Skills-based hiring is one of the most significant shifts in talent acquisition thinking in a generation. The logic is well-founded, the research supports it, and the momentum behind it is real. Across the industry, organisations are moving away from degree requirements and credential filters toward a more direct question: can this person actually do the job?
The direction is right. Getting there takes more than a policy update, and it takes more than swapping one rigid filter for another.
Why the Shift Is Happening
For decades, the degree requirement served as a convenient proxy. It was easy to apply, familiar to hiring managers, and perceived, often incorrectly, as a reliable indicator of capability and potential. In practice, it was more often an indicator of access: access to the right schools, the right socioeconomic background, the right starting point.
By the mid-2010s, a tight labour market and growing awareness of structural inequality began to reshape the conversation. Large employers, IBM, Google, Apple, Walmart, publicly removed degree requirements from significant portions of their hiring. State governments followed. Pennsylvania opened 92% of government positions to non-degree candidates. Maryland saw a 41% increase in the number of people without degrees hired by the state in the first six months after removing degree requirements from public sector roles.
The argument was backed by data. Skills-based hiring, when implemented with rigour, is five times more predictive of job performance than education-based screening. At companies leading the transition, identified by joint research from Harvard Business School and the Burning Glass Institute, non-degree hires achieved retention rates ten percentage points higher than their degree-holding colleagues in equivalent roles, with an average salary uplift of 25%.
These are meaningful outcomes. They represent hiring done more accurately, more equitably, and with a clearer line of sight to actual performance.
Where the Complexity Lives
The Harvard Business School and Burning Glass Institute research, which examined more than 11,000 job postings across large US employers, identified where the transition becomes difficult in practice.
Removing a degree requirement from a job posting is a single, visible change. What it does not do, on its own, is change how candidates are actually evaluated once they are in the process.
Professor Joseph Fuller, who led the Harvard research, put the challenge plainly: “If you are doing the hiring and you haven’t been guided through a process of how to assess somebody based on their work experiences as opposed to whether they graduated from Columbia, that is going to be a real challenge.”
Fuller’s point is important. The shift to skills-based hiring is fundamentally a shift in what evidence counts. Credentials were a proxy for capability. Skills-based hiring demands something more direct: evidence of what a person has actually done, what they have built, what problems they have solved, and what they are genuinely capable of delivering in the role.
That is a harder question to answer than “do they have a degree?” And answering it well requires a different kind of evaluation, one that takes each candidate seriously as an individual, rather than running everyone through the same predetermined sequence.
The Problem With One-Size-Fits-All Evaluation
When organisations transition to skills-based hiring, many reach for standardised question sets as a replacement framework. The intention is sound: consistency, fairness, a common basis for comparison. But a uniform question bank applied identically to every candidate creates its own limitations.
A candidate with fifteen years of hands-on engineering experience and one who has just completed an intensive bootcamp are not the same person presenting the same evidence. Asking them identical questions does not create fairness. It creates a process optimised for people who are good at answering prepared questions, which is its own form of credential, just a less visible one.
What skills-based hiring actually calls for is evaluation that is anchored to the individual. To what this specific person says they have done. To the specific experience they are presenting as evidence of their capability. The right question is not the same for every candidate, it is the question that probes most directly into the claim this candidate is making about themselves.
A 2026 review of hiring bias research published by Cogn-IQ captured the underlying principle clearly: the goal of fair evaluation is not procedural uniformity, it is consistent rigour applied to each individual’s actual evidence. The difference matters. Procedural uniformity treats fairness as a question of process design. Consistent rigour treats it as a question of evaluation quality. One can be gamed. The other cannot.
What Genuine Skills Assessment Looks Like
The organisations achieving the best outcomes from skills-based hiring, identified in the Harvard and Burning Glass research as representing 37% of those studied, with a nearly 20% increase in hiring workers without degrees, share a consistent characteristic. They evaluate candidates against what those candidates actually bring to the table.
That means starting from the candidate’s own account of their experience. What did they work on? What was their specific contribution? What did they learn? What would they do differently? These are not questions drawn from a fixed bank. They are questions that emerge directly from what the candidate has presented, and they are the questions that reveal whether the claimed experience is real, whether the understanding runs deep, and whether the capability will transfer to the role in question.
This approach is more demanding than running a standard question set. It requires evaluators, human or AI-assisted, to engage with each candidate’s specific background rather than processing all candidates against the same template. The payoff is a significantly higher quality of signal. When evaluation is grounded in a candidate’s own claims, it becomes genuinely hard to perform. There is no script to prepare. The evidence either holds up under examination or it does not.
The documentation that matters in this model is not a record of which questions were asked. It is a record of what each candidate claimed, how those claims were examined, what evidence emerged, and how the evaluation reached its conclusion. That is an audit trail that reflects real thinking, and it is the kind of record that stands up to scrutiny from legal teams, regulators, or candidates who want to understand why a decision was made.
Why This Matters Now
The regulatory environment gives this conversation a sharper edge in 2026.
Illinois law, effective January 2026, requires employers to notify candidates whenever AI is used in hiring decisions and prohibits the use of AI that produces discriminatory outcomes. New York City’s bias audit requirements are under active enforcement, with the city’s oversight body having identified at least 17 instances of potential non-compliance among audited organisations. The EU AI Act classifies all recruitment AI as high-risk, with full compliance obligations, including documented human oversight and explainability requirements, taking effect in August 2026. California’s automated decision system regulations, live since October 2025, extend accountability directly to any system that influences employment decisions.
Across every jurisdiction, the direction is the same: employers must be able to explain how hiring decisions were reached. Not just what decision was made, but why, and on the basis of what evidence.
An evaluation model anchored to each candidate’s own claims and experience is, by design, explainable. The reasoning is traceable. The evidence is specific. If a candidate asks why they were not selected, there is a real answer, grounded in what they presented and how it was assessed, rather than a score produced by a process nobody can fully account for.
The EEOC recorded 88,531 new discrimination charges in FY2024, a 9.2% year-on-year increase, resulting in $700 million in recoveries, the highest total in the agency’s history. The organisations best positioned in that environment are those whose hiring processes can demonstrate, clearly and specifically, that every candidate was evaluated on their merits.
The Practical Questions for HR Leaders
For HR Directors and talent leaders thinking through where their organisation stands, a few questions are worth sitting with.
When a candidate is evaluated today, is the assessment grounded in what that specific person has claimed about their experience, or is it a standardised process applied identically regardless of background? When a hiring decision is challenged, is there a record of the actual reasoning behind it, what was examined, what evidence was considered, how the conclusion was reached? And does the evaluation process reward people who can perform a rehearsed answer, or people who can demonstrate genuine capability?
These are not compliance questions. They are quality questions. The organisations getting skills-based hiring right are the ones asking them.
What the Shift Actually Requires
Skills-based hiring is a genuine improvement on what came before. The talent pools it opens up are broader and more capable than credential-filtered pipelines. The outcomes, when the approach is implemented with rigour, are better for employers and candidates alike.
Realising those outcomes requires evaluation that is genuinely grounded in each candidate as an individual. That means moving beyond credentials as a proxy, and also beyond standardised question banks as a replacement proxy. It means building evaluation processes that engage with what candidates actually present: their specific experience, their demonstrated capabilities, their real track record.
The consistency in that model does not come from asking everyone the same questions. It comes from applying the same quality of scrutiny, the same depth of examination, and the same standard of documented reasoning to every candidate who comes through the process.
That is what skills-based hiring, at its best, looks like in practice.
Sources
Skills-based hiring — performance and adoption data
- TestGorilla (2025). State of Skills-Based Hiring Report. Skills-based hiring is 5x more predictive of job performance than education-based screening.
- Harvard Business School & Burning Glass Institute (2024). Skills-Based Hiring: The Long Road from Pronouncements to Practice. Analysis of 11,000+ job postings. Findings on retention rates (+10 percentage points), salary uplift (+25%), and the 37% of firms classified as Skills-Based Hiring Leaders (+20% increase in non-degree hires).
- Harvard Business School (2025). Quote from Professor Joseph Fuller, faculty co-chair of the Project on Managing the Future of Work, via Institute for Business in Global Society.
Government policy — degree requirement removal
- Commonwealth of Pennsylvania, Office of Governor Josh Shapiro (January 2023). Executive Order: 92% of state government jobs (approximately 65,000 positions) opened to non-degree candidates. Corroborated by SHRM, HR Dive, Fortune.
- Maryland Department of Labor, via Joseph Farren, Chief Strategy Officer (2022), as reported by NewsNation (December 2022) and Stand Together (2025). 41% year-on-year increase in non-degree hires from May to August 2022, following Governor Larry Hogan’s March 2022 initiative.
Bias in hiring — evaluation and process
- Cogn-IQ (April 2026). Hiring Bias Statistics: What the Research Shows. Review of hiring bias literature; finding that the degree of process structure is a primary determinant of bias in hiring outcomes.
- Johnson, S.K., Hekman, D.R., & Chan, E.T. (2016). If There’s Only One Woman in Your Candidate Pool, There’s Statistically No Chance She’ll Be Hired. Harvard Business Review.
Regulatory environment
- Illinois Human Rights Act, HB 3773 (effective January 1, 2026). Prohibits the use of AI that discriminates based on protected classes; requires candidate notification when AI is used in hiring.
- New York City Local Law 144 (in effect since July 2023; enforcement review December 2025). Bias audit requirements for automated employment decision tools; NYC Department of Consumer and Worker Protection identified 17 instances of potential non-compliance.
- EU Artificial Intelligence Act, Regulation (EU) 2024/1689. Recruitment AI classified as high-risk under Annex III, Category 4. Full high-risk obligations enforceable from 2 August 2026.
- California Civil Rights Department, Final Employment Regulations Regarding Automated Decision Systems (effective 1 October 2025). Extends the Fair Employment and Housing Act to automated decision systems used in employment.
- U.S. Equal Employment Opportunity Commission (EEOC), FY2024 Annual Report. 88,531 new discrimination charges filed; 9.2% year-on-year increase; $700 million in recoveries, the highest total in EEOC history.
