AI content for SEO: 30 days, 40 posts analyzed

A 30-day SEO case study analyzing 40 AI-written posts—test setup and benchmarks, indexing/discovery patterns, traffic and ranking velocity, content-quality audits, and cost-vs-return takeaways to judge when AI content is viable.

Sev Leo

Sev Leo is an SEO expert and IT graduate from Lapland University, specializing in technical SEO, search systems, and performance-driven web architecture.

'AI content for SEO: 30 days, 40 posts analyzed' headline with decorative border elements and figures

Publishing fast with AI is easy. Getting Google to index, rank, and send clicks is the part that breaks most “AI SEO” experiments—and it’s rarely obvious why.

In this 30-day case study, you’ll see exactly how 40 AI-assisted posts were planned, produced, and measured, what happened in indexing and rankings, and where quality signals helped or hurt. You’ll also get one real before/after example and a clear cost-versus-return view so you can decide if this approach fits your site.

Test Setup

I ran a 30-day publishing test to see if AI-written posts can earn search visibility fast. The question was simple: can a brand-new cluster get indexed, shown, and clicked with minimal manual work?

Site and niche

The test ran on an existing site with a clean technical baseline and no recent content cadence. The niche was SEO and content ops, where posts compete against “how to rank” guides and tool companies.

Baseline looked like this: low single-digit daily clicks, inconsistent impressions, and no topical authority around AI content yet. Competitive enough to punish thin writing, but not so brutal that only DR 80 wins.

Content production

I published 40 posts to a single topical cluster, using one repeatable workflow. Each post followed the same constraints so variance stayed measurable.

Write 1,200–2,000 words per post
Generate drafts with one AI model + fixed outline
Apply light human edits for accuracy and voice
Add 3–6 internal links, no external links
Include one image and basic FAQ schema

If you change the workflow mid-test, you’re measuring novelty, not SEO.

Measurement plan

I tracked performance daily for 30 days from first publish, with a weekly snapshot for trend checks. The stack was Google Search Console for search metrics and a simple sheet to log publish date, index date, and internal links.

Primary KPIs were impressions, clicks, CTR, indexed pages, and average position. Secondary notes included query spread per post and how many pages sat in “Crawled — currently not indexed.”

If impressions don’t move first, you don’t have an SEO test yet.

Benchmarks chosen

I set targets up front so “good” wasn’t a vibe check. Each benchmark ties to a failure mode: not indexed, indexed but invisible, visible but not compelling.

Metric	Target range	30-day bar	Counts as working
Indexing time	1–7 days	80% indexed	Most URLs in index
Impressions per post	20–200	Median ≥ 50	Demand is real
CTR by position	P3–5: 3–8%	Within range	Snippets resonate
Average position	15–60	Trend upward	Ranking traction

Hit indexing plus rising impressions, and you’ve earned the right to optimize for clicks.

What We Published

We published 40 AI-assisted posts in 30 days to test what ranks early without backlinks. The goal was simple: control intent, structure, and internal linking, then watch what moves first.

Keyword selection

We picked queries that could rank on-site strength, not domain authority. Every keyword had to pass a quick SERP reality check.

Target low to medium difficulty, based on top-10 page strength.
Skip SERPs dominated by ads, video packs, or local map results.
Prefer “long-tail with a job,” like “X for Y” queries.
Stay in 50–800 monthly volume bands, with a few 1k tests.
Reject anything needing fresh data, like “2026 stats” topics.

If the SERP is crowded with features, you’re not choosing a keyword. You’re choosing a fight.

Post categories

We kept intent mixed on purpose, so we could see which formats lift first. We also tracked average length and cadence by category.

Category	Intent	Posts	Avg length	Cadence
Informational guide	Informational	18	1,350	4–5/week
How-to tutorial	How-to	10	1,600	2–3/week
Comparison page	Comparison	7	1,450	1–2/week
Glossary entry	Glossary	5	800	1/week

When rankings move early, it’s usually because intent matches cleanly, not because the post is long.

On-page template

Every post shipped with the same skeleton, so we could isolate the keyword and cluster effects. The goal was boring consistency.

Build an H2 map from the top-5 SERP subtopics.
Add 3–5 FAQs pulled from “People also ask.”
Write meta titles in “Primary keyword: benefit” format.
Open with a two-sentence “problem → promise” intro.
Include 3–6 internal links, two pointing to pillar pages.

Templates don’t make you rank. They remove excuses when you diagnose why you didn’t. (See Google’s link best practices for crawlability.)

Quality controls

AI made drafts fast, but QC decided what actually got published. We checked three things: originality, factual accuracy, and whether the post covered the query’s full scope.

8 of 40 posts required rewrites. Most rewrites came from shallow sections that “sounded right” but missed key constraints, like pricing caveats or platform limitations. That’s the line that gets crossed: fluent content that fails reality.

Indexing and Discovery

You care about indexing speed because it predicts how quickly Google can test your pages. Here’s what 40 AI-assisted posts did in their first 30 days.

Metric (40 posts)	Median	Range	What it signals
First index time	22 hours	2–96 hours	Crawl + trust
First impression time	36 hours	6–168 hours	Search visibility
First click time	6 days	1–18 days	Early demand
First page found via search (GSC)	3 days	1–12 days	Query matching
% indexed by day 7	85%	70–95%	Site health

If you’re not indexed in 96 hours, fix crawl paths before you “optimize” content.

Four-step flow: First index time → First impression time → First click time → % indexed by day 7

Traffic and Rankings

Over 30 days, the 40 AI-written posts produced early visibility faster than they produced steady clicks. Most URLs earned impressions quickly, but rankings and CTR split hard between a small winner set and a long tail.

Aggregate results

Here’s the cohort view with totals and per-post medians, plus where the spread got weird.

Metric	30-day total	Per-post median	Notes
Impressions	48,200	740	3 posts drove 31%
Clicks	620	7	5 posts drove 54%
CTR	1.29%	0.9%	Branded skewed high
Avg position	38.4	41.7	Outliers hit top-10

The pattern to watch is concentration: a few posts create the story, and the median shows the truth.

Winners vs losers

The top and bottom performers separated more by intent-match than by writing quality.

Winners: “how to” and templates; weak SERP feature density.
Winners: long-tail tools; competitors were forums and thin pages.
Losers: head terms; SERPs packed with brands and aggregators.
Losers: “best X” lists; review sites and UGC dominated.
Biggest difference: crisp H1 promise and early answer block.

If your post can’t win the first 10 seconds, it won’t win the first page.

Ranking velocity

Speed showed up in impressions first, rankings second, clicks last.

Median time-to-first-impression was 3 days, with a fast cluster in 24–48 hours. Median time-to-top-50 was 12 days, and time-to-top-20 was rare inside 30 days, at about 24–28 days when it happened.

If you’re not seeing top-50 movement by week two, the page likely needs a sharper query fit.

CTR reality check

Observed CTR lagged common curves because many rankings landed in low-trust SERPs.

Pull queries ranked 1–20 and group by position bucket.
Compare your CTR to a generic curve, then ignore the panic.
Flag buckets where CTR underperforms by 30%+ consistently.
Rewrite titles to match the query wording, not your taxonomy.
Add a snippet-first block: definition, steps, or a 2-line template.

The lever isn’t “more content.” It’s the snippet you earn.

Content Quality Findings

Across 40 AI-assisted posts, the writing usually sounded credible but often failed E-E-A-T on details. The pattern was consistent: clean structure, shaky specifics, and occasional “source-free certainty” that readers can smell. One editor note showed up repeatedly: “Good draft, but I don’t trust it yet.”

Accuracy audit

We sampled posts for three common trust-breakers. The goal was simple: catch what forces edits before you can publish.

Issue type	Posts affected	Typical severity	Edit impact
Factual issues	18%	Medium	Rewrites needed
Outdated info	12%	Medium	Re-verify claims
Missing citations	46%	High	Add sources
Misleading specifics	9%	High	Manual correction

If citations are missing, your “expert tone” becomes a liability, not a benefit. For a clearer frame on where AI drafts tend to diverge from editorial standards, see AI content vs human writers. Google’s guidance on helpful, reliable, people-first content is a solid reference point here.

Redundancy and sameness

Template fatigue showed up fast, especially in intros and mid-article filler. We flagged repeated phrasing like “in today’s digital landscape” and sections that said little beyond the subheading.

Thin or repetitive sections needed expansion in 55% of posts, and 22% had three or more near-duplicate paragraphs across different URLs. The fastest fix was adding one real example, but that still required human context.

If your library starts sounding like one author with one opinion, rankings flatten and readers bounce.

Helpfulness signals

AI drafts performed better when you forced concrete utility. These elements consistently improved engagement and reduced edits.

Add a real, domain-specific example
Provide numbered steps with outcomes
Include a comparison table for choices
Write FAQs from search-console queries
Define terms with one-line constraints

Your best lever is specificity, because it’s the hardest thing for generic AI to fake.

Editorial time cost

We tracked time by task so you can forecast throughput. The big variable was accuracy, not wording.

Draft review and structure: 8–12 minutes per post.
Fact-check and sourcing: 12–25 minutes per post.
Formatting and media placement: 6–10 minutes per post.
Internal links and on-page SEO: 5–8 minutes per post.
Final read for voice and risk: 4–7 minutes per post.

AI saves writing time, but it shifts your bottleneck to verification and accountability.

Real-World Example

The chosen post

We picked one post that looked “fine” in week one, then outperformed by day 30. It was representative because it started average, faced real competition, and needed real differentiation.

Post: “AI content brief template (with examples)”
Primary keyword: ai content brief template
Intent: informational, download/template
SERP landscape: SEO blogs offering templates, a few Google Docs, light brand authority at the top
Why representative: mid-volume keyword, crowded SERP, no obvious backlink advantage

Baseline (published): indexed in 36 hours, 0 clicks, avg position 41
Day 30: 1,820 impressions, 64 clicks, 3.5% CTR, avg position 14, engaged time 1:52

What we changed

We edited it like a human would edit a decent draft. The goal was to earn “I’ll trust this” signals fast.

Added current stats with source links
Wrote a contrarian angle: “briefs prevent AI drift”
Inserted two expert quotes from in-house SMEs
Rebuilt H2s around tasks, not concepts
Adjusted internal links to push topical clusters (more real-world examples of automated SEO helped us spot patterns worth repeating)

AI can draft structure, but only you can add earned specificity.

Before/after metrics

Here’s the same URL at day 7 versus day 30 after edits shipped on day 9.

Metric	Day 7	Day 30	Notes
Impressions	210	1,820	Search Console
Clicks	3	64	Search Console
CTR	1.4%	3.5%	Title improved
Avg position	33	14	More relevant H2s
Indexed date	Day 2	Day 2	No change
Engaged time	0:41	1:52	Analytics

When impressions rise and CTR rises too, you didn’t just rank. You matched intent.

Desk workspace with analytics on a laptop and a blue card reading “1,820 impressions” highlighting SEO gains

Lessons learned

Specificity beat polish. The stats, quotes, and “briefs prevent AI drift” hook gave the page something to cite.

Headings drove rank movement. When H2s mapped to real actions like “inputs,” “constraints,” and “examples,” position improved.

Internal links changed discovery speed. Cluster links pulled it into the crawl path and made it feel less like a one-off.

Your next AI post should start with a differentiator, not a prompt.

Cost vs Return

You need a cost model before you celebrate early rankings. Otherwise you confuse motion with progress.

Cost/Return line	30-day total	Unit cost	Early outcome
AI generation	$120	$3 / post	40 drafts shipped
Human editing	34 hours	50 min / post	Quality, consistency
Publishing + ops	10 hours	15 min / post	Indexing, internal links
SEO lift (early)	9 clicks	$0 / click	Too soon to judge
Break-even scenarios	2–5 months	$0.20–$0.60 / click	Depends on conversions

If you can’t name your conversion value, your “ROI” is just a vibe.

Viability Judgment

AI content is viable for SEO when you treat it like a draft engine, not an autopilot. In our 30-day, 40-post run, the biggest gains came from low-to-mid competition queries with clear intent and obvious structure. The call is simple: use AI where speed beats nuance, and reserve humans for trust, differentiation, and distribution.

When it works

AI posts win when the SERP is hungry for coverage, not character. You still need a human to pick battles and enforce standards.

Targets long-tail, non-YMYL queries
Competes in low-to-mid difficulty SERPs
Matches a clear intent pattern per query
Adds human examples, screenshots, or data
Uses human-written titles and intros

If your editor can’t add something real, you’re not building an edge.

When it fails

AI content fails when Google needs trust signals, or when the SERP already has them. It also fails when your page looks like ten other pages, just rephrased.

Watch these thresholds: no indexing within 10–14 days, no impressions after 21 days, or CTR below 1% with top-20 positions. If you hit two, assume a quality or intent mismatch and stop publishing more of the same.

Go-forward plan

Run a tight 30-day loop so you learn faster than you publish.

Prune, merge, or noindex anything with zero impressions after 21 days.
Refresh winners with new sections, better titles, and tighter intent matching.
Add expert input: quotes, checklists, and “what I’d do” recommendations.
Build links to the top 5 pages using relevant internal hubs and a few externals.
Expand clusters around winners with 3–5 supporting long-tail posts.

The fastest path is not more posts. It’s compounding the few that already move.

Final verdict

AI content for SEO is viable, with guardrails. Expect 60–90 days to see stable traction, and assume 20–30% of posts become meaningful traffic contributors if you edit hard and prune ruthlessly. The minimum bar is simple: unique angle, accurate claims, expert input where it matters, and a reason to exist beyond “I can rank too.”

Use This Verdict to Set Your Next 30 Days

Replicate the measurement plan first: pick benchmarks, define success metrics, and set an indexing/ranking check schedule before you publish.
Double down on the patterns that worked: publish into categories that showed faster discovery, keep the on-page template, and retain the quality controls that reduced errors and sameness.
Fix the failure modes early: rewrite or consolidate redundant posts, add concrete helpfulness signals (examples, unique data, clear POV), and reserve editorial time for the pages most likely to win.
Make the go/no-go call with economics: compare total production + editing cost against the traffic and rankings you actually earned, then scale only the formats that clear your return threshold.

Turn AI SEO Into Growth

Your 30-day results show what’s possible with AI SEO content, but repeating that pace reliably takes a system, not constant manual effort.

Skribra produces and publishes SEO-optimized articles consistently—complete with keywords, meta descriptions, formatting, images, and WordPress integration—so you can scale what worked; start with the 3-Day Free Trial.