Oliver Feakins writes about travel-nurse and healthcare staffing, with a focus on how AI and data can support recruiters, strengthen candidate relationships, and improve agency efficiency.
Oliver Feakins is most useful for healthcare staffing agencies seeking practical AI and data advice for recruiter productivity and margin.
Best for
A mid-size travel-nurse staffing agency with recovering volume and thin margins wanting leadership sessions and advisory follow-up.
Not for
Direct-to-clinician brands wanting clinician-facing lifestyle content and travel-nurse reach at scale.
Key takeaways
AI can sort a large candidate list into twelve conversations worth having.
Marketing without recruiter capacity creates unanswered candidates; hiring without candidate acquisition leaves stale databases.
Margin measures include cost per placement and redeployment rates.
Check first: Confirm whether their audience includes agency decision-makers and ask for recent comparable results.
Main ideas
Use AI to surface the conversations recruiters should have · Keep the human work centered on trust and fit · Build candidate acquisition and recruiter capacity as a pair
Oliver Feakins writes and speaks about travel-nurse and healthcare staffing, especially how agencies can use AI and data without losing the human work of recruiting.
Their posts connect staffing technology to practical questions: which candidates need attention, how to respond quickly while understanding what clinicians actually want, and whether rising demand can translate into stronger margins.
They also share a recruiter-training perspective, including material from a TravCon session and a TalentTrack Podcast conversation.
Readers interested in the changing craft and economics of healthcare staffing can follow their work for concrete operating examples and arguments rather than technology predictions alone.
Oliver Feakins makes broad staffing changes tangible by showing how they affect a recruiter’s day and an agency’s decisions.
In one post, AI sorts a large candidate list into twelve conversations worth having, while recruiters use their time to build trust and handle complex choices.
In another, they explain why a demand recovery can still leave agencies behind: marketing without recruiter capacity creates unanswered candidates, while hiring without renewed candidate acquisition leaves teams with stale databases.
A margin-focused post adds measures such as cost per placement and redeployment rates.
The posts present arguments and selected examples, not independent proof that the proposed practices produce particular results.
They also do not describe the referenced podcast conversation.
If a book, business, or other earlier work is central to why people search Feakins’s name, ask about it.
Feakins argues that travel-nurse agencies should use technology to handle more administrative work while preserving recruiters’ time for judgment and relationships.
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They connect that idea to practical recruiting habits and an operating challenge: rebuilding candidate acquisition and recruiting capacity together, then measuring whether growth improves margins.
Use AI to surface the conversations recruiters should have
Feakins argues that recruiter activity is a poor measure of value when software can screen applications, search databases, recommend jobs and send follow-ups.
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AI and data are better used to identify timely opportunities, leaving recruiters to focus on conversations that need human judgment.
Feakins imagines a system that flags 12 nurses to contact: someone whose assignment ends in 30 days, for example, and someone who applied for a closed ICU role but qualifies for seven others. These are illustrations of how a system might work, not results from a named system.
Feakins presents AI as support for recruiters, not a replacement.
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Technology can find information and handle volume, but recruiters still need to understand a clinician’s constraints, explain tradeoffs and build enough trust to reach a decision.
Feakins describes a recruiter helping a nurse consider a move of 1,500 miles, navigate credentialing or assess a pay package, while remembering preferences such as avoiding night shifts or traveling with a dog. In a separate post, they argue that recruiters should respond quickly when a clinician makes contact, then take time to understand which jobs might actually fit.
Build candidate acquisition and recruiter capacity as a pair
Feakins warns that a recovering market can expose gaps left by cuts to marketing and recruiting teams.
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More candidate leads do not help if nobody can respond; more recruiters do not help if they have no fresh candidates to work with.
Feakins compares marketing to gas, recruiting to an engine and technology to a transmission. In their example, increased marketing without enough recruiter capacity leaves candidates unanswered, while hiring recruiters without rebuilding acquisition leaves them working a stale database.
For Feakins, rising job volume is not the same as a healthier agency.
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They urge leaders to track operating measures that show whether recruiting and candidate relationships are producing more efficient placements, rather than assuming revenue growth will improve profit.
They cite a 4.3% median EBITDA margin in the SIA/NATHO travel-nurse staffing benchmark, compared with 11% in 2019, and name cost per placement, placements and revenue per recruiter, redeployment, re-engagement and application-to-placement conversion as measures to watch.
Treat recruiting as a continuing pipeline, not a one-call placement
Feakins says recruiters should move fast when a clinician signals interest but slow down once they connect.
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Rather than forcing a match to the job the clinician first applied for, recruiters should learn what would make a move work and maintain the relationship even when there is no immediate placement.
After a TravCon session, Feakins recommends asking why a clinician wants a location, whether they would work nights or float, and what their backup plans are. They frame one conversation as a possible start to several future assignments, not just a single placement.
Feakins uses an example from Ryan McGuire at Adex Medical Staffing to show that letting AI communicate with clinicians takes preparation and oversight.
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The implementation details come from McGuire’s example; Feakins’s broader point is that technology can improve recruiters’ access to information.
Feakins says McGuire’s team reviewed and corrected messages and used tens of thousands of recruiter conversations before allowing the AI to speak directly with clinicians. The post contrasts the AI’s rapid access to job information with a recruiter putting a nurse on hold to search for ICU jobs in Georgia.
Answers the practical question behind the topic: once AI takes the administrative work, what is the recruiter actually doing all day?
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It quickly explains Feakins's position on AI in staffing without a vendor argument.
Feakins argues that agencies have measured recruiters by calls made, texts sent, candidates sourced, resumes reviewed and follow-ups completed — exactly the work AI can now absorb.
What remains, in their framing, is recruiting: the ten conversations a day that matter most.
They bring the argument to life with a morning screen that surfaces twelve nurses to call today, each with a reason attached: an assignment ending in 30 days, three Florida jobs viewed, four recent emails opened, an ICU application to a closed role matched against seven open ones.
Then they list what software cannot do: talk a nurse through moving 1,500 miles, calm a credentialing problem, tell someone the pay package is weak, or remember that a candidate hates nights, travels with a dog and needs to be near an airport.
The argument is a before-and-after, not a product pitch.
It closes on a line an audience can remember: AI handles volume, data finds opportunity, humans create trust.
For brands planning a campaign
A strong opening frame for a talk or advisory session aimed at agency leaders and recruiter managers: it reframes the productivity question rather than announcing a technology. The twelve-nurse example gives the audience a concrete scene, not a slide of claims.
Answers whether the recovery is real and what breaks first when it arrives.
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Feakins turns a market statistic into a decision about headcount and budget.
This post leads with a number: open travel nurse jobs reached 10,824 on September 28, up 16% in 30 days and 67% year over year, attributed to Aya's Index.
Feakins stresses that the market is not returning to 2021.
The rest of the piece uses an operating metaphor — marketing is the gas, recruiting is the engine, technology is the transmission — to name two failure modes.
Turn up marketing without recruiter capacity and candidates go unanswered, helping a competitor place them.
Hire recruiters without rebuilding candidate acquisition and you pay people to work a stale database.
Both have to be rebuilt together; the gap will show up in placement numbers only after the agency is already behind.
For brands planning a campaign
Shows Feakins tracking market data and translating it into operating choices, making this a good fit for a session on growth planning. The gas-engine-transmission framing gives a non-technical audience a way to grasp the argument.
Answers the question owners ask after demand returns: does the money come back with it?
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It clearly lays out the numbers Feakins thinks agency leaders should manage.
Feakins draws on one figure from the SIA/NATHO travel nurse staffing benchmark — a 4.3% median EBITDA margin against 11% in 2019 — to distinguish revenue growth from profit.
More jobs, they note, usually mean more recruiters, more marketing spend and more overhead, so volume can climb while margins stay thin.
They name the measures they watch: cost per placement, placements and revenue per recruiter, redeployment rates, re-engagement of candidates already in the applicant tracking system, and application-to-placement conversion.
The claim is specific: if 100 recruiters deliver what once took 130, or a previously acquired traveler is reactivated, the acquisition spend goes further.
Feakins calls the shift an efficiency era.
For brands planning a campaign
A strong entry point for a margin-focused audience, such as agency owners or finance-adjacent operators, and a natural basis for a session on measuring AI adoption against cost per placement and redeployment rather than technology novelty.
Answers what AI doing the talking actually looks like inside a staffing agency, and what it took to get there.
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This is the closest thing here to a firsthand operator case study.
Rather than describe an AI capability in the abstract, Feakins recounts a specific build by Ryan McGuire of Adex Medical Staffing, who let an AI system message clinicians directly.
The post highlights the discipline required first: reviewing messages, correcting mistakes and feeding tens of thousands of real recruiter conversations into the system before it spoke to candidates.
The case is concrete: no recruiter can hold every job, shift, hospital, location and pay package in memory, but the system can.
That matters because nothing kills momentum faster than an eight-minute hold when a nurse asks what ICU jobs are open in Georgia.
Feakins makes the case for augmentation rather than replacement.
The conversation came from the TalentTrack Podcast.
For brands planning a campaign
Supports a talk built around a named case study rather than general claims, and shows Feakins interviewing operators about their builds. Ask for the podcast transcript before quoting the conversation in detail.
Answers how recruiter craft changed once the easy volume disappeared, with practical questions recruiters can use in their next call.
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It shows Feakins as a trainer as well as a commentator.
This post comes out of a session Feakins led at TravCon and turns on a deliberate contradiction: recruiters need to move faster and slower at the same time.
Faster means responding the moment a clinician raises a hand, because an hour's delay cuts the chance of connecting and a day may lose them entirely.
Slower means resisting the urge to match the applicant to the job they applied for and asking instead why the location matters, whether they will work nights or float, and what their Plan B and Plan C are.
Feakins argues that this expands the number of jobs a clinician could realistically accept and rebuilds the pipeline so one conversation becomes several assignments rather than one placement.
They contrast this with the COVID period, when, in their words, the market came to you.
For brands planning a campaign
Shows conference speaking and workshop-style material: the discovery questions and the speed/slow-down split are immediately usable in a session or advisory engagement for recruiter teams.
AI takes the volume, recruiters take the conversation
Feakins' central argument is that the travel-nurse recruiter's day is splitting in two.
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AI can increasingly handle screening applications, summarizing candidate history, searching the database, recommending jobs, sending follow-ups and re-engaging old candidates.
That shifts recruiting value to the few conversations that decide an assignment.
They picture a morning list of twelve nurses flagged for a reason: an assignment ending in 30 days, three Florida jobs viewed, four emails opened after six months of silence, or an ICU applicant matched to seven other openings.
The human step is the one that closes: persuading a nurse to move 1,500 miles, steering them away from the job they applied for, calming credentialing problems, or remembering that a nurse hates nights or travels with a dog.
Measure recruiter productivity by the quality of the ten conversations technology flags as urgent, not by calls made, texts sent, candidates sourced or follow-ups completed.
For brands planning a campaign
For an agency campaign aimed at recruiter productivity, this is core session material: present the AI prioritization layer and the human closing role as one workflow, not competing claims. Feakins is explicit that this is not replacement: AI handles volume, data finds opportunity, and humans create trust.
Redeployment and re-engagement as a margin strategy
Feakins points agency owners to a number rather than job volume: the 4.3% median EBITDA margin in the latest SIA/NATHO travel nurse staffing benchmark, against 11% in 2019.
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More jobs tend to bring more recruiters, marketing spend and overhead, so revenue can climb while margins stay thin. They call the current period an efficiency era and track cost per placement, placements and revenue per recruiter, redeployment rates, re-engagement of candidates already in the ATS, and application-to-placement conversion.
If 100 recruiters deliver what previously required 130, and a traveler already paid to acquire is reactivated, acquisition spend goes further. Build redeployment into the process from day one rather than adding it later.
For brands planning a campaign
Gives a leadership audience a measurable frame that an advisory session can turn into a working scorecard, positioning AI as a margin lever rather than a headcount decision.
Rebuilding marketing and recruiting capacity together
When travel-nurse demand returns, Feakins argues, the risk is capacity, not demand.
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They cite Aya's Index, which showed open travel nurse jobs at 10,824 on September 28, up 16% in 30 days and 67% year over year, after three years in which agencies cut marketing budgets, job board spend, recruiter teams and operations.
Their frame: marketing is the gas, recruiting is the engine, and technology is the transmission.
Turning up marketing without enough recruiter capacity produces more ignored candidates, who apply elsewhere and get placed by another agency's recruiter.
Hiring recruiters without rebuilding candidate acquisition means paying people to work a stale database.
Rebuild both sides together, using the ATS, matching, automation, re-engagement and AI to get more output without recreating 2021 headcount.
For brands planning a campaign
Useful for market-timing conversations with agency leadership: the recommendation is a paired rebuild, not a single spend decision. A session can show what the pipeline and capacity gap looks like before it appears in placement numbers.
Feakins' rule for recruiters comes from a Modern Recruiter Playbook session at TravCon.
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Speed up when a clinician raises their hand: wait an hour and the chance of connecting falls sharply; wait a day and they may already be gone.
Once connected, slow down and stop trying to match them to the job they applied for.
Feakins urges recruiters to ask deeper qualifying questions and treat a conversation that produces nothing today as assignment one next month—and assignments two, three and four after that.
Ask why that location, why no nights, whether they would float, what Plan B and Plan C are, what would make them move faster and what would make them walk away. Then widen the set of jobs a clinician could realistically say yes to.
For brands planning a campaign
This is the practical training segment of a talk or workshop—the part a recruiter team can rehearse the same week—and supports the wider point that the COVID-era fire hose is gone and pipeline work is back.
One case study Feakins returns to is Ryan McGuire at Adex Medical Staffing, who moved AI beyond paperwork and eventually let it talk directly to clinicians.
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Before that, they reviewed messages, corrected mistakes and fed it tens of thousands of real recruiter conversations so it could absorb the nuances of healthcare staffing.
The point that landed with Feakins: a human recruiter cannot hold every job, shift, hospital, location and pay package in memory, while AI can instantly answer a question such as which ICU jobs exist in Georgia—without the eight-minute hold that kills momentum.
The account comes from a conversation on the TalentTrack Podcast.
Build on real recruiter conversations, keep a person reviewing and correcting the output, and only then let the AI talk directly to clinicians.
For brands planning a campaign
A concrete rollout story a staffing audience can follow, and a talk segment on implementation risk: the payoff is instant access to job and pay detail; the cost is the review and correction work required before the tool can speak for the agency.
Oliver Feakins’s saved posts show one named industry collaboration—a recruiter-playbook session at the TravCon travel healthcare conference—and a case study and podcast reference featuring staffing agency ADEX Medical Staffing.
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No posts document sponsorship, affiliate arrangements, clients, ownership, employment or investments.
Industry data citations (Aya’s Index, the SIA/NATHO margin benchmark) appear as research references rather than brand relationships.
The post records have no publication dates, so ask when these examples were published.
TravCon (The Travel Healthcare Conference)
Disclosed partnership
Feakins describes delivering a “Modern Recruiter Playbook” session at TravCon, writing, “During our Modern Recruiter Playbook session at TravCon,” and drawing the main points from it.
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The post identifies the conference and session but gives no commercial terms; ask whether the session was paid.
Feakins writes about Ryan McGuire, an ADEX Medical Staffing staffer who trained an AI system on recruiter conversations and then let it talk directly with clinicians.
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They also point readers to a related podcast episode. The post presents ADEX as an example of AI adoption in staffing, not as a work, sponsorship or client relationship with Feakins.
Oliver Feakins writes short, framework-driven LinkedIn posts about travel-nurse and healthcare staffing, focusing on how AI can absorb screening, database search and follow-up work so recruiters can spend time on the conversations that close assignments.
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Their posts pair practical recruiting habits — speed up when a clinician raises a hand, then slow down to learn what would make a move work — with operating measures such as cost per placement, redeployment and EBITDA margin.
Start with their picture of the 2027 recruiter, then read their TravCon notes on qualifying conversations and posts on rebuilding marketing and recruiting capacity together.
Explore their healthcare staffing market commentary and AI-enabled recruiting ideas; similar creators below offer adjacent views on staffing technology, talent acquisition and recruiter workflow.
Writes about AI agents in healthcare staffing and how the recruiter role changes as AI takes on sourcing, engagement and scheduling.
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Like Feakins, argues that AI should amplify recruiters rather than replace them; both use healthcare staffing examples to explain the shift.
Rahman teaches through longer video episodes and framework breakdowns of the AI stack — language models, RAG, vector databases and agentic AI — while Feakins works from travel-nurse market numbers and specific recruiting habits in short LinkedIn posts.
Posts about AI in recruiting and how it changes what great recruiters spend time on, including a customer story about AI-sourced candidates and a reorganized recruiting operating model.
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Both connect AI adoption to recruiter focus and talent acquisition outcomes.
Clifford often promotes LinkedIn Talent Solutions products and employer-branding tactics such as job-posting structure, while Feakins concentrates on travel-nurse agency economics, redeployment and the quality of recruiter conversations.
Argues that recruiters should not act as human middleware between an ATS and communication tools, and that automation should remove copy-paste work so recruiters can focus on relationships.
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That overlaps with Feakins’s case for letting AI handle administrative volume while people handle trust and closing.
Smyth’s examples center on a specific product, GLYDE Unity, and Microsoft 365, Outlook and Teams integration, with detailed workflow mechanics. Feakins discusses AI-enabled prioritization and travel-nurse market capacity without naming a single tool.
Oliver Feakins writes for people who run or staff healthcare recruiting teams: agency owners, travel-nurse recruiters, and recruiting leaders weighing AI, redeployment, and margin pressure.
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The five posts address that audience, with commentary aimed at staffing operators rather than traveling clinicians.
For example, they address “agency owners” directly and tell recruiters to rebuild candidate pipelines, speed up first contact, and consider cost per placement and placements per recruiter.
That suggests the intended readership, not who actually reads or follows the account.
The only published audience figure is 13,708 LinkedIn followers on October 8, 2026.
For a healthcare staffing company planning an AI-enabled recruiting and redeployment talk or advisory session, the key question is whether the account reaches agency decision-makers — recruiters, recruiting managers, and owners — rather than clinicians.
The content suggests recruiters and agency leaders are the intended readership, but ask for recent, comparable results: a post or sponsored item on AI-enabled recruiting or recruiter productivity, with impressions and engagement, plus any audience survey or follower demographics showing how many followers are agency owners, recruiting leaders, or individual recruiters.
Results for comparable topics and formats matter more than total follower count, which reflects accumulated following rather than current reach or sponsorship delivery.
The posts contain no sponsor policy, media kit, rate information or partnership inquiry route for Oliver Feakins.
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They show participation in industry conversations, not an advertised offer: a session billed as the Modern Recruiter Playbook at TravCon, where Feakins discussed speed and depth in travel-healthcare recruiting, and an appearance on the TalentTrack Podcast discussing AI in recruiting with a guest from Adex Medical Staffing.
Neither post states terms, pricing, sponsorship or how a brand can request a collaboration, so no purchasable format can be documented.
Before budgeting or setting a timeline for a talk, advisory session or sponsored placement, ask Feakins directly whether they offer it, and request a delivery format, lead time and contact channel.
A mid-size travel-nurse staffing agency (roughly 80–150 recruiters) with recovering job volume and thin margins wants a leadership session plus a short advisory follow-up on using AI to raise recruiter productivity and protect margin.
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This is a strong fit.
The campaign is aimed at the same people Oliver Feakins writes for — agency owners and recruiting leaders — and their posts already answer the two questions it would raise.
On productivity, Feakins argues the day is splitting: screening, database search, job recommendations, follow-ups and re-engagement of old candidates can increasingly be handled by software, leaving recruiters the few conversations that decide an assignment.
A concrete example — a morning list of twelve nurses flagged for a reason, from an assignment ending in 30 days to four emails opened after six months of silence — could open the session.
On margin, Feakins points to a number rather than job volume: a 4.3% median EBITDA margin in the SIA/NATHO travel nurse staffing benchmark, against 11% in 2019, and measures to manage — cost per placement, placements and revenue per recruiter, redeployment rates, re-engagement of candidates already in the ATS, and application-to-placement conversion.
They frame redeployment as the cheapest placement available because the acquisition spend is already paid.
That is an actionable margin argument, not a technology pitch.
Two further pieces support the advisory follow-up.
The TravCon posts offer a training component: respond within minutes when a clinician raises a hand, then slow down and qualify hard — why this location, will they work nights, would they float, what are Plan B and Plan C — to widen the set of jobs a clinician could realistically accept.
Adex Medical Staffing describes a rollout in which the system was checked and corrected, then fed tens of thousands of real recruiter conversations before it was allowed to message clinicians directly.
Before budgeting, confirm the speaking fee, format, lead time and inquiry route directly.
Roughly 13,700 LinkedIn followers were observed on 8 October 2026; no engagement or demographic data was provided.
The value rests on the operating argument, not audience reach, so sell the session on that basis.
When to choose a specialist
A direct-to-clinician brand — travel-nurse apparel, gear, or a nurse-facing education app — wants Oliver Feakins to produce clinician-facing lifestyle content and reach travel nurses at scale, citing account size.
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This is a poor fit because of the audience, not the quality.
Feakins's posts address agency owners and recruiters, not clinicians: they cover recruiter productivity, cost per placement, redeployment rates and application-to-placement conversion, with practical advice for the people making calls and setting budgets.
The posts offer no evidence that the account reaches nurses as buyers of apparel or consumer products; follower count alone cannot establish that.
A second problem is deliverability.
Feakins's posts show participation in industry conversation — a Modern Recruiter Playbook session at TravCon and a podcast appearance — but publish no media kit, rate card, format specification or inquiry route.
A brand planning a paid placement, sponsored post series or consumer content program would be building on an unconfirmed offer, with no documented lead time or contact channel.
To assess the opportunity, ask directly for a media kit or confirmation of formats and pricing.
A third mismatch is subject matter.
A clinician-lifestyle campaign would need content about pay packages, housing, contracts and life on assignment from the nurse's side.
Feakins writes about how agencies should staff and retain those nurses — almost the opposite vantage point.
If a brand still wants to work with this account, it could reframe the campaign as an agency-side talk, a recruiter-training workshop, or a sponsored piece on travel-nurse demand and agency capacity.
Those topics fit the demonstrated subject matter and would reach recruiting leaders rather than clinicians.
A worked campaign example
Campaign need: an 80-recruiter travel-nurse agency, after three years of trimmed marketing and headcount, sees job volume rising but margins flat. It wants a leadership session on AI-enabled recruiting and redeployment, with a working scorecard the team can keep using.
Campaign angle: “The Efficiency Era — More Jobs, Same Margin.” A 45-minute leadership session followed by a half-day working session with recruiting managers, positioned as an operating review rather than a technology briefing.
Opening (10 minutes): the 4.3% versus 11% margin comparison, paired with rising open travel-nurse job counts.
Land the argument early: volume is returning, but profit does not automatically follow because more jobs usually mean more recruiters, marketing spend and overhead.
Marketing is the gas, recruiting is the engine, and technology is the transmission — turn up the gas without engine capacity and the spend helps a competitor place the candidate.
Middle (20 minutes): what the recruiter's day becomes.
Feakins's twelve-nurse morning screen, with a reason attached to each name, is the concrete scene: an assignment ending in 30 days, a clinician who viewed three Florida jobs, four emails opened after six months of silence, an ICU applicant whose role closed and who matches seven open ones.
Then list what software cannot do — talk someone through a 1,500-mile move, calm a credentialing problem, say the pay package is weak, or remember that a candidate hates nights or travels with a dog.
Close with the line the room will repeat: AI handles volume, data finds opportunity, humans create trust.
Working session (half day): build the scorecard the agency will actually report on.
Start with the measures Feakins names — cost per placement, placements and revenue per recruiter, redeployment rate, re-engagement of candidates already in the ATS, and application-to-placement conversion — and pick three to instrument in the next 90 days.
Then set the recruiter-training component: respond within minutes when a clinician raises a hand, then slow down and qualify — why this location, will they work nights, would they float, what are Plan B and Plan C, what would make them move faster, and what would make them walk away.
Use the Adex rollout as a cautionary story: review, correct and train on real recruiter conversations before any system messages clinicians directly.
Deliverable: a one-page scorecard with owners and targets, a recruiter call checklist of qualifying questions, and a 30/60/90 plan for the AI rollout, with checked output first and direct clinician contact later.
Measure of success: three named metrics instrumented, a documented redeployment target, and a pipeline of re-engagement conversations from the existing database.
Before budgeting, confirm Feakins's speaking and advisory fee, availability and delivery format directly. Treat the session as an operating-advisory engagement rather than an audience-reach buy: no engagement or demographic data was provided, so the case rests on the substance of the argument, which is strongest on this campaign.
Choose Rahman when the session is about how to evaluate or build AI for staffing.
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In a post promoting an AI-stack video, Rahman names language models, agents, retrieval and vector databases, and argues that data and workflows can matter more than model size.
That offers a more technical, vendor-evaluation angle than Oliver Feakins’s examples about recruiter conversations, redeployment and agency efficiency.
Ask for the video transcript to assess the framework’s depth and operational detail. Feakins’s posts provide concrete examples of recruiter practice and agency measures, including redeployment and cost per placement.
The examples center on job-posting craft and one customer story, rather than the travel-nursing pipeline, redeployment and margin framework in Feakins’s posts. The customer post reports placement revenue associated with AI-sourced candidates.
These examples focus on a particular product and proposed workflow, not broad agency strategy for margins, redeployment or recruiter conversations. Feakins is the closer match when a session needs to connect recruiter practice with business measures.
This profile draws on the full text of five LinkedIn posts from Oliver Feakins's account, read in full rather than sampled from titles or previews.
They cover the recruiter role and AI-assisted prioritisation, a Modern Recruiter Playbook session at the TravCon travel healthcare conference, returning travel-nurse demand and agency capacity, staffing margins and operating measures, and a podcast conversation about an AI rollout at ADEX Medical Staffing.
The posts carry no publication dates, so the order of their arguments and how current they are cannot be established from them.
The account listing was observed on 8 October 2026 and showed roughly 13,700 followers; no engagement figures, audience demographics or posting history are published in the posts here.
Six dated LinkedIn posts from three comparable creators, published between April and September 2026, provide the similar-creator comparisons.
Feakins's posts do not say anything about other channels, longer-form work, speaking or advisory terms, or earlier business and career material.
In particular, no signature book, show or business is identified in the posts, so whether an earlier body of work is also central to why people search the surname remains an open question rather than a settled answer.
