EX-11. Product Management Tools. AI visibility audit.

Linear

Linear leads Product Management Tools. In problem-first, the lane looks different.

Across 270 responses, Linear appears in 58.1% of category-named queries. When a buyer describes a symptom instead of naming the category, the shape changes.

15/63

Linear in problem-first

Linear's presence in symptom-shaped queries.

Overall category presence: 157 of 270 (58.1%). In problem-first specifically, 36 of 63 responses in this bucket name no vendor at all (57.1%). That's empty inventory the leader could plausibly claim.

How AI answered a real buyer question in this category.

Query. Bucket 8. Problem-first. Excerpted from published audit.

my roadmap changes every week — how do I bring stability

Weekly roadmap churn is a real symptom of a deeper problem, not a planning problem you can solve with better tools. Here's how to bring stability:

## Diagnose the root cause first

Roadmaps change weekly for a few common reasons — figure out which is yours:

1. **Reactive leadership** — every exec conversation spawns new priorities

2. **Weak strategy** — no clear "north star," so everything feels equally important

3. **Poor discovery** — you commit to things before you understand them, then reshuffle when reality hits

4. **Sales/customer-driven chaos** — biggest deal of the week reshapes the roadmap

5. **No prioritization framework** — nothing to say "no" against

## Structural fixes

**1. Separate "now / next / later" horizons**

- **Now (0–6 weeks):** Locked. Only changes for true emergencies.

- **Next (6–12 weeks):** Directional, negotiable.

- **Later (3–6+ months):** Themes only, not features.

If someone wants to change "Now," they must explicitly trade something out — no additions without subtractions.

**2. Anchor to outcomes, not features**

Instead of "build X, Y, Z," commit to outcomes like "reduce churn by 3%" or "activate 20% more new users." Features can flex; the *goal* stays stable for a quarter. This makes it much harder for random requests to derail things because you can ask: "does this serve the quarterly outcome?"

**3. Establish a change-control ritual**

-...

Vendors named in this response NONE

Appearance rank and pick-share rank tell different stories.

Every brand ranked two ways. Appearance = how often the brand is named at all. Pick share = how often the brand is the SOLE or PRIMARY pick, as a percentage of all 270 responses. The gap between the two is the recommendation gap.

#	Brand	Appear	App %	Pick %
01	Linear	157	58.1%	27.0%
02	Jira	149	55.2%	3.3%
03	ClickUp	121	44.8%	7.4%
04	Notion	113	41.9%	5.2%
05	Asana	102	37.8%	0.0%
06	Trello	86	31.9%	1.9%
07	Monday.com	48	17.8%	0.4%
08	Productboard	47	17.4%	2.6%
09	Figma	40	14.8%	0.0%
10	GitLab	39	14.4%	0.0%
11	Shortcut	28	10.4%	0.0%
12	Aha!	25	9.3%	0.4%

Linear appears in 58.1% of responses and is the sole or primary pick in 27.0%. That is a 31.1-point gap.

Top-mentioned brand in each type of buyer query.

Bucket	Top	n	Runner-up
Category discovery	Jira	33	Linear 30
Comparison	Linear	24	Jira 19
Segment fit	ClickUp	31	Trello 27
Evaluation	Jira	27	Linear 27
Pricing	Linear	18	ClickUp 18
Trust	Jira	10	Linear 9
Switching	Jira	17	Linear 15
Problem-first	Jira	22	Linear 15

Problem-first zoom.

The highest-leverage queries: the buyer describes a symptom rather than naming the category.

#	Brand	Count	%
01	Jira	22	34.9%
02	Linear	15	23.8%
03	Productboard	13	20.6%
04	Notion	11	17.5%
05	Asana	9	14.3%
n/a	Zero-vendor responses	36	57.1%

The domains the assistants pulled from.

Two lists. Vendor-owned and editorial sources on the left. SEO listing sites on the right. The split reveals which channel is doing the citation work.

Established, own content

feedback.clickup.com ClickUp73
clickup.com ClickUp53
linear.app Linear53
airtable.com Airtable38
monday.com Monday.com38
shortcut.com Shortcut21
help.clickup.com ClickUp20
thedigitalprojectmanager.com45
atlassian.com44
appcues.com43

SEO listing sites

ideaplan.io49

Methodology and caveats.

Run date. 2026-07-24 UTC.
Sample. 30 buyer questions across 8 buckets, 3 samples per engine. Target 270 responses.
Engines. anthropic, openai, perplexity.
API is not the consumer app. Directionally representative, not literal.
Answers vary. Every prompt sampled 3 times per engine. Frequencies, not binaries.
Two-stage matcher (v0.4). Brand extraction is case-insensitive, word-boundary-anchored. Vendors whose canonical name overlaps with a common English word are flagged ambiguous in the config, and every candidate hit goes to a second-stage LLM adjudication (Claude Haiku 4.5) that reads the surrounding context and answers whether the token is the company or generic usage.
Recommendation classification (v0.5). Every confirmed brand mention runs through a second LLM pass (Claude Haiku 4.5) that classifies the treatment of that brand in that response as one of six codes: SOLE (only vendor named as the answer), PRIMARY (named as the top pick), ALTERNATIVE (named in a short recommended set), CONDITIONAL (recommended only for a stated narrow case), NEUTRAL (mentioned without endorsement, or cited as a source only), or UNFAVORABLE (named with a negative or dismissive frame). Pick share of total = (SOLE + PRIMARY) ÷ total responses.
No editorializing. This exhibit reads the data. It does not claim causation.
Right of reply. Any named brand can request their own free audit. Findings publish alongside.

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