In 2022, Google added a second “E” to its quality evaluator framework, turning EAT into E-E-A-T. That small change had large implications: Google was now explicitly evaluating whether a source had real-world experience, not just claimed expertise. For small businesses, this is actually good news, experience is something local businesses have in abundance. The problem is most of them aren’t showing it.
What E-E-A-T stands for, and why it matters beyond Google
First E
Experience
Has this person or business actually done the thing? A dentist who has treated 2,000 patients has experience. A website that just lists dentistry facts does not. Google looks for first-hand evidence: case studies, before and after results, customer stories, photos of real work.
Second E
Expertise
Does this business have the knowledge and credentials to be authoritative in its field? Relevant for professional services especially, HVAC certification, dental licensing, legal bar admission. But expertise can also be demonstrated through depth of published content, not just credentials.
A
Authoritativeness
Do others in your field recognize you? This is about third-party signals, mentions in local press, citations from industry associations, links from trusted sources. It’s the equivalent of professional reputation, expressed in digital signals.
T
Trustworthiness
Can users and AI systems verify that your business is what it claims to be? Accurate contact information, a real address, a functioning website, and consistent data across the web all contribute to trustworthiness. Inconsistencies undermine it.
E-E-A-T was developed as guidance for Google’s human quality raters. But the same signals now feed directly into AI recommendation systems. When ChatGPT or Google AI evaluates whether to recommend your business, it applies analogous reasoning, can it verify your experience, your expertise, your reputation, your trustworthiness?
Where most small businesses fail E-E-A-T
Experience: the evidence problem
Most small business websites describe what they do but don’t show what they’ve done. A plumbing company might have a “Services” page listing every service they offer, but no photos of completed work, no case studies, no specific examples. From an E-E-A-T perspective, that’s a claim without evidence.
The fix is concrete: add before-and-after photos of real jobs. Write brief case studies (“we replaced a 30-year-old water heater in a 1960s home, here’s what we found and how we fixed it”). Include specific project details. AI systems can distinguish between generic service descriptions and specific, experience-grounded content.
Expertise: credentials are invisible when they're not stated
A licensed electrician who doesn’t list their license number, their certification bodies, or their years of experience on their website is functionally invisible to E-E-A-T evaluation. The expertise exists, the signals don’t.
For licensed trades, include your license number. For medical practices, list the degrees and board certifications of every provider. For legal services, list bar admissions and practice areas. These facts need to be findable as text, not buried in a PDF or only visible on a state licensing board’s website.
Authoritativeness: the citation gap
Small businesses often have excellent local reputations but no digital evidence of them. The chamber of commerce knows you. The local newspaper covered your expansion last year. You’re a preferred vendor for three large property managers. None of this exists online in a form AI can find.
Authoritativeness is built through citations, third-party sources that mention your business by name. Press coverage, association memberships, partner pages, industry directories, and even well-attributed customer testimonials all contribute.
Trustworthiness: the consistency problem
The most common trustworthiness failure is data inconsistency. Your phone number is different on your website versus your GBP versus an old Yelp listing. Your business name has “LLC” in some places and not others. Your address used “Suite 4” in some places and “#4” in others.
AI systems treat inconsistencies as uncertainty. Uncertain businesses get lower confidence scores. Lower confidence means fewer recommendations.
The practical E-E-A-T audit
Experience signals to add
✓
Photos of real completed work, including interior, exterior, and before-and-after.
✓
At least 3 case studies describing specific jobs or patient outcomes
✓
Years in business stated explicitly on About page and in About schema
✓
Number of customers, jobs completed, or patients treated (if you have the data)
Expertise signals to add
✓
License numbers and certification bodies listed on website
✓
Founder or key team bio with credentials
✓
Person schema markup for key team members
✓
Published content demonstrating depth of knowledge in your field
Authority and trust signals to add
✓
Press coverage, even local newspaper coverage, linked from your site
✓
Association and chamber memberships with badge or link
✓
Identical NAP across all online sources
✓
Physical address and phone number in footer of every page
✓
LocalBusiness schema markup with complete entity data
✓
Privacy policy and terms of service (trust signals for YMYL queries)
E-E-A-T is not a one-time fix
The businesses that do best in AI era search are the ones that systematically build E-E-A-T signals over time, publishing experience demonstrating content, earning new citations, maintaining data consistency, and updating credentials as they grow.
The good news: for most small businesses, the experience and expertise are real. The signals just aren’t visible. Getting them online is a technical and content problem, not a fundamental problem with your business.
We audit and build your E-E-A-T signals
Revisible’s Authority plan includes a pillar + cluster content architecture, next month’s content plan, dedicated account manager.
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