Jodi Marchewitz focuses on recruiter training and hiring strategy, including helping recruiters approach unfamiliar technical roles and use AI to research searches more effectively.
They are useful for B2B recruiting-tech vendors selling enablement training or AI tools to talent acquisition leaders.
Best for
A B2B recruiting-tech vendor selling enablement training or AI-assisted search tools.
Not for
Campaign needs broad consumer reach or targeting job seekers and applicants.
Key takeaways
Define what a role requires before starting a search.
Training should be built around live searches and practical application.
AI provides generic answers unless the recruiter knows what to ask.
Check first: Request LinkedIn follower demographics and recent post performance data.
Main ideas
Set the direction of a search before contacting candidates · Recruiters can work unfamiliar technical roles without learning to code · AI is useful only if the recruiter can ask the right questions
Jodi Marchewitz writes about recruiter training, hiring strategy, technical recruiting, and AI in hiring.
A recurring message is that a search can go off course before sourcing begins: recruiters need to define what a role requires, where to find suitable talent, and how to assess candidates.
Marchewitz also argues that training is more useful when it is built around live searches and reinforced in practice.
The posts offer practical ideas for people helping recruiters take on unfamiliar roles and use AI without mistaking generic answers for sound search strategy.
Marchewitz brings recruiter enablement back to the decisions that shape a search.
In a post about recruiting software engineers, the advice is not to learn to code, but to understand the role, find the relevant talent, and recognize strong candidates.
In another, Marchewitz warns that AI can give confident but generic answers when a recruiter does not yet know what to ask.
The underlying point: better tools and faster sourcing cannot make up for aiming a search at the wrong target.
A separate post lays out a practical training approach—learn on live roles, get an early win, and reinforce the framework across searches.
Marchewitz also connects these ideas to team capacity, promoting a conversation about how Denver’s city and county supports 900 job classifications with 19 recruiters.
Jodi Marchewitz’s posts return to one practical idea: a search should be aimed well before sourcing begins.
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Recruiters need a clear read on the role, the talent pool, and what strong candidates look like—especially when a role is unfamiliar. Training and AI can help, but only when they support that groundwork and fit into the recruiter’s actual searches.
Set the direction of a search before contacting candidates
Marchewitz argues that searches often go wrong before sourcing starts.
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Recruiters should first decide what the role really requires, where relevant talent can be found, and how to distinguish stronger candidates from weaker ones. In this view, getting those decisions right makes later work more effective.
In a post about recruiting strategy, Marchewitz reduces the preparation to those three decisions and says they affect everything downstream.
Recruiters can work unfamiliar technical roles without learning to code
For a recruiter handling an engineering role for the first time, Marchewitz says the essential gap is not coding knowledge.
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It is understanding the role, where suitable engineers are, and what signals distinguish a strong candidate. That makes technical recruiting a role-research and evaluation problem, not a demand that every recruiter become an engineer.
A post opens with a software engineering role landing on the desk of someone who has never filled one, then names the three areas an experienced tech recruiter brings to the search.
AI is useful only if the recruiter can ask the right questions
Marchewitz warns that AI can produce confident but generic answers when a recruiter researches a role they do not understand.
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The recruiter may not know what to ask, and the resulting search can target the wrong candidates. This is a caution about how to use AI, rather than a rejection of it: role understanding must guide the tool.
Marchewitz describes a recruiter using ChatGPT on an unfamiliar role and explains how weak questions can point the search at the wrong target. In another post, Marchewitz says AI may save more than 20 hours, but argues that misdirected searches and misfit interviews can cost more.
Training should change the work recruiters do, not just deliver information
Marchewitz says one-off workshops fade and video libraries may go unfinished.
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Training is more likely to stick when recruiters apply it to live roles, get an early win, revisit the framework on each search, and learn from someone who has filled those roles. Marchewitz adds that training should help recruiters use AI confidently as leverage.
A post contrasts a one-day workshop and an unfinished video library with those four conditions for learning that lasts beyond the initial session.
Build recruiting capacity by helping generalists handle unfamiliar work
Marchewitz also highlights the challenge of doing more with a small recruiting team.
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The emphasis is on building expertise and helping generalists become stronger talent advisors, rather than treating capacity as a headcount question alone.
A post promoting a conversation with Allison Wagner of the City and County of Denver cites 19 recruiters supporting 900 job classifications. The figures and discussion topic are presented in Marchewitz’s post; Wagner is the invited practitioner, not the source of Marchewitz’s other arguments.
This is the clearest single statement of how Jodi Marchewitz thinks recruiter enablement works, and it answers the first question a training buyer or content partner would ask: what makes it last?
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Jodi Marchewitz argues that a one-day workshop fades, a video library never gets finished, and a quarter later the team recruits exactly as it did before.
Four things make training stick, in this account: recruiters learn on their own live roles, they get a fast first win, the framework is reinforced on every search, and the teaching comes from someone who has actually filled the roles.
The post adds that in the age of AI, training also has to leave recruiters confident enough to use AI as leverage rather than fear it.
For brands planning a campaign
A co-marketing plan built around reinforcement tied to live searches — rather than a one-off workshop or a library of videos — matches the position already argued publicly here.
It names a specific, common problem for generalist recruiters moving into technical hiring and states what the gap actually is, which is more useful to a newcomer than a general claim about upskilling.
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The post opens with the situation it solves: a software engineering role lands on the desk of a recruiter who has never filled one.
The argument is that the recruiter does not need to learn to code.
They need what an expert tech recruiter brings before the search starts — a real read on the role, where those engineers actually are, and how to tell a strong candidate from a weak one.
For brands planning a campaign
This is the natural anchor piece for content aimed at generalist recruiters who inherit technical requisitions without technical backgrounds.
It explains a concrete failure mode of AI-assisted recruiting research rather than treating AI as either a fix or a threat, which is a more specific starting point for anyone weighing AI in a search process.
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Jodi Marchewitz writes that AI only answers what you already know to ask. On a role the recruiter has never filled, they do not know what to ask, so they get confident, generic answers and walk into the search pointed at the wrong target, with hours piling up on the wrong things.
For brands planning a campaign
A co-marketing angle here is an evaluation-or-checklist piece on how to stress-test AI output before trusting it on an unfamiliar requisition — a more defensible hook than generic AI enthusiasm.
It gives a compact, repeatable framework that a newcomer can apply to any role, and it locates the failure earlier in the process than most recruiting advice does.
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The premise is that most searches fail before anyone has been contacted, not in sourcing.
The post frames a recruiting strategy as three decisions made before sourcing a single person: what the role truly needs, where that talent is, and how you will tell top talent from the rest.
Get those right, the post says, and everything downstream gets faster.
For brands planning a campaign
Framework-shaped content like this converts well into a downloadable worksheet or a co-branded session, since the three decisions can be turned into a working template.
It reframes the value of AI in recruiting around misdirected effort rather than saved sourcing time, which is a sharper way for a newcomer to think about where the real loss sits.
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Jodi Marchewitz states that AI can save a recruiter more than 20 hours on a single search, then argues that is not the expensive part. The larger cost is the weeks or months of recruiter and hiring manager time spent on wrong profiles and misfit interviews, because the search was aimed wrong from day one.
For brands planning a campaign
A co-marketing plan can frame the offer around the cost of a wrongly aimed search — wrong profiles and misfit interviews — instead of competing on hours saved alone.
It shows the format Jodi Marchewitz already uses — a named practitioner describing a real staffing constraint — and gives a concrete public-sector example of the do-more-with-less problem the other posts address.
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This post promotes a free live conversation on an October 21 date at 1 PM ET / 10 AM PT with Allison Wagner, a talent acquisition leader at the City and County of Denver. The stated case is 19 recruiters supporting 900 job classifications, and the discussion covers building recruiter expertise, improving alignment with hiring managers, and increasing recruiting capacity with fewer resources.
For brands planning a campaign
A co-marketing plan could follow this same live-conversation format with a named practitioner, using a specific resourcing constraint as the hook rather than a general topic.
Marchewitz argues that most recruiter training is forgotten within a month: the one-day workshop fades, the video library never gets finished, and a quarter later the team recruits the way it always did.
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The post names four conditions for training that sticks — recruiters learn on their own live roles, get a fast first win, have the framework reinforced on every search, and are taught by someone who has actually filled the roles. In AI-era hiring, Marchewitz adds a fifth requirement: training has to make recruiters confident enough to use AI as leverage rather than fear it.
For brands planning a campaign
A contributor or trainer role should be scoped around these conditions rather than general 'enablement' language. Live-role practice, a fast first win, reinforcement on every search, and practitioner credibility are the specific promises the content would have to keep.
Equipping generalist recruiters for unfamiliar roles
Marchewitz hosted a free live conversation with Allison Wagner, a talent acquisition leader at the City and County of Denver, to discuss how 19 recruiters support 900 job classifications.
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The framing is capacity, not headcount: generalist recruiters need to handle unfamiliar roles with the confidence of specialists and to become stronger talent advisors, while improving alignment with hiring managers. The post is addressed directly to leaders whose recruiting teams are being asked to do more with less.
For brands planning a campaign
Marchewitz already works in a live interview/webinar format with a named practitioner guest, which a sponsoring recruiting-tech company could co-host. Public-sector talent teams doing more with less is a clearly identified audience segment for recruiter enablement content.
Recruiting strategy decided before sourcing starts
Marchewitz's diagnosis is that most searches fail before anyone is contacted, not in sourcing.
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The proposed method is three pre-sourcing decisions: what the role truly needs, where that talent is, and how to tell A talent from B talent. Getting those three right is presented as what makes everything downstream faster.
Three-decision pre-sourcing framework: role requirements, talent location, and a working standard for distinguishing stronger from weaker candidates.
For brands planning a campaign
This is a reusable framework a brand can build a co-marketing asset around — a template, checklist, or workshop structured on the three decisions rather than on tool features.
Marchewitz makes a specific case against simply asking ChatGPT to research a role you have never filled: AI only answers what you already know to ask.
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On an unfamiliar role, the recruiter does not know what to ask, so the output is confident and generic and points the search at the wrong target, with hours then spent on the wrong things.
Use AI for role research only after the recruiter has enough role knowledge to form useful questions; the post links to a longer explanation of why generic AI research stalls a search.
This is a caution about method, not a rejection of AI. Marchewitz's other posts treat AI as a genuine time saver, so the position is conditional on the recruiter's grasp of the role.
For brands planning a campaign
A tool vendor should not pitch this as generic 'AI for recruiting' endorsement. The angle that matches the evidence is AI used after a recruiter builds role knowledge — a 'know what to ask first' campaign rather than a speed claim.
Time saved by AI, and the larger cost it does not remove
Marchewitz puts AI time savings at 20-plus hours on a single search, then argues that is not the expensive part.
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The bigger cost is the weeks, sometimes months, of recruiter and hiring manager time spent on the wrong profiles and misfit interviews because the search was aimed wrong from day one.
For brands planning a campaign
Supports an ROI argument built on avoided misfit interviews and wasted hiring-manager time rather than hours-per-search alone. A brand can use that two-part cost structure to frame a co-marketing case study or calculator.
Technical recruiting for recruiters who are not technical
The scenario is a software engineering role landing on a recruiter's desk when they have never filled one.
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Marchewitz's answer is that the recruiter does not need to learn to code; they need what an experienced tech recruiter brings before the search starts — a real read on the role, where those engineers actually are, and how to tell a strong engineer from a weak one.
Close the knowledge gap on the role, the talent pool's location, and candidate quality signals, rather than on engineering skills.
For brands planning a campaign
Practical, non-technical framing of technical recruiting is a strong niche for enablement content aimed at generalist recruiters at smaller teams. It pairs naturally with the AI-research caution above as a single 'unfamiliar role' content series.
Jodi Marchewitz's saved LinkedIn posts name two organizations.
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Four posts end with links to HireBoost.ai articles on recruiter training, technical recruiting, and AI-assisted search, so HireBoost.ai is listed as a disclosed partnership with the exact nature of the relationship unstated — the posts promote the brand but do not say whether Marchewitz works for it, owns it, or partners with it.
A fifth post promotes a live conversation with a talent acquisition leader at the City and County of Denver; Denver is the case-study subject and the guest's employer, not a documented client or sponsor, so it is listed as a mention only.
No sponsorship, affiliate, investment, or employment relationship is stated anywhere in the reviewed posts.
HireBoost.ai
Disclosed partnership
Four posts on recruiter training and search strategy close with a link to a HireBoost.ai article and the tag #HireBoost.ai, including "How do I recruit for a software engineer if I've never filled one before?", "How much time can AI save recruiters on each search?", and "How do I build a recruiting strategy for a specific role?".
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The posts market HireBoost.ai content, but none states whether Marchewitz owns, works for, or contracts with the business, so the relationship is recorded as an unstated commercial association rather than employment or ownership.
A post advertising a free live conversation on 21 October features Allison Wagner, Talent Acquisition Leader at the City and County of Denver, describing how 19 recruiters cover 900 job classifications.
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Denver appears as the guest's employer and as the subject of the case example; the post shows no client, sponsor, or employment tie between Denver and Marchewitz.
Jodi Marchewitz writes for recruiters and recruiting leaders about aiming a search before sourcing starts.
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The clearest entry point is the three pre-sourcing decisions: what the role truly needs, where that talent is, and how to tell a strong candidate from a weak one.
From there, follow the posts on filling unfamiliar technical roles without learning to code, and the caution about using AI for role research — a model only answers what you already know to ask, so weak questions point a search at the wrong people.
Two related threads run alongside: recruiter training that lasts past the first month, and building capacity in small teams by helping generalist recruiters handle unfamiliar work, with a public-sector example of 19 recruiters covering 900 job classifications.
Several posts link out to longer articles or to a live practitioner conversation, so following those links is part of getting the full argument.
Readers who want the same ground from other angles can look at Mark Wojcik on repeatable hiring systems, Aaron "Big Country" McCann on recruiter and hiring manager communication, and Rob Walker on proactive, relationship-led searches seen from the candidate's side.
Both argue for making hiring repeatable instead of reactive.
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Wojcik's summit session covers five disciplines for a hiring system that outlasts the individual — stronger role profiles, protected time for hiring, reliable sourcing routines, and more confident interview and selection decisions — which is the ground Marchewitz covers with the three pre-sourcing decisions and with training that gets reinforced on every search.
Wojcik writes from the staffing-agency side and is recruiting agency-trained, reduced-schedule recruiters for a flex-recruiter model, while Marchewitz writes about equipping in-house recruiters and generalists and hosts practitioner conversations rather than staffing a team.
Both publish practical LinkedIn commentary for recruiters and both treat communication and follow-through as where searches break: McCann's dating metaphor turns on recruiters and hiring managers leaving each other in silence, the same failure Marchewitz locates before anyone has been contacted.
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McCann writes in comic narrative form about agency competition, candidate experience and talent brand, while Marchewitz writes instructional posts about role research, recruiter training and AI-assisted search.
Walker's client stories and Marchewitz's posts make the same argument from opposite chairs: a search is won by targeting the right people and building relationships before you need them, not by sending more applications.
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Walker reports a 50% response rate from direct, personalised messages; Marchewitz reaches the same conclusion about redirecting effort before sourcing.
Walker coaches individual job seekers through a proactive search and writes up client outcomes, while Marchewitz writes for the recruiters and talent teams running those searches, including training and AI tooling that the candidate-side work does not cover.
That subject matter is directly relevant to a B2B recruiting-tech audience and to a brief about recruiter enablement, but the supplied evidence does not measure who follows or reads the account.
The only supplied audience measurement is a LinkedIn follower count of 3,403 observed 2026-10-11.
The six posts carry no engagement, demographic, employer, seniority, or geography data, so they show content appeal rather than audience composition.
For a recruiter-enablement co-marketing or training brief, the practical next step is to request LinkedIn follower demographics (job function, seniority, geography) and recent performance for posts on recruiter training and AI-assisted search; if those are unavailable, ask for audience survey results.
Treat the current fit as subject-matter relevance, not verified audience match.
LinkedIn followers
3,403
LinkedIn account metadata supplied in the creator packet · 2026-10-11
Jodi Marchewitz does not publish partnership terms, sponsorship formats, or a media kit in the material reviewed here.
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The six LinkedIn posts cover recruiter training, hiring strategy, technical recruiting, and AI-assisted search, and several of them link out to articles on an external recruiting resource site, but none describes a sponsorship arrangement, a rate card, a content-collaboration process, or a way for a brand to pitch a partnership.
This means a buyer cannot currently confirm what Jodi Marchewitz offers commercially, what it costs, or how to start a conversation from published evidence alone.
The gap is an open research item rather than a finding that no offers exist: an official media kit, rate card, or partnership page would need to be located and reviewed before the formats list can be populated.
A B2B recruiting-tech vendor that sells recruiter enablement training, sourcing workflow software, or AI-assisted search tools, and whose buyers are talent acquisition leaders and generalist recruiters who inherit requisitions outside their specialty.
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Jodi Marchewitz fits this brief on subject matter and on method.
The reviewed posts argue that recruiter training fails because a one-day workshop fades and a video library never gets finished, and they name what makes it last: practice on the recruiter's own live roles, a fast first win, reinforcement on every search, and teaching from someone who has filled the roles.
That is a usable position for a training or enablement offer rather than a generic endorsement.
The three-decision framing for a search — what the role truly needs, where the talent is, and how to tell stronger candidates from weaker ones — converts directly into a worksheet, a workshop outline, or a co-branded session.
The AI caution is equally specific: AI only answers what you already know to ask, so on an unfamiliar role it returns confident, generic output that points the search at the wrong target.
A vendor selling AI sourcing can build a defensible campaign around knowing what to ask first instead of claiming raw speed.
The format already exists in public: a free live conversation with a named practitioner describing a real staffing constraint, in that case 19 recruiters covering 900 job classifications.
Two constraints belong in the decision.
The measured audience is small — a LinkedIn follower count of 3,403 observed 2026-10-11 — with no engagement, seniority, or geography data supplied, so reach cannot be projected from the evidence reviewed.
And no media kit, rate card, or partnership process appears in the material reviewed, so the commercial route has to be established directly before a plan is costed.
When to choose a specialist
A campaign that needs broad consumer reach or a job-seeker audience.
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The content reviewed is written for recruiters and the leaders who manage them, not for candidates, and it assumes the reader is running searches.
A consumer hiring app, a career-change brand, or an employer-branding push aimed at applicants would be buying an audience the reviewed material does not show.
The audience evidence is a single LinkedIn follower count of 3,403 observed 2026-10-11, with no demographics, engagement rates, or past campaign performance supplied, so a reach or awareness brief cannot be underwritten from what is available.
A second poor fit is a brand looking for a straightforward sponsored-post placement with published formats and pricing.
None of the six posts describes a sponsorship arrangement, a content-collaboration process, or a way to pitch a partnership, and several carry a HireBoost.ai label whose commercial nature is not documented in the reviewed material.
A buyer cannot confirm from published evidence what is offered, what it costs, or how to start.
Performance-marketing briefs that need conversion tracking against the account are also a mismatch on the current evidence: the posts link out to articles and a registration page that were not part of the reviewed material, so click-through, registration, or lead quality cannot be assessed.
This is a documented gap, not proof that no commercial offers exist, and the gap should be resolved before the brief is rejected outright.
A worked campaign example
Brief: a recruiting-tech company wants Jodi Marchewitz as an expert contributor or trainer for recruiter enablement content, with a short co-marketing plan around recruiter training and AI-assisted search strategy. Worked angle — run the plan as a short enablement series anchored on one promise: recruiters learn to aim a search before they source or automate it.
Step one, a co-hosted live conversation in the format the Denver post already demonstrates, pairing Jodi Marchewitz with a practitioner from the sponsor's customer base who is working unfamiliar roles with a small team.
The hook is a real constraint, not a topic, and the session title should name it.
Step two, a companion worksheet built on the three pre-sourcing decisions — role requirements, where the talent is, how to separate stronger from weaker candidates — with a worked example for one hard-to-fill role and a badly aimed AI research prompt beside a corrected one that shows the difference between confident generic output and questions a recruiter can actually use.
Step three, reinforcement between sessions: short check-ins tied to the recruiters' own live searches rather than a recorded library, which is the position the training post already argues against.
Step four, a closing session on where AI fits after role knowledge exists, framed around avoided misfit interviews and hiring-manager time rather than hours saved on sourcing.
Before costing this, resolve the open items: request audience demographics by job function and seniority, recent performance on recruiter-training and AI posts, and any existing partnership or media materials.
Clarify the relationship behind the HireBoost.ai label in several posts and confirm whether contributing, training delivery, or sponsorship is the vehicle.
Until then the co-marketing case rests on demonstrated subject fit, not on measured audience match.
Choose Mark Wojcik when the campaign has to sell employers on a repeatable hiring process, or when recruiting capacity itself is part of the offer.
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The reviewed posts show a named live session built for HR teams and hiring managers — 'Recruit Your Replacement,' organised around five disciplines: stronger role profiles, protected hiring time, reliable sourcing routines, and more confident interview and selection decisions — and a pitch for reduced-schedule, agency-trained recruiters supplied to growing companies at project rates. If the brief is to equip employers to keep hiring steady as individuals come and go, or to source flexible recruiting labour, the reviewed material maps to that decision more directly.
The tradeoff is direction.
Wojcik's reviewed posts teach hiring systems to employers and sell recruiting labour; they do not cover training a generalist recruiter to run an unfamiliar search, and they say nothing about where AI fits in a search.
A campaign whose promise is recruiter enablement and AI-assisted search would be buying a different subject.
The recruiter-recruitment post is also an ad for agency-trained people willing to work a few hours a day for project pay, which points at a recruiting-services relationship between Wojcik and the clients, not at a content or training collaboration.
Choose Aaron McCann when the goal is attention and a shareable point of view among agency recruiters and sales-recruiting audiences.
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The agency-competition post makes a specific, quotable argument a vendor could build a differentiated claim on: putting many agencies on one search damages the candidate experience, pushes early-career recruiters out of agency work, and cheapens the talent brand. The dating-analogy post is the kind of narrative hook that travels in recruiter feeds, and the reviewed material includes a longer video, so the format range runs past short text.
The tradeoff is substance over reach.
The reviewed posts are opinion and storytelling — a dating analogy and a rant about agency competition — with no training framework, no published method for hard-to-fill roles, and no AI-in-search content.
A recruiter enablement campaign needs teaching material a reader can act on, and the reviewed evidence does not supply it.
The commercial message in the competition post is also agency-side and aimed at prospects choosing a search firm, which is not the same buyer as a recruiting-tech vendor.
Two posts is a limited basis for judging how consistently the point of view is carried.
Choose Rob Walker when the audience is job seekers and the message is a proactive search method.
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The reviewed posts give a worked, numbered example: targeting chosen employers and messaging directors, CEOs, and founders directly (about a 50% response rate on personalised messages), following up with hiring managers rather than only recruiters or talent acquisition — including one automated rejection reversed within 24 hours — and building a network of recruiters before it is needed. For a career-services, candidate-community, or talent-attraction brief, that is directly usable material with concrete outcomes.
The tradeoff is who is being served.
The audience in the reviewed posts is candidates looking for their next role, not recruiters or talent acquisition leaders, and nothing in the reviewed material covers recruiter training or AI-assisted search.
For a brief about equipping recruiters, this would put a candidate-facing voice in front of a recruiter-facing problem.
The posts also rest on individual client stories rather than a published programme, so a sponsor would be buying the method and the anecdotes, not a curriculum.
This profile rests on six publicly posted LinkedIn posts by Jodi Marchewitz covering recruiter training, hiring strategy, technical recruiting, and AI-assisted search, plus an account profile showing 3,403 LinkedIn followers observed 11 October 2026.
Five of the posts carry dates in September 2026; one announces a 21 October session and is undated, so no claim is made about how the ideas developed over time.
Each post's own text was read in full.
Articles, registration pages, and videos linked from the posts were not part of the reviewed material, so anything said about them is reported as the posts describe it.
The posts name HireBoost.ai and the City and County of Denver; the nature of those relationships is stated as the posts state it, without inferring employment, ownership, sponsorship, or client status.
No audience demographics, engagement rates, media kit, rate card, or partnership terms were available, so reach and commercial fit are not projected beyond the follower count above.
Unverified items are kept as open questions rather than settled facts.
Recent engagement
Across their last 6 LinkedIn posts, the median post got 3 reactions and 0 comments. Checked 2026-11-02.
