솔라윈드가 15일 발표한 보고서에 따르면, 인공지능(AI)이 IT 역할을 재편하고 있는 것으로 나타났다. 전체 전문가의 80%는 기존의 직접 운영 중심 업무에서 벗어나, 자동화된 시스템과 워크플로를 감독하는 방향으로 역할이 이동하고 있다고 응답했다.

솔라윈드에 따르면, 기업들이 AI 기반 도구와 자동화를 확대 도입하면서 IT 실무자들은 점차 오케스트레이션 중심의 책임을 맡고 있다. 이번 조사는 기업 환경에서 AI 도입의 효과와 과제를 분석하기 위해 1,048명의 IT 전문가를 대상으로 진행됐다.

보고서는 “IT 실무자 10명 중 8명은 기술 인력이 운영자에서 오케스트레이터로 이동하고 있다는 데 동의한다”며 “업무를 직접 수행하는 시간은 줄어드는 대신, 시스템과 워크플로, 그리고 이를 대신 실행하는 AI 도구를 통제하고 관리하는 데 더 많은 시간을 투입하고 있다”고 밝혔다.

인식과 현실 사이의 격차

보고서는 AI 준비 수준을 둘러싸고 경영진과 기술 인력 간 뚜렷한 인식 차이가 존재한다고 지적했다. C레벨 임원의 47%는 자사가 AI 기반 변화에 “매우 잘 준비돼 있다”고 평가한 반면, 실무 기술 인력 중 같은 응답은 13%에 그쳤다.

한편, 일상적인 IT 운영에서 AI가 가져온 실질적인 효과도 확인됐다. 응답자의 65%는 수작업이 감소했다고 밝혔고, 61%는 근본 원인 분석 속도가 빨라졌다고 답했다. 또한 49%는 AI가 의사결정에 대한 확신을 높이는 데 기여한다고 평가했다.

그러나 이러한 성과는 동시에 업무 부담 증가로 이어지고 있다. 응답자의 71%는 AI 도입 이후 업무가 더 까다로워졌다고 답했으며, 이는 AI가 생성한 결과를 검증하고 관련 리스크를 관리해야 하는 요구가 늘어난 데 따른 것으로 분석된다. 특히 신뢰 문제는 여전히 핵심 과제로 남아 있다. IT 전문가의 71%는 AI 결과를 반드시 재확인해야 한다고 밝혔고, 62%는 AI의 권고를 신뢰하는 데 어려움을 겪고 있다고 답했다.

AI 도입 수준 역시 조직별로 차이를 보였다. 전체 응답자의 절반(부분 도입 34%, 전면 도입 16%)은 AI를 수용했다고 답한 반면, 37%는 인프라, 예산, 시스템 복잡성 등의 이유로 도입에 저항이 존재한다고 응답했다.

보고서는 IT 역할이 점차 전략 중심이면서 자동화 기반으로 전환되고 있다고 분석했다. 응답자의 52%는 전략성과 자동화 비중이 모두 증가했다고 답했다. 또한 역할은 점점 더 부서 간 협업 중심(47%)으로 확대되고 있으며, 복잡성 역시 증가(41%)하고 있는 것으로 나타났다. 이는 AI가 IT를 넘어 전사 비즈니스 프로세스 전반에 통합되고 있기 때문이라는 설명이다.

AI는 IT 조직의 시간 활용 방식에도 변화를 가져오고 있다. IT 인력은 전략 수립이나 시스템 성능 분석과 같은 선제적 업무에 더 많은 시간을 투자하는 반면, 장애 대응 등 일부 사후 대응 업무는 감소하는 추세다.

거버넌스·교육·데이터 과제

보고서는 AI 도입을 효과적으로 추진하기 위해 조직이 해결해야 할 과제로 거버넌스, 교육, 데이터 품질을 제시했다.

응답자의 56%는 보다 명확한 AI 정책과 가이드라인이 필요하다고 밝혔으며, 50%는 체계적인 교육의 필요성을 지적했다. 특히 데이터 품질은 AI 성과를 좌우하는 핵심 요소로 꼽힌다. 응답자의 83%는 AI의 효과가 활용 가능한 데이터의 범위와 품질에 달려 있다고 답했다.

이와 함께 도구 간 파편화와 통합 부족 문제도 AI 활용을 저해하는 주요 요인으로 지목됐다.

향후 전망에 대해 응답자들은 AI와 자동화의 역할이 더욱 확대될 것으로 내다봤다. 응답자의 77%는 향후 2~3년 내 조직이 자동화 확대와 데이터 기반 인사이트를 바탕으로 보다 선제적인 운영 체계를 갖추게 될 것으로 예상했다.

다만 동시에 역량 격차, 거버넌스 요구, AI 기반 시스템의 정확성과 신뢰성 확보 등 해결해야 할 과제도 지속될 것으로 전망된다.

솔라윈즈의 최고기술책임자(CTO) 크리슈나 사이는 공식 보도자료를 통해 “AI는 IT를 단순하게 만드는 것이 아니라, 오히려 더 중요한 영역으로 만들고 있다”며 “이 환경에서 성과를 내는 조직은 단순히 AI 도구를 많이 보유한 곳이 아니라, 이를 신뢰할 수 있도록 거버넌스와 구조를 구축한 조직”이라고 말했다.
dl-ciokorea@foundryco.com

A technical interview goes exceptionally well. The candidate answers every question with confidence, explains complex concepts fluently and demonstrates impressive knowledge of modern tools and architectures. The hiring team leaves the interview convinced they have found a strong addition to the engineering team.

Weeks later, after onboarding, a different picture begins to emerge. Routine tasks take longer than expected. Basic troubleshooting requires more assistance than anticipated. Design discussions reveal gaps that were not visible during the interview.

Situations like this are not new in the technology industry. But the growing use of artificial intelligence (AI) in job preparation is making them harder to detect.

In cybersecurity, we rarely blame attackers for exploiting weaknesses in a system. Instead, we examine the conditions that allowed the breach to occur and focus on strengthening controls, detection and response mechanisms so the organization becomes more resilient.

A similar mindset may now be needed in the hiring process. Artificial intelligence (AI) is rapidly changing how candidates prepare for technical roles. Many applicants now use AI tools to refine resumes, rehearse interview responses and organize complex ideas before interviews.

In many ways, this is a positive development. AI can help candidates communicate their experience more clearly and prepare more effectively. However, it also introduces a new challenge for hiring teams: distinguishing between candidates who are genuinely capable and those whose interview performance may be heavily assisted by external tools.

This is not about blaming candidates for using AI. Technology inevitably changes how people learn and present themselves. The more important question is whether our hiring processes still provide enough visibility into a candidate’s true capability in an AI-enabled world.

For CIOs and CISOs, this issue extends beyond talent acquisition. Hiring the wrong technical candidate, whether a developer, system administrator, engineer or security professional, can introduce operational weaknesses that eventually translate into reliability, resilience or even security risks. As organizations adopt AI-assisted workflows, technical hiring increasingly becomes a shared responsibility between technology leadership and HR teams, requiring new approaches to evaluation, validation and post-hire observation. This shift is already becoming visible across the technology hiring landscape.

The talent gap is real, and the pressure to hire is increasing

Roles such as software developers, system administrators, cloud engineers, AI specialists and cybersecurity professionals are increasingly difficult to fill. As digital transformation accelerates, companies compete aggressively for individuals who can design, build and secure modern systems.

Across the technology industry, organizations face a persistent shortage of experienced professionals. The challenge is particularly visible in cybersecurity, where demand continues to exceed supply. According to the ISC² Cybersecurity Workforce Study, the global industry faces a shortage of more than 3.4 million cybersecurity professionals. Similar findings appear in the ISACA State of Cybersecurity report, which consistently highlights hiring and skills shortages as major barriers for security teams.

This pressure can place significant strain on hiring teams.

Recruiters must evaluate large numbers of applications. Hiring managers must assess candidates across multiple technical domains. Decisions often must be made quickly to avoid losing strong candidates to competitors.

In this environment, the hiring process itself becomes a critical operational function. Hiring the right person can accelerate innovation and strengthen teams. Hiring the wrong person can delay projects, introduce operational risk and require months to correct. Against this backdrop of talent scarcity, organizations are also navigating a new variable: the growing influence of artificial intelligence on the hiring process itself.

AI can also strengthen hiring

While AI introduces new complexities, it also offers opportunities to improve recruitment.

Organizations can use AI to:

• Analyze large volumes of candidate data
• Identify skill patterns across roles
• Support recruiters in preparing structured interviews
• Highlight inconsistencies in candidate histories

Used responsibly, AI can help hiring teams spend more time evaluating substance rather than presentation. For technology leaders, this dual role of AI, both enabling candidates and assisting recruiters, reinforces the need to rethink how hiring decisions are made.

AI is changing the candidate experience

AI is now widely accessible to professionals across industries. Candidates are increasingly using AI to:

• Improve the structure and clarity of their resumes
• Prepare responses to common interview questions
• Research technical concepts before interviews
• Simulate interview scenarios using AI coaching tools

In many cases, these uses are entirely legitimate. Learning how to use AI effectively is becoming an important professional skill. The challenge emerges when AI tools begin to influence the hiring process in ways organizations did not anticipate.

Some recruiters report that AI-generated resumes now appear highly polished and perfectly aligned with job descriptions. Interview responses may be structured, technically accurate and delivered with impressive fluency. Yet when candidates move into practical assessments or real work environments, the depth of knowledge sometimes does not match the initial impression.

This phenomenon is not necessarily the result of intentional deception. Often, it reflects the growing ability of AI tools to enhance presentation beyond the underlying experience.

For hiring teams, this creates a new kind of risk.

The polished profile paradox: When strong presentation outpaces technical depth

As AI becomes a common tool in job preparation, many organizations are noticing an unexpected side effect: candidate profiles are becoming increasingly polished, and increasingly similar.

AI-powered tools help applicants refine resumes, structure achievements and align their profiles closely with job descriptions. As a result, many applications now feature highly consistent language, well-structured narratives and carefully optimized technical terminology.

Concepts such as cloud architecture, DevOps pipelines, automation frameworks, zero-trust security and AI integration appear repeatedly across resumes, often described in nearly identical ways.

In many cases, these experiences may indeed be valid. However, when AI tools standardize how candidates present their backgrounds, it becomes harder for hiring teams to differentiate between individuals who have deep, hands-on expertise and those who are primarily familiar with the terminology.

The challenge is not that candidates are presenting themselves well; clear communication is a valuable skill. The paradox emerges when the quality of presentation begins to outpace the depth of underlying capability, making it more difficult for recruiters and hiring managers to identify truly exceptional technical talent.

In this environment, simply receiving more applications does not necessarily improve hiring outcomes. Without evaluation methods that surface real experience and practical thinking, organizations risk selecting candidates based on polished profiles rather than demonstrated capability.

The challenge of remote interviews

Remote hiring has become the norm across the technology industry. It allows organizations to recruit globally and provides flexibility for both employers and candidates.

But virtual interviews also introduce blind spots. Candidates may have access to:

• Multiple screens or monitors, allowing them to search for information or reference external materials during the interview.
• Secondary devices, such as tablets or smartphones, which can be used to quickly look up answers without being visible to the interviewer.
• Real-time AI tools, capable of generating structured responses to technical questions within seconds.
• Third-party assistance, where another individual may be providing prompts or guidance to the candidate behind the scenes during the interview.

These possibilities do not automatically imply misconduct. However, they highlight a growing challenge for hiring teams: ensuring that interview responses accurately reflect the candidate’s own reasoning, experience and technical capability, rather than external assistance.

Interview answers may appear polished and technically precise. Long pauses before responses, structured explanations and highly consistent phrasing sometimes raise questions about whether answers are being generated independently.

However, attempting to detect AI use during interviews is unlikely to be a sustainable strategy. Technology evolves faster than detection methods, and overly intrusive monitoring risks undermining trust between candidates and employers. Instead, organizations may need to rethink the design of interviews themselves.

The goal should be evidence of capability

The most effective hiring processes focus on one core objective: gathering evidence that a candidate can actually perform the role. Rather than trying to determine whether AI was used during preparation or interviews, hiring teams should ask a more practical question: Do we have enough evidence to be confident this person can do the job?

When hiring processes generate clear evidence of capability, concerns about AI assistance become far less significant. This requires shifting from traditional question-and-answer interviews toward more evidence-based evaluation methods.

Practical examples include asking candidates to:

• Explain real projects they worked on
• Describe decision-making processes behind technical solutions
• Walk through incident or troubleshooting scenarios
• Discuss trade-offs made during system design

Experienced professionals can usually describe how problems unfolded, why certain decisions were made and what lessons were learned. These details are much harder to reproduce artificially.

Strengthening the hiring process

Based on observations from recent hiring and interviewing experiences, it has become increasingly clear that organizations may need to revisit how technical hiring processes are structured. As candidates gain access to more sophisticated tools to prepare for interviews, traditional evaluation methods may not always provide sufficient insight into real capability.

Several approaches can help strengthen confidence in hiring decisions.

• Scenario-based discussions can be particularly useful. Instead of relying solely on theoretical questions, interviewers can present practical situations and ask candidates how they would approach the problem. This often reveals how individuals think, how they prioritize and how they reason through unfamiliar situations.
• Real-time problem solving can also provide valuable insight. Observing how a candidate works through a technical issue step by step often reveals far more about their mindset and problem-solving approach than prepared responses alone.
• Cross-functional interview panels: Another helpful approach is the use of cross-functional interview panels, where professionals from different technical backgrounds participate in the evaluation. Engineers, system administrators, architects or other practitioners can often explore different dimensions of a candidate’s experience and provide a more balanced assessment.
• Finally, skills-based assessments, when designed thoughtfully, can shift the focus from resume claims to practical capability. Tasks that reflect real-world work scenarios often provide clearer signals about how a candidate might perform in the role.

Importantly, the objective of these methods is not to trap candidates or place them under unnecessary pressure. The goal is to create opportunities where genuine experience, thinking patterns and technical understanding can naturally emerge.

Observing capability beyond the interview

Even with improved interview methods, hiring decisions should not rely entirely on a single conversation or assessment. Much like technology systems and processes are monitored and refined after deployment, organizations can treat onboarding and probation periods as part of a broader validation process. These early months provide valuable opportunities to observe how individuals operate within real environments.

During onboarding and probation, teams can better understand:

• How individuals approach unfamiliar problems
• How they collaborate and communicate within teams
• How they translate theoretical knowledge into operational decisions
• How quickly they adapt to existing tools, processes and organizational practices
  
  

These observations often provide a more accurate picture of capability than interviews alone. Viewing hiring as a continuum rather than a single decision point can help organizations reduce risk while supporting new employees as they integrate into the team.

A human-centered hiring mindset

AI is undoubtedly changing how candidates learn, communicate and prepare for professional opportunities. This shift is unlikely to slow down, and organizations will need to adapt accordingly.

However, it is important to remember that hiring processes are ultimately designed to evaluate people, not just technical answers. Candidates bring more than knowledge to a role, they bring personality, professional values, cultural perspectives and individual ways of thinking.

Differences in communication style, body language or cultural background can sometimes influence how candidates present themselves during interviews. In an environment where AI assistance is becoming more common, organizations should remain mindful not to make incorrect assumptions or unfair accusations based on isolated signals.

The objective of hiring is not to identify who delivers the most polished interview responses. It is to identify individuals who can collaborate with others, solve problems and contribute meaningfully once they become part of the organization.

As AI becomes more embedded in the professional landscape, the most effective hiring processes will be those that remain balanced, combining structured evaluation with thoughtful human judgment.

For technology leaders, the implications extend beyond recruitment efficiency. Hiring decisions influence system reliability, operational resilience and in some cases the organization’s overall security posture. When the wrong expertise enters critical engineering, infrastructure or security roles, the downstream impact can reach far beyond the hiring process itself.

Addressing this challenge will require closer collaboration between CIOs, CISOs, hiring managers and HR teams to design hiring approaches that emphasize evidence of real capability rather than polished presentation alone.

Organizations that rethink their hiring processes today — through stronger technical assessments, thoughtful onboarding observation and better interviewer training — will be better positioned to identify authentic talent in an AI-assisted world.

Because in the end, hiring is not about selecting the candidate who interviews the best. It is about identifying the individuals who can actually build, operate and secure the systems organizations depend on.

In an AI-enabled hiring landscape, the organizations that succeed will not be those trying to detect every tool candidates use, but those designing hiring processes that reveal real expertise regardless of it.

This article is published as part of the Foundry Expert Contributor Network.
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