Danilo Dukanovic works at the intersection of behavioural science, psychometrics and AI. Their work helps companies make more structured decisions about hiring, promotion and employee development.
They are most useful for practical arguments on structured people decisions across hiring, promotion and development.
Best for
B2B HR technology thought leadership partnership on AI-driven talent assessment.
Not for
Performance-led recruitment lead generation with free ATS audit and AI keyword screening.
Key takeaways
AI screening does not fix weak hiring; clear criteria, skills assessments and work samples matter.
HR teams lose full-time work to fragmented data synthesis.
The bigger opportunity is talent decisions after hiring, not only recruitment.
Check first: Confirm commercial terms, audience data, and review process for product and performance claims.
Main ideas
AI amplifies hiring problems instead of fixing them by itself · HR teams lose full-time work to fragmented data synthesis · The bigger opportunity is talent decisions after hiring, not only recruitment
Danilo Dukanovic works at the intersection of behavioural science, psychometrics and AI.
In LinkedIn posts, they write about using more structured methods to make decisions about hiring, promotion and employee development.
Their examples range from AI screening and work-sample tests to consolidating HR data and identifying who may be ready for a new role.
Follow their work for practical arguments about how organizations assess and develop people—and where automation alone falls short.
Danilo connects workplace problems to questions about how people decisions are made.
In one post, they describe the time HR teams spend combining information from different systems and frame AI as a way to free staff from repetitive synthesis, not simply reduce headcount.
In another, they argue that AI screening does not fix a weak hiring process: clear criteria, skills assessments and work samples matter too.
Their account of expanding beyond recruitment adds an important counterpoint: the same structured approach can inform internal mobility, development and succession.
Together, these posts show a focus on better-supported decisions across the employee lifecycle, rather than on AI as an end in itself.
Danilo Dukanovic's ideas focus on making people decisions more structured and less dependent on gut feel, rushed screening or fragmented manual work.
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The recurring argument is that AI does not replace human judgment; it amplifies the process already in place.
When hiring is unstructured, AI screening exposes that weakness.
When HR teams are buried in disconnected data, AI infrastructure can free them from repetitive synthesis.
Danilo's stated priority is to work at the intersection of behavioural science, psychometrics and AI to help companies hire, promote and develop the right people with clearer criteria.
The posts also show a shift in emphasis from recruitment alone toward internal mobility, succession, leadership pipelines and workforce visibility.
AI amplifies hiring problems instead of fixing them by itself
Danilo argues that candidates and recruiters both use AI, exposing weaknesses in hiring systems rather than creating them.
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A recruiter reviewing CVs in seconds and AI screening at scale both feel impersonal, but the alternative is not a perfect human evaluator.
Instead, look beyond the CV: use psychometrics to assess potential, mindset and soft skills, alongside skills-based assessment and work-sample tests.
Employers that use AI as blind automation should not complain about poor application quality or employer-brand damage.
Danilo describes a recruiter who reads 200 CVs, wants to go home for lunch and spends eight seconds per CV.
They cite Alan Price, Global Head of Talent Acquisition at Deel, who said AI screening was used for 500,000 candidates and that people complain about it.
The examples prompt a question: what is the realistic alternative?
Danilo recommends structured assessment over rushed CV review or informal interview impressions.
HR teams lose full-time work to fragmented data synthesis
Danilo argues that capable HR business partners often spend their time on work they were not hired to do: reading PDFs, moving between Excel files, consolidating reports from different countries, and preparing executive and audit documents.
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The problem grows when a regional enterprise has legacy software from headquarters, a group HRIS competing with a local ATS, and assessments from local vendors.
Danilo estimates the cost in hours and money, then proposes AI infrastructure to automate integration across those sources while keeping data inside the company's ecosystem.
The goal is to free HR staff, not reduce the team.
For a regional group with offices in Montenegro, Serbia, Croatia and Bosnia and Herzegovina, Danilo itemizes talent review prep at 1,376 hours, exit interview synthesis at 1,377 hours, engagement survey free-text processing at 1,216 hours, cross-system consolidation for executive reports at 3,168 hours, and audit prep across four jurisdictions at 448 hours.
The total is 15,650 hours a year, described as nine full-time HR employees and about €344,000, or 15 to 20 percent of a regional HR budget.
Danilo says the response is AI infrastructure, naming ModernBERTić, a BCMS-language encoder model, and BalkanBench, an open-source benchmark for the language, as part of that work.
The bigger opportunity is talent decisions after hiring, not only recruitment
Danilo explains that recruitment pain was real but often not strategic enough in larger companies.
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It was treated as a workflow issue, folded into broader HRIS decisions, or too weak to justify a complex enterprise sale.
Meanwhile, the team used its own tool internally for performance review, development, internal mobility and talent mapping.
That changed how they saw the product: its greater value was helping companies decide whom to promote, develop, move or prepare for succession, not only whom to hire.
Danilo says market conversations then shifted toward internal mobility, succession, leadership pipelines and workforce visibility.
The team began asking not just who performs well, but who is ready for more, who is in the right role, who has leadership potential, and who needs development rather than promotion. Danilo says these questions changed the product's direction, and that stronger market conversations became less about hiring faster and more about internal talent decisions.
Danilo describes a deliberate shift from recruitment-only use to internal mobility, development and succession. The company expanded beyond recruitment not because recruitment stopped mattering, but because the buying logic and the product's stronger value pointed to broader talent decisions.
Structured criteria should replace gut feel and politics in people decisions
Danilo's stated work sits at the intersection of behavioural science, psychometrics and AI.
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The priority is to help companies hire, promote and develop people with more structure and less politics.
Across the recruitment post and internal-mobility story, Danilo argues against relying on informal impressions, rushed screening or political judgment.
The alternative is a system based on data, criteria and technology, with assessment methods that evaluate potential, skills and soft skills.
In the recruitment post, Danilo writes that reading a CV and then having a 30-minute conversation to see how a candidate feels is not selection but guesswork.
They also say a manager's impression should not be the value justification, and recommend building a system based on data, criteria and technology.
In the story post, Danilo describes the work as helping companies hire, promote and develop the right people with more structure and less politics.
See how Danilo links a specific HR workload problem to AI infrastructure.
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The post describes HR teams combining information from different systems and estimates that five recurring tasks could consume 15,650 hours a year in a four-country regional group.
Rather than describing AI in the abstract, the post names the work: preparing talent reviews, synthesizing exit interviews and survey comments, consolidating executive reports, and preparing for audits.
It estimates the time for each task, then puts the total at nine full-time employees and about €344,000 in annual cost.
They frame the product’s purpose as freeing HR staff from repetitive synthesis, not reducing the team.
The post also names ModernBERTić and BalkanBench as part of the approach.
For brands planning a campaign
For a B2B HR technology collaboration, build around one concrete data-consolidation process and show what changes for the HR team. Present the time and cost figures as Danilo’s estimates, not independently verified results, and confirm the assumptions before using them in campaign claims.
Open this post to understand Danilo’s position on AI in recruitment from both sides of the process.
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It answers whether they see AI screening as a standalone fix: the argument is that it exposes weaknesses in hiring systems that already rely on rushed CV review and unstructured judgment.
The post sets up a cycle: candidates use AI to generate applications, recruiters use AI to screen them, and both sides blame the other.
Danilo contrasts automated screening with a recruiter spending about eight seconds on a CV, then argues that neither blind automation nor an informal interview impression is a sound selection method.
They recommend looking beyond the CV through skills assessments, work samples, and measures of potential and soft skills, with clearer criteria.
For brands planning a campaign
A thought-leadership partnership could explore how to make AI-supported assessment more structured and useful, rather than promoting automation alone. Address evaluation criteria, work samples, and candidate experience; present the post’s recruiter scenario as illustrative, not as a measured industry-wide statistic.
Read this for Danilo’s explanation of how the work moved from hiring into internal mobility and development.
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It shows which people decisions they consider connected, and why a campaign about talent assessment need not focus only on recruitment.
Danilo explains the shift through two signals: recruitment pain was real but often difficult to sell as a complex enterprise priority, while the team found a stronger use for its tool in internal mobility, development, and management decisions.
They cite questions raised at work: who is ready for more, suited to the right role or leadership, or in need of development rather than promotion.
They say market conversations reinforced interest in succession, leadership pipelines, and workforce visibility.
For brands planning a campaign
A collaboration could pair recruitment assessment with a related internal-talent decision, such as development or succession. Since the post describes Danilo’s account of the product’s evolution, ask for a specific, documented example before making claims about customer outcomes.
Danilo Dukanovic argues that both candidates and recruiters now use AI, and that the technology has exposed weaknesses in hiring rather than created them.
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They point to recruiters reviewing hundreds of CVs in seconds and to AI screening at scale as reasons the process feels impersonal.
They recommend that employers look beyond the CV to signals such as psychometrics for potential, mindset and soft skills, skills-based assessments, and work-sample tests.
They also warn that blind AI automation will damage application quality and employer brand.
The post makes a direct, two-sided argument: candidates and recruiters are treated as parallel users of AI, then it closes with concrete alternatives to CV-only screening.
The post cites Alan Price of Deel on AI screening for 500,000 candidates, drawing on external practice as well as first-hand experience.
For brands planning a campaign
A B2B HR technology partner can build thought leadership around practical hiring signals and process design, not generic AI replacement claims. A strong angle is a joint piece on where AI screening helps, where it fails, and how psychometrics or work samples can support a fairer funnel.
Danilo Dukanovic describes regional enterprise HR teams spending full-time hours on manual synthesis: reading PDFs, moving between Excel files, consolidating reports across countries, preparing talent reviews, exit interviews, engagement survey text, executive reports, and audit prep.
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Using a four-country example, the post totals 15,650 hours a year and roughly €344,000 in cost, or 15–20% of a regional HR budget. The proposed response is AI infrastructure that automates integration across Excel, PDFs, manager feedback, and local tools while keeping data inside the company's ecosystem.
The post quantifies the problem with an itemized time-and-cost model for a specific regional group, rather than relying on general productivity claims.
The post also introduces ModernBERTić and BalkanBench, a first encoder model and an open-source AI benchmark for BCMS languages, tying the argument to language-specific infrastructure.
For brands planning a campaign
For an HR technology partner, this opens a content collaboration on the operational case for AI in HR: quantifying administrative drag, making data integration concrete, and discussing privacy-preserving deployment. A vendor-neutral explainer or customer-facing calculator could extend the argument beyond a single post.
From recruitment to internal mobility and succession
Dukanovic explains why the company expanded beyond recruitment: although recruitment pain was real, larger companies often treated it as a workflow issue or folded it into broader HRIS decisions, weakening the buying case relative to the problem's complexity.
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Meanwhile, the team used its own tool for internal mobility, development, performance review, and talent mapping.
The greater value lay in helping companies decide who to promote, develop, move, or prepare for succession.
The post uses a first-person story to narrate a product and market pivot, moving from customer pain to internal use to external demand.
The post names no customers or performance metrics; ask for these before assessing the pivot's traction.
For brands planning a campaign
A thought leadership partnership can position Danilo Dukanovic around talent decisions after hiring: internal mobility, succession planning, and workforce visibility. This can complement hiring-focused AI content and support co-created research on how companies turn assessment data into promotion and development decisions.
Danilo Dukanovic's LinkedIn posts document a business connection with Recrewty.
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In a post about HR data work, they describe Recrewty building AI infrastructure and say “we” are presenting the ModernBERTić model and BalkanBench benchmark. Ask them to clarify whether they own, work for, or partner with Recrewty.
Recrewty
Disclosed partnership
Dukanovic writes in first-person plural that Recrewty spent the last 18 months building AI infrastructure and says “we” are publicly presenting ModernBERTić and BalkanBench.
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Ask them to clarify their title, ownership, or contractual role with Recrewty.
Danilo Dukanovic writes short LinkedIn posts about how organizations decide who to hire, promote, and develop.
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Three posts offer clear entry points, each taking one problem and arguing it through: AI screening in recruitment, the hours HR teams lose consolidating fragmented data, and the company's move from hiring tools into internal mobility and succession.
Start with the recruitment post, which argues that AI amplifies whatever process is already in place; then read the data-consolidation post for the quantified version of the same claim, followed by the story post for where the work is heading.
Expect first-person narration, itemized numbers, and a stated preference for structured criteria over interview impressions.
Both write for business and policy audiences in the same region about whether organizations are actually adopting AI.
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They build posts around figures, named institutions, and their own public appearances — Vazdar's at a national council session and in a television interview, Dukanovic's at a product launch and around a regional cost estimate. Both argue that adoption, not awareness, is the hard part.
Vazdar works on cybersecurity, NIS2 compliance, and national AI policy, writing as an outside analyst addressing the whole economy. Dukanovic stays within HR, writing about assessment, psychometrics, and the internal data systems that shape hiring and promotion decisions.
Both write practical HR posts for people who run hiring systems, using concrete regulatory or operational detail rather than general commentary — Diaz on state screening rules and HRIS selection, Dukanovic on fragmented data and assessment criteria.
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Both also discuss vendor partnerships openly as part of their advice.
Diaz focuses on United States employment law compliance and HR system selection, targeting employers who need to stay on the right side of changing rules. Dukanovic focuses on the science of assessment and AI infrastructure for HR data in a Balkan and BCMS-language context.
Both urge talent acquisition to act as a strategic business partner rather than a transaction desk, and write short, structured posts for HR practitioners — Buntin on accountability and global recruitment governance, Dukanovic on data-based criteria replacing interview impressions.
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Both close with a direct question to the reader.
Buntin's posts summarize interviews and third-party research and offer leadership and employer-branding advice, without a product behind them. Dukanovic grounds posts in their company's tooling and reports specific hour and cost figures from their own work.
The posts show clear topical relevance for a thought-leadership partnership on AI in talent decisions, covering AI-assisted recruitment, structured assessment, and talent data for internal mobility and development.
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Before committing, request recent LinkedIn audience data and documented evidence, such as an audience survey, of fit by employer, role, or seniority.
The posts show Danilo Dukanovic writing in a professional capacity about their product work: an AI infrastructure build for HR data, a language model trained for BCMS languages, a benchmark, and a pivot from recruitment into internal mobility and succession.
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They also include opinion posts about AI in screening.
None is presented as paid content: the posts disclose no sponsored post, affiliate arrangement, or brand collaboration.
For a thought leadership collaboration on AI-driven talent assessment, ask directly about terms, budget, and the approval process.
Good fit for a B2B HR technology thought leadership partnership on AI-driven talent assessment, provided the parties agree terms directly and review all product and performance claims before publication.
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The campaign needs co-created content for HR and talent leaders, and Danilo Dukanovic has already published on the subject.
They argue that AI in hiring does not replace judgment; it amplifies whatever process is already there.
Their practical alternative is to look beyond the CV through psychometrics, skills-based assessment, and work-sample tests, alongside clearer evaluation criteria.
That gives a partner a real point of view to build around rather than a generic AI endorsement.
Their posts also describe an operational angle: regional HR teams lose time to manual synthesis across PDFs, Excel files, and local tools.
The proposed response is AI infrastructure that keeps data inside the company ecosystem.
A third post extends the same assessment logic beyond hiring into internal mobility, development, and succession, letting the campaign speak to talent decisions after recruitment.
The fit is not unconditional.
Their posts do not publish a sponsorship policy, rate card, media kit, or partnership inquiry route, so commercial terms would need to be discussed directly.
Audience composition by role and seniority is not established; ask for separate audience data before making claims about reach or buyer influence.
The 15,650-hour and €344,000 figures in the HR data post are presented as an illustrative regional estimate, not an independently verified benchmark.
The 500,000-candidate screening example is attributed to Alan Price of Deel, not to Danilo.
Label or confirm these points before using them in campaign claims.
When to choose a specialist
Poor fit for a performance-led recruitment lead-generation campaign promising faster CV screening through AI keyword matching and a free ATS audit as the call to action.
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That campaign conflicts with positions Danilo Dukanovic has published.
They call ATS keyword matching from the 2000s a broken approach, warn that using AI as blind automation damages application quality and employer brand, and argue for structured signals such as psychometrics, skills-based assessment, and work samples.
A direct-response campaign built on volume screening would require them to contradict that published argument or stay silent on the point that makes the campaign work.
The commercial format is also unsupported.
Their posts do not contain a sponsorship policy, media kit, rate card, disclosed brand collaboration, or official inquiry route for paid campaigns.
There is nothing published to quote, budget against, or follow as an approval process.
Audience data by buyer role is unavailable, making a cost-per-application or reach guarantee especially risky.
This does not prove they never accept sponsorships; their posts publish no offer or terms.
If the brand wants to work with them, reframe the campaign as diagnostic thought leadership about when AI screening helps, when it fails, and what structured assessment adds.
That is materially different from the original lead-generation request.
A worked campaign example
Worked campaign angle for the good-fit brief: “Structured signals, not blind automation.”
Audience: HR and talent leaders at regional enterprises evaluating AI screening and assessment tools.
Core promise: after the series, a buyer should be able to decide where AI screening fits in their funnel and which structured signals to add before automating more.
Format: a three-part thought leadership package built from Danilo Dukanovic’s published positions, with the brand as sponsor and co-publisher.
Part one is a co-authored article on the hidden cost of fragmented HR data.
It uses their example of regional teams manually reading PDFs, moving between Excel files, and consolidating reports across countries, then explains why automating that work is different from cutting the HR team.
Part two is a LinkedIn post and short explainer on the screening problem.
It develops their argument that candidates and recruiters both use AI, rushed CV review was already weak, and psychometrics, work samples, and skills-based assessment are better signals than keyword matching.
Part three is a worksheet and webinar on talent decisions after hiring.
It draws on their account of extending assessment from recruitment into internal mobility, development, and succession, and asks HR leaders to map who is ready for more, who is in the wrong role, and who needs development rather than promotion.
The resource should be a one-page assessment design brief with fields for role, decision, signal to be measured, method, evidence source, and review owner.
It must work outside the article, so include both a blank version and a completed example.
The webinar should be a working session, not a product demo: Danilo presents the framework, a brand subject expert responds, and the audience leaves with the completed worksheet.
Before the campaign runs, agree terms directly and review every claim.
Do not present the 15,650-hour or €344,000 figures as verified benchmarks or imply that Danilo ran the 500,000-candidate screening example.
Measure qualified worksheet downloads, webinar attendance from HR leadership roles, replies to the follow-up assessment brief offer, and sales conversations that cite the framework.
Do not judge the campaign by follower count or raw impressions alone.
This angle uses their existing argument instead of asking for a product endorsement, gives buyers a usable decision tool, and turns the absence of a published rate card into a direct commercial conversation rather than an assumed offer.
Choose Tomislav Vazdar when the partnership needs to reach AI-governance, policy, and cybersecurity decision-makers rather than HR buyers.
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The posts show national-level work: presenting seven AI priorities to Croatia's National Council for Digital Transformation and serving as a CroAI board member, plus a television appearance explaining the Collins Aerospace outage that disrupted airport check-in systems. For a campaign about the rules and risks around AI in hiring — regulatory sandboxes, cross-border compute, compliance obligations like NIS2 — a partner who can speak to regulation and security carries more weight than an assessment specialist.
The tradeoff is depth on hiring.
Vazdar's posts cover AI adoption rates, sandboxes, compute access, and deepfake guidelines, but not assessment methods, psychometrics, candidate screening, or talent decisions.
A brand hiring Vazdar for a talent-assessment campaign would need to supply the HR substance and accept a policy-and-security framing rather than a practitioner one.
Andrea Diaz is the better choice when the campaign angle is legal and compliance risk in AI hiring.
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The posts analyze Illinois HB-3773, amended California FEHA rules that expose employers to liability for discriminatory automated decisions even from third-party tools, and Washington limits on background checks.
They also advise employers to document individualized review, bias testing, and human oversight.
Diaz shows a working HR-tech partnership model: a 200-employee client moved off spreadsheets onto Rippling through a referral-partner route with preferred pricing.
For a brand whose need is AI-hiring compliance plus a referral partnership, this is the closer match.
The tradeoff is method.
Diaz explains what the law requires and recommends defensible process, but the posts present no assessment framework, scoring approach, or measurement design.
A campaign built around how assessment works would get risk framing and a partnership case from Diaz, not the signal-and-criteria model Danilo Dukanovic discusses.
Ask for an assessment framework if the campaign needs one.
Eric Buntin is the better choice when the campaign centers on talent-acquisition leadership, employer brand, and workforce strategy rather than assessment technology.
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The posts turn leadership interviews into practice — accountability as the basis for talent acquisition's strategic seat, governance across regions — and lay out a sector-specific playbook for manufacturing's Gen Z perception gap, with steps on rebranding the work, modernizing candidate experience, building early pipelines, and leading with purpose. For a brand selling a broad TA or employer-branding offer, this speaks the buyer's everyday language.
The tradeoff is specificity to AI assessment.
Buntin's posts name no AI screening or assessment tool and give no method for measuring candidate signals; the advice stays at the strategy and branding level.
A campaign promising a concrete assessment framework would need to add that content.
Ask for an assessment method if the campaign depends on one.
Danilo Dukanovic's three LinkedIn posts cover AI screening in recruitment, the hours regional HR teams spend consolidating fragmented data across systems, and a shift from recruitment tooling into internal mobility, development and succession.
No post dates appear, so the posts cannot be placed on a career timeline.
Danilo Dukanovic's posts do not include external interviews, transcripts, books or broadcasts, and the claims here have not been independently verified.
One post describes company work at Recrewty in the first-person plural, but does not state whether Danilo owns, works for, or partners with that business.
Two figures in the HR data post — 15,650 hours and roughly EUR 344,000 a year — are presented as an illustrative estimate for a regional group rather than a verified benchmark, and the 500,000-candidate screening example is attributed to Alan Price of Deel, not to Danilo.
The comparisons with other named profiles draw on two posts each, dated September 2025 to June 2026.
The posts do not establish audience composition by buyer role or seniority, sponsorship terms, or customer outcomes; this page describes posts and arguments, not verified performance.
