond traditional blue links, such as featured snippets, answer boxes, and “People Also Ask” sections. Tracking these is paramount.

4.1 Set Up Position Tracking

1. From your project dashboard, navigate to Position Tracking.
2. Click Set up tracking.
3. Enter your primary target keywords. Critically, include a mix of traditional head terms and long-tail, question-based queries (e.g., “what is vertical farming,” “how to grow hydroponic lettuce indoors”).
4. Select your target location (e.g., “United States,” “Georgia,” or even “Atlanta, GA” for local businesses).
5. Choose your device type (desktop, mobile, or both).
6. Click Start Tracking.

Common Mistake: Only tracking traditional organic rankings. In 2026, a significant portion of user queries are answered directly by AI interfaces or within SERP features, bypassing the need to click through to a website. Ignoring these metrics means missing a huge part of your potential visibility.

Expected Outcome: Clear insights into your content’s performance in AI-driven search results, identifying opportunities to capture more featured snippets and answer boxes.

Step 5: Discovering Conversational Keywords with the Keyword Magic Tool

AI search thrives on natural language and conversational queries. The Keyword Magic Tool is excellent for uncovering these opportunities.

5.1 Generate Keyword Ideas

1. From the Semrush main menu, go to Keyword Research.
2. Select Keyword Magic Tool.
3. Enter a broad topic or seed keyword (e.g., “cloud security”).
4. Click Search.

5.2 Filter for Conversational Queries

1. On the left-hand filter panel, click on Questions. This will filter the results to show only queries phrased as questions.
2. Further refine your search by using the “Word count” filter, setting it to 4 or more, to identify longer, more specific conversational queries (e.g., “what are the best cloud security practices for small businesses”).
3. Review the list for keywords that align with your content strategy and user intent.

Pro Tip: Don’t just look for high search volume. Conversational queries, even with lower individual volumes, often indicate high intent and are more likely to be processed by AI assistants. A collection of well-optimized answers to these specific questions can drive significant, qualified traffic. We’ve found that targeting these conversational queries can lead to a higher conversion rate, even if the overall traffic volume is lower than head terms.

5.3 Export and Map Keywords to Content

1. Export your filtered list of question-based keywords.
2. Map these keywords to existing content that can be updated or new content you plan to create. Ensure your content directly answers these questions concisely and comprehensively.

Common Mistake: Over-reliance on short-tail keywords. While they have volume, they are often too broad for AI to provide a definitive answer, and competition is fierce. Conversational, long-tail queries are where AI search truly shines, and where you can carve out a competitive advantage.

Expected Outcome: A complete list of conversational, question-based keywords that can be integrated into your content strategy to improve AI search visibility.

Adapting your SEO strategy to AI search is not an option. It is a necessity for maintaining digital visibility in 2026. By diligently applying these steps within your chosen marketing technology platforms, you proactively position your content to be discovered by evolving algorithms and directly answer user queries. For CMOs working through this new field, understanding CMO AI strategy is important. Also, ensuring your AI content maintains authenticity will be key to long-term success.