Maria Christopoulos Katris writes about employer branding and talent acquisition, with a focus on how AI is changing the way candidates discover and evaluate employers and how organizations measure recruiting impact.
They are most useful for framing employer brand measurement in the AI era beyond last-touch attribution.
Best for
B2B talent acquisition technology brands running thought leadership on AI employer branding and recruiting measurement.
Not for
Programmatic job-advertising and source-of-hire attribution vendors wanting a keynote that argues against Katris's published position.
Key takeaways
91% of surveyed leaders believed AI influences employer discovery; 33% were confident in accurate representation.
A company case reported improved perception and application figures after changes to its employer story.
Three-question framework covers visibility, reputation and outcomes, cut by market, function and role.
Check first: Ask for the survey and case-study measurement methods before using the figures as benchmarks.
Main ideas
AI reputation is a recruiting performance problem, not a marketing one · The 58-point gap between what candidates do and what employers can see · Attribution is being asked the wrong question
Maria Christopoulos Katris writes about employer branding and talent acquisition, especially how AI is changing the way candidates discover and judge employers.
Their posts connect that shift to a practical question: how can organizations measure visibility and reputation when a candidate’s research may happen before the application—and across channels that application data cannot capture?
The posts include a three-part measurement framework, a reported company case study, and a challenge to the assumption that recruiting investments must be tied to a single hire before they are worth measuring.
Follow them for arguments and examples aimed at talent acquisition and employer-brand leaders navigating those questions.
Katris makes a hard-to-measure change concrete.
Rather than treating AI visibility as a standalone marketing metric, they link it to candidate trust, recruiting-funnel outcomes, and differences between markets, functions, and roles.
Their most usable framework asks whether the right talent can find an employer, what candidates think when they research it, and whether visibility and reputation improvements accompany outcomes such as applicant quality, conversion, offer acceptance, and hires.
They also use specific examples: one post reports a Built In survey in which 91% of surveyed leaders believed AI influences employer discovery while 33% felt confident their employer brand was represented accurately; another reports a company case with improved perception and application figures after changes to its employer story.
Those posts make the argument easier to discuss.
Before using the figures as benchmarks, ask for the survey and case-study measurement methods.
The writing is most useful to readers looking for a way to frame employer-brand measurement beyond last-touch attribution, not a fully specified measurement protocol.
Maria Christopoulos Katris argues that employer reputation is recruiting infrastructure, not brand sentiment.
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Candidates now form impressions inside AI tools — ChatGPT, Gemini, Claude, Google AI Overviews — before they visit a career site or hear from a recruiter, so a company's visibility and standing in those answers shape response rates, conversion and offer acceptance.
From that starting point, they push back on how talent acquisition measures its work: the standard question, which source generated this hire, describes less and less of the journey and should become one input rather than the strategy.
The alternative is a framework built on visibility, reputation and outcomes measured across all channels, cut by market, function and role because a single location or function can diverge sharply from the company average.
Katris writes long-form posts that open with a named misconception or a live question, lay out observations in numbered or arrowed form, credit peers by name, and close by asking readers how they handle the same problem.
They present these as positions to pressure-test rather than settled answers.
AI reputation is a recruiting performance problem, not a marketing one
Katris treats the belief that AI reputation belongs to marketing as the biggest current misconception, arguing it is increasingly a recruiting performance issue.
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The reasoning is a chain of funnel effects: when candidates trust a company before a recruiter reaches out, response rates improve; when AI reinforces the employer story during evaluation, conversion improves; when candidates see the company positively during offer-stage comparisons, acceptance rates improve. Candidate perception is largely formed before the recruiting process begins, through AI-assisted discovery, comparison, culture evaluation, leadership assessment and decisions about which opportunities are worth pursuing.
One enterprise technology company strengthened its employer story on Built In by adding structured FAQs, employee-generated content and richer company insights. As perception improved, its Workplace Perception score rose from 43% to 91% and its apply rate from 10.84% to 12.47%.
The 58-point gap between what candidates do and what employers can see
Katris reports a Built In survey of talent acquisition and employer brand leaders that produces two numbers held against each other: 91% believe AI tools influence how candidates discover and research employers, while only 33% feel confident their employer brand is accurately represented inside AI.
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The distance between them, 58 points, is presented as one of the defining recruiting challenges of the next few years. The underlying problem they point to is visibility: most companies do not know what AI says about them as an employer, which sources it draws on, or how it shifts candidate decisions.
The post lists what candidates do inside ChatGPT, Gemini, Claude and Google AI Overviews — discover and compare employers, evaluate culture, assess leadership, decide which opportunities to pursue — and links the full survey report so readers can go past the two headline figures.
Across hundreds of conversations with leaders responsible for employer branding, talent acquisition, talent attraction and talent brand, Katris keeps hearing the same request: prove that these investments produce hires.
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The argument is that the question is mis-framed.
Candidates now discover and research employers inside AI before applying wherever is convenient, so the recorded application source captures only part of what shaped the decision.
Attribution does not disappear on this account; it becomes one input rather than the strategy.
The comparison that carries the argument is marketing: CMOs invest in visibility and reputation without waiting for perfect attribution, while talent acquisition asks for proof before acting on the same shift in behaviour.
The line the post turns on is that the field is not losing influence, it is losing visibility into influence.
Katris writes that candidates who once researched across LinkedIn, Built In, Google, review sites and company career sites increasingly ask a single question of an AI assistant and act on the answer — compressing, rather than eliminating, the candidate journey. They call this an argument they want pressure-tested, noting it could look self-serving given the work.
Measure employer brand on visibility, reputation and outcomes — cut by market, function and role
The proposed replacement for source-based attribution is three questions.
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Visibility: is the company showing up for the talent it wants to reach.
Reputation: what candidates think when they research the company, especially in the markets, functions and roles that matter most.
Outcomes in total: whether improvements in visibility and reputation translate into higher-quality applicants, better mid-funnel conversion, higher offer acceptance and more hires in previously underperforming areas, counted across all channels rather than per channel.
The practical rule attached to the framework is that employer brand should not be measured only at company level, because one location can have exceptional visibility while another has none, and one function can have a strong reputation while another struggles.
Katris argues those local and functional differences explain hiring outcomes better than attribution alone, and that they are where the biggest opportunities tend to surface — a market-by-market or role-by-role view of reputation rather than a single company score.
The arguments follow a consistent format: posts open with a named misconception or a live question, lay out the reasoning in a numbered or arrowed sequence, and close by asking readers how their own organisation handles the problem.
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Katris presents conclusions as positions to pressure-test rather than settled answers, and credits others for their ideas.
In the attribution post, Katris credits Kathleen Booth's recent post for the marketer's-side version of the same argument about AI-driven discovery, describing the two as parallel worlds acted on very differently. This is Booth's argument, quoted and credited, not Katris's own claim.
This post answers a newcomer’s first question: is there evidence behind the topic they write about?
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It carries the clearest number in the material.
Reports a Built In survey of talent acquisition and employer brand leaders.
Two findings anchor the post: 91% believe AI tools influence how candidates discover and research employers, while only 33% feel confident their employer brand is accurately represented inside AI—a 58-point gap.
The post lists what candidates do with ChatGPT, Gemini, Claude and Google AI Overviews: discover and compare employers, evaluate culture, assess leadership, and decide which opportunities to pursue.
It adds that most companies have no visibility into what AI says about them, where it draws information from, or how it shapes decisions, and links the full report.
For brands planning a campaign
For a campaign seeking a speaker who can open with a measured number, this survey gap is a natural frame. It sets up a measurement session for TA and employer brand leaders who accept that AI matters but cannot yet measure its effect.
The clearest worked example in Maria Christopoulos Katris’s posts of reputation work tied to recruiting funnel numbers.
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Argues that the biggest current misconception is treating AI reputation as a marketing problem when it is increasingly a recruiting performance problem.
Chains three effects: candidates who trust a company before a recruiter reaches out respond more often; AI reinforcing the employer story during evaluation lifts conversion; and positive perception at the offer stage lifts acceptance rates.
Then gives the example of an enterprise technology company that strengthened its employer story on Built In by adding structured FAQs, employee-generated content, and richer company insights.
Workplace Perception scores went from 43% to 91%, and apply rates from 10.84% to 12.47%.
For brands planning a campaign
The published before-and-after gives a webinar or executive roundtable a concrete point to debate.
Use it as the worked example in a session on measuring employer brand return in the AI era.
Ask how the numbers were measured; the post does not say.
The most directly usable framework in the material for preparing a measurement discussion or brief.
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Frames a recurring question for leaders responsible for employer branding, talent acquisition and talent brand: how do you know whether these investments pay off?
Argues that the final application source tells only part of the story now that candidates start and stay inside AI before applying through a career site, LinkedIn, Indeed, Built In or an ATS.
Proposes three questions: visibility—whether the company shows up for the talent it wants to reach; reputation—what candidates think when they research the company in the markets, functions and roles that matter; and total outcomes—higher-quality applicants, better mid-funnel conversion, higher offer acceptance and more hires in previously underperforming areas, across all channels.
Adds one practical rule: measure by market, function and role rather than at company level.
One location can have strong visibility while another has none, and those differences explain hiring outcomes better than attribution alone.
For brands planning a campaign
Roundtable-ready: three headings, sub-questions under each, and a rule for segmenting measurement give the session structure and a concrete output.
Shows how this person argues and where the argument challenges the standard talent acquisition position.
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Describes the same conversation with TA leaders about tying employer branding, recruitment marketing, AI visibility and reputation back to hires, then asks whether that is the wrong question.
Notes that candidates started behaving like consumers more than a decade ago, while measurement never changed.
Points to marketing as the comparison: CMOs invest in visibility and reputation without waiting for perfect attribution.
The post’s central line: the field is not losing influence; it is losing visibility into influence.
Credits Kathleen Booth with the marketer’s-side version of the argument and closes with an open question about what would have to happen for TA to think about measurement the way marketing does.
For brands planning a campaign
Useful for a panel that needs disagreement, not consensus. The marketing-versus-TA framing draws out objections from TA leaders, while the credited parallel post brings a natural second voice to the roundtable.
A short read that shows which audience this person speaks to and how clearly they name its pressures.
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Written for National Talent Acquisition Day, which falls on the author’s birthday.
Says more than 15 years have been spent working alongside the people responsible for finding the people who build companies.
Lists what TA leaders describe: doing more with less; building an employer brand that competes with consumer brands on a fraction of the budget; proving impact while losing visibility into familiar metrics; handling an explosion of fraudulent and AI-generated applications; working out how to use AI in their own teams and whom to hire; and managing hiring plans that change overnight because of a stock price, a government mandate or a new business priority.
Ends by arguing that the job is important, not merely hard, because growth depends on finding the right people.
For brands planning a campaign
A human-register piece for campaigns aimed at TA leaders rather than marketing leaders. It shows that this person speaks the audience’s language and can open or close a session with the pressures the room already feels.
AI reputation as a recruiting performance problem1
A three-part measurement framework1
Survey evidence on the AI representation gap1
What talent acquisition teams are being asked to carry1
AI reputation as a recruiting performance problem
Katris argues that how a company shows up in AI answers is a recruiting performance issue, not a marketing one.
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When candidates trust the employer story before a recruiter reaches out, they say response rates improve; when AI reinforces that story during evaluation, conversion improves; and when candidates see the company positively during offer-stage comparisons, acceptance rates improve. As evidence, they cite an enterprise technology company that added structured FAQs, employee-generated content, and richer company insights to its Built In profile, after which its Workplace Perception score rose from 43% to 91% and its apply rate from 10.84% to 12.47%.
Ties employer reputation to named recruiting funnel metrics, not just brand awareness.
Ask how the company measured perception and what else changed over the same period.
For brands planning a campaign
Gives a campaign on AI-era employer branding a funnel-level argument, not just a sentiment one, with before-and-after numbers to anchor a webinar or roundtable discussion.
Katris describes hundreds of conversations with talent acquisition and employer brand leaders who want to know whether their talent brand spending produces hires.
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Their answer: the question is misframed.
Candidates increasingly discover, compare, and evaluate employers inside AI tools before they ever reach a career site, so the application source records only part of what shaped the decision.
In their framing, attribution does not disappear; it becomes one input rather than the strategy.
Contrasts how marketing teams respond to AI-shaped buyer behavior with how talent acquisition teams respond to AI-shaped candidate behavior.
For brands planning a campaign
Suits a debate or roundtable where the premise can be challenged, with a client's own attribution model as the live case.
Katris proposes measuring employer brand through three questions: visibility (are we showing up for the talent we want to reach?), reputation (what do candidates think when they research the company, especially in the markets, functions, and roles that matter most?), and total outcomes (higher-quality applicants, better mid-funnel conversion, higher offer acceptance, and more hires where the company was previously underperforming).
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They stress cutting the analysis by market, function, and role, because one location or function can differ sharply from the company average.
For brands planning a campaign
Offers a ready-made workshop segment or takeaway template. The market, function, and role cuts set it apart from generic employer brand advice.
Katris reports a Built In survey of talent acquisition and employer brand leaders in which 91% said AI tools influence how candidates discover and research employers, while only 33% were confident their employer brand is accurately represented in AI—a 58-point gap they call one of the defining recruiting challenges of the next few years.
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They note that most companies have no visibility into what AI says about them, where it pulls from, or how it shapes candidate decisions.
Leads with the two headline percentages and points readers to the full report for the rest.
Ask for the survey sample and method; the post summarizes results without describing either.
For brands planning a campaign
Provides a quantified hook and survey-backed story for a campaign on AI visibility. The post links the full report, giving a session a foundation in the underlying data.
What talent acquisition teams are being asked to carry
Katris frames the pressures on their audience: do more with less; build an employer brand that competes with consumer brands on a fraction of the budget; prove impact while losing visibility into metrics they have relied on; handle a surge of fraudulent and AI-generated applications; work out how AI changes who to hire; and absorb hiring plans that can change overnight because of a stock price, a mandate, or a new business priority.
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They argue the role is consequential because growth depends on finding the right people.
The post is personal and celebratory, offering evidence of audience rapport and recurring themes rather than analytical claims.
For brands planning a campaign
Useful for audience framing: it identifies the specific anxieties a campaign message must address to resonate with talent acquisition and employer brand leaders.
Across the material, Katris uses long-form LinkedIn posts built around a named misconception or live question, a numbered or arrowed set of observations, and a closing question inviting readers to share how they handle the same problem.
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They credit peers whose work they build on, notably Kathleen Booth on the marketing-side view of AI-driven discovery, and describe their conclusions as something to pressure-test rather than settle.
Presents ideas as peer-tested working positions and explicitly invites disagreement in the comments.
For brands planning a campaign
Points to formats where discussion matters—roundtables, panels, or co-created content—and an easy handoff to a hosted conversation rather than a one-way keynote.
The posts document one business affiliation: Built In, the recruiting and employer-brand platform.
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In their National Talent Acquisition Day post, Katris writes as part of the company: “To our Built In customers, partners, friends and the entire TA community.”
They also present Built In’s survey findings on AI and hiring as in-house research.
ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews are discussed as tools that shape candidate research.
Ask about any other sponsorship, affiliate, client, or investment relationships before a campaign; none appear in the posts.
Built In
Employment
Katris's own writing positions them inside Built In.
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The National Talent Acquisition Day post addresses "our Built In customers, partners, friends and the entire TA community," and Katris's post on employer reputation in AI reports "Built In recently surveyed talent acquisition and employer brand leaders about AI and hiring," citing its results (91% believe AI tools influence how candidates discover and research employers; only 33% are confident their employer brand is accurately represented in AI).
Katris also references outcomes for "our Built In customers."
The posts do not state Katris's title or how long they have been with the company.
Maria Christopoulos Katris writes long-form posts about measuring employer brand and talent acquisition as candidates research employers inside AI tools.
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Start with the two attribution posts: they make the central argument and set out a three-question framework—visibility, reputation and outcomes counted across all channels.
A post built on a Built In survey supplies the numbers behind the argument, while the case-study post shows what improved employer reputation looks like when tied to funnel metrics.
A fifth post about the day-to-day pressures on talent acquisition teams offers community writing rather than an argument, so it works best as context.
If you want one piece, choose the measurement framework post; for the disagreement, read the attribution posts, which are written to be argued with.
Ko treats hiring as a commercial pipeline that must be run deliberately: define the ideal candidate profile, source proactively like an outbound sales team, keep candidates engaged, sell the role and keep good near-misses warm.
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That aligns with Katris’s argument that talent acquisition should borrow marketing’s habits and measure the funnel, not just a single source.
Ko stays with the mechanics of sourcing, qualifying and selling candidates, writing short posts about a recruiter’s day-to-day. Katris works a level up, examining how the employer story takes shape in AI answers and how to measure visibility and reputation.
Ricketts examines what can go wrong before the interview: whether the job description reflects the real role, expectations are clear and success is defined.
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Katris applies the same thinking at a larger scale, arguing that candidate perception is largely settled before a recruiter makes contact.
Ricketts focuses on individual applicants and hiring fundamentals, ending posts with an offer of workforce consultation to employers. Katris writes about measurement and AI visibility across the whole function, using survey figures and funnel numbers.
Hobbs argues that employers lose under-represented early-careers candidates before they apply because of signals sent by content, process and employer brand, naming the mechanism self-deselection.
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Katris applies the same before-the-process logic to AI-shaped candidate perception. Both build their arguments around a named mechanism, not reputation alone.
Hobbs writes about Gen Z and entry-level hiring, diversity and a published playbook for early-careers professionals. Katris covers employer brand visibility and measurement across the talent function, and asks readers how they handle the problem.
For a campaign on AI-driven employer branding and recruiting measurement, place this LinkedIn audience in the consider-but-verify category.
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The posts speak directly to talent acquisition, employer brand, and talent attraction leaders, aligning with the buyer’s subject.
The LinkedIn profile lists 10,664 followers as of 8 October 2026, but does not show their location, seniority, job function, company size, or decision-making authority.
Before booking a webinar or roundtable, ask for a recent audience survey or LinkedIn follower demographics showing how many followers work in TA, employer brand, or recruiting leadership, and the split between practitioners and executives.
LinkedIn followers
10,664
LinkedIn profile record attached to the research · 2026-10-08
Maria Christopoulos Katris's posts do not document a public partnership, sponsorship, or speaking offer.
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Across five LinkedIn posts published between late May and early September 2026, the visible commercial relationship is their work at Built In.
They cite a Built In survey of talent acquisition and employer brand leaders (91% say AI tools influence how candidates discover employers; 33% are confident their employer brand is accurately represented in AI), describe an enterprise technology company that improved its employer story on Built In and saw Workplace Perception scores rise from 43% to 91% and apply rates from 10.84% to 12.47%, and thank Built In customers, partners, and the wider talent acquisition community in a National Talent Acquisition Day post.
These are references to their organisation's research and customers, not a public offer brands can buy.
For a webinar or executive roundtable on measuring employer brand return, ask directly about partnership terms, format, and availability; no media kit, rate card, sponsorship policy, or inquiry form appears in the posts.
A B2B talent acquisition technology company wants a thought leadership partner or speaker for a campaign on AI-driven employer branding and recruiting measurement, with a webinar or executive roundtable on measuring employer brand return in the AI era.
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This is a strong fit, for specific reasons.
Katris brings together two things such a session needs and rarely gets: a measured gap and a usable framework.
The reported survey finding — 91% of talent acquisition and employer brand leaders say AI tools influence how candidates discover and research employers, while only 33% are confident their employer brand is represented accurately in AI — gives the session an opening number.
The three-question framework (visibility, reputation, outcomes in total, each cut by market, function and role) gives it a spine that produces decisions rather than discussion.
The reported before-and-after case, an enterprise technology company whose Built In profile work moved Workplace Perception from 43% to 91% and apply rates from 10.84% to 12.47%, gives the room something concrete to debate.
The format fit is as important as the topic fit.
They write long-form posts that name a misconception, list observations, credit peers and close by asking readers how they handle the same problem.
They describe their conclusions as positions to pressure-test rather than settle.
That suits a roundtable or a webinar with live Q&A far better than a one-way keynote.
They also name the audience's pressures accurately (do more with less, prove impact while losing visibility into familiar metrics, absorb hiring plans that change overnight), which matters when the room is the audience being sold to.
Before booking, ask about terms and availability: the posts describe their work alongside Built In but provide no rate card, media kit, booking channel or sponsorship policy.
The reported case numbers also lack detail on how perception or apply rates were measured, so present them as a reported before-and-after, not a proven causal model.
They flag that the argument can read as self-serving given their work; invite live objections and include a second voice from the marketing side of the same problem rather than relying on a monologue.
When to choose a specialist
A programmatic job-advertising and source-of-hire attribution vendor wants a keynote for a talent acquisition conference presenting channel-level cost-per-hire benchmarks and a product-led demonstration of its attribution dashboards, plus a paid job-board sponsorship with a fixed package.
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This is a poor fit: the premise conflicts with Katris's published position.
They argue that source-of-hire attribution is the wrong question: candidates increasingly discover, compare and evaluate employers inside AI tools before reaching a career site or job board, so the final application source records only part of what shaped the decision, and attribution becomes one input rather than the strategy.
A keynote using their name to argue the opposite would work against their own material, and they would likely contest the vendor's central claim from the stage.
The framing of marketing versus talent acquisition is the point they keep returning to.
That is a reasonable roundtable, but not a product demo.
The commercial structure is also unclear.
The posts document no sponsorship offer, partner programme, rate card or inquiry route.
A sponsor buying a fixed deliverable should ask Katris or their employer for documented terms and availability.
Shared audience is not fit when the offer and the message pull in opposite directions.
The assessment could change for a vendor whose product measures employer visibility and reputation rather than source attribution, or if Katris or their employer provides a documented commercial offer.
A worked campaign example
Worked angle for the campaign: a 60-minute executive roundtable (or a 45-minute webinar plus live Q&A) titled around the measurement question rather than the AI trend — for example, "Beyond source of hire: measuring employer brand when candidates decide inside AI."
Opening, roughly 10 minutes: Katris frames the problem with the reported survey gap, 91% against 33%, and the claim that the employer's AI representation is a recruiting performance issue rather than a marketing one. This is the slot for the number, not an explanation of what AI is.
Worked example, roughly 15 minutes: the enterprise technology company case, presented as reported.
Add structured FAQs, employee-generated content and richer company insights; Workplace Perception moved from 43% to 91% and apply rates from 10.84% to 12.47%.
The method behind those numbers is not documented, so ask the room what they would need to see before trusting the same claim in their own business.
That question turns a case study into an exercise.
Roundtable segment, roughly 25 minutes: each attendee takes the three questions — are we visible for the talent we want to reach; what do candidates find when they research us in the markets, functions and roles that matter; are visibility and reputation improvements showing up as higher-quality applicants, better mid-funnel conversion, higher offer acceptance and hires where we previously underperformed. The key rule is to measure by market, function and role, because one location can have strong visibility while another has none, and those differences explain hiring outcomes better than a company-level score.
Close, roughly 10 minutes: a short hand-off, not a pitch.
Each attendee leaves with their own three-question draft for one market or function, and the vendor takes the follow-up conversation.
A marketing-side counterpart would strengthen the room, since Katris builds explicitly on the marketer's view of AI-driven discovery.
If no counterpart is booked, say so and keep the framing to the talent acquisition side they document.
Before booking, ask Katris or their employer to confirm the fee, format, whether they appear in a personal or employer capacity, and who the counterparty is.
Pick Ko when the session needs recruiting operations rather than measurement.
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The posts argue that hiring is a sales process: employer branding works like the marketing engine that generates inbound interest; recruiters qualify candidates against a defined ideal candidate profile; and proactive outbound sourcing mirrors an outbound sales team.
The funnel is managed stage by stage, while strong candidates who are not right today stay warm for later roles.
A second post details what business development and sales development representatives do day to day: keep CRM data clean, pick the right companies and people, write buyer-centric outreach, absorb rejection, and prioritise across prospects and internal stakeholders.
For a campaign about sourcing-team enablement, pipeline discipline, or recruiters borrowing sales practices, this material is the most directly usable of the three alternatives.
The tradeoff is that it does not address how to measure employer brand return when candidates decide inside AI.
Neither post discusses how candidates research employers in AI tools, how employers appear in AI answers, or how to measure employer brand return.
The posts date from 2019 and 2021, before that shift.
Choosing Ko moves the campaign from “how do we measure employer brand when candidates decide inside AI” to “how do we run recruiting like a sales pipeline”—a different buyer conversation, with no reputation or attribution framework attached and no case data comparable to the reported perception and apply-rate movement in Katris's posts.
Pick Ricketts when the campaign problem is the applicant pool rather than the employer's visibility.
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The posts address what happens when applications arrive: one cites a survey in which 73% of HR professionals report challenges with fabricated or misleading candidate information and 36% say AI-generated candidate content has reduced their confidence in hiring decisions, then argues for clear success profiles, stronger interviews and candidate verification.
The other starts with clarity—whether the job description reflects the actual role, whether expectations are set in the interview, and whether the candidate understands what they will own and what success looks like—and ties mismatches between the posting, interview and first day to recruiting and retention problems.
For a webinar or roundtable on hiring-process integrity, application fraud and screening confidence, this is the closer match.
The tradeoff is the other half of the funnel.
Ricketts does not address how candidates discover, compare and evaluate employers inside AI tools, employer reputation scoring, or how brand investment is attributed to hiring outcomes—the core of a campaign about measuring employer brand return in the AI era.
Their posts offer practitioner advice that closes with an offer of a workforce consultation rather than a documented measurement method, so a session built on them reaches the buyer after the application and gives the room tactics instead of a framework.
Ask for comparable before-and-after case numbers if those are needed.
Pick Hobbs when the campaign targets early-careers and entry-level hiring and a Gen Z audience.
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The posts cover building employer reach on social and TikTok to reach young candidates directly, a £1.7 million seed round for urfuture, and the argument that the entry-level problem is distribution and infrastructure rather than candidate quality: assess on potential, not polish.
The second post names the mechanism that keeps underrepresented candidates out: self-deselection, where young people from lower socioeconomic backgrounds and high-potential candidates without the polish opt out before applying, driven by signals in content, process and employer brand.
Shortlisting fixes, blind CVs and diverse interview panels all arrive too late.
For a session on attracting Gen Z, or on the diversity pipeline before the application stage, this material is the most direct of the three.
The tradeoff is scope and posture.
Hobbs' content focuses on early careers and Gen Z; it offers no employer brand measurement model, AI visibility or reputation framework, or attribution discussion, so it cannot carry the core question of measuring employer brand return in the AI era.
The posts also promote a playbook download and webinar sign-up, making them as much about list growth as argument, and describe a company-building journey rather than a measurement practice.
Choosing Hobbs shifts the campaign to early-talent distribution rather than employer brand measurement—a useful but different need.
Maria Christopoulos Katris's five long-form LinkedIn posts discuss employer branding, AI-driven candidate research and talent acquisition measurement.
The posts carry no publication dates, so nothing here claims a chronology.
Two posts report third-party material — a Built In survey and an unnamed company case study — and those figures are described as the posts present them rather than independently verified.
The posts mention one business affiliation, Built In, and no sponsorship, client or partner relationships; no rate card, booking channel or sponsorship policy appears, and the posts do not establish a book, show or earlier career history, so those questions remain open.
Similar-creator comparisons use posts from the named creators' accounts, considered the same way.
