Most marketers use AI to write faster. I use it to think better.

There is a difference. Faster means more output. Better means more outcomes, higher rankings, stronger positioning, client revenue that actually moves. I have grown organic traffic by 70% in six months, managed 700+ articles for one company, and run a $100K GTM campaign. None of that happened by pasting prompts into ChatGPT and hitting publish.

Here is my actual AI content strategy: the stack I run, the process I follow phase by phase, and what it has produced for me and for the clients I work with.

What Is an AI Content Strategy?

The key distinction is where AI fits. AI handles research synthesis, outline structuring, draft generation, and optimization passes. The human handles the angle, the proof, the perspective, and the editorial filter that makes the content worth reading. Remove the human layer and you get content that sounds like everyone else’s AI content. Keep it, and you get something that actually ranks, resonates, and converts.

Why Most AI Content Strategies Fail

Two failure modes appear in almost every marketing team I have worked with or advised:

The fix is not less AI. The fix is a workflow where AI and human judgment each do what they are actually good at.

My AI Content Stack

I do not use every tool on every piece. The stack scales to the job. A LinkedIn post is Claude only. A full client content strategy brief is Perplexity plus NotebookLM plus Gemini plus Claude.

Here is what each tool does in my workflow:

Google Search Console sits underneath all of this as the reality check. No AI tool tells me what my specific audience is actually searching for. Connecting GSC and GA4 directly to Claude via MCP changed how fast I can move from data to insight to content plan.

Phase 1: Research and Planning

Before I write a single word, I need four things confirmed:

• What the target keyword is and what the current top 3 results actually say
• What question the reader is really asking (not just what they typed)
• What my angle is that the existing results do not cover
• What proof point I can bring that no one else in the results can match

For keyword research, I start with Google Search Console data to find underperforming URLs and hidden opportunities — pages already getting impressions but low clicks. Then Perplexity to understand what the current search results are actually saying. Then I cross-reference with free Google SEO tools like Search Console’s performance report, Google Trends, and People Also Ask to confirm search intent.

I ask Perplexity: “What are the top 5 articles ranking for [keyword] saying, and what are they missing?” That gap becomes my angle.

Phase 2: Content Creation Workflow

Once the brief is locked, here is my step-by-step process:

Step 1: Brief Generation with Perplexity + Claude

I give Perplexity the keyword and three competitor URLs. It comes back with what those articles cover, what claims they make, and what questions they leave unanswered. I paste that into Claude with my positioning angle and ask for a structured content brief with an H2 skeleton and the key point each section needs to make.

Step 2: Outline Review (Human)

I review the brief and make the decisions AI cannot make for me: what to cut, what my angle adds that the AI missed, and where my personal proof points belong. This is the most important step in the whole workflow. If I skip it, the article reads like everyone else’s.

Step 3: Section-by-Section Drafting with Claude

I draft one section at a time, not the full article at once. I give Claude the brief, the section goal, and a tone note. “Write the introduction that opens on the specific tension of [X], max 3 short paragraphs.” Scoped prompts produce tighter output than “write the full article.”

Step 4: Human Editorial Pass

Every draft gets a human pass before it moves forward. I add proof points from my own work, opinions I actually hold, examples I have seen in practice, and the lines that make it sound like me rather than a model. This is where the article becomes worth reading.

Step 5: SEO and LLM Optimization

After the draft is solid, I run a two-layer optimization pass: traditional SEO and LLM optimization. More on the second layer in the next section.

Phase 3: SEO and LLM Optimization

These are two separate layers, and most marketers only work on one.

Traditional SEO

The primary keyword appears in the H1, within the first 100 words, in at least two H2s, the meta title, and the meta description. Internal links connect the article to related content on the same domain. Page speed is clean. Schema markup is in place for FAQ sections. This is table stakes, and it is not optional.

LLM Optimization (GEO and AEO)

This is where the real opportunity sits in 2026. AI search systems, Google AI Mode, Perplexity, Claude, ChatGPT — are now surfacing content in their answers. To get cited rather than skipped, your content needs four things:

• A clear definition block early in the article — something an AI can lift and quote as the direct answer
• FAQ sections with direct, specific answers — not hedged, not vague, not “it depends” without an actual answer following it
• Named, specific proof — company names, numbers, outcomes. AI systems prefer citable evidence over generic claims
• A clear author perspective — something that distinguishes the content from the generic version of the same answer

The structure of this article follows those principles. The definition box at the top exists so AI systems can cite a clean answer. The FAQ at the bottom exists because People Also Ask and AI overviews pull directly from well-structured Q and A content.

Phase 4: How I Apply This for Clients

The same stack and workflow applies when I am working for a brand. Two things change:

• I replace my personal proof points with the client’s product data, customer stories, and internal knowledge I gather in onboarding
• Every AI output runs through their brand voice constraints before delivery — tone guidelines, banned phrases, preferred vocabulary, and 3 examples of content that sounds like them

The brand voice comes first. The AI speeds up what comes after.

For eCommerce clients specifically, the content strategy connects directly to proven eCommerce growth strategies — where content is not just SEO infrastructure but a direct acquisition channel.

Results at a Glance

Frequently Asked Questions