Using AI as Your In-House SEO Expert (Without Hiring One)
Can AI replace an SEO consultant for a small B2B team?
For a lean B2B team, AI can absorb most of the work an external SEO consultant would do — at least to the point where hiring a full-time expert becomes optional.
The Storetasker CMO describes rebuilding the company website and leveraging AI heavily across copywriting and site structure, saying he could not have done the work without it and would have spent thousands of dollars getting equivalent input from people.
The reframing is not "AI replaces the expert" but "AI reduces the need for one." With enough baseline understanding, an operator can make confident SEO decisions in-house and reserve human hiring for the areas where it actually compounds.
How do I pressure-test AI's SEO recommendations?
Treat the AI like a junior consultant you actively challenge.
The Storetasker workflow is a back-and-forth with a custom GPT ("SEO by AI Research Plus") on concrete decisions — for example, whether a page should live at /developers , /shopify-developers , or with a subfolder for Shopify Plus.
Every call is made by the operator applying critical thinking against the GPT's suggestions.
Pressure-testing also means catching the model contradicting itself: Push back when the AI gives different answers across sessions.
Cross-check keyword suggestions against real tool data — e.g. pasting the list into Semrush, screenshotting results, and feeding them back to the GPT to refine which keyword to focus on.
Use AI to clean and structure scraped or copy-pasted resources for context before asking for recommendations.
“I don't think I could have done any of the work I've done today. On this project without AI. And it's like it's so valuable.”
What baseline SEO knowledge do I still need to work with AI?
You need enough SEO literacy to spot when the AI is wrong or inconsistent.
As the Storetasker CMO puts it, baseline knowledge lets you say "hey, I think you're wrong here.
You said this this time, but you said something else the next time" and draw connections faster than someone net new to SEO.
That baseline is also what lets you decide where to trust AI and where to hold the line: Identify where your human edge actually is and trust your judgment there.
For low-hanging fruit you can get 85% right , lean on AI tools and stitch the pieces together.
Free up time to cover more ground across your role — SEO one day, case studies the next.
“if you just apply good thinking for generic SEO practices, it's going to help you in the LLMs, and that's we see that today.”
How do I build pages that rank in Google and get cited by LLMs?
The Storetasker observation is that you don't need a separate "LLM strategy" — solid organic SEO fundamentals already drive citations in ChatGPT, Claude, and Perplexity.
Leads are already arriving via LLMs without anything fancy done to appeal to them, simply because the site is strong on classic organic factors.
What did change in the new build was adding Q&A-style sections near the top of the page , specifically for LLMs.
On a Shopify developers page that means a "what you need to know" block answering: When should you hire a developer?
What skills should I look for in a developer?
How much does it cost?
This sits close to the hero — distinct from a traditional FAQ at the bottom — and is built through back-and-forth with the AI.
“hey. I think you're you're you're wrong here. You said this this time, but you said something else the next time.”
When should I build, buy, or hire for AI-powered SEO?
David Arnoux offers a simple decision framework that applies cleanly to SEO work: Buy when the workflow is proven, the team isn't technical, and speed matters — e.g.
Clay for enrichment, Gamma for decks, HubSpot AI features.
Build when it's a core differentiator with proprietary data and feedback loops — programmatic SEO is named explicitly here, alongside ad creative loops and custom ABM automation.
Hire for long-term strategic stuff: one AI architect (fractional or full-time), not ten prompt engineers — for data unification, repo architecture, and strategic oversight.
SEO and generative engine optimization keep coming up as recurring buckets in B2B AI work, so deciding where it sits on this matrix matters before tooling up.
“One AI architect, fractional or full time, not 10 prompt engineers.”
How fast is AEO/GEO actually changing, and what does that mean for my playbook?
AEO and GEO are moving fast.
The Truffle founder notes that classic SEO playbooks worked well to build the foundations, "but we saw that change very rapidly" — and AEO/GEO is also changing really rapidly.
There was a window where you could game it by getting listed in listicles, and that window is closing.
The practical implication: don't over-invest in a single tactic that might be obsolete next quarter.
Keep the foundations strong, stay close to how LLMs are surfacing your content, and use AI itself as the fastest way to iterate on page structure as the rules shift.
Frequently asked questions.
- Can a small B2B team really run SEO without an agency or consultant?
- Yes, if the team has baseline SEO literacy. The Storetasker CMO rebuilt the site's positioning, structure, and copy largely through back-and-forth with AI, and says he couldn't have done the work without it and would have otherwise spent thousands on external input. AI doesn't eliminate the expert role entirely, but it reduces the need to pay for a full-time specialist whose only job is SEO.
- What's the workflow for picking URLs and keywords with AI?
- Treat it as a structured conversation. The Storetasker workflow uses a custom GPT (SEO by AI Research Plus) to debate URL structure — for example, /developers vs. /shopify-developers vs. a Shopify Plus subfolder. For keywords, the operator pastes candidate lists into Semrush, screenshots the results, sends them back to the GPT, and lets it argue which keyword to prioritize. Every final call is the human's, made via critical thinking.
- Do I need a separate strategy for ChatGPT, Claude, and Perplexity citations?
- Not a wholly separate one. Strong classic organic SEO already drives LLM citations — Storetasker reports leads arriving via LLMs without doing anything fancy specifically for them. What helps is adding Q&A-style sections near the top of key pages (when should you hire, what skills to look for, how much does it cost), distinct from a bottom-of-page FAQ, written explicitly with LLMs in mind.
- Should I build my own AI SEO tooling or buy something off the shelf?
- David Arnoux's heuristic: buy when the workflow is proven, the team isn't technical, and speed matters (Clay, Gamma, HubSpot AI). Build when it's a core differentiator with proprietary data and feedback loops — programmatic SEO is called out as a build candidate. Hire only for long-term strategic roles like an AI architect for data unification and repo architecture, not a swarm of prompt engineers.
- How do I pressure-test AI's SEO advice so I don't get burned?
- Use baseline SEO knowledge as your bullshit detector. Call out contradictions across sessions ("you said this this time, but you said something else the next time"), cross-check keyword suggestions against tools like Semrush, and feed cleaned, scraped context back into the AI before trusting its recommendations. The operator's job is critical thinking on top of the model's output, not blind execution.
- Is AEO/GEO stable enough to build a long-term playbook on?
- The Truffle founder notes that classic SEO playbooks built strong foundations, but the landscape changed very rapidly, and AEO/GEO is also changing rapidly. Tactics that worked briefly — like gaming listicle inclusion — degrade fast. The safer bet is investing in fundamentals plus a tight iteration loop using AI itself, rather than over-committing to any single AEO trick.