# Richard Rosenow

Richard Rosenow writes about people analytics, HR data foundations, and applying AI to HR processes. Their work emphasizes understanding and documenting a process before choosing tools, and building reliable workforce data that supports trusted analysis.

- Profile: https://b2binfluencer.marketing/creators/richard-rosenow/
- LinkedIn: https://www.linkedin.com/in/richardrosenow
- LinkedIn followers: 40,743
- Topics: artificial-intelligence, recruiting, hr
- Based on 9 dated posts, latest 2026-10-05

## Summary

Richard Rosenow is useful for explaining HR AI, people analytics data foundations and process-first tool choices.

- Best for: HR technology vendors running practitioner-focused people analytics or HR AI campaigns.
- Not for: Paid product endorsements or campaigns needing verified employer, occupation and seniority audience data.
- Check first: Confirm sponsorship terms, rate card and audience employer, occupation and seniority data before committing.

### Takeaways

- Uniper cut time-to-sign by 27 days, screening by 13, and added no HR AI vendors.
- Copilot assisted screening and scheduling, not candidate selection, using process data as context.
- Fixing HRIS-to-ATS joins and rebuilding headcount data people can trust is less visible work.

## Main ideas

- **See the process before you buy the AI tool**: Rosenow argues that most HR AI roadmaps start with the wrong question. HR cannot apply AI to a process it cannot see, so the first step is to document the process; the AI layer comes second. In Rosenow's framing, a roadmap that starts with "which tool should we buy" gets the sequence wrong. Visibility came first, the assistant shipped inside Teams where recruiters already worked, and no new vendor was needed.
- **Scope the automation so it stays out of the hiring decision**: A second argument in the same post is about boundaries. Rosenow treats the line Uniper drew around its recruiting agents as what keeps a high-risk HR use case defensible under EU AI regulation. The agents assist screening and scheduling; they explicitly do not touch candidate selection. This is presented not as a limitation that weakened the result but as part of why the result holds up.
- **Trustworthy workforce analytics rests on unglamorous data plumbing**: Rosenow argues that trustworthy workforce dashboards depend on foundational work that is largely invisible. Practitioners rebuild headcount tables nobody trusts and fix the join between the HRIS and ATS so the dashboards can finally be believed. Because that work rarely shows up as activity or self-promotion, Rosenow says they meet fewer of these people the further they get from their own time as a practitioner. That is why the post asks publicly for names rather than launching a private search. Clients are now asking for people earlier in their careers alongside senior practitioners: data engineers, data foundations builders, and workforce intelligence analysts who know the plumbing.
- **People analytics is a second career, not an entry-level track**: Rosenow argues that people analytics is a field people arrive at rather than grow up in. In the second year of the People Analytics Network Census, median experience held around nine years, with a mean of 10.3. Practitioners came from recruiting, economics, compensation, finance, psychology programs, and, in two cases, wildlife biology. Rather than reading the thin early-career numbers as a talent-pipeline failure, Rosenow suggests respondents already identify with the community, and that identification tends to arrive five or more years into a career. This reframes an apparent pipeline problem as a self-selection effect among respondents.

## Work to read

- [Uniper cut recruiting time with tools it already owned](https://www.linkedin.com/posts/richardrosenow_they-skipped-the-recruiting-ai-and-still-activity-7505622904051650560-W_mH)
- [When people stop feeling like they work in HR and start feeling like they work in People Analytics](https://www.linkedin.com/posts/richardrosenow_when-did-you-stop-feeling-like-you-worked-activity-7505372931267444736-0pte)
- [A public search for HR data foundations and data engineering talent](https://www.linkedin.com/posts/richardrosenow_request-to-my-network-who-is-the-best-hr-activity-7501610440796561409-R2JY)

## Brand relationships

- **Ikona Talent Network** (disclosed_partnership)
- **People Analytics Network** (mention)

## Partnerships

Richard Rosenow's posts do not identify sponsorship, paid partnership, or commercial placement formats, and publish no sponsor policy, specification, rate card, media kit, or partnership inquiry route. They show editorial and community activity: a newsletter breaking down HR AI case work, a People Analytics Network Census session on 17 September 2026, and an open call for HR data-foundation practitioners directed to a business email address. These are content and networking activities, not verified partnership products. The posts do not establish whether they accept sponsorship. Ask for availability, pricing, and deliverables through an official partnership route; all remain unconfirmed.

## Alternatives

- Gabrielle Carlson, M.Ed., PHR: Choose Gabrielle Carlson when the collaboration is about building or rebuilding a talent acquisition function, not applying AI to people data. The two posts offer a five-point account of what works when building TA from scratch: structure and decision rights before speed, workforce planning ahead of filling requisitions, metrics that drive decisions and show results, hiring-manager discipline as the biggest variable, and employer brand read from inside the business. A second post argues that recruiting drives revenue but is still measured like an administrative function. Vendors selling workforce planning, TA operating-model design or recruiter enablement would find their buyer conversation already reflected here. The language of revenue velocity, time-to-productivity and opportunity cost is also closer to a finance-facing pitch than the process-first analytics argument.
- Lindzi Cox: Choose Lindzi Cox when the session needs a practitioner talking with practitioners about whether to redesign the hiring process instead of automating it. The “don't pave the cowpath” post asks what would be built from a blank page today and notes that the most interesting work is being done by people quietly building something different, rather than speeding up the inherited funnel. That is a natural frame for a vendor whose product pays off only when a team is willing to change its process. The other post shows how Cox works: taking the arguments of a TEDx talk—hiring managers wanting someone good at everything, hiring for likeness rather than performance, and resumes screening out what the speaker calls whispering talents—and translating them into daily recruiting decisions, including an explicit case for structure and tools over untrained interview instinct. That translator role suits a co-hosted session for recruiters sceptical of vendors.
- Lauren B. Jones: Choose Lauren B. Jones when the audience is a staffing or recruiting firm seeking a practical adoption session rather than an argument about foundations. The post offers an operator's list of five moves for using Claude on a recruiting desk: load job specs, rate cards and meeting notes once in a project for each client; turn one intake call into a job description, outreach email, client recap, interview questions and a social post; turn a top recruiter's submittal style into a reusable skill; connect email, calendar and drive to a scheduled Monday brief; and keep drafting with the tool while people make the hiring decision. The post closes by promoting a two-day live session on Copilot and Claude, showing a workshop format this creator already runs and an event pattern a co-host could join.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/