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OPM details expectations for the ‘rule of many’ in federal hiring 21 January 2026 at 18:56

OPM details expectations for the ‘rule of many’ in federal hiring

By: Drew Friedman

21 January 2026 at 18:56

Agencies are getting more information on how to implement the recently finalized “rule of many.” The federal hiring strategy, several years in the making, aims to create broader pools of qualified job candidates while adding flexibility for federal hiring managers.

A series of guidance documents the Office of Personnel Management published earlier this month outlined the steps agencies should take to begin using the “rule of many” when hiring. OPM’s new resources also detail how the “rule of many” intersects with other aspects of the federal hiring process, such as shared certificates, skills-based assessments and veterans’ preference.

Under the “rule of many,” federal hiring managers score job candidates on their relevant job skills, then rank the candidates based on those scores. From there, hiring managers can choose one of several options — a cut-off number, score or percentage — to pare down the applicant pool and reach a list of qualified finalists to select from.

OPM’s new guidance comes after the agency finalized regulations last September to officially launch the “rule of many.” The concept was initially included in the fiscal 2019 National Defense Authorization Act, and OPM during the Biden administration proposed regulations on the “rule of many” in 2023.

“Coupled with the use of functional skills assessments … the [rule of many] gives hiring managers the much-needed flexibility to distinguish candidates based on their demonstrated functional merit-based qualifications for the role in question,” OPM Director Scott Kupor wrote in a Sept. 8 blog post, the same day OPM issued the final rule.

The “rule of many” aligns with some aspects of the Trump administration’s merit hiring plan, OPM said, such as using technical assessments and shared certificates. OPM said the “rule of many” in particular aligns with skills-based hiring, since it can expand candidate pools with applicants who have more fitting skillsets.

The “rule of many” also encourages agencies to use more “comprehensive” assessments, like structured interviews or job simulations, OPM said in its new guidance. And it can “support improved hiring outcomes, particularly for nontraditional candidates, veterans and those with varied career paths,” OPM added.

But for many agencies, the actual adoption of the “rule of many” may be put on the back burner, according to Jenny Mattingley, vice president of government affairs at the Partnership for Public Service. She said without enough funding or staffing, agencies are not likely to overhaul their current and already well-established hiring practices in the short term.

“The ‘rule of many’ is a good tool, but until those ingredients are all put together, I don’t think that you’ll see it rolled out immediately,” Mattingley said in an interview.

OPM’s finalization of the “rule of many” last September officially ended agencies’ ability to use the past “rule of three” hiring practice. The older candidate assessment technique already had been largely phased out, but previously restricted agencies to only selecting from the top three ranked applicants.

The “rule of many” also differs from most agencies’ current candidate-vetting technique, called “category rating,” which lets federal hiring managers assort job applicants into categories such as “qualified,” “better qualified,” and “best qualified,” then select a candidate for the job from the highest category.

When “category rating” was introduced years ago, it was an improvement over the “rule of three,” but Kupor said “category rating” created other challenges — namely, that all candidates within a single category would be considered equally qualified.

“In other words, the categories are minimum hurdles for consideration, but they don’t distinguish between applicants within a category,” Kupor said in September. “For example, if a score of 80% is the minimum hurdle to qualify into the ‘best qualified’ category, an applicant who scores 100% is treated no differently than one who scores 80%.”

OPM said in its new guidance that the “rule of many” uses the strengths of “category rating,” while adding flexibility to the process. It also allows for “finer distinctions” between candidates and broadens the range of applicants available for selection.

In most cases, OPM said the “rule of many” is preferable over “category rating.” But there are also best use cases for each hiring mechanism. Higher-level positions with more robust assessments will usually require the finer distinctions between candidates that the “rule of many” provides. But for more entry-level positions that don’t require highly technical qualifications, the “category rating” system may be just as effective.

Adopting the “rule of many” will also require a significant cultural shift at agencies, which the Partnership’s Mattingley said can be difficult. Existing strategies like skills-based hiring have not yet been fully adopted at agencies, which may indicate that the uptake of the “rule of many” will also be slow, she explained.

“Until agencies crack the nut on really leveraging skills-based hiring, I don’t think it’s going to be this big change in the immediate future,” Mattingley said. “You need skills-based hiring in order to leverage the rule of many, because you have to be able to make much finer technical assessments on the skills between candidates if you’re going to rank them in the way rule of many does.”

OPM’s “rule of many” guidance comes a few months after President Donald Trump officially lifted the governmentwide hiring freeze. But the White House has emphasized that when hiring, agencies should still focus on maintaining their now-smaller staffing sizes.

“Hiring is still a big question this year,” Mattingley said. “It does look like the administration is going to encourage agencies to hire, except at the same time, agencies are still facing budget uncertainty. They’re facing downward pressure on headcount.”

The post OPM details expectations for the ‘rule of many’ in federal hiring first appeared on Federal News Network.

Human resources search, resume and recruitment, human resources department holding magnifying glass to select resume

Coinmonks
A Structural Diagnosis of Silence: Why So Many Qualified Candidates Hear Nothing Back 14 January 2026 at 10:19

A Structural Diagnosis of Silence: Why So Many Qualified Candidates Hear Nothing Back

Coinmonks

By: Er Nabal Kishore Pande

14 January 2026 at 10:19

Visual representation of automated hiring systems, ATS filtering, and the structural silence experienced by job applicants.

In the last decade, job search has quietly changed shape. Not in visible ways like resumes becoming digital or interviews moving online, but in something more subtle: silence has become the default response.

Applications disappear. Portals show “under review” for months. Follow-ups receive no reply. Rejections, when they arrive, are generic and delayed. For many Gen Z and mid-level professionals, this silence is no longer an exception. It is the normal experience.

A Structural Diagnosis of Silence approaches this reality without advice, motivation, or corrective strategies. It treats silence not as a personal problem, but as a system outcome.

Silence Is Not Feedback

Most applicants assume silence means evaluation is still happening. The book challenges this assumption early and directly.

Modern hiring systems are designed to operate as filters, not conversations. Applicant Tracking Systems (ATS) ingest data, scan for predefined tokens, and discard anything that does not align with known patterns. No signal match means no progression. No progression means no response.

Silence, in this context, is not delayed judgment. It is the end state of a process that was never activated.

Understanding this distinction matters because it prevents misinterpretation. When candidates read silence as personal failure, they often respond by increasing effort — rewriting resumes, collecting certificates, applying more aggressively. The book explains why this often changes nothing.

Automation Is About Risk, Not Talent

One of the central arguments in the artifact is that automation in hiring exists primarily to reduce organisational risk, not to discover ability.

Hiring at scale introduces uncertainty. Human judgment is slow, inconsistent, and difficult to defend internally. Automated filters provide something safer: repeatability. They replicate past hiring patterns using compressed proxies like job titles, keywords, company names, and duration thresholds.

This means originality, non-linear careers, international experience, and unconventional paths often become invisible — not because they lack value, but because they lack encoding.

The system does not ask, “What might this mean?” It asks, “Have we seen this before?”

The Loss of Human Judgment at Scale

A recurring belief among applicants is that “someone must have seen my resume.” The book dismantles this gently but firmly.

At high volume, most applications never reach human eyes. Recruiters act as confirmation points for machine output, not evaluators of raw profiles. Their role is constrained by filters, score thresholds, and workflow capacity.

This explains why follow-ups often go unanswered. There is no active decision-maker to reply. The file is inert.

By naming this, the artifact removes a common source of psychological damage: the assumption that silence reflects hidden negative judgment.

Psychological Containment, Not Reassurance

Importantly, A Structural Diagnosis of Silence does not attempt to comfort the reader. It does something quieter.

It creates psychological containment.

Containment here means providing a stable explanatory frame that prevents the reader from internalising systemic outcomes as personal defects. The book does not promise success, clarity of the future, or improved results. It offers orientation.

For readers who have experienced months or years of non-response, this distinction matters. Clarity without optimism can still be stabilising.

Why This Is Not a Career Guide

The artifact explicitly rejects the categories it is often mistaken for. It is not a guide, not a strategy manual, not a workaround catalogue.

There are no templates. No hacks. No “do this instead.”

This refusal is deliberate. The book argues that adding tactics without addressing misinterpretation simply deepens confusion. Explanation comes first. Action, if any, is the reader’s responsibility.

This makes the work unusual in a landscape dominated by motivational language and success narratives. It is closer to a system note than a self-help text.

Who This Explanation Serves

The book is written for readers who already know how to apply, already meet qualifications, and already suspect that something structural is happening — but lack language for it.

Gen Z professionals are encountering automated hiring for the first time. Mid-career candidates are facing repeated stalls. International applicants navigating opaque filters. Anyone whose confidence is being eroded by non-feedback rather than failure.

For such readers, naming the system correctly can be more useful than being told to try harder.

Silence, the book argues, is not a message waiting to be decoded. It is a design feature.

Understanding that does not solve the market. But it can stop the market from damaging the person.

https://gum.new/gum/cmkdv6a4g000204l465g35ywc

A Structural Diagnosis of Silence: Why So Many Qualified Candidates Hear Nothing Back was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

CIO
로버트 월터스 코리아, 2026 연봉조사 발표 “자동차·반도체 분야 시니어 소프트웨어 엔지니어 수요 높아” 13 January 2026 at 03:47

로버트 월터스 코리아, 2026 연봉조사 발표 “자동차·반도체 분야 시니어 소프트웨어 엔지니어 수요 높아”

CIO

13 January 2026 at 03:47

조사 결과에 따르면 2026년 국내 채용 시장은 글로벌 경제의 불확실성 속에서도 반도체, 배터리, AI, 바이오헬스 등 기술 기반 산업을 중심으로 일정 수준의 성장 흐름을 보일 것으로 전망됐다. 정부의 디지털 전환 정책과 공급망 정상화 기조에 따라 IT 기반 신산업 전반에서 고급 기술 인력에 대한 수요가 유지될 가능성이 있는 것으로 분석됐다. 이에 따라 제조업 종사자의 94%, AI 및 데이터 기술 분야 구직자의 90%가 2026년 임금 상승을 예상한다고 응답했다.

산업별로 수요가 높은 직무를 살펴보면, 제조업에서는 자동차 분야 시니어 소프트웨어 엔지니어의 수요가 가장 높은 것으로 나타났다. 이어 반도체 분야의 수석 회로설계 엔지니어와 시니어 소프트웨어 엔지니어에 대한 수요도 높은 수준으로 조사됐다. 해당 직무의 예상 연봉은 자동차 및 반도체 분야 시니어 소프트웨어 엔지니어가 최대 1억 2,000만 원, 수석 회로설계 엔지니어가 최대 1억 3,000만 원 수준으로 제시됐다.

이직 의향과 관련해서는 금융 및 회계 분야 응답자의 81%가 향후 12개월 이내 이직을 계획하고 있다고 답했으며, 테크(64%)와 제조업(63%) 분야에서도 과반수가 이직을 고려 중인 것으로 나타났다. 이직을 고려하는 주요 요인으로는 경력 개발 기회와 연봉 인상이 꼽혔다. 재무·회계와 테크 분야에서는 경력 개발이, 인사 및 제조업 분야에서는 연봉 인상이 상대적으로 중요한 요인으로 분석됐다.

한편, 로버트 월터스는 저성장 국면이 이어질 가능성이 있는 가운데 정부의 고용 안정화 정책과 노동시장 구조 개편 논의가 인력 수요에 일정 부분 영향을 미칠 것으로 전망했다. 특히 정년 연장 논의가 향후 기업의 인력 운영 전략에 변수로 작용할 수 있다고 분석했다.

계약직 및 파견직에 대한 수요 역시 증가하는 추세로 나타났다. 로버트 월터스 코리아에 따르면 지난해 계약직 채용 관련 기업 문의는 전년 대비 2배 이상 증가했으며, 직접 고용 후 파견직 인원도 약 4.5배 늘어난 것으로 집계됐다. 이는 기업들이 불확실한 경영 환경 속에서 정규직 채용보다는 단기 프로젝트 중심의 인력 운영 방식을 병행하고 있는 흐름으로 해석된다. 또한 육아·출산휴가로 인한 인력 공백을 보완하기 위한 수단으로 계약·파견직 활용이 확대되고 있는 것으로 나타났다.

최준원 로버트 월터스 코리아 지사장은 “기술 중심 산업을 중심으로 기회가 지속되는 가운데, 기업들은 생산성과 경쟁력을 높이기 위해 시니어 인재의 활용을 비롯해 유연근무제, 계약·파견직, 단기 및 프로젝트형 채용 등 다양한 고용 형태를 적극적으로 검토해야 할 시점이다”라며 “계약·파견직은 시니어 인재나 경력이 단절된 전문 인력이 다시 노동시장에 참여할 수 있는 하나의 진입 경로로 활용될 수 있다”라고 밝혔다.

로버트 월터스는 2000년부터 전 세계 30개국에서 자사를 통해 이직한 지원자들의 연봉 데이터를 기반으로 고용 동향과 산업·직군별 연봉 정보를 분석해 매년 디지털 연봉 조사서를 발간하고 있다.
jihyun.lee@foundryco.com

CIO
칼럼 | 2026년, 피하고 싶지만 그럴 수 없는 AI의 현실이 드러나는 해 13 January 2026 at 03:04

칼럼 | 2026년, 피하고 싶지만 그럴 수 없는 AI의 현실이 드러나는 해

CIO

13 January 2026 at 03:04

대화 주제가 AI로 넘어가면 대개 새로운 가능성과 역량부터 논의되기 시작한다. 그러나 이번에는 불편한 현실을 짚어볼 필요가 있다. 2026년이 단순히 AI에 대한 기대가 이어지는 해에 그치지 않을 수 있다는 점이다. 사회적·경제적·기술적 차원에서 그동안 누적돼 온 변화가 본격적으로 현실화될 가능성이 크다.

많은 리더가 논의를 피하려 할 수 있지만, 다음 3가지 변화는 예상보다 훨씬 빠르게 다가오고 있다.

AI로 인한 대규모 해고는 계속 늘어난다.
개인이 통제할 수 있는 프라이버시 영역은 점점 사라진다.
끝없이 이어지던 AI 파일럿의 시대가 막을 내리고, 이른바 ‘AI 프로젝트의 구조조정’이 시작된다.

1. AI로 인한 대규모 해고가 계속 확대된다

지금 일자리를 지키고 싶다면, 역량을 빠르게 끌어올릴 필요가 있다. 이미 마이크로소프트(MS), 지멘스, 구글, 메타, 아마존 등 글로벌 기업이 전 세계에서 수천 명 규모의 인력 감축을 단행하는 모습을 목격했을 것이다. 실시간으로 해고 현황을 집계하는 지표까지 등장한 상황이다. 역사상 최대 수준의 비용 절감 움직임은 이미 시작됐으며, 공개적으로 잘 언급되지는 않지만 그 동력의 상당 부분은 AI에 있다.

이는 경영진이 냉정하거나 의도적으로 인력을 줄이려 해서 벌어지는 일이 아니다. 수학적, 논리적으로 보면 대규모 조직에서 가장 큰 비용 항목은 인건비다. 많은 기업의 운영 비용 중 60~80%가 인력에 쓰인다. 자동화 기술이 직원이 수행하던 업무의 상당 부분을 더 효율적이고 더 낮은 비용으로 처리할 수 있다는 결과를 내기 시작하면, 이사회와 최고재무책임자(CFO)는 자연스럽게 움직일 것이다.

따라서 AI를 활용해 업무 흐름을 개선하려 하지 않거나 그렇게 할 수 없다면 점차 경쟁에서 밀려날 가능성이 높다. 일부 직원은 내부 이동 프로그램을 통해 재배치되거나, 더 높은 가치 영역의 역할을 맡게 될 수 있다. 그러나 모두가 그런 기회를 얻는 것은 아니다. 이런 이유로 AI 활용 역량을 높이고, AI 도구를 활용해 산출물과 업무 효율을 개선할 방법을 적극적으로 고민해야 한다. 가능한 한 다양한 방식으로 AI를 활용해 생산성을 끌어올리는 노력이 요구되는 시점이다.

이때 대규모 감원이 오랜 시간에 걸쳐 복잡하게 진행되는 과정이라고 생각하는 이들이 많다. 그러나 현실은 정반대다. AI 도입이 이 과정을 더욱 가속화한다. AI 투자의 성과는 보통 24~36개월 후에 본격적으로 나타나는데, 이는 챗GPT 출시 이후 시작된 AI 생산성 프로그램이 지금 시점에서 성숙 단계에 접어들고 있음을 의미한다. 실제로 글로벌 컨설팅사 맥킨지의 관련 보고서에서도 이 같은 흐름이 확인된다.

현재는 AI 전환의 ‘수확기’에 가까워지고 있다. 일부 개인의 역할은 점차 대체 가능해지고 있으며, 2년 이상 전에 투입된 투자금이 실제 성과로 돌아오고 있는 시기다.

그렇다면 “기업이 빠르고 과감하게 감원을 추진한 것을 후회하게 되지는 않을까”라는 의문이 제기될 수 있다.

일부 기업이나 부서는 그런 판단을 내릴 수도 있다. 조직 내부에 축적된 지식과 경험이 생각보다 쉽게 대체되지 않는다는 사실을 뒤늦게 인식하는 경우도 있다. 신규 인력을 채용하고 교육하는 데 드는 시간이 예상보다 길고 비용도 더 크다는 점을 체감하는 사례도 나올 수 있다. 그럼에도 AI가 조직 전반의 효율을 30~50% 끌어올린다면 인력 감축 흐름 자체가 바뀌지는 않는다. 이는 지금이야말로, 더 늦기 전에 AI 역량을 강화해야 할 시점이라는 의미일 수 있다. 시장은 냉정하며, 스스로를 최적화하지 못하는 개인에게는 불리하게 작용할 것이다.

그렇다면 직원은 어떻게 해야 할까? 현실적인 선택지는 3가지로 압축된다.

전략적·창의적·기술적 업무 역량을 중심으로 빠르게 역량을 강화한다.
AI 도구를 누구보다 능숙하게 활용하는 개인 기여자로 자리 잡는다.
아직 AI의 직접적인 영향을 받지 않은 새로운 분야로 완전히 전환한다.

아무것도 하지 않고 상황을 지켜보는 것은 전략이 될 수 없다. AI로 인한 일자리 감소는 추측이나 미래의 가능성이 아니라 이미 진행 중인 현실이다. 더 냉정한 사실은 다수의 사람이 적성과 역량을 다시 찾기 위해 1년 이상 시간을 쓸 수 있을 만큼의 재정적 여유를 갖고 있지 않다는 점이다.

2. 프라이버시는 점점 사라진다

오랫동안 데이터는 ‘새로운 석유’로 불려왔다. 그러나 이 표현은 이제 더 이상 적절하지 않다. 데이터는 석유 수준을 넘어 AI 시대의 새로운 ‘금광’이 됐다. 경쟁력 있는 AI 모델의 기반이 예외 없이 데이터에 있기 때문이다.

앞으로 2년은 이 금광을 둘러싼 경쟁으로 인해, 개인 데이터 수집과 활용이 그 어느 때보다 광범위하게 이뤄지는 시기가 될 가능성이 크다. AI는 방대한 데이터 위에서 작동하는데, 그 규모는 사람이 직관적으로 이해하기 어려울 정도다. 이로 인해 기술 기업은 법적으로 허용되는 범위 내에서 가능한 한 많은 고객 데이터를 확보하는 데 집착하고 있으며, 그 과정에서 합법성의 경계라고 인식되는 선을 넘는 사례도 빈번하게 나타나고 있다.

이미 주요 AI 기업이 사용자가 삭제했다고 믿었던 콘텐츠를 포함해 사용자 데이터로 학습했다는 유출 사례와 의혹이 제기된 바 있다. 공개적으로 한 차례 드러난 일이 있다면, 대규모로 조용히 이뤄지는 사례는 얼마나 많을지 짐작하기 어렵다. 수익과 경쟁 우위가 걸린 상황에서는 기준과 경계가 쉽게 느슨해지고, 협상의 대상이 되기 쉽다.

하지만 데이터 관련 법과 제도는 기술 발전 속도를 따라가지 못하고 있다. AI가 여전히 새로운 영역에 속하는 만큼, 법적 체계가 완전히 정비되지 않은 상태이며 이로 인해 해석의 여지가 큰 회색지대가 존재한다. 이는 AI 기업 입장에서 활용하기 쉬운 환경을 만든다. 막대한 자본력을 가진 기업은 잠재적인 법적 분쟁을 큰 위협으로 여기지 않는다. 설령 소송에서 지더라도, 그 시점에는 이미 데이터를 통해 충분한 가치를 확보한 뒤이기 때문이다. 이후 부과되는 벌금 역시 데이터 활용으로 얻은 수익에 비하면 극히 제한적인 수준에 그친다. 결국 벌금은 규제 수단이 아니라, 혁신 과정에서 감수하는 비용이 된다.

또 하나 간과되기 쉬운 점은 흔히 프라이버시 침해가 개인이 스스로 데이터를 입력할 때만 발생한다고 믿는다는 것이다. 실제로는 그렇지 않은 경우가 훨씬 많다. 개인이 AI에 어떤 정보도 직접 제공하지 않았더라도, AI는 이미 상당한 수준의 정보를 파악하고 있을 수 있다. 이는 주변 사람들이 제공한 데이터가 개인의 정보를 간접적으로 드러내기 때문이다. 충분히 많은 데이터가 서로 겹치면, 플랫폼은 당사자의 동의 없이도 대부분의 정보를 추론할 수 있다. 지인이 연락처에 전화번호를 저장하는 순간 신원이 식별될 수 있고, 휴대전화가 매일 밤 특정 위치에서 포착되면 거주지가 추정된다. 특정 인물과 정기적으로 만나는 패턴이 반복되면 관계 역시 파악된다. 개인이 직접 입력하지 않았더라도, 행동과 네트워크가 이미 그 정보를 대신 전달하고 있는 셈이다.

이미 스마트폰은 방대한 양의 데이터를 수집하고 있다. 수면 패턴과 심박수, 위치 정보, 걸음 수, 검색 기록은 기본이고, 그 밖에 어떤 정보까지 추적되는지는 사용자도 정확히 알기 어렵다. 필자 역시 스마트워치를 하루 종일 착용하고 있으며, 애플이 눈을 깜빡이는 순간까지 알고 있어도 이상하지 않다고 느낀다. 개인적으로는 일상의 편의성과 기술 발전을 위해 일정 수준의 프라이버시를 내주는 것에 큰 거부감이 없다.

그러나 모든 사람이 같은 생각을 하는 것은 아니다. 많은 이들은 프라이버시를 중요한 가치로 여기며, 자신의 데이터에 대해 강한 보호 의식을 갖고 있다. 이들에게 이러한 데이터 공유는 단순한 편의의 대가가 아니라, 신뢰를 저버리는 행위처럼 받아들여질 수 있다.

이 과정이 일부 사람에게 불편하게 느껴질 가능성은 분명하다. 그렇다면 이런 흐름이 멈출 수 있을까? 가능성은 낮다. AI는 데이터를 필요로 하고, 현대 경제는 AI에 점점 더 의존하고 있기 때문이다.

3. AI 프로젝트의 구조조정이 시작된다

올해 초부터 이사회와 경영진의 시선은 “AI로 무엇을 할 수 있는가”에서 “ROI를 보여주지 못하면 중단하라”로 빠르게 옮겨갈 가능성이 높다. 수개월 안에 기업이 추진 중인 AI 파일럿 프로젝트의 80% 이상이 종료되거나 중단될 것이라는 전망도 나온다. 상당수 프로젝트가 명확한 비즈니스 사례 없이 진행돼 왔기 때문이다. AI는 이제 실험의 단계를 지나 실행과 성과의 단계로 넘어가고 있다.

이는 단순한 비관이 아니라 현실적인 흐름이다. 과도한 기대, 실험, 가치 회수, 구조적 경제 변화, 규제는 모든 대규모 기술 혁신이 거치는 공통된 사이클이다. AI는 불과 2년 전 대중화 단계에 진입했지만, 이제는 실험을 넘어 성과와 효율을 요구받는 국면에 접어들고 있다. 더 이상 AI를 ‘미래의 기술’로만 부르기 어려운 이유다.

2026년은 산업과 정부, 사회 전반이 이 변화의 실제 규모를 받아들이는 ‘AI 조정의 해’가 될 전망이다. 즉, AI의 가능성을 이야기하는 사람과, 실제 성과를 만들어내는 사람을 가르는 기준점이 될 것이다. 지금 개인과 조직이 할 수 있는 선택은 단순하다. 그 경계선의 어느 편에 설 것인지를 결정하는 일이다.
dl-ciokorea@foundryco.com

CIO
3 AI truths no one wants to hear — But will become reality in 2026 12 January 2026 at 07:40

3 AI truths no one wants to hear — But will become reality in 2026

CIO

12 January 2026 at 07:40

As the topic turns toward AI, everyone usually starts discussing new possibilities and capabilities that AI can provide. But today, I must put on my evil hat and offer some uncomfortable truths: 2026 will not merely be another AI hype year. I suspect that this year will be the one when painful realities hit scale — socially, economically and technologically.

Three things are coming faster than most leaders want to accept:

Mass layoffs driven by AI will only increase.
Privacy will (sort of) disappear.
The era of endless AI pilots will end — and the AI “killing season” is about to begin.

Let’s discuss each.

1. Mass layoffs driven by AI will only increase

If you want to safeguard your job, better improve your skills fast. I bet you have seen numerous well-known, global companies, such as Microsoft, Siemens, Google, Meta and Amazon, laying off thousands of people globally. There is even a real-time firing chart to follow that. The greatest mass cost-cutting event in history has already begun and even though many don’t talk about it openly, it is, in fact, powered by AI.

Not because business leaders are villains thirsty for some bloodshed. But because, mathematically and logically speaking, salary cost is the largest expense in most large-scale organizations, for many, 60% to 80% of operational cost is people. Once automation provides results that it can perform a large portion of work that employees perform more efficiently and economically, the board and CFOs will take action.

Individuals who are not willing or able to improve their workflows using AI will stay behind. Some employees will get redeployed through internal mobility programs or will take up positions moving up the value chain. But many won’t. Which is why it should be a wakeup call for everyone to increase their AI literacy and think of ways — in fact, the more the better — to improve their output and work efficiency using AI tools.

Unfortunately, a lot of people think that mass firing is a long and tedious process that takes a lot of time. Quite the opposite. AI adoption compounds it. The fruits of the AI harvest usually take 24–36 months to mature. That implies that the first major wave of AI productivity programs initiated after ChatGPT’s launch is about to mature right now. Check out this McKinsey report for more details.

We are, in fact, about to reach the harvest phase of AI transformation, when individuals are beginning to become obsolete and the money that was invested more than two years ago is beginning to pay off.

“But won’t companies regret cutting their workforce so ruthlessly and so quickly?” you may ask.

Some might. Some departments will discover that institutional knowledge cannot be replaced at ease. Others will soon find out that training new hires is a slow and much more expensive process than they thought. But if AI gives organizations a 30% to 50% efficiency lift, the job cuts will still come. Which is why now (if not yesterday) is the best time to improve your skills in AI so you improve your game. The market is ruthless; it will only penalize those who fail to optimize.

But then what should employees do?

There are only three options, really:

Upskill fast (pick up additional strategic, creative or technical work skills)
Become an AI-empowered individual contributor (that is, someone who uses AI tools better than others)
Reskill entirely into a new domain not yet impacted

Sitting around and doing nothing is not a strategy. The loss of jobs due to AI is not hypothetical; it is already happening. The harsh reality is that, unfortunately, the majority of people do not have a financial safety net that would allow them to spend a year rediscovering their passions and talents.

2. Privacy will (sort of) disappear

For years, we said that data is the new oil. That phrase is now outdated – data is not oil; it has become the whole new gold mine. Data became the foundation of every competitive AI model.

The following two years will be the most invasive period in human history due to the AI quest for this gold. Why? Because AI is data-driven. AI runs on volumes of persistent and diverse data — and the volume of this data is so big, that is even difficult to digest. This implies that computer companies are now obsessed with extracting as much customer data as they legally can, frequently going beyond what many would consider legal.

We’ve already seen leaks and allegations that major AI companies trained on user data, including content users believed was deleted. And if that happened publicly once, imagine what happens quietly at scale. When incentives are this high, boundaries become stretched and negotiable.

Unfortunately, data laws are slow. Since AI is such a fresh and new field, it is not surprising that the legal framework is still developing, making it an ideal area for legal interpretations. Companies with billion-dollar pockets are not intimidated by the potential legal battles: even if they eventually lose, they will have already captured the value of the data. And the fine that they might pay is as insignificant as a droplet in an ocean, since they have already profited from the usage of that data. In other words, fines turn into a cost of innovation.

Yet another aspect here — many believe that privacy loss happens only when they feed their own data to the AI willingly and consciously. That is not always correct. In fact, you may have never provided any specific data to the AI, but AI already knows plenty about you. Privacy disappears because others surrender data that reveals information about you. This is the birthday paradox applied to digital identity: with enough overlapping data points, platforms can infer almost everything — even without your consent. As your friends save your number into their contacts, the system can identify you. If your phone can be located in a specific location every night, AI knows where you live. If you meet someone regularly, AI knows your relationship. You never said this, but your actions and/or network did.

Our phones already track large amounts of repetitive data: the phones track our sleep patterns, our heart rate, geolocation, step count, search history and only God knows what else. I wear my smartwatch day and night. Apple might even know when I blink. I personally don’t care and I am okay with giving up my privacy in exchange for daily convenience and progress. But many people value their privacy deeply and feel rather protective of their own data inputs. To them, sharing such input feels like betrayal.

Will it feel uncomfortable for some people? Absolutely. Will it be stopped? No. Because AI needs data and economies need AI.

3. The AI-killing season will begin

I expect that already in the first months of this year, the boardroom mood will shift from “What can AI do?” to “Show me ROI or shut it down.” It is likely that over four-fifths of the corporate AI pilots will die or will be killed within the coming months. This is about to happen simply because too many pilots lacked real business cases and were more of an AI theatre. Now, AI is shifting from experimentation to execution.

This is nothing but realism. Hype, experimentation, value extraction, structural economic impact and regulatory catch-up are all part of the cycle that every significant technology revolution goes through. AI started its mass-market phase only two years ago and we are already approaching stage four, so it is about time we stop labelling AI as being “about the future.” The future has already come. And 2026 will become the AI correction year, when industries, governments and societies absorb the true scale of the shift.

2026 will separate futurists from executioners. And for now, all you can do is be on the right side of that line.

This article is published as part of the Foundry Expert Contributor Network.
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CIO
7 challenges IT leaders will face in 2026 12 January 2026 at 05:01

7 challenges IT leaders will face in 2026

CIO

12 January 2026 at 05:01

Today’s CIOs face increasing expectations on multiple fronts: They’re driving operational and business strategy while simultaneously leading AI initiatives and balancing related compliance and governance concerns.

Additionally, Ranjit Rajan, vice president and head of research at IDC, says CIOs will be called to justify previous investment in automation while managing related costs.

“CIOs will be tasked with creating enterprise AI value playbooks, featuring expanded ROI models to define, measure, and showcase impact across efficiency, growth, and innovation,” Rajan says.

Meanwhile, tech leaders who spent the past decade or more focused on digital transformation are now driving cultural change within their organizations. CIOs emphasize that transformation in 2026 requires a focus on people as well as technology.

Here’s how CIOs say they’re preparing to address and overcome these and other challenges in 2026.

Talent gap and training

The most often cited challenge by CIOs is a consistent and widening shortage of tech talent. Because it’s impossible to meet their objectives without the right people to execute them, tech leaders are training internally as well as exploring non-traditional paths for new hires.

In CIO’s most recent State of the CIO survey 2025, more than half the respondents said staffing and skills shortages “took time away from more strategic and innovation pursuits.” Tech leaders expect that trend to continue in 2026.

“As we look at our talent roadmap from an IT perspective, we feel like AI, cloud, and cybersecurity are the three areas that are going to be extremely pivotal to our organizational strategy,” says Josh Hamit, CIO of Altra Federal Credit Union.

Hamit said the company will address the need by bringing in specialized talent, where necessary, and helping existing staff expand their skillsets. “As an example, traditional cybersecurity professionals will need upskilling to properly assess the risks of AI and understand the different attack vectors,” he says.

Pegasystems CIO David Vidoni has had success identifying staff with a mix of technology and business skills and then pairing them with AI experts who can mentor them.

“We’ve found that business-savvy technologists with creative mindsets are best positioned to effectively apply AI to business situations with the right guidance,” Vidoni says. “After a few projects, new people can quickly become self-sufficient and make a greater impact on the organization.”

Daryl Clark, CTO of Washington Trust, says the financial services company has moved away from degree requirements and focused on demonstrated competencies. He said they’ve had luck partnering with Year Up United, a nonprofit that offers job training for young people.

“We currently have seven full-time employees in our IT department who started with us at Year Up United interns,” Clark says. “One of them is now an assistant vice president of information assurance. It’s a proven pathway for early career talent to enter technology roles, gain mentorship, and grow into future high impact contributors.”

Coordinated AI integration

CIOs say in 2026 AI must move from experimentation and pilot projects to a unified approach that shows measurable results. Specifically, tech leaders say a comprehensive AI plan should integrate data, workflows, and governance rather than relying on scattered initiatives that are more likely to fail.

By 2026, 40% of organizations will miss AI goals, IDC’s Rajan claims. Why? “Implementation complexity, fragmented tools, and poor lifecycle integration,” he says, which is prompting CIOs to increase investment in unified platforms and workflows.

“We simply cannot afford more AI investments that operate in the dark,” says Flexera CIO Conal Gallagher. “Success with AI today depends on discipline, transparency, and the ability to connect every dollar spent to a business result.”

Trevor Schulze, CIO of Genesys, argues AI pilot programs weren’t wasted — as long as they provide lessons that can be applied going forward to drive business value.

“Those early efforts gave CIOs critical insight into what it takes to build the right foundations for the next phase of AI maturity. The organizations that rapidly apply those lessons will be best positioned to capture real ROI.”

Governance for rapidly expanding AI efforts

IDC’s Rajan says that by the end of the decade organizations will see lawsuits, fines, and CIO dismissals due to disruptions from inadequate AI controls. As a result, CIOs say, governance has become an urgent concern — not an afterthought.

“The biggest challenge I’m preparing for in 2026 is scaling AI enterprise-wide without losing control,” says Barracuda CIO Siroui Mushegian. “AI requests flood in from every department. Without proper governance, organizations risk conflicting data pipelines, inconsistent architectures, and compliance gaps that undermine the entire tech stack.”

To stay on top of the requests, Mushegian created an AI council that prioritizes projects, determines business value, and ensures compliance.

“The key is building governance that encourages experimentation rather than bottlenecking it,” she says. “CIOs need frameworks that give visibility and control as they scale, especially in industries like finance and healthcare where regulatory pressures are intensifying.”

Morgan Watts, vice president of IT and business systems at cloud-based VoIP company 8×8, says AI-generated code has accelerated productivity and freed up IT teams for other important tasks such as improving user experience. But those gains come with risks.

“Leading IT organizations are adapting existing guardrails around model usage, code review, security validation, and data integrity,” Watts says. “Scaling AI without governance invites cost overruns, trust issues, and technical debt, so embedding safeguards from the beginning is essential.”

Aligning people and culture

CIOs say one of their top challenges is aligning their organization’s people and culture with the rapid pace of change. Technology, always fast-moving, is now outpacing teams’ ability to keep up. AI in particular requires staff who work responsibly and securely.

Maria Cardow, CIO of cybersecurity company LevelBlue, says organizations often mistakenly believe technology can solve anything if they just choose the right tool. This leads to a lack of attention and investment in people.

“The key is building resilient systems and resilient people,” she says. “That means investing in continuous learning, integrating security early in every project, and fostering a culture that encourages diverse thinking.”

Rishi Kaushal, CIO of digital identity and data protection services company Entrust, says he’s preparing for 2026 with a focus on cultural readiness, continuous learning, and preparing people and the tech stack for rapid AI-driven changes.

“The CIO role has moved beyond managing applications and infrastructure,” Kaushal says. “It’s now about shaping the future. As AI reshapes enterprise ecosystems, accelerating adoption without alignment risks technical debt, skills gaps, and greater cyber vulnerabilities. Ultimately, the true measure of a modern CIO isn’t how quickly we deploy new applications or AI — it’s how effectively we prepare our people and businesses for what’s next.”

Balancing cost and agility

CIOs say 2026 will see an end to unchecked spending on AI projects, where cost discipline must go hand-in-hand with strategy and innovation.

“We’re focusing on practical applications of AI that augment our workforce and streamline operations,” says Pegasystems’ Vidoni. “Every technology investment must be aligned with business goals and financial discipline.”

When modernizing applications, Vidoni argues that teams need to stay outcome-focused, phasing in improvements that directly support their goals.

“This means application modernization and cloud cost-optimization initiatives are required to stay competitive and relevant,” he says. “The challenge is to modernize and become more agile without letting costs spiral. By empowering an organization to develop applications faster and more efficiently, we can accelerate modernization efforts, respond more quickly to the pace of tech change, and maintain control over cloud expenditures.”

Tech leaders also face challenges in driving efficiency through AI while vendors are increasing prices to cover their own investments in the technology, says Mark Troller, CIO of Tangoe.

“Balancing these competing expectations — to deliver more AI-driven value, absorb rising costs, and protect customer data — will be a defining challenge for CIOs in the year ahead,” Troller says. “Complicating matters further, many of my peers in our customer base are embracing AI internally but are understandably drawing the line that their data cannot be used in training models or automation to enhance third-party services and applications they use.”

Cybersecurity

Marc Rubbinaccio, vice president of information security at Secureframe, expects a dramatic shift in the sophistication of security attacks that looks nothing like current phishing attempts.

“In 2026, we’ll see AI-powered social engineering attacks that are indistinguishable from legitimate communications,” Rubbinaccio says. “With social engineering linked to almost every successful cyberattack, threat actors are already using AI to clone voices, copy writing styles, and generate deepfake videos of executives.”

Rubbinaccio says these attacks will require adaptive, behavior-based detection and identity verification along with simulations tailored to AI-driven threats.

In the most recent State of the CIO survey, about a third of respondents said they anticipated difficulty in finding cybersecurity talent who can address modern attacks.

“We feel it’s extremely important for our team to look at training and certifications that drill down into these areas,” says Altra’s Hamit. He suggests the certifications such as ISACA Advanced in AI Security Management (AAISM) and the upcoming ISACA Advanced in AI Risk (AAIR).

Managing workload and rising demands on CIOs

Pegasystems’s Vidoni says it’s an exciting time as AI prompts CIOs to solve problems in new ways. The role requires blending strategy, business savvy, and day-to-day operations. At the same time the pace of transformation can lead to increased workload and stress.

“My approach is simple: Focus on the highest-priority initiatives that will drive better outcomes through automation, scale, and end-user experience. By automating manual, repetitive tasks, we free up our teams to focus on higher-value, more engaging work,” he says. “Ultimately, the CIO of 2026 must be a business leader first and a technologist second. The challenge is leading organizations through a cultural and operational shift — using AI not just for efficiency, but to build a more agile, intelligent, and human-centric enterprise.”

GeekWire
Seattle tech job postings remain far below pre-pandemic levels 9 December 2025 at 17:35

Seattle tech job postings remain far below pre-pandemic levels

GeekWire

By: Taylor Soper

9 December 2025 at 17:35

The Seattle skyline. (GeekWire File Photo / Kurt Schlosser)

Tech-related job postings remain stuck well below pre-pandemic levels in Seattle, according to a new hiring trends report from Indeed.

The site uses a measure called the Indeed Job Postings Index, which treats Feb. 1, 2020 as the “normal” baseline of 100. Numbers below 100 mean fewer job postings than before the pandemic.

In Seattle, the index for Software Development was 32 as of Nov. 27, 2025 — meaning postings are about two-thirds lower than the pre-COVID benchmark. Data & Analytics is even lower at 29.

Those numbers haven’t moved much over the past two years. Software Development was 31 in late 2023 and Data & Analytics was 38, for example.

Nationally, tech job postings are almost a third lower compared to early 2020, according to Indeed.

Seattle is seeing a more concentrated pullback in tech-related hiring. It makes for an unfamiliar economic environment in the Emerald City, which has seen its tech industry surge for much of the past decade, including a hiring spree early in the pandemic.

A report from CBRE in 2021 showed that the Seattle region added more than 48,000 tech jobs from 2016 to 2020, an increase of more than 35% — growing at a faster rate than any other large U.S. tech market for that time period. Amazon was growing exponentially, Microsoft had a massive revival, and the startup scene was producing multiple billion-dollar companies.

It’s a different climate now, just as the artificial intelligence era gets going amid broader macroeconomic uncertainty.

Microsoft and Amazon had substantial layoffs this year, though both are still hiring in select areas as they invest heavily in AI infrastructure. Some startups, once so-called “unicorns,” have also shed staff due to financial trouble.

The tech slowdown in Seattle got the national spotlight in September, when The Wall Street Journal detailed the broader fallout from widespread layoffs, including decreased retail spending in tech-heavy districts and record-high office vacancies.

The latest trends may help explain why some job seekers, including longtime leaders, are having trouble landing tech gigs in Seattle.

The tech industry accounts for a whopping 30% of the economy in the Seattle region, according to a report from CompTIA. That ranks second in the U.S. behind San Jose. Tech also accounts for more than 12% of the overall workforce in the Seattle area.

Workers in the “computer and mathematicals” occupation category in the Seattle area had the highest median earnings in 2024 by a wide margin ($163,609), according to the Seattle Times.

Other hiring trends in Seattle and nationally

As of late 2025, only seven of 45 sectors in the Seattle area were above 100, per the Indeed Job Postings Index — and all of them were in healthcare. Two years earlier, 22 sectors were still above 100, showing a much broader economy with stronger hiring demand. Overall, Seattle had a 35% decline in job postings from February 2020 to October 2025, Axios reported.

The weakest Seattle sectors right now include Data & Analytics, Software Development, Project Management, Human Resources, and Media and Communications.

Some of the largest declines over the past two years came in non-tech areas such as Driving, Pharmacy, Cleaning and Sanitation, Civil Engineering, and Childcare — though Indeed notes that Pharmacy and Civil Engineering still remain relatively high compared with pre-pandemic levels.

Indeed said in nearly every state, the highest job posting levels are found in smaller and mid-sized regions, rather than big cities.

“Employment in many of the largest MSAs tends to be skewed towards tech, business, and professional services, which are seeing lower levels of job postings,” the company wrote in a blog post. “Smaller MSAs, however, tend to have heavier employment shares in sectors, including manufacturing, leisure and hospitality, and healthcare, which generally have job postings that remain near or higher than pre-COVID norms.”

Indeed said the most probable outcome for next year’s labor market is an extension of the current “low-hire, low-fire” environment. It noted that large coastal metro areas with slower population growth and more exposure to tech and professional services “are likely to face tougher conditions.”