SJK Labs • Inaugural Report • May 2026
The B2B AI Legibility Report
Finance Edition
What Machines Tell Buyers Before You Do
"If AI can't explain your business, buyers won't either — and the deal dies before you know it existed."
50
Companies audited
4
AI platforms tested
6
Dimensions scored
Introduction
In 2026, the assumption that the audience reading your story is human no longer holds. Buyers are asking AI systems what your company does, who it is for and whether it is credible before they reach your website or book a sales call.
Journalists are drafting with AI assistance. Analysts are summarising at machine speed. And the infrastructure for machine led commerce is now live, not theoretical.
What we already know
Why legibility? Why now?
This study exists because narrative architecture is now the work of building a story that holds up under both human and machine reading.
We audited 50 regulated and capital intensive businesses across ChatGPT, Claude, Gemini and Perplexity using the same six buyer questions a human would ask, scored against the SJK Labs Legibility Score.
This is not really about what AI gets wrong. It is about what happens when your narrative was built only for human readers.
“This is not a search problem to be solved with more SEO, and it is not a content problem to be solved with more output. It is a narrative architecture problem.”Sarah Kemp, Narrative Architect and Founder of SJK Labs
The SJK Labs Legibility Score
The Legibility Score is the scoring framework SJK Labs developed to run this study consistently across 50 companies.
It measures how accurately AI systems describe your business when asked buyer style questions, scoring each platform response across six dimensions.
Part of that test is whether your story makes your customer the protagonist, with a clear pain point, clear stakes, and clear proof.
Clarity
Accuracy
Differentiation
Customer pain point
Proof / credibility
Category fit
Results
Headline problem patterns across 50 companies
Category fit
32
In 32 companies, at least one AI platform placed the business in the wrong or incomplete competitive frame.
Proof / credibility
17
17/50 companies with a proof, credibility or evidence surfacing issue.
Crawlability
11
11/50 companies with a crawler, access, JavaScript, Cloudflare, gated site or homepage visibility issue.
Average Legibility Score per platform
Claude
21.6
out of 30
ChatGPT
19.5
out of 30
Perplexity
18.7
out of 30
Gemini
15.6
out of 30
Claude was the highest scoring platform in this dataset, largely because it was better at synthesis and at preserving commercial context.
But the platform picture is uneven - your company can be legible in Claude, invisible in Gemini and misclassified in Perplexity.
Cross-platform consistency comes from the authority layer — the same story corroborated across press, analyst coverage, partner pages and structured data is the single biggest lever for closing platform gaps.
Seven things the audit shows
01
AI can often name the category, but it still loses the commercial difference
The central problem is not total ignorance. In many cases, AI systems can identify your broad category but the reason to buy gets compressed.
02
Name collisions are now a board level communications risk
The most damaging problems are wrong company answers.
23/50 companies with entity recognition, name collision or wrong company issue.
03
The same company can be legible in one AI system and invisible in another
26/50 had a 10+ point gap between platforms. 3/50 had a 20+ point gap. 16/50 scored 20/30+ on all four.
04
AI often knows the previous version of the company better than the current one
A rebrand without a machine readable cascade leaves AI telling your old story.
05
Proof exists, but AI often cannot use it
Proof hidden in carousels, visual modules, JavaScript heavy pages, footnotes, PDFs or isolated testimonial blocks does not reliably travel into AI answers.
06
Crawlability is now a communications problem, not just a technical one
When AI systems cannot read your website, they do not stop answering. They answer from whatever is available.
07
AI is weakest when asked to compare
When you do not define your competitive set, AI fills the gap with scraped database logic, category adjacent guesses, outdated competitors or entirely wrong industries.
Companies included
All 50 companies included in the report
Download the full list of companies and scores.
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05
06
07
08
09
10
11
12
13
14
15
16
17
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26
27
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31
32
33
Dino Merchant Bank
34
GCI / Global Credit Investments
35
36
37
38
39
AIMS Financial Group
40
41
42
Mercore
43
RateOne
44
45
46
47
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49
50
Fill in the form above to get The B2B AI Legibility Report 2026 (Finance Edition).
What companies should do now
The practical lesson is to make your company easier for AI systems to explain accurately.
You should treat AI legibility as a distinct layer of communications strategy — part positioning, part PR, part technical visibility, part proof architecture.
1
Positioning
Clear narrative architecture that holds under machine reading. Your story must be parseable, not just persuasive, and it must make your customer the protagonist.
2
PR / Authority Layer
Corroborated story across press, analysts, partners, and structured data. Third-party validation AI can retrieve.
3
Technical Visibility
Crawlable pages, structured data, accessible proof points. If AI cannot read it, AI cannot repeat it.
4
Proof Architecture
Evidence that travels into AI answers, not hidden in carousels or PDFs. Make your proof portable.
Methodology
How the audit was run
We audited 50 regulated financial, fintech, insurance, wealth, payments, crypto and capital-intensive businesses.
Sample
50 regulated and capital-intensive businesses
The sample included companies with strong public footprints, recent rebrands, generic or collision-prone names, and websites difficult for crawlers to access.
Question
Could AI resolve the business clearly?
The audit tested whether AI could resolve entities, explain businesses clearly, and preserve differentiation.
Platforms
Four web-search-enabled AI platforms
Each company was tested under consistent real-world conditions across ChatGPT, Claude, Gemini and Perplexity.
Scoring
The same six buyer questions and score
The audit used the same six buyer questions a human would ask and scored responses against the SJK Labs Legibility Score across clarity, accuracy, differentiation, customer pain point, proof / credibility and category fit.
The scoring framework is rooted in the Scriptwriter Test: protagonist, stakes, proof and category.
The point was consistency: the same question set, the same platforms, and the same scoring logic across the full company set.
What is narrative architecture
A narrative architect helps you become more clearly understood.
It is not just copywriting. It is diagnosing how your authority, expertise, and identity are being perceived, then building the structure that helps other people grasp your value properly.
This is where the Scriptwriter Test matters: your customer should be the protagonist, not your company.
You know what your business does. But the market, media, and AI systems do not always understand it accurately.
If you want to understand how AI systems describe your company, where your narrative breaks, and what to fix first, book a 30 minute call to talk through it.
Talk it through
Book a 30 minute call
If you want to understand how AI systems describe your company, where your narrative breaks, and what to fix first, book a 30 minute call to talk through it.
Self-serve
The Narrative Gap Audit
If you want to see how your company would score, use the Legibility Audit Tool for a self-serve starting point. The version on the website is a lite version and does not use all LLMs from the report methodology.
Get the data
Download the report
Fill in the form to get The B2B AI Legibility Report 2026 (Finance Edition).