The State of AI SEO in 2026
There's no shortage of advice about using AI for SEO. Most of it falls into two camps:
- "Just use ChatGPT to write blog posts" — which produces generic, undifferentiated content that Google has no reason to rank.
- "AI content is spam and will get you penalised" — which ignores the fact that AI-assisted content at scale is now standard practice across successful sites.
The truth, as usual, is more nuanced. AI-powered SEO works extremely well when you get the architecture right. It fails spectacularly when you treat it as a replacement for strategy.
What Programmatic SEO Actually Is
Programmatic SEO is the practice of generating large numbers of pages from structured data using templates. Think of how Zapier has a page for every integration (Zapier: Connect Slack to Google Sheets), or how G2 has a comparison page for every software category.
These aren't "AI-generated content" in the spammy sense. They're well-structured, genuinely useful pages that happen to be produced at scale through automation rather than manual writing.
The AI component comes in when you need the content to be unique, contextual, and genuinely informative — rather than just template fill-in-the-blanks.
The Architecture That Works
Based on what we've built and shipped (including this very site), here's the architecture pattern that produces results:
1. Start with the Data Model
Before writing a single word of content, define your data model. What are the entities? What are the relationships? What does each page need to contain?
For a software alternatives site, this means:
- Software entities (name, category, pricing, use cases)
- Alternative entities (name, description, pros, cons, pricing, rating)
- Page entities (which software, which use case, which alternatives)
- FAQ entities (question, answer, per page)
This isn't an AI step. This is an architecture step. Get it wrong, and no amount of AI will save you.
2. Design Templates Before Generating Content
Every page type needs a template that enforces:
- Consistent heading hierarchy (H1 > H2 > H3)
- Required sections (comparison table, detailed reviews, verdict, FAQ)
- Structured data (schema.org markup computed from the data)
- Internal linking (algorithmically determined from relationships)
The template is your quality guarantee. The AI generates content that fits the template, not the other way around.
3. Generate Structured Content, Not Freeform Text
This is the critical insight most people miss. Don't ask AI to "write a blog post about Notion alternatives." Instead, ask it to produce structured data that your template will render.
The difference:
- Freeform: Inconsistent format, variable quality, hard to validate
- Structured: Consistent schema, validatable, guaranteed to render correctly
When the AI outputs JSON that matches your TypeScript interface, you get compile-time guarantees about the content structure. You can programmatically verify that every page has the required fields, that ratings are within range, that pricing information is present.
4. Layer SEO Into the Template, Not the Content
Many people try to make their AI produce "SEO-optimised content." This is backwards.
SEO elements should be computed by your template:
- Title tags — derived from the data (
Best ${software} Alternatives for ${useCase} in ${year}) - Meta descriptions — generated from the data with a consistent pattern
- Canonical URLs — computed from the slug structure
- Structured data — assembled from the data model
- Internal links — algorithmically determined from entity relationships
- Breadcrumbs — derived from the URL hierarchy
The AI doesn't need to "do SEO." Your architecture does.
5. Build Quality Control Into the Pipeline
At scale, you can't manually review every page. But you can:
- Validate every output against the TypeScript schema (malformed content won't build)
- Check for minimum content length, required fields, and rating ranges
- Verify internal links resolve to real pages
- Run the build and check for errors
If a page fails validation, regenerate it. The marginal cost of regeneration is near zero.
What Doesn't Work
Thin content at scale
Generating 1000 pages with 200 words each is a fast track to a penalty. Each page needs to provide genuine value. Our comparison pages average 2000+ words of unique, contextual content per page.
No editorial oversight
AI makes mistakes. It hallucinates pricing details. It attributes features to the wrong product. A human needs to review output, especially for factual claims.
Ignoring technical SEO
Great content on a slow, poorly structured site won't rank. Performance, Core Web Vitals, proper HTML semantics, and clean URL structures matter as much as the content itself.
Treating it as set-and-forget
Markets change. Pricing updates. New tools launch. Programmatic SEO at scale requires a maintenance strategy, not just a launch strategy.
The Results You Can Expect
Done well, AI-powered programmatic SEO can:
- Launch a comprehensive content library in days instead of months
- Cover long-tail keywords that would be uneconomical to target manually
- Maintain consistent quality across hundreds of pages
- Scale content production without scaling headcount
Done poorly, it produces generic noise that search engines rightfully ignore.
The difference between the two isn't the AI model you use. It's the architecture you wrap around it.
What's Next
We're working on a detailed course that covers the exact workflows, prompts, and quality control systems we use for AI-powered programmatic SEO. If you're interested, keep an eye on the blog — we'll share more specifics as we go.
For now, the actionable takeaway is this: invest in your data model and templates before you invest in AI content generation. The architecture is the strategy. The AI is just the engine.