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.
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.
Key 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.
Check first: Confirm sponsorship terms, rate card and audience employer, occupation and seniority data before committing.
Main ideas
See the process before you buy the AI tool · Scope the automation so it stays out of the hiring decision · Trustworthy workforce analytics rests on unglamorous data plumbing
Richard Rosenow writes about people analytics, workforce data and practical AI in HR.
Their posts look past new tools to the conditions that make them useful: reliable data, visible processes and clear limits on automation.
In their account of Uniper’s recruiting work, they highlight how process data gave Microsoft Copilot useful context without adding a recruiting vendor—and why the system assisted screening and scheduling, not candidate selection.
They also explore how people enter the People Analytics field and the often-overlooked work behind trustworthy HR data.
Richard Rosenow connects HR technology decisions to the people, data and workflows underneath them.
In a post about the People Analytics Network Census, they note that practitioners often arrive from other fields and may identify with the profession only after years of work—a useful caution against reading a survey’s experience profile as a simple pipeline measure.
In a request for HR data-foundations talent, they make the less visible work concrete: fixing HRIS-to-ATS joins and rebuilding headcount data people can trust.
These examples will resonate with readers weighing HR technology choices or trying to understand the foundations of people analytics.
Richard Rosenow argues that HR gets more from AI and analytics by fixing what sits underneath them than by shopping for tools.
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Across the posts, the sequence is consistent: document and understand the process, build data foundations people can trust, then add the AI or dashboard layer. Rosenow also draws boundaries around where automation should stop in HR and describes people analytics as a field people arrive at mid-career, rather than a track they start on.
See the process before you buy the AI tool
Rosenow argues that most HR AI roadmaps start with the wrong question.
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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.
Rosenow walks through Uniper's recruiting work with Microsoft Copilot and Celonis: 27 days off time-to-sign, 13 days off screening time, and zero net-new AI vendors purchased.
The approach uses a business-context knowledge model built from process-mining data and fed to the Copilot recruiters already had.
Rosenow reports that the same copilot and recruiters produced dramatically more specific answers once the agent understood how Uniper's recruiting process runs.
Scope the automation so it stays out of the hiring decision
A second argument in the same post is about boundaries.
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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.
Uniper's agents assist with screening and scheduling but stop short of candidate selection, a boundary Rosenow connects directly to EU AI regulation. The post also identifies a gap in most HR AI readiness work: organizations lack documented processes for AI to act on, not ambition.
Trustworthy workforce analytics rests on unglamorous data plumbing
Rosenow argues that trustworthy workforce dashboards depend on foundational work that is largely invisible.
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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.
Rosenow asks his network to comment with or send referrals for "the person you would hire again without an interview" — senior or rising — naming HR data foundations specialists and HR data engineers.
They offer an email address for referrals.
The post describes the Ikona Talent Network, which matches former HR executives and senior practitioners with client work.
Assignments vary: some are short pieces of work, some projects alongside a day job, and some just a conversation for now.
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.
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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.
The census is the vehicle: Rosenow reports median experience holding near nine years and a mean of 10.3.
The post opens by asking when someone stopped feeling they worked in HR and started feeling they worked in People Analytics.
Rosenow describes the follow-up discussion as a team effort with Patrick Coolen, Maria Nolazco Masson, and Andrew Pitts, moderated by Caitlin Gillespie of SPA, and credits the census respondents.
Shows what a process-first HR AI rollout can look like and what it can deliver.
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Start here for the most concrete piece in the set: named numbers, named tools, a stated boundary, and a specific failure mode it argues against.
Rosenow walks through a recruiting AI case at Uniper: 27 days off time-to-sign, 13 days off screening time, and no new AI vendors purchased.
The approach used process mining on Celonis data to feed a business-context knowledge model into Microsoft Copilot.
The same recruiters and copilot produced more specific answers once the agent had context about how the recruiting process actually runs.
They draw three signals from the case: visibility came first, tooling second; agents assist with screening and scheduling but do not touch candidate selection, a boundary they frame as making a high-risk HR use case defensible under EU AI regulation; and the work shipped inside Teams, where recruiters already worked, rather than asking them to adopt a new interface.
The post argues that HR teams usually lack documented processes for AI to draw on, not ambition to use AI.
For brands planning a campaign
For a vendor positioning itself around AI-enabled people analytics, this offers ready-made third-party framing for the case that process documentation should precede tool selection.
It could anchor a webinar or content piece built around a process-mapping exercise rather than a tool demo.
Rosenow's scope boundary also offers a substantive angle on where AI should stay out of HR decisions.
Answers who works in people analytics and how they got there.
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Start here for the audience picture: practitioners are converts from other functions, making entry into the field slow and identity-driven rather than a straight career track.
Rosenow opens with a question: when did you stop feeling like you worked in HR and start feeling like you worked in People Analytics?
The Year Two People Analytics Network Census finds that most people arrive from elsewhere.
They list recruiting, economics, compensation, finance, psychology programs, and wildlife biology, and report median experience holding near nine years with a mean of 10.3.
Rather than read the thin early-career numbers as a pipeline failure, they suggest respondents are people who already identify with the community, and that identification tends to arrive five or more years into a career.
The post also names the discussion panel and its scheduled session, and credits the census respondents.
For brands planning a campaign
Useful for audience framing in a people analytics content collaboration.
The census and the identification lag make a case for programming around mid-career practitioners rather than entry-level recruiting.
The named census discussion also offers a live community hook for an event or co-authored content.
Shows what the network around Rosenow does and what it is hiring for.
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Start here for the commercial and community side: the post describes the work being staffed, the skill gap clients report, and how Rosenow brings practitioners together directly.
Rosenow asks their network to name the best HR data foundations or HR data engineer they have worked with, senior or rising.
The post explains why those people are hard to find: they are heads down rebuilding a headcount table nobody trusts and fixing the join between the HRIS and the ATS, so their work is invisible on LinkedIn and known only to their manager and a couple of others.
Rosenow writes from inside the Ikona Talent Network, which the post says has been matching former HR executives and senior practitioners with client work.
They note that clients are now asking for people earlier in their careers, including data engineers, data foundations builders, and workforce intelligence analysts.
The ask: tag names in comments or send referrals to an email address.
For brands planning a campaign
Shows a convening, referral-driven practitioner network rather than a single-author channel, which shapes how a partnership could work.
It also surfaces the HR data foundations gap as a subject a vendor could own.
The assignments described—short assignments and project work alongside a day job—hint at the kinds of collaboration the network is set up to broker.
Rosenow's read of Uniper's HR AI work is deliberately anti-tool-shopping.
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Uniper bought no dedicated recruiting AI and cut 27 days off time-to-sign and 13 days off screening time by feeding Microsoft Copilot a business-context model built from Celonis process data.
The argument: the same copilot and recruiters produced dramatically different output once the agent had context about how the recruiting process actually runs.
Their stated rule is that HR organizations cannot apply AI to a process they cannot see; a roadmap starting with "which tool should we buy" solves problems in the wrong order.
Start with process mining or documented process, then add the AI layer; ship the assistant inside Teams so recruiters do not adopt a new interface; scope agents to assist screening and scheduling.
Rosenow draws a hard line at candidate selection, which the agents explicitly do not touch, and connects that boundary to keeping a high-risk HR use case defensible under EU AI regulation.
For brands planning a campaign
A strong angle for a webinar or long-form piece aimed at HR technology buyers: an "AI readiness" session that starts with a process-mapping exercise rather than a product demo, using the Uniper numbers as the worked example and the candidate-selection boundary as the governance talking point.
Rosenow describes the unglamorous work underneath workforce analytics: rebuilding a headcount table nobody trusts, fixing the join between the HRIS and the ATS, and getting the foundation right so dashboards can be believed.
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The practitioners doing this are heads down and largely invisible — their manager knows, maybe two other people know — which is why they ask their network to tag the HR data engineers and data foundations builders they would hire again without an interview. The post also shows Rosenow placing former HR executives and senior practitioners into client work through the Ikona Talent Network, and reports that clients now ask for people earlier in their careers: data engineers, data foundations builders, and workforce intelligence analysts who know the plumbing.
For brands planning a campaign
Useful for a data or HR technology vendor that wants to reach the technical buyer rather than the CHRO: a content collaboration on "what a trustworthy headcount table actually requires" puts the brand next to the plumbing work Rosenow treats as the precondition for analytics credibility.
A career path into people analytics, and the community around it
The People Analytics Network Census, in its second year, is Rosenow's vehicle for describing who works in this field and how they arrived.
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Median experience held at roughly nine years, with a mean of 10.3.
They note that few people grew up in people analytics or aimed for it deliberately; the strongest leaders came from recruiting, economics, compensation, finance, psychology programs, and, in two cases, wildlife biology.
Their interpretation is that people analytics identity forms about five years into a career, reframing an apparent early-career pipeline problem as a self-selection effect in who answers the census.
They co-run the follow-up discussion with Patrick Coolen, Maria Nolazco Masson, and Andrew Pitts, moderated by Caitlin Gillespie of SPA.
For brands planning a campaign
A route for a brand that wants association with the practitioner community rather than a single creator: sponsor or co-host census results programming, or commission a career-path series for experienced practitioners entering analytics from adjacent functions.
Richard Rosenow speaks on behalf of Ikona Talent Network, a staffing and matching business for HR data and analytics practitioners, and takes part in the People Analytics Network Census as a member of its core team.
Ikona Talent Network
Disclosed partnership
Richard Rosenow posts a public call for HR data foundations and HR data engineer recommendations, writing as “we” with “our clients” and directing names and referrals to info@ikonaanalytics.com.
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The post describes the network as matching former HR executives and senior practitioners with client work in 2026 and says 2027 has more in it. Ask whether they own Ikona Talent Network or work for it.
Rosenow names themselves as one of the core team on the People Analytics Network Census, alongside Patrick Coolen, Maria Nolazco Masson, and Andrew Pitts, and thanks respondents who completed the survey.
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The post describes the group’s work and an upcoming results session moderated by Caitlin Gillespie of SPA.
Richard Rosenow writes for people who run HR data and HR technology, with a through-line of order of operations: see and document the process, fix the data foundations, then add the AI or the dashboard.
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The clearest starting point is the post on Uniper's recruiting work with Microsoft Copilot and Celonis process data, where they report 27 days off time-to-sign and 13 days off screening time with no new AI vendor bought, and argue that an HR team cannot apply AI to a process it cannot see.
The companion point is a boundary: Uniper's agents assist screening and scheduling and stop short of candidate selection, which they tie to keeping a high-risk HR use case defensible under EU AI regulation.
For the human side, read the request for HR data foundations specialists and HR data engineers, which describes the unglamorous work beneath trustworthy headcount tables and dashboards, and the People Analytics Network Census post, which treats people analytics as a second career most practitioners reach about five years in.
Readers who want practical HR AI craft, process thinking, and community research rather than vendor promotion will find the most to follow here.
Carlson writes the same kind of practitioner lesson from inside HR leadership work: build the structure, plan the workforce rather than fill requisitions, and measure talent acquisition against the business instead of counting requisitions and time-to-fill.
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Like Rosenow, Carlson writes about how the HR function itself is built and judged, for other senior HR practitioners rather than as tool promotion.
Carlson's material sits on the operating-model and business-case side, arguing that recruiting drives revenue and should be measured that way. Rosenow's goes one layer lower, into the process data, HRIS-to-ATS joins, and AI deployment boundaries that make the numbers trustworthy.
Cox reflects on how talent acquisition works and where AI should change it rather than speed up the old sequence, borrowing the warning against paving the cowpath and asking what building from a blank page would look like.
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Both use a specific outside source or idea, draw a practitioner lesson, and put a question back to the reader.
Cox's focus is recruiting practice and hiring judgment: role definition, bias in who gets hired, and the shape of the hiring funnel. Rosenow's is the HR data and process infrastructure an AI would have to sit on, with attention to regulation and scope limits.
Jones writes about where AI belongs in recruiting work, including the same human-decision boundary Rosenow draws: let the AI draft, keep people making the hiring call, and note that Anthropic uses Claude across its own recruiting without letting it decide.
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Both offer step-by-step, practical accounts of HR AI adoption for staffing and HR audiences.
Jones's posts offer tactical tool instruction for staffing firms, with named workflows, a platform claim about applicant lift, and a live training invitation. Rosenow starts from process visibility and documented process data, treating the governance line as what makes a high-risk HR use case defensible rather than as one step in a tool tour.
The available audience information supports a LinkedIn collaboration for an AI-enabled people analytics campaign, but does not show who makes up the audience.
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Richard Rosenow had 40,743 LinkedIn followers as of 9 October 2026, and their posts address people analytics, HR data, and recruiting-technology practitioners.
Examples ask HR data engineers to identify colleagues, invite people analytics professionals to a census discussion, and argue that process visibility should come before AI tooling.
For a campaign aimed at people analytics professionals, the audience is plausible but unverified on employer, occupation, and job seniority.
Before committing sponsored delivery, ask whether Richard Rosenow has audience survey results or another documented source covering those attributes, and request recent results for comparable topics, depth, and formats.
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.
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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.
An HR technology company wants to establish itself as a credible voice in AI-enabled people analytics. It wants a co-hosted webinar and two supporting content pieces for HR operations, recruiting operations and people analytics practitioners. The creator carries the argument; the vendor supplies the platform and product context.
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Reasons
Rosenow already argues the vendor-friendly version of this brief’s thesis. The Uniper post makes the case that process visibility comes before tooling, grounding it in named numbers: 27 days off time-to-sign, 13 days off screening time, and no new HR AI vendors purchased. For HR and recruiting operations practitioners, that is a concrete, checkable argument rather than a claim about AI potential.
The subject matter matches the audience. The data foundations post addresses HR data engineers, data foundations builders and workforce intelligence analysts; the census post addresses people analytics practitioners and reports that median experience in the field sits near nine years. That is the same practitioner layer the campaign aims to reach.
Rosenow convenes the community, not just publishes to it. Their posts show a newsletter breakdown of the Uniper work, a scheduled census discussion with named co-panelists, and a public call for practitioners to be named. Co-hosted programming needs someone who can bring people into a room, and this shows that activity.
The governance boundary gives the vendor a defensible segment, not a hype segment. In the Uniper case, agents assist with screening and scheduling but are explicitly kept out of candidate selection. Rosenow connects that boundary to keeping a high-risk HR use case defensible under EU AI regulation. That is the part of the topic HR buyers most need explained, and a subtopic a vendor can credibly sponsor without overclaiming.
Rosenow identifies the failure this brief addresses: the gap between AI ambition and documented process. That is a natural framing for a vendor selling to HR teams that want AI but cannot yet point it at a process.
Limits
Employer, occupation and seniority data are not documented. Plan the webinar around topic pull, not a promised seniority mix, and use registration data as the first audience read.
No sponsorship terms, rate card or media kit are published. Scope the collaboration directly with Rosenow rather than buying a published product.
Verdict
Strong fit on the material reviewed.
When to choose a specialist
A recruitment marketing vendor wants a paid sponsorship: a fixed-format post or short video endorsing its sourcing product, distributed as paid media and measured on click-throughs and demo requests from a defined list of enterprise talent acquisition leaders.
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Reasons
The posts contain no sponsor policy, rate card, media kit or example of a paid placement, so availability, price and deliverables are unconfirmed. Ask Rosenow whether this format is available and request terms before scoping the campaign.
The audience cannot be validated against the brief’s targeting requirement. A LinkedIn following of 40,743 was observed on 9 October 2026, but employer, occupation and seniority data, as well as engagement and click-through history, are unavailable. A brief promising delivery to enterprise talent acquisition leaders cannot be underwritten by follower count alone.
The content form is at odds with a product endorsement. The strongest reviewed piece argues that a company got results without buying a new recruiting AI vendor, using tools it already owned. A direct endorsement of a competing sourcing product would undercut the reason the audience reads the work.
The measurement requirement has no baseline. Click-through and demo-request targets depend on prior results from comparable sponsored topics and formats, which Rosenow’s posts do not report.
Verdict
Poor fit on the evidence reviewed, chiefly because of the format and targeting requirement, not the topic.
What would change this
A published partnership route or rate card, recent results from comparable sponsored work, and audience data on employer or occupation would make this brief assessable. Until then, it remains a poor fit on evidence, not a disqualification.
A worked campaign example
Angle
A 45-minute co-hosted webinar built around the process-first argument—for example, “Map the process before you buy the AI: what Uniper’s recruiting results actually depended on”—supported by one long-form companion piece and one working worksheet.
How to measure it without audience data
Because audience attributes are undocumented, measure success through behaviour in the session, not a promised seniority mix: worksheet completions submitted, questions asked during the mapping and governance segments, and requests for a process review afterward. Registration counts alone will not show whether the right practitioner layer attended.
Open gap
The newsletter piece linked from the Uniper post is not included here. If the session will rely on the mechanics of that rollout, ask Rosenow for the underlying detail before building the agenda around it.
Session shape
Opening, about 8 minutes: Rosenow walks through the Uniper case as reported—process mining on existing process data feeding a business-context model into Microsoft Copilot; the same recruiters and the same copilot producing different output once the agent had context; and the resulting 27-day and 13-day reductions. The tools were already owned.
Middle, about 15 minutes: the vendor demonstrates a process-mapping exercise on a sample recruiting process, covering topics from the data foundations post: rebuilding a headcount table people trust, fixing the join between the HRIS and the ATS, and naming the steps and system of record for each stage.
Governance, about 7 minutes: Rosenow covers where the AI layer should stop, using the boundary of screening and scheduling in, candidate selection out, and explaining why it matters under EU AI regulation.
Working segment, about 10 minutes: participants complete a blank process inventory grid—step, system of record, owner, current elapsed time, known failure point—and leave with it filled in for one recruiting stage.
Close, about 5 minutes: the vendor frames the next step as reviewing a documented process, not booking a product demo.
What each side brings
The vendor brings a sample process, a facilitator for the mapping walkthrough and platform context. Rosenow brings the case framing and numbers, the governance boundary, and an invitation to the practitioner community around the census and the talent network. The companion piece should use their analytical register and describe the vendor’s platform only where the mapping exercise requires it.
Why this angle rather than ademo
The argument is that HR teams cannot apply AI to a process they cannot see, and that a roadmap focused first on which tool to buy tackles problems in the wrong order. A session on process mapping positions the vendor as an adviser within that argument, rather than asking the audience to accept a product claim. It also gives the vendor a reason to share the stage with a creator whose stated premise is scepticism about new AI purchases.
Choose Gabrielle Carlson when the collaboration is about building or rebuilding a talent acquisition function, not applying AI to people data.
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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.
The posts explain what works but do not show a named rollout with numbers, so there is no equivalent of the Uniper case for a session to walk through step by step.
They also say almost nothing about AI tooling, data foundations, process mining or where governance should draw the line on AI—central parts of the need being compared.
A vendor pitching AI-enabled people analytics would have to supply the technical half of the session; Carlson would be speaking from general practice rather than work the audience can inspect.
Choose Lindzi Cox when the session needs a practitioner talking with practitioners about whether to redesign the hiring process instead of automating it.
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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.
Both posts reflect on other people's ideas and the field at large, rather than document an engagement.
The striking statistic in the December post is quoted from a speaker, not drawn from Cox's own measurement, so a session building on it would need to attribute it carefully and would deliver an argument rather than a case with numbers.
The posts offer little on people analytics data work, workforce data foundations or where an AI layer should be allowed to operate.
A vendor seeking the foundations-and-governance angle that anchors the Rosenow comparison would need to supply most of that material.
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.
The post is product-adjacent from start to finish: it promotes named AI tools and an event, and gives numbers—roughly a full workday saved per week and a 25% lift in applicants for one platform's job descriptions—without a source readers can check.
A vendor sponsoring a session alongside this creator would share the stage with another product's playbook and need clear ground rules about whose tooling the audience is being told to adopt.
The frame is the staffing industry, rather than the HR operations and people analytics practitioner layer the session aims to reach, and the post offers instruction rather than analysis of why a process works.
The same piece also appears on two channels as a single text, so it demonstrates one piece of work rather than a wider body of writing.
Richard Rosenow's three LinkedIn posts here cover Uniper's AI-assisted recruiting work using Microsoft Copilot and Celonis process data, a public request for HR data foundations and data engineering practitioners, and the second-year People Analytics Network Census.
The posts have no publication dates, and there are no transcripts, videos or long-form articles here, so the topics, recurring ideas and priorities described here come from these posts rather than a full-lifetime inventory of Rosenow's work.
The three other HR and recruiting writers discussed for similar-creator comparisons each have two posts.
Rosenow's LinkedIn profile showed 40,743 followers on 9 October 2026.
No sponsorship terms, rate card, media kit, engagement data or audience employer and seniority data are published here, so partnership availability and audience composition remain unconfirmed rather than ruled out.
