If your search strategy relies entirely on manual efforts from that team, which often comes about due to a siloed organizational structure, it won’t thrive.
The value of search expertise isn’t just in one-off tasks but from best practices incorporated into your company’s SOPs. This maximizes the work (and shows additional value) from what other teams already do.
Here are some examples:
- Standardize Copy: Use on-page SEO & emerging AI search guidelines to create a resource for content writers. This limits the inefficient cycle of tweaking live pages after the fact, which may harm results too.
- Automate Technical Web Design: Build page speed rules into reusable UI widgets & schema markup into CMS template blocks. Technical SEO then happens natively, without devs rethinking it for every page.
- Deploy AI Agents for Site Maintenance: Turn inefficient chores into automated background processes. Use AI agents to fix broken links, add internal links pointing to new content and optimize images.
- Scale Brand Associations: While some customization makes sense, digital marketers should have boilerplates to support topical authority from business descriptions.
- Train for AI Sentiment: Establish an SOP for community & partnership teams to encourage user reviews with high sentiment & specificity, often by asking them the right questions, which reinforces consistent AI responses.
- Create a Support-to-FAQ Pipeline: Have your customer support team flag repetitive user tickets, turning them into structured Q&As in FAQs.
- Fix the AI Attribution Blind Spot: Add a “How Did You Find Us?” field immediately after conversion. Because standard analytics often masks LLM attribution as direct traffic, this simple step is the most accurate way to properly measure AI search success.
Tactical work gets the foundations right, but process optimization is how you actually scale. The most effective partners don’t just deliver spreadsheets of fixes, they integrate best practices throughout your company’s existing processes.