Guide15 March 2026·13 min read

Employer Brand Audit Template: The Complete Checklist for 2026

Most employer brand audits haven't caught up with how candidates actually research employers. They check your careers page copy, your Glassdoor rating, and maybe your LinkedIn presence. Then they call it done.

That was fine in 2023. In 2026, 80% of candidates under 30 use AI to research employers before applying. Your audit template needs to account for what AI says about you, whether AI can even access your content, and whether your data is structured in formats machines can parse.

This is the complete employer brand audit checklist — 47 items across 10 categories. It covers everything from traditional employer branding fundamentals to AI-era requirements that most companies are still ignoring.

Use it as a self-assessment. Or run the automated version and get scored across every dimension in under 60 seconds.

Source: OpenRole audit framework, developed from analysis of 517 UK employers, March 2026

How to Use This Template

Score each item on a simple scale:

✅ Done — Fully implemented and current
⚠️ Partial — Exists but incomplete or outdated
❌ Missing — Not implemented

Count your totals at the end. Anything below 70% coverage means candidates — and AI — are filling in the gaps with guesswork.

1. Careers Page Content

Your careers page is the primary source of truth about your employer brand. But most careers pages are invisible to AI because they're built on ATS templates with thin, generic content.

Unique company description — Not boilerplate. Specific to your company, industry, and stage. At least 300 words.
Mission and values — Concrete, not generic. "We ship weekly" beats "We value innovation."
Team size and structure — How many people, how teams are organised (squads, departments, chapters).
Office locations with addresses — Every office, with full postal addresses. Not just "London" — the actual postcode.
Day-in-the-life content — At least one detailed description of what a typical day looks like for a common role.
Founding story and company history — When founded, key milestones, current trajectory.
Leadership team with bios — Names, titles, brief backgrounds. AI uses this to verify company legitimacy.
Photos and video — Real employees, real offices. Stock photos actively damage credibility.

Why this matters for AI: When candidates ask ChatGPT "What's it like to work at [your company]?", AI synthesises its answer from whatever content it can find. If your careers page has thin ATS-template content, AI falls back on Glassdoor reviews and Reddit threads instead. Unique, specific content gets cited. Generic content gets ignored.

2. Salary Transparency

Salary information is the single highest-impact factor in AI accuracy. Our research found that AI underestimates salaries by an average of £14,800 when employers don't publish structured compensation data.

Salary ranges published on job listings — Minimum and maximum for every open role.
Salary bands by level — A published framework showing compensation at each career level.
Total compensation breakdown — Base salary, bonus structure, equity (if applicable), pension contribution.
Benefits monetary value — Quantified value of non-salary benefits where possible (e.g., "private health insurance worth £1,200/year").
Pay review cycle documented — When and how salary reviews happen (annual, biannual, promotion-linked).

Why this matters for AI: Salary is the first question candidates ask AI. If you don't publish ranges, AI guesses — and consistently guesses low. Employers who publish structured salary data see AI accuracy within £2,000 of reality.

3. Benefits Documentation

Vague benefits descriptions ("competitive package") are functionally invisible to AI. Specifics get cited.

Complete benefits list — Every benefit, named and described. Not "and more" — the actual list.
Holiday allowance — Exact days, including bank holidays, buy/sell schemes.
Pension details — Employer contribution percentage, provider name.
Health and wellbeing — Private medical, dental, mental health support, gym memberships. Named providers.
Family policies — Maternity, paternity, shared parental leave — exact duration and pay (e.g., "16 weeks full pay").
Learning and development — Budget amount, what it covers, conference policy.
Financial perks — Share schemes, bonuses, referral bonuses, cycle-to-work, season ticket loans.

Why this matters for AI: When AI can list your specific benefits — "28 days holiday, Vitality health insurance, £1,500 learning budget" — candidates see a real offer. When it says "competitive benefits package," they see nothing.

4. Remote and Flexible Working Policy

Ambiguity around remote policy is one of the fastest ways to lose candidates in 2026. If AI can't confirm your flexibility, candidates choose employers where it can.

Remote policy clearly stated — Fully remote, hybrid, or office-based. No ambiguity.
Hybrid details specified — Exact days required in office per week.
Geographic scope — Which countries/regions remote employees can work from.
Work-from-anywhere policy — If you offer temporary international working, state the duration and conditions.
Core hours — If flexible hours are offered, specify any core hours or overlap requirements.
Equipment policy — What you provide for home offices (budget, specific items).

Why this matters for AI: "Does [company] allow remote work?" is one of the top 7 questions candidates ask AI. A clear, specific answer beats "some roles offer hybrid arrangements."

5. Interview Process Documentation

Interview process transparency reduces candidate anxiety and increases application rates. It also gives AI specific content to cite.

Process overview — Number of stages, approximate timeline from application to offer.
Stage descriptions — What happens at each stage (phone screen, technical test, panel interview, etc.).
Time commitments — How long each stage takes (30-minute call, half-day onsite).
Assessment criteria — What you're evaluating at each stage, in plain language.
Preparation guidance — What candidates should prepare or expect.
Decision timeline — How quickly candidates can expect feedback after each stage.

Why this matters for AI: When AI can describe your process — "4 stages, starting with a 30-minute recruiter call, offer within 5 working days" — candidates feel informed. When it says "typically involves multiple rounds," candidates feel uncertain and apply elsewhere.

6. Employee Testimonials and Social Proof

AI models weight authentic employee voices. But they weight them differently depending on format and source.

Written testimonials on careers page — Real employees, named (or at least with role title and team).
Attributed quotes — Not anonymous. AI treats named testimonials as more authoritative.
Role-specific testimonials — At least one testimonial per major department (engineering, sales, operations, etc.).
Video testimonials — Transcribed and embedded (AI can't watch videos, but it can read transcripts).
Recent testimonials — Updated within the last 12 months. Dated content loses authority.

Why this matters for AI: When employers don't provide testimonials, AI defaults to Glassdoor reviews — which skew negative and are often outdated. Your own testimonials, structured and attributed, give AI a narrative you've chosen.

7. Glassdoor and Review Platform Presence

You don't control Glassdoor, but you can influence how AI uses it.

Claimed Glassdoor profile — Your company page is claimed and managed.
Updated company information — Logo, description, benefits, photos are current.
Response to reviews — Management responses on at least the 10 most recent reviews.
Rating above 3.5 — Below this threshold, AI actively flags negative sentiment in responses.
Recent reviews — At least 5 reviews from the past 6 months. Stale profiles signal problems to AI.

Why this matters for AI: AI synthesises review data from multiple platforms. If your Glassdoor profile is outdated or unclaimed, AI gives disproportionate weight to the few (often negative) reviews it can find.

8. Schema Markup and Structured Data

Schema markup is the technical foundation of AI visibility. It tells search engines and AI models exactly who you are, what you pay, and what it's like to work at your company — in machine-readable format. Only 18% of UK employers currently implement any structured data on their careers pages.

Organisation schema on homepage — Company name, description, founding date, employee count, logo, social profiles.
JobPosting schema on every job listing — Title, description, salary range, location, employment type, date posted.
FAQPage schema on careers page — Structured Q&A covering the 7 questions candidates ask AI.
EmployerAggregateRating schema — If you have enough reviews to warrant it.
Schema validation — Tested with Google Rich Results Test and Schema.org validator. Zero errors.

Why this matters for AI: Companies with schema markup see 32% higher citation rates in AI responses. Structured data is the single most reliable way to ensure AI presents accurate facts about your company.

9. AI Crawler Access

You can have perfect content and perfect schema — but if AI crawlers can't access your site, none of it matters.

robots.txt allows AI crawlers — GPTBot, ClaudeBot, Google-Extended, PerplexityBot, and Bytespider are not blocked.
No blanket bot blocking — Some CDNs and security tools block all non-standard crawlers by default. Check yours.
ATS pages are crawlable — If your careers page is on a subdomain (e.g., jobs.company.com), confirm that subdomain has its own robots.txt allowing AI crawlers.
JavaScript-rendered content is accessible — If your careers page requires JavaScript, test that AI crawlers can access the rendered content (most can't execute JS).
Page load speed — Careers pages should load in under 3 seconds. Slow pages get abandoned by crawlers.

Why this matters for AI: Our UK audit found that a significant portion of employer careers pages are functionally invisible because AI crawlers are actively blocked — often unintentionally.

10. AI Response Accuracy

This is the output layer — what AI actually says about you. Everything above feeds into this.

Company description accurate — Ask ChatGPT, Claude, and Perplexity to describe your company. Is the answer correct?
Salary data accurate — Ask AI what you pay for three roles. Is it within 10% of reality?
Benefits accurately listed — Ask AI about your benefits. Does it cite specifics or vague generalities?
Culture accurately represented — Ask AI about your culture. Does the answer reflect your current reality?
No hallucinations — Check for fabricated facts — wrong founding dates, incorrect products, made-up leadership names.

Why this matters: This is the candidate experience. Everything else on this checklist exists to make these five answers accurate.

Scoring Your Audit

Count your checkboxes:

Score	Rating	What It Means
40–47	Excellent	You're in the top 5% of UK employers for AI visibility
30–39	Good	Strong foundation with room to optimise
20–29	Average	Significant gaps that candidates and AI are noticing
10–19	Poor	AI is guessing about most of your employer brand
0–9	Invisible	You're in the 68% of UK employers that AI can't reliably describe

Priority Order

If you're starting from scratch, tackle these sections first:

Salary transparency — Highest impact on AI accuracy
Schema markup — Highest impact on AI citation rates
AI crawler access — Prerequisite for everything else
Benefits documentation — Second most-asked candidate question
Interview process — Reduces application drop-off

What This Template Doesn't Cover

This checklist is comprehensive but manual. It takes 2–3 hours to complete thoroughly, and the AI response accuracy section requires testing across multiple platforms.

The OpenRole automated audit covers every item on this checklist — plus real-time AI response testing across ChatGPT, Claude, Perplexity, and Google AI Overviews — in under 60 seconds. You'll get a scored report with specific, prioritised recommendations.

Run your free audit now →

It's the difference between a checklist you'll work through over a week and a report that tells you exactly where you stand today.

Source: OpenRole employer brand audit framework, March 2026. Based on analysis of 517 UK employers across six AI platforms. For full methodology and findings, see the UK AI Employer Visibility Report 2026.