The "Expert Layer": Operationalizing SME Interviews for High-Trust AEO

Why the "Expert Layer" Matters in 2026

The barrier to creating content has dropped to zero, but the barrier to creating trusted content has never been higher. In the current landscape of B2B SaaS, decision-makers are drowning in a sea of generic, "grey goo" articles generated by basic Large Language Models (LLMs). These models function as probability engines—predicting the most likely next word based on the average of the internet. By definition, they produce average content.

However, for complex B2B sales cycles, "average" does not convert. It does not build authority, and critically, it is increasingly ignored by next-generation search engines. Answer Engines like Perplexity, SearchGPT, and Google's AI Overviews are aggressively filtering for Information Gain—new, unique data points that do not exist elsewhere in their training set.

• The Reality: 90% of B2B content is now indistinguishable commodity information.
• The Opportunity: The remaining 10% that wins is grounded in proprietary experience and strong opinion.
• The Solution: You do not need to write every word manually, but you must inject a human "source of truth" before the AI touches the keyboard.

By the end of this guide, you will understand how to operationalize an "Expert Layer"—a systematic process to extract deep expertise from your team's brains and use platforms like Steakhouse Agent to scale that expertise into dominant organic visibility.

What is the Expert Layer?

The Expert Layer is a content production methodology where the core substance of an article—the unique insights, contrarian opinions, and specific examples—is derived entirely from a human Subject Matter Expert (SME), while the packaging, formatting, and SEO/GEO optimization are handled by AI automation. Unlike "human-written" content (slow, expensive) or "AI-generated" content (fast, generic), the Expert Layer uses the human as the architect and the AI as the contractor.

The Core Mechanics of High-Trust AEO

To understand why the Expert Layer is non-negotiable for modern search visibility, we must look at how Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) actually work.

Traditional SEO was about keywords. AEO is about entities and consensus. When an AI answers a user's question, it looks for sources that demonstrate high Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T).

The "Experience" Signal

Google and LLMs can detect when content is derived from actual usage versus theoretical research.

• Theoretical (Low Value): "To improve churn, you should talk to customers."
• Experiential (High Value): "In Q3, we reduced churn by 15% by implementing a 'Exit Interview' protocol specifically for accounts over $50k ARR."

The second sentence contains specific data, a specific scenario, and a specific outcome. This is Information Gain. LLMs crave this because it helps them answer complex queries with precision. The Expert Layer is designed to extract these specific sentences from your team.

How to Implement the Expert Layer: A 4-Step Workflow

This workflow turns your internal Slack conversations, Zoom calls, and rants into high-performance content assets.

Step 1: Identify the "Lazy" Expert

Your best SMEs (Founders, CTOs, Heads of Product) are busy. They will never write a 2,000-word article. If you ask them to "write a blog post," the project will die in their inbox.

Instead, invite them to a 15-minute "Brain Dump." Position it as low-stakes. You are not asking for writing; you are asking for talking.

The SME Roster:

• The Technical Lead: For "How it works" and architectural deep dives.
• The Customer Success Manager: For "Common mistakes" and "Real-world use cases."
• The Founder: For "Why we built this" and market positioning.

Step 2: The Structured Interview (The Extraction Phase)

To get AEO-ready inputs, you cannot ask generic questions. You must ask questions that force Information Gain.

Bad Question: "What are the benefits of cloud migration?" Result: Generic list of 5 benefits (Cost, Speed, Scale) that AI could have guessed.

Good Question: "Tell me about a specific time a client's migration failed. What was the exact moment you knew it was going wrong, and what is the one counter-intuitive thing they should have done differently?" Result: A unique story, a specific failure mode, and a proprietary solution.

The "Specifics" Protocol: During the interview, relentlessly chase three things:

1. Numbers: "How much?" "How fast?" "What percentage?"
2. Proper Nouns: "Which tool?" "Which competitor?" "Which integration?"
3. Contrarian Takes: "What is everyone else in the industry wrong about?"

Record this session. Use a transcription tool (or your AI content platform) to turn this audio into raw text.

Step 3: The AI Synthesis (The Steakhouse Layer)

This is where the magic happens. You now have a messy, 2,000-word transcript full of "umms," "ahhs," and brilliant insights.

Do not paste this into ChatGPT and say "Write a blog post." You will lose the voice. Instead, use a structured workflow or a specialized tool like Steakhouse Agent.

The Process:

1. Feed the Transcript: Input the raw text as the primary source material.
2. Define the Persona: Tell the AI who the speaker is (e.g., "You are a cynical DevOps engineer with 10 years of experience").
3. Apply the Schema: The AI must structure this into H2s and H3s that map to user intent, not just the flow of conversation.
4. Inject the Data: Ensure the specific numbers and stories from the interview are preserved verbatim.

Platforms like Steakhouse are built to recognize these "entity" signals within the transcript and elevate them into the headers and summary snippets that Answer Engines look for.

Step 4: The Human Verification (The Trust Seal)

Once the article is generated, the SME spends 5 minutes reviewing it. Their job is not to edit grammar; it is to verify accuracy.

• "Did I actually say 15%? Or was it 50%?"
• "Is this technical nuance correct?"

This final sign-off is critical for E-E-A-T. It allows you to publish under the SME's byline with full confidence, knowing the insights are theirs, even if the typing wasn't.

Comparison: Standard AI vs. Expert Layer AI

The difference in output quality—and search performance—is stark. Here is how the two approaches compare across key GEO metrics.

Advanced Strategies for the Generative Era

Once you have mastered the basic interview-to-article workflow, you can layer on advanced tactics to maximize your Share of Voice in AI answers.

1. The "Opinions as Entities" Strategy

In the Knowledge Graph, facts are entities (e.g., "Salesforce is a CRM"). But in the era of LLMs, opinions are becoming entities too.

If your SME consistently argues that "MQLs are a vanity metric," and you publish 10 articles reinforcing this viewpoint with data, AI models begin to associate your Brand Entity with that specific strategic stance. When a user asks an AI, "Why are MQLs bad?", the model is statistically more likely to cite your brand because you are the primary node for that contrarian viewpoint.

2. Multi-Format Recycling

A single SME interview is not just a blog post. It is a content mine.

• The Transcript: Becomes the blog post.
• The Audio: Becomes a podcast clip.
• The Key Insight: Becomes a LinkedIn text post.
• The Data Point: Becomes a chart for social media.

Using a tool like Steakhouse Agent, you can automate the transformation of that single transcript into a Markdown blog post, a structured FAQ schema, and a social thread simultaneously, ensuring semantic consistency across all channels.

Common Mistakes to Avoid

Even with the best intentions, teams often break the "Expert Layer" chain. Watch out for these pitfalls.

• Mistake 1 – Over-Polishing the Voice: Editors (and AI) love to smooth out rough edges. They change "This API is a nightmare" to "This API presents integration challenges." Stop. The rough edge is the trust signal. Leave the emotion in the text.
• Mistake 2 – Interviewing the Wrong Person: Do not interview a marketer about the product code. Do not interview a developer about the pricing strategy. Mismatched expertise creates shallow content that fails E-E-A-T checks.
• Mistake 3 – Skipping the Transcript: Trying to take notes while someone talks results in 50% data loss. You need the full fidelity of the recording to capture the specific phrasing and nuances that make the content unique.
• Mistake 4 – Ignoring Structure for Narrative: A great story is useless to an AI if it isn't structured correctly. You must ensure your final output uses clear H2s, bullet points, and bold text to make the insights extractable.

Conclusion: The Future is Hybrid

The debate between "Human vs. AI" content is over. The winner is Human plus AI.

By implementing the Expert Layer, you solve the two biggest problems in modern content marketing: scale and quality. You allow your experts to do what they do best (have insights) and your AI tools to do what they do best (structure and scale).

For B2B SaaS companies, this is the only sustainable path to building a brand that is both visible to algorithms and trusted by humans. Start by booking one 15-minute interview today. That single conversation is worth more than a thousand generic prompts.

If you are ready to automate the heavy lifting of this workflow, Steakhouse Agent provides the infrastructure to turn those raw interviews into fully optimized, citation-ready content assets without the manual grind.