Tech employers can verify whether a candidate knows a programming language or holds a certification, but determining whether that person will question a flawed AI response, warn colleagues about a slipping deadline or resolve conflict without damaging relationships is much harder.
An Experis survey found 74% of U.S. tech and IT services employers report talent shortages, with professionalism and a strong work ethic among the capabilities they struggle to find.
AI is increasing the value of judgment, accountability and communication while making those qualities more difficult to separate from polished résumés and rehearsed interviews.
AI Makes Judgment a Core Technical Requirement
Human skills remain difficult to identify because employers lack the verification systems they use for technical proficiency, according to Gina Smith, senior research director for IT Skills for Digital Business at IDC.
“All human skills are harder to find because they aren't legible in the way technical skills are,” she says. “Tech skills are associated with certifications, degrees, take-homes, a whole verification industry. Professionalism, unfortunately, has no credential.”
The distinction has greater consequences when employees oversee AI agents. IDC's 2026 Global IT Skills Survey of 1,043 respondents found 43% of organizations require analytical judgment from employees directing agentic workflows, making it the most cited skill.
Cross-functional collaboration followed at 40%, with workflow redesign and orchestration at 38% and systems thinking at 37%.
Employees need enough subject knowledge to recognize when an AI-generated answer sounds convincing but contains an error. They also need the confidence to interrupt an automated process rather than treating its output as authoritative.
“If employees are unable to push back on confident-looking AI outputs, spot hallucinations and use their experience and knowledge to fact check AI, they will just be making mistakes at the speed of AI,” Smith says.
That requirement creates a related concern for newer talent. Workers who repeatedly accept completed AI output may miss the practice, recall and correction that build durable knowledge. Employers can address the risk by asking employees to explain their reasoning, verify results independently and retain responsibility for the final decision.
Interviews Should Reveal Behavior
However, Heather Miller, chief information officer at Randstad, questions whether employers are seeing a genuine decline in professionalism and work ethic. Candidates have received a powerful market signal that technical capabilities deserve top billing, so they emphasize those credentials when applying.
“I don’t actually think there’s a shortage of strong work ethic or professionalism out there,” Miller says. “The issue is that the current demand for AI skills has convinced candidates they need to lead with a ‘tech-first’ mindset to stand out.”
Hiring managers can make human capabilities visible by asking candidates to describe a specific conflict, mistake or high-pressure problem.
Follow-up questions should establish what the candidate did, how the person communicated and what changed afterward, while references can show whether the behavior appeared consistently.
“Human qualities like empathy and critical thinking are often hard to judge on paper,” Miller says. “Asking for concrete examples of how a candidate navigated conflict or solved a high-pressure problem makes these traits visible.”
Workplace conditions influence whether employees continue demonstrating those capabilities after hiring.
Miller points to Randstad data indicating 83% of employees consider work-life balance more important than pay, while 44% have left a job because of a toxic environment. Burned-out employees may concentrate on clearing tasks and devote less attention to collaboration, careful reasoning or improvement.
Work Samples Make Vague Qualities Concrete
Joel Carusone, senior vice president of Data and AI at NinjaOne, recommends defining broad labels through observable behavior.
For example, accountability may mean admitting an error early, explaining its effect and proposing a correction. Critical thinking becomes visible when a candidate asks useful questions, tests assumptions and recognizes a failing approach.
“Employers should define these qualities through specific behaviors and seek evidence of them throughout the hiring process,” Carusone says. “Rather than simply asking whether a candidate is a hard worker, employers should give candidates a problem to solve in real time to see their thought process.”
A practical exercise does not need one predetermined solution. The rubric can evaluate how the candidate frames the problem, requests missing information, explains trade-offs and responds when presented with new evidence. That approach provides stronger evidence than confidence or personal chemistry during an interview.
Human judgment also determines where organizations should apply AI. Employees with direct knowledge of a workflow understand its exceptions, informal dependencies and consequences when something fails.
“The people closest to the work are often best positioned to know what will improve it and what will not,” Carusone says.
Human Skills Develop Through Repeated Practice
Marci Paino, chief learning officer at Cisco, says automation is moving employees away from routine execution and toward direction, evaluation and intervention.
Cisco-led AI Workforce Consortium research found demand in cybersecurity job postings increased 533% year over year for ethical reasoning and 125% for stakeholder engagement.
“With AI automating and augmenting more routine and technical work, people are increasingly responsible for directing that technology, applying judgment to its outputs, and knowing when to challenge it,” Paino says.
Hiring alone cannot keep pace with changing requirements. Tools and models can change within months, while experienced employees already understand the organization's customers, systems and operating history.
“The shelf life of technical skills is getting shorter, with some of the tools and models people rely on today likely to look very different in the next 6 to 12 months,” Paino says.
Experiential learning gives employees opportunities to collaborate, solve ambiguous problems and explain decisions while receiving feedback. Internships, apprenticeships, industry projects and workplace assignments can create that practice for students and employees.
Cisco's consortium research found 50% of security leaders view a strong technical foundation combined with continued learning as the most important preparation for future skills.
Early-Career Talent Needs Room to Develop
Kevin Spektor, co-founder and chief technology officer at Coddy.Tech, says professionalism depends partly on the employer's culture and expectations. Communication norms for a formal office may differ from those of a globally distributed startup, so employers should define the behavior they need instead of assuming every candidate shares the same interpretation.
Remote work adds time zones, cultural differences, communication styles and collaboration tools. Employees must know when to document a decision, whom to inform and how to disagree clearly without turning routine friction into a larger conflict.
Companies should also reconsider expectations for young workers who have had limited opportunities to acquire professional judgment. Refusing to hire inexperienced candidates because they still need development ensures that the experience gap persists.
“It’s unrealistic to expect a young Gen Z worker to come in and have it all already,” Spektor says.
Employers must provide coaching, useful feedback and enough psychological safety for employees to surface mistakes before they become expensive.
“Work is messy. Jobs are messy,” Spektor says. “Sometimes you’re going to get it wrong, adjust, and try again. The sooner we stop pretending otherwise, the better off everyone will be.”
