How do you make sure your D&I story is correctly represented by LLMs?
The representation dilemma
A candidate asks Claude: “Which tech companies in the Netherlands are good for female developers?”
Claude searches the web. It finds your D&I page (“We believe in diversity”). It finds a news item about your female CTO. It finds a Glassdoor review that says “men's stronghold.” It finds statistics you never published.
What will be the answer? That depends on which signals are strongest, and you can influence that.
AI and bias: the double challenge
The challenge is twofold:
1. AI systems can contain bias
LLMs are trained on historical data that reflects societal biases. Research shows that AI systems in recruitment gender and racial bias may be present in CV screening, patterns that may also carry over into how they describe employers.
2. Your D&I story must go through the AI filter
Even if your D&I efforts are excellent, that information needs to be findable, citable and compelling for AI systems.
What works: data over claims
AI systems (like critical candidates) are sceptical of vague D&I claims. What does work:
| Weak (unquote) | Strong (quotable) |
|---|---|
| “We believe in diversity” | “42% of our tech teams are women (2025)” |
| “Inclusive workplace” | “Employee Resource Groups for five communities” |
| “Equal opportunities” | “Pay gap audit: 2.3% difference, action plan published” |
| “Diverse leadership” | “4 of 8 board members are women; 2 have migration background” |
The rule: specific, verifiable data is cited; vague claims are ignored.
AI tools for D&I in recruitment
Interesting: AI itself is being used to reduce bias. Research shows effective applications:
- Vacancy text optimisation: NLP tools identify and remove gender-coded language. Cisco reported 10% more female candidates After implementation of Textio
- CV screening: AI ignoring demographic information and focusing on skills. Amazon saw improvement in diversity of invited candidates
- Personalised outreach: AI that adapts communication to diverse audiences without stereotyping
Structuring your D&I content for AI
1. Dedicated D&I page with data
- Concrete figures on team composition
- Historical trend (improvement over time)
- Specific initiatives with measurable results
- External validation (certifications, awards)
2. Employee stories of underrepresented groups
- Authentic stories, not corporate scripts
- Specific experiences and growth
- Published on LinkedIn and your website
3. Transparency on challenges
Counterintuitive but effective: recognise where you still need to improve. “Our tech teams are 28% women, our goal for 2026 is 35%” is more credible than “We are a diverse employer”.”
The Glassdoor factor
D&I-related Glassdoor reviews weigh heavily in AI responses. Actions:
- Monitor D&I sentiment in reviews
- Respond professionally to criticism (AI sees responses)
- Encourage staff from underrepresented groups to review
Practical steps
This week:
- Test: ask LLMs about D&I at your company. What comes out?
- Audit your D&I page: does it contain specific, recent figures?
This month:
- Publish or update your D&I data with concrete metrics
- Collect and publish 2-3 employee stories from various employees
This quarter:
- Implement AI tools for bias-free job ads
- Build a monitoring system for D&I sentiment in AI responses
The bottomline
In the AI era, your D&I reputation is not what you say it is, it is what the data says. Vague claims are ignored; specific numbers are cited.
The employers who win on D&I are those who are transparent about their progress, including where they still need to improve. Because authenticity scores, even on AI.
Next article
In the following article, we ask the uncomfortable question: Where is the line between optimising and manipulating? The ethics of GEO and why honesty is the best strategy.
This article is part of a series on GEO and employer branding.
Sources:
- Harvard Business Review, “AI and Bias in Hiring: A Research Review” (2024)
- Textio, “Gender-Neutral Job Descriptions Impact Study” (2024)
- McKinsey, “Diversity Wins: How Inclusion Matters” (2023)
- SHRM, “AI Tools for Inclusive Recruitment” (2025)