Feature
White Paper
The Future of Leadership Development: AI-Driven Manager Enablement
There was a time in recruiting when the resume was the whole story, but that’s not the case anymore. Candidates today aren’t simply people presenting themselves to employers. The view employers now see isn't a person, but a structured dataset — a composite of scores, signals, behaviors, skills and predictive traits.
This was bound to happen as applicant volume soared, assessments became powered by AI and pressure increased on recruiting and hiring managers to justify — or “defend” if you prefer — their decisions. The result is a recruiting world where a candidate's identity is parsed into measurable variables, then reassembled into a ranked profile.
Today’s ATS uses algorithms to convert human attributes into numerical scores. For example, a formula might weigh skills matches at 40%, previous experience at 30%, assessment at 20% and educational background at 10%, all to produce one number to compare applicants and potentially aid in skills-based hiring.
Besides speeding things up, scores help companies be more consistent in their hiring, identify which channels work and create analytics that help monitor aspects such as time-to-hire. For high-volume employers especially, this helps recruiting function.
But scoring models are only as useful as the signals that feed them. That’s why software vendors have built a sophisticated infrastructure to capture “signals” at every stage of the hiring process. Pre-employment tests, asynchronous video interviews, skills assessments, personality inventories, exercises and live coding environments generate data points that flow into a composite profile.
Moreover, these profiles are granular. Talent platforms don’t capture only whether a candidate solved a coding problem correctly, but track the candidate’s problem-solving process. Companies can then evaluate job skills more accurately by seeing the candidate’s thought process. In some cases, a candidate’s hesitations or pivots from one approach to another can also become data.
Table of Contents
- Technology Speeds the Verification Process
- Hiring the Invisible Candidate
- The Power of Prediction – or Not
- Human Judgment, Structured Candidates
Technology Speeds the Verification Process
At the same time, platforms have become more sophisticated in verifying a candidate’s history and credentials. Employment history, degrees and certifications, reference quality, right-to-work status and criminal records previously required manual checking, which was slow, inconsistent and often shallow. Here again, AI-driven platforms automate much of the work, pulling data from multiple sources and flagging discrepancies at a speed people can’t hope to match.
Because of this, verification becomes a structured data problem. Rather than relying on traditional references alone, today’s systems treat references as quantitative inputs, and use AI to automate reference collection and link pre-hire reference data to post-hire outcomes.
This technology also helps combat tools many candidates use to improve their odds. AI-generated resumes and cover letters allow applicants to submit more applications in less time, which has increased volume while decreasing the quality of submissions. Verification systems show to what extent a candidate's profile reflects reality.
Hiring the Invisible Candidate
The most radical development in this datafication of candidates is intent signals, meaning behavioral data that reveals not just who a candidate is, but whether they are emotionally and practically ready to move into a new role. Here, the structured dataset extends beyond what the candidate submits and incorporates what they imply.
On LinkedIn, for example, signals, behavioral data and metadata give recruiters a well-matched talent pool to pick from. When a professional updates their profile, engages with certain content or begins researching companies they’ve never worked for, they generate signals for recruiters and their AI tools to read.
Passive candidates become visible not because of what they say but through their behavior. With LinkedIn classifying more than 70% of the global workforce as passive talent, this has become a key to sourcing.
AI recruiting platforms approach this at scale by analyzing behavioral signals across the entire candidate journey and helping employers understand a candidate’s intent. That allows recruiters to reach out when candidates are most open to change. It’s also reversed the age-old recruiting model. Once, candidates initiated contact with employers. Today, employers identify and approach before a candidate has even consciously decided to look.
The Power of Prediction – or Not
When they work, these changes bring benefits. Companies that use recruitment analytics see offer acceptance rates about 18% higher than average, according to the assessment firm PMaps.
Still, problems remain. For one, algorithms learn from historical data, and historical data encodes bias. As many analysts note, bias doesn't start in the AI’s model, but in the history fed into it.
Regulators are catching up. Some research shows the European Union’s AI Act has led to seeing AI in hiring as a high-risk application that requires deep documentation and human oversight. In New York City, local regulations require bias audits of automated talent tools. As the legal and regulatory landscape changes, employers that use scoring and assessment systems without the necessary governance can find themselves exposed.
Transparency is another issue. When a candidate is rejected by an algorithmic filter, they’re rarely told why. Research from Cornell University found that AI models are usually kept private, which makes independent auditing nearly impossible.
Information candidates submit is no longer read just by a recruiter. It’s parsed, scored, verified and stored, perhaps to be looked at again if they apply to the same organization even years later.
Behavioral signals generated on professional platforms such as LinkedIn are not private. A candidate’s profile completeness, engagement patterns and the size of their network are all inputs into systems designed to assess employability and openness to opportunity.
Candidates need to understand the infrastructure they’re engaging with. Their data profile is stronger when they demonstrate skills through verifiable work samples, maintain accurate professional records across platforms and engage with professional communities.
Human Judgment, Structured Candidates
The idea of skills-based hiring aligns with using data, because evaluating competency requires structured, measurable criteria.
Of course, there are challenges that come along with all this. The same infrastructure that supports fairer, more consistent, more skills-based hiring decisions encodes discrimination into itself or inadvertently reveals information that’s supposed to be kept private.
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The key to using data in talent acquisition is to treat structured datasets as an input for human judgment, not a replacement for it. Despite AI's expanding role, 71% of adults in the U.S. oppose using technology to make final hiring decisions without human oversight. In other words, data should sharpen the decision, but not make it. Assessment, verification and signals are only tools. What they build depends on the people who use them.
And remember: if today’s candidate is a structured dataset, they’re a person first. Hiring systems that prove most durable will be those that remember the difference.
Editor's Note: How else is hiring changing?
- Forget Skills, Hire Someone Who Can Figure Things Out — Forget the know-it-alls. Companies now want "learn-it-alls" — people who adapt fast, stay curious and thrive when everything changes.
- AWS Wants to Sell You AI Hiring. Remember 2018? — Amazon's new AI hiring tool, Connect Talent, could be technically flawless and still fail for reasons unrelated to the product itself.
- What Happens When Candidates Choose AI Over Recruiters? — A fact that should make every talent leader sit up: when over 70,000 applicants were offered a choice between a recruiter and an AI interviewer, 78% chose AI.