AIO Case Studies: What Changed and What Happened

AIO means preparing your site’s information and structure so AI search and AI answers can recommend your business. When you ask for budget internally, it is tempting to line up success stories from other companies.

But “4x,” “83% up” and “25x” measure different things. Putting them in one table as-is can mislead your manager.

This article first sets three axes for comparing cases, then lines up two cases whose primary sources we read in full on those axes. The list is short, but it is built to keep metrics from getting mixed up. Sources were checked on September 23, 2026.

Three Axes for Comparing AIO Cases: Action, Success Metric and Reporter

Multipliers cannot be compared unless you align what was changed, what was counted and who reported it.

Axis	What to confirm	Misreading if you skip it
Action	Which pages or settings changed, and how	Generalizing to “more articles means growth”
Success metric	What counts as one, and what it is compared with	Reading 4x customers as 4x revenue
Reporter	The company itself, a vendor, or a third party	Treating a promotional report as neutral verification

For the metric, also check the type of comparison. A before-and-after comparison means something different from a comparison against another channel in the same period.

AIO Case Studies by Action

We present two cases under two actions: making body text readable, and building pages that answer buyer questions.

Action	Case	Reporter
Fix pages so AI can read the body text	Runpod	Scrunch, the tool vendor
Build pages that answer buyer questions	Go Fish Digital	Go Fish Digital itself

Both ran several initiatives at once. Be careful not to read the results as the effect of a single action.

A Case of Making Body Text Readable to AI

Runpod found articles whose titles were captured but whose body text was not fully captured, and fixed the pages.

Runpod rents computing capacity for AI development over the internet [1]. In early 2025, new paying customers from AI search had reportedly plateaued at around 300–400 a month [1].

From April 2025, it expanded the questions it tracked from 50 to 300 [1]. A site audit found that some articles’ body text was not being captured, and the team updated metadata, restructured pages and corrected formatting [1].

The report says monthly new paying customers grew 4x after adopting Scrunch, presented as a change within 90 days [1]. The exact measurement window is not stated. The reporter, however, is the tool vendor. Because the question expansion happened at the same time, the effect of the page fixes alone is unknown.

A Case of Building Pages That Answer Buyer Questions

Go Fish Digital mapped the questions buyers ask when choosing an agency and built fact-dense cornerstone pages.

The company is a US marketing agency, and it published this case about itself on September 24, 2025 [2]. Over roughly three months, it mapped buyer questions, recorded baseline numbers, built 5–8 cornerstone pages and created pages answering related questions [2].

It reports a 43% increase in monthly visitors from AI referrals and an 83.33% increase in monthly conversions [2]. What counted as one conversion is not published, so it cannot be confirmed as revenue.

The company also notes it already had reputation, authority and external mentions before starting [2]. We cover the details in our Go Fish Digital case article.

Metrics Side by Side: Don’t Mix Revenue, Inquiries and Customer Counts

Putting the original metric, comparison type, period and reporter in one table makes the differences visible.

Case	Original metric	Meaning	Comparison	Period	Reporter
Runpod	New paying customers per month: 4x	Monthly new paying customers grew 4x	Before/after	Within 90 days after adoption (details not stated)	Vendor
Runpod	Conversion rate 8% (2,100 from ~28,000 visitors)	Share of visits resulting in conversions (definition not published)	Rate	Not stated	Vendor
Go Fish Digital	Monthly visitors from AI referrals: +43%	Monthly AI-referred visits up 43%	Before/after	~3 months	Self
Go Fish Digital	Monthly conversions: +83.33%	Monthly conversions up 83.33% (definition not published)	Before/after	~3 months	Self
Go Fish Digital	Conversion rate: 25X	AI-referred conversion rate 25x that of traditional search	Channel comparison	~3 months	Self

The table shows:

• None of these is an increase in revenue.
• 25x does not mean 25 times better than before. It compares AI referrals with traditional search in the same period.
• 4x and +83.33% count different things, so they cannot tell you which action worked better.

Google also says meeting the requirements does not guarantee appearance in its AI features [3]. Case figures are not a forecast of your own results.

How to Use Cases When Explaining Internally

Present the actions you can try together with the reasons the same results are not guaranteed.

An example explanation:

Two overseas companies reported growth in customers or conversions after work for AI search—one fixed how body text is read, the other built pages answering buyer questions. Both are reports by the company or its vendor, and neither is a revenue increase. We would like to first check whether our main pages’ body text is being read and whether they answer customer questions, then compare records before and after changes.

Phrasings to avoid:

Avoid	Why	Say instead
AIO quadrupled revenue	It was a customer count	Reported 4x monthly new paying customers
The action raised conversion rate 25x	It was a channel comparison	Reported AI-referred conversion rate 25x that of traditional search
Results come in three months	Conditions differ	The company reported roughly three months of work

If you try something first, pick one main page and check whether its body text is being read and whether it answers customer questions. For reviewing existing pages before adding articles, see Before Adding Articles for AI Traffic, Review Your Existing Pages; for where AI visitors land, see the Ahrefs case.