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오픈AI·앤트로픽, SI 영역 넘본다…엔터프라이즈 AI 경쟁 ‘구현 영역’으로

오픈AI와 앤트로픽은 합작 투자와 인수 협상을 통해 전문 서비스 영역으로 사업 범위를 확장하며, 기존 시스템 통합 기업이 맡아온 구현 역할에 한층 더 가까이 다가가고 있다.

로이터의 5일 보도에 따르면, 두 AI 기업과 연계된 합작사는 기업의 AI 도입을 지원하는 서비스 업체 인수를 논의해 왔으며, 이 가운데 오픈AI 측은 3건의 협상에서 상당한 진척을 이룬 것으로 알려졌다.

또한 기업 고객들이 생성형 AI를 실험 단계에서 실제 운영 환경으로 전환하는 과정에서, 엔지니어와 컨설턴트 인력을 확충하려는 움직임도 나타나고 있다.

한편 앤트로픽은 블랙스톤, 헬만앤프리드먼, 골드만삭스의 투자를 기반으로 새로운 엔터프라이즈 AI 서비스 기업 설립 계획을 발표했다. 이 회사는 중견 기업이 ‘클로드(Claude)’를 핵심 업무에 적용할 수 있도록 지원하는 것을 목표로 한다.

앤트로픽은 자사의 응용 AI 엔지니어들이 신설 기업의 엔지니어링 팀과 협력해 유즈케이스를 발굴하고, 맞춤형 시스템을 구축하며, 장기적으로 고객 지원을 수행할 것이라고 밝혔다.

서비스 확장 배경…엔터프라이즈 AI 주도권 경쟁 본격화

CIO들에게 이번 변화의 핵심은 AI 벤더가 기존 컨설팅 기업, 시스템 통합(SI) 업체, 매니지드 서비스 제공업체가 맡아온 역할을 점차 대체하고 있는지 여부다. 이번 흐름은 모델 기업들이 엔터프라이즈 AI 구현 과정에서 더 큰 주도권을 확보하려는 의지를 보여준다. 다만 대규모 구축 프로젝트에서는 여전히 SI 기업의 역할이 중요하다는 점도 함께 드러난다.

이 같은 움직임은 이미 많은 CIO들이 직면한 문제를 반영한다. AI 파일럿은 빠르게 시작할 수 있지만, 이를 보안과 안정성을 갖춘 운영 시스템으로 전환하는 데에는 수개월에 걸친 통합과 프로세스 작업이 필요하다.

컨설팅 기업 테크아크의 설립자이자 수석 애널리스트인 파이살 카우사는 “엔터프라이즈 IT 구축은 전통적으로 컨설팅이나 자문 중심으로 이뤄져 왔다”라며 “실질적인 수익이 발생하는 도입 속도를 높이기 위해서는 기존 엔터프라이즈의 프레임워크와 시장 진출 모델에 맞춰야 한다”고 설명했다.

이어 카우사는 “현재 AI 기업들은 가치 사슬의 최상단에 위치해 있으며, 단순한 IT 공급업체로 전락하기보다는 ‘주도권을 쥔 상태’를 유지하려 한다”고 분석했다.

IDC 아시아태평양 지역 AI·데이터 분석·데이터 부문 리서치 총괄 디피카 기리는 “이번 변화는 엔터프라이즈 AI 전반의 구조 재편으로 이어질 가능성이 있다”라며 “AI 모델 기업들이 플랫폼 공급자를 넘어 전체 AI 가치 사슬을 적극적으로 설계하는 방향으로 이동하고 있다”고 말했다. 이어 “구현, 컨설팅, 매니지드 서비스까지 확장함으로써 단순 기술 공급을 넘어 기업의 실제 성과에 더 밀접하게 관여하려는 전략”이라고 덧붙였다.

카우사는 일부 IT 서비스 기업들이 AI 도입에 신중한 태도를 보이는 이유로 기술의 불확실성과 역할 축소 가능성을 지목했다. 그는 “시장 진출 전략의 변화 속에서 AI 기업들이 주도권을 잡고 있다”고 평가했다.

도입 리스크는 낮추지만…‘락인’ 심화 우려

AI 모델 기업으로부터 직접 서비스를 도입하면 초기 구축은 한층 수월해질 수 있다.

카덴스 인터내셔널의 수석 부사장 툴리카 쉴은 “기업이 더 긴밀한 통합과 전문 인력 지원을 받을 수 있어 단기적으로는 구축 리스크를 줄일 수 있다”고 설명했다.

다만 이러한 편의성은 장기적인 부담으로 이어질 수 있다는 지적도 나온다.

쉴은 “모델부터 데이터 파이프라인, 워크플로우에 이르기까지 전체 스택 전반에서 의존도가 더욱 심화될 수 있다”라며 “시간이 지날수록 락인이 강화돼, 큰 혼란 없이 벤더를 교체하기 어려워질 수 있다”고 말했다.

카운터포인트 리서치의 부사장이자 파트너인 닐 샤는 “AI 모델 기업들은 사용량 기반 비즈니스 모델과 애플리케이션, 서비스 간 결합을 강화하며 기업 고객을 위한 ‘원스톱 서비스’ 제공자로 자리매김하려 한다”고 분석했다.

이어 “애플리케이션과 서비스 계층을 직접 통제하면 기업을 자사 생태계에 묶어둘 수 있을 뿐 아니라, 고객의 요구와 문제, 업무 방식까지 직접 이해해 모델 최적화에도 활용할 수 있다”고 설명했다.

IDC의 기리는 락인이 불가피한 것은 아니라고 진단했다. 다만 이를 피하기 위해서는 초기 단계에서의 전략적 설계가 중요하다고 강조했다.

기리는 “모듈형 아키텍처를 통해 모델 계층은 점차 추상화할 수 있지만, 락인을 피하려면 의도적인 설계 선택이 필요하다”라며 “그렇지 않으면 특정 모델뿐 아니라 데이터 파이프라인, 워크플로우, 거버넌스 프레임워크까지 포함한 전체 스택에 종속될 위험이 있다”고 말했다.

한편 이번 흐름은 엔터프라이즈 AI가 여전히 많은 구현 작업을 필요로 한다는 점도 보여준다.

쉴은 “생성형 AI 플랫폼은 강력하지만, 실제 비즈니스 프로세스를 지원하려면 기업 내부 데이터와 워크플로우, 거버넌스 시스템과의 깊은 통합이 필수적”이라며 “이는 모델 성능과 실제 현장 적용 사이에 간극이 존재한다는 것을 의미한다”고 짚었다.

이러한 변화는 CIO들이 단순히 어떤 AI 모델의 성능이 더 뛰어난지를 넘어서, 해당 모델이 기업 시스템에 적용된 이후 구현과 운영을 누가 주도할 것인지까지 함께 고려해야 함을 시사한다.
dl-ciokorea@foundryco.com

Coherence: Where leadership and AI success intersect

In an era where AI is accelerating faster than most organizations can absorb, many IT leaders are grappling with how to move quickly without creating fragmentation. For Leigh-Ann Russell, BNY’s CIO and global head of engineering, the answer comes down to a single word: coherence.

For Russell, coherence isn’t a slogan. It’s a leadership discipline that connects strategy to execution, technology to trust, and ambition to sustainability. In a recent episode of the Tech Whisperers podcast, she discussed why coherence is so integral to the modern CIO playbook and how to leverage it to scale impact instead of chaos.

Russell’s perspective is shaped by a career defined by range and resilience, from her formative years in Scotland to leading complex, high-stakes transformations across industries and geographies. After the podcast, we spent more time exploring how that journey has informed her leadership operating system, how she translates coherence into enterprise-scale AI execution, and why IT leaders must learn to navigate the intersection of innovation, control, and reusability. What follows is that conversation, edited for length and clarity.

Dan Roberts: Are there experiences from your formative years growing up in Scotland that shaped how you lead today?

Leigh-Ann Russell: I credit my father as the biggest inspiration in my life. He worked seven days a week and had two jobs, yet he was always present. When I look back, I don’t know how that was possible, for him to have a 7-day-a-week job and still take me to the park and to ballet lessons, or even afford ballet lessons. He was this embodiment of a combination of work ethic and family, and that’s how I’ve led.

There was also a lot about my life growing up that I hid. I hid the fact that I was a single parent. I hid the fact that I grew up in a council estate, similar to public housing in the US. I hid a number of things about me. It wasn’t until I got to be a bit more mature in life that I realized these were not things to hide. These are the stepping stones that make me the person I am.

My father taught me to do more with less and the power of work ethic. My daughter taught me ninja productivity skills. These were not things you should be uncomfortable about. These are things you should celebrate. My whole virtuous feedback on that, as a leader, if you share what makes you human, then it will be much easier to connect with other leaders and other people in your team. And then they feel comfortable sharing what makes their human foundation.

When the environment is complex, fast-moving, and high stakes, like it is today with AI, what are the core operating principles you return to as a leader, when you’re tired, under pressure, or being watched?

My core operating model centers on two things: talent and clarity. My philosophy is, my job is to find great talent and help them be the best version of themselves. If you achieve that, then real, magical things happen, because it’s people who create magic, not technology.

The second part of my role is about creating clarity. Life is complex, leadership is complex, and what teams need is simplicity. It’s about trying to simplify the problem, understand the trade-offs, and align people. Take AI as an example: The technology can create enormous value while also creating friction at scale if we’re not redesigning the work thoughtfully around it. That’s why it feels like the next leadership challenge — it’s not just about deploying AI well but designing the system around it with clarity and consistency in mind.

During the podcast, we talked about how you were very intentional in choosing coherence as your word of the year. Can you give some examples of coherence applied as a leadership discipline?

Coherence is a hard thing to build and a fast thing to lose. It starts with humans. It’s something Robin Vince, our CEO, does really well in bringing our leadership team together. He talks publicly about the fact that we all have a coach, and we have the same coaches from the same company and come together around that. He’s very intentional about creating coherence as a leadership structure.

As you go vertically down to the different meanings of coherence, it also applies equally to technology. It’s very easy to chase the shiny thing, and there’s a very tenuous conflict between people being empowered to do great innovation but also doing it in a structured way so that you avoid lack of controls or duplication or issues with architecture and costs firing out of control.

That double meaning of coherence and being very tight on the balance between individual innovation and empowerment and leadership becomes critical.

Can you talk about what your AI strategy looks like across the enterprise?

We set up the AI hub in 2023, and when I came to BNY in 2024, we started thinking about adoption and enablement across the enterprise, guided by our mantra: AI for everyone, for everywhere, for everything. In 2025, we set the goal of having 65% of the bank trained on AI, but we made 100% as early as June, and we’ve had to rewrite the training program twice since then to enable our employees to continue deepening their proficiency.

That enablement was important and we’ve had an amazing uptake, with over 220 AI solutions now in production accessible in Eliza, our enterprise AI platform. Eliza is built on the premise of foundational, reusable capabilities and is designed to enhance client service and company operations and drive cultural transformation through the power of AI. We talk more about Eliza and how we are advancing responsible and ethical AI in financial services on our BNY website.

Over half the bank have built their own agents, and we also have digital employees at the bank — 140 autonomous agentic employees who work alongside our human employees and that have direct human managers who monitor what logic is applied to decision-making. This is truly agentic.

So, 2025 was about widespread adoption and literacy, and now we’re moving from AI adoption to AI at the core. Even in the most complicated use cases that we’ve put into production, I still think they’re somewhat at the edges — anomaly detection, pulling together client briefing documents, or looking at contract reviews. Very advanced use cases compared to most enterprises, but we are pivoting in 2026 to having AI at the core of everything that we do at the bank. That’s truly transformational, and it’s the next step in our journey.

You have 140 digital employees, an idea that wasn’t even on the radar at the beginning of 2025. How did your organization move and adapt to scale that up so quickly?

It goes back to the philosophy that leadership is all about having talent and enabling them. We have an amazing team in engineering — and across the bank, because this is not just an engineering piece. If you look at our first digital employee, it was in the payment space, looking at reconciliations, and that was born out of a collaboration between engineering and operations. Our first human manager of a digital employee was in the operations side of the business.

Reimagining how work gets done can’t just be an engineering issue. It has to be in partnership with the business. Those individuals in the businesses and in engineering who can think back to first principles about how work gets done are leaping ahead on their AI journeys because they’re not just thinking about adding in AI as an afterthought; they’re thinking about redesigning their workflows with AI at the core.

A great recent example of that is in our onboarding process. We now have a multi-agentic model that has taken the research part of the process down from double-digit hours to single-digit minutes. This partnership and ability for our people to reimagine how we work with AI now at the core is foundational.

The strategy you’ve laid out really is a journey, and it seems there are some key foundational steps that many organizations are trying to skip, which is creating all sorts of problems for them.

In the podcast, we talked about the flip side of coherence being chaos. If you have chaos, AI will just amplify that chaos. That’s at a stack level or a leadership level. As the pressure is on companies to go out and adopt, this is where Eliza, our platform, has been truly instrumental to us because everything AI-related at the company is centralized in Eliza. It’s our tech stack; it’s our governance framework. Having one place for AI so you’re not chasing multiple tools and multiple companies, and having that very clear AI strategy embedded in a single platform, has been really differentiating for us.

In truth, there has been no single silver bullet in our AI journey. We have a tech-enthusiastic CEO in Robin Vince, who realized very early on that AI would be transformative for our company and has been determined that BNY remain at the forefront. With his leadership from the top, we invested early in our people to cultivate the AI-literate workforce we have today. So the fact that it’s a CEO-led strategy, plus the platform, plus the enablement has really helped us get to the speed of having 220 AI solutions in production supporting the enterprise.

Considering the strategic priority you’ve placed on AI adoption, how do you balance innovation and control?

Our goal is to enable innovation across the firm, which speaks to this mindset that being “AI first” is more important than just control. Obviously, we need control from a risk, compliance, legal, resilience, and cyber point of view, but from a financial and leadership perspective, we’re not trying to control and damp down and make everyone justify every single use case. As a result, we’ve said no to a lot less than I think most other companies would have done.

When I think about the trade-offs of that, it’s understanding “what is innovation?” and making sure there’s a reusable core. Because people have in their mind, “If I just hire my own engineers, and I have my own architecture, and I have my own software, I’m really innovative.” People tie innovation to having the new shiny thing, and it’s very hard to shift to a mindset that understands sometimes innovation is reusing what other teams have already developed and building on that. So, sometimes the more painful conversations involve trying to help people reground in reusability, common architectures, and common data platforms, understanding that isn’t hampering their innovation, it’s providing a solid foundation they can build on to go faster.

That intersectionality of innovation and control and reusability is the difficult thing we have to get right, because if you have too little control, you have the chaos we spoke about, and we know that AI amplifies chaos. If you have too much control and too much centralization, then you do hamper innovation. It’s something I’m very conscious of, that we don’t want to do either.

Great leadership often comes down to managing those tensions. What is the central tension you’re learning to navigate right now and how is it shaping the leader you’re becoming?

It goes back to a quote I mentioned in the podcast about Atticus Finch: How do you maintain your convictions without being rigid? Because one thing I know for sure, there are no right answers right now. Does one discipline shift left and one discipline shift right? What is the role of engineering in the future? There is absolutely no right answer to that. There’s only a set of choices that’s right for your institution, and what might be right for a marketing company will not be right for a regulated bank.

I use my digital twin to make sure I’m not too over-convicted, because I have that tendency, in my past growing up in the oil field and it’s a slightly Scottish tendency. So that’s the thing I’m really pushing myself on: How do I retain my conviction, but not become rigid, and stay really open to what is coming at us, because the change is like we’ve never seen — in a very positive way as an engineer, but we have to remain convicted, not rigid.

In world of “double VUCA,” where the impact of external volatility, uncertainty, complexity, and ambiguity is being compounded by fragmented AI journeys, a lack of clear AI strategy, and ineffective leadership internally, Leigh-Ann Russell shows us why coherence is an essential strategic discipline. In the age of AI, it can spell the difference between leaders who scale impact and those who simply scale chaos. For more insights from Russell’s leadership playbook, tune in to the Tech Whisperers.



칼럼 | 기술을 넘어선 경쟁력, 뛰어난 FDE는 어떻게 다른가

전례 없는 규모의 AI 투자가 이뤄지고 있음에도 불구하고, 대부분의 기업은 ‘통합의 벽’에 부딪힌 상태다. 기술은 개별 환경에서는 제대로 작동하고, 개념검증(PoC)도 충분히 인상적인 결과를 낸다.

하지만 실제 고객과 접점이 생기고 매출에 영향을 미치며 실질적인 리스크가 발생하는 운영 환경에 AI를 적용하려는 순간, 기업들은 주저하게 된다. 이는 충분히 타당한 이유에서다. AI 시스템은 본질적으로 비결정적(non-deterministic) 특성을 지니기 때문이다.

예측 가능한 방식으로 동작하는 기존 소프트웨어와 달리, 대규모 언어모델(LLM)은 예상치 못한 결과를 만들어낼 수 있다. 틀린 정보를 확신에 차서 제공하거나, 존재하지 않는 사실을 생성하는 ‘환각(hallucination)’, 브랜드 톤과 맞지 않는 응답을 내놓을 위험도 존재한다. 리스크 관리에 민감한 기업일수록 이러한 불확실성은 어떤 수준의 기술적 고도화로도 극복하기 어려운 장벽으로 작용한다.

이 같은 현상은 산업 전반에서 공통적으로 나타난다. 기업의 AI 도입을 지원해 온 경험을 돌아보면, 많은 조직이 인상적인 AI 데모를 구축하고도 통합 단계를 넘어서지 못하는 사례를 반복해왔다. 기술은 준비돼 있었고 사업적 타당성도 충분했지만, 조직의 리스크 수용도가 이를 따라가지 못했다. 또한 실험 환경에서 가능한 AI 활용과 실제 운영 환경에서 허용되는 범위 사이의 간극을 메울 방법을 아는 사람도 없었다. 이 지점에서 문제의 본질은 기술이 아니라 이를 실제로 적용할 인재라는 결론에 이르게 된다.

필자는 몇 달 전 IT 인력 제공 플랫폼인 안델라(Andela)라는 기업에 합류했다. 이 관점에서 보면, 기업이 필요로 하는 역량은 보다 분명해진다. 바로 파견형 엔지니어 정확히 말해 FDE(Forward Deployed Engineer)다. 해당 개념은 데이터 분석 기업 팔란티어(Palantir)가 정부 기관과 기업 내부에 자사 플랫폼을 배포하는 과정에서 필수적인 고객 중심 기술 인력을 설명하기 위해 처음 사용했다. 최근에는 선도 AI 연구소와 하이퍼스케일러, 스타트업까지 이 모델을 채택하고 있다. 예를 들어 오픈AI는 고가치 고객의 플랫폼 도입을 촉진하기 위해 숙련된 FDE를 배치하고 있다.

다만 CIO가 반드시 이해해야 할 점이 있다. 지금까지 이러한 역량은 주로 AI 플랫폼 기업의 성장 전략을 위해 집중적으로 활용돼 왔다. 기업이 통합의 벽을 넘어 AI를 실제 운영에 적용하기 위해서는, 이 같은 FDE 역량을 내부적으로 확보하고 육성해야 한다.

FDE를 만드는 요소

FDE의 핵심 특징은 기존 엔지니어가 하지 못하는 방식으로 기술적 솔루션과 비즈니스 성과를 연결하는 능력에 있다. FDE는 단순히 시스템을 구축하는 개발자가 아니다. 엔지니어링, 아키텍처, 비즈니스 전략이 교차하는 지점에서 작동하는 ‘번역자’에 가깝다.

이들은 생성형 AI라는 미지의 영역을 조직이 탐색할 수 있도록 이끄는 ‘탐험대장’과 같은 존재다. 특히 AI를 실제 운영 환경에 배포하는 과정은 단순한 기술 문제가 아니라 리스크 관리 문제라는 점을 명확히 이해한다. 따라서 적절한 가드레일 설정, 모니터링 체계, 위험 통제 전략을 통해 조직의 신뢰를 확보하는 것이 필수적이다.

필자는 구글 클라우드와 안델라에서 15년간 일하며 이러한 역량을 모두 갖춘 인재를 극소수만 만나왔다. 이들을 구분 짓는 요소는 단일 기술이 아니라 네 가지 핵심 역량의 결합이다.

첫째는 첫째는 문제 해결 능력과 판단력이다. AI의 출력은 대체로 80~90% 정확하지만, 나머지 10~20%는 오히려 더 위험한 오류를 포함할 수 있다. 때로는 그럴듯하게 보이지만 잘못된 결과이거나, 불필요하게 복잡해 실무 적용을 어렵게 만들기도 한다.

뛰어난 FDE는 이러한 오류를 식별할 수 있는 맥락적 이해를 갖추고 있다. 이들은 AI가 생성한 저품질 결과나, 중요한 비즈니스 제약을 무시한 권고를 빠르게 찾아낸다. 무엇보다 중요한 점은 이러한 리스크를 통제할 수 있는 시스템을 설계할 수 있다는 것이다. 출력 검증, 인간 개입 프로세스, 모델이 불확실할 때 작동하는 결정적 대체 응답 체계 등을 통해 위험을 관리한다. 이러한 역량이야말로 단순히 인상적인 데모와, 경영진이 실제 도입을 승인할 수 있는 운영 시스템을 가르는 결정적 차이다.

둘째는 솔루션 엔지니어링과 설계 역량이다. FDE는 비즈니스 요구사항을 기술 아키텍처로 전환하는 동시에 비용, 성능, 지연 시간, 확장성 등 현실적인 트레이드오프를 균형 있게 고려해야 한다. 특정 활용 사례에서는 추론 비용이 낮은 소형 언어모델이 최신 대형 모델보다 더 나은 성과를 낼 수 있으며, 이러한 선택을 기술적 완성도가 아닌 경제적 관점에서 설명할 수 있어야 한다.

무엇보다 중요한 것은 단순성을 우선하는 접근이다. 통합의 벽을 가장 빠르게 넘는 방법은 대부분 적절한 가드레일을 갖춘 최소기능제품(MVP)으로 전체 문제의 80%를 해결하는 데서 시작된다. 모든 예외 상황을 포괄하려다 통제 불가능한 리스크를 초래하는 복잡한 시스템이 아니라, 현실적으로 관리 가능한 수준의 솔루션이 더 효과적이다.

셋째는 고객 및 이해관계자 관리 역량이다. FDE는 비즈니스 조직과의 주요 기술 접점 역할을 수행하며, AI 경험이 많지 않은 경영진에게 기술적 작동 원리를 설명해야 한다. 다만 이들이 실제로 주목하는 것은 기술 자체가 아니라 리스크, 일정, 그리고 사업적 영향이다.

바로 이 지점에서 FDE는 조직의 신뢰를 확보하고, AI를 실제 운영 환경으로 확장할 수 있는 기반을 마련한다. FDE는 비결정적 AI의 특성을 경영진이 이해할 수 있는 리스크 프레임워크로 전환한다. 예를 들어 문제 발생 시 영향 범위는 어디까지인지, 어떤 모니터링 체계가 구축돼 있는지, 그리고 롤백 계획은 무엇인지 등을 명확히 제시한다. 이러한 과정은 AI의 불확실성을 가시화하고 관리 가능한 형태로 전환함으로써, 리스크에 민감한 의사결정자들이 이를 수용할 수 있도록 만드는 핵심 역할을 한다.

넷째는 전략적 정렬 능력이다. FDE는 AI 구현을 측정 가능한 비즈니스 성과와 직접 연결한다. 어떤 기회가 실제 성과를 만들어낼 수 있는지, 혹은 기술적으로는 흥미롭지만 가치 대비 과도한 리스크를 수반하는지에 대해 판단하고 조언한다.

또한 초기 도입 단계뿐 아니라 운영 비용과 장기적인 유지보수까지 함께 고려한다. 이러한 사업 중심의 시각에 더해, 리스크를 객관적으로 평가하는 능력이 결합될 때 비로소 FDE는 단순히 뛰어난 소프트웨어 엔지니어를 넘어서는 차별화된 역할을 수행하게 된다.

이 네 가지 역량을 모두 갖춘 인재는 공통된 특성을 보인다. 대부분 개발자 등 기술 중심 직무에서 커리어를 시작했고, 컴퓨터공학 기반의 교육을 받았을 가능성이 높다. 이후 특정 산업에 대한 전문성을 쌓고, 빠르게 변화하는 환경 속에서도 지속적으로 학습하는 유연성과 호기심을 갖추게 된다. 이러한 희소한 조합 때문에 이들은 주로 대형 기술 기업에 집중돼 있으며 높은 보상을 받는 경향이 있다.

CIO의 딜레마

FDE가 이처럼 희소한 자원이라면, CIO에게 남은 선택지는 무엇일까.

인재 시장에서 자연스럽게 공급이 늘어나기를 기다리는 방법이 있지만, 이는 상당한 시간이 필요하다. 그 사이 AI 프로젝트가 통합의 벽에서 멈춰 있는 매달, 실제 가치를 창출하는 기업과 여전히 이사회에 데모만 보여주는 기업 간 격차는 더욱 벌어진다. AI의 비결정적 특성은 앞으로도 사라지지 않는다. 오히려 모델 성능이 향상될수록 예측 불가능한 행동의 가능성은 더 커질 수 있다. 결국 성공하는 기업은 기술이 완전히 무위험 상태가 되기를 기다리는 조직이 아니라, AI를 책임감 있고 자신 있게 운영 환경에 적용할 수 있는 내부 역량을 갖춘 조직이다.

대안은 내부에서 FDE를 육성하는 것이다. 이는 채용보다 더 어렵지만, 확장 가능한 유일한 해법이다. 다행히 FDE 역량은 체계적으로 개발이 가능하다. 적절한 인재 풀과 집중적이고 구조화된 교육이 필요하다. 안델라(Andela)는 경험 많은 엔지니어를 FDE로 전환하는 교육 과정을 구축했으며, 이를 통해 효과적인 방법론을 축적해왔다.

FDE 인재 풀 구축 전략

우선 적합한 후보자를 선별하는 것이 중요하다. 모든 뛰어난 엔지니어가 FDE로 전환할 수 있는 것은 아니다. 기술 영역을 넘어서는 호기심을 갖춘 숙련된 소프트웨어 엔지니어를 찾아야 한다. 기본적인 개발 역량이 탄탄하고 데이터 과학과 클라우드 아키텍처에 대한 경험이 있는 인재가 적합하다. 특히 특정 산업에 대한 이해는 빠른 적응을 돕는 중요한 요소다. 의료 규제나 금융 리스크 프레임워크에 대한 경험이 있는 인재는 해당 분야를 처음 배우는 경우보다 훨씬 빠르게 성장할 수 있다.

기술 교육 과정은 세 단계로 구성된다. 기초 단계에서는 AI와 머신러닝에 대한 기본 이해를 다진다. LLM 개념, 프롬프트 설계 기법, 파이썬 활용 능력, 토큰 구조, 기본적인 에이전트 아키텍처 이해가 포함된다. 이는 기본 역량에 해당한다.

중간 단계는 실전 도구 활용 역량이다. FDE가 수행하는 ‘세 가지 역할’에 대응하는 핵심 기술이 요구된다.

  • 첫째는 RAG(검색 증강 생성)로, 기업 데이터와 모델을 정확하고 안정적으로 연결하는 능력이다.
  • 둘째는 에이전트형 AI로, 다단계 추론과 작업 흐름을 적절한 통제와 검증 단계와 함께 설계하는 역량이다.
  • 셋째는 운영 환경 대응 능력으로, 모니터링 체계와 가드레일, 장애 대응 프로세스를 갖춘 상태에서 솔루션을 실제 배포할 수 있어야 한다.

이러한 역량은 실제 운영 환경의 리스크를 고려한 시스템을 직접 구축하고 배포하는 과정을 통해 습득된다.

고급 단계에서는 모델 내부 구조와 파인튜닝 등 심화 지식을 익힌다. 이는 표준적인 접근 방식이 통하지 않을 때 문제를 해결할 수 있는 능력으로 이어진다. 단순히 정해진 절차를 따르는 수준을 넘어, 새로운 상황에 맞춰 즉각적으로 대응할 수 있는 역량이다. 또한 보안 책임자(CISO)와 같은 이해관계자에게 특정 접근 방식의 안전성을 설명할 수 있는 수준의 전문성이 요구된다.

기술 역량만큼 중요한 것이 비기술적 역량이다. FDE는 기술 중심의 대화에서 벗어나 비즈니스 문제와 리스크 완화 중심으로 논의를 재구성할 수 있어야 한다. 프로젝트 범위 변경, 일정 지연, 비결정적 시스템의 불확실성 등 민감한 이슈를 포함한 고난도 이해관계자 관리도 필수다. 무엇보다 중요한 것은 판단력이다. 불확실한 상황에서도 합리적인 결정을 내리고, 새로운 유형의 기술 리스크를 받아들여야 하는 경영진에게 신뢰를 줄 수 있어야 한다.

조직과 후보자 모두에게 현실적인 기대치를 설정하는 것도 중요하다. 아무리 체계적인 프로그램을 갖추더라도 모든 인재가 FDE로 전환되는 것은 아니다. 그러나 소수의 FDE 인재만 확보하더라도 통합의 벽을 넘는 속도는 크게 빨라질 수 있다. 실제로 비즈니스 조직에 배치된 한 명의 FDE는, 비즈니스 맥락 없이 분리된 환경에서 일하는 다수의 기존 엔지니어보다 더 큰 성과를 낼 수 있다. 이는 문제의 본질이 기술이 아니라는 점을 FDE가 정확히 이해하고 있기 때문이다.

AI 시대의 승부처

FDE 역량을 확보한 기업은 통합의 벽을 넘어설 수 있다. 이들은 인상적인 데모를 실제 가치를 창출하는 운영 시스템으로 전환하고, 성공 경험을 바탕으로 조직의 신뢰를 점진적으로 확대해 나간다.

반면 이러한 역량을 확보하지 못한 기업은 AI 투자에도 불구하고 실질적인 성과를 내지 못한 채 정체 상태에 머물 가능성이 크다. 그 사이 더 높은 리스크를 감수하는 경쟁 기업들이 시장을 선점하게 된다.

안델라에 합류할 당시 필자는 AI가 인간의 역량을 완전히 대체하지는 못할 것이라고 판단했다. 지금도 그 생각은 변함없다. 다만 인간 역시 진화해야 한다. FDE는 그 진화의 방향을 보여주는 대표적인 인재상이다. 깊이 있는 기술 이해, 비즈니스 감각, 리스크 관리 능력, 그리고 지속적인 변화에 대응하는 유연성을 모두 갖춘 존재다.

AI 시대에 CIO가 지금 이 역량에 투자한다면, 단순히 기술 발전 속도를 따라가는 수준을 넘어 그동안 쉽게 확보되지 않았던 기업의 AI 가치를 실질적으로 실현하는 주체가 될 수 있다.
dl-ciokorea@foundryco.com

OpenAI, Anthropic expand services push, signaling new phase in enterprise AI race

OpenAI and Anthropic are expanding their reach into professional services through joint ventures and acquisition talks, moving model providers closer to implementation roles traditionally held by systems integrators.

Joint ventures tied to the two AI companies have held talks to acquire services companies that help businesses deploy AI, with OpenAI’s venture in advanced stages on three deals, Reuters reported.

The companies are reportedly looking to add engineers and consultants as enterprise customers try to move generative AI from experiments into production.

Separately, Anthropic announced plans for a new enterprise AI services company backed by Blackstone, Hellman & Friedman, and Goldman Sachs, aimed at helping mid-sized businesses bring Claude into core operations.

Anthropic said its applied AI engineers will work with the new company’s engineering team to identify use cases, build custom systems, and support customers over time.

Market drivers for services expansion

For CIOs, the issue is whether AI vendors are starting to take over more of the work traditionally handled by consulting firms, systems integrators, and managed service providers. The developments suggest model providers want a stronger hand in enterprise AI implementation, even as systems integrators remain central to large-scale rollouts.

The push also reflects a problem many CIOs are already facing: AI pilots can be launched quickly, but turning them into secure production systems usually requires months of integration and process work.

“IT deployments in the enterprise domain have been consultations or advisory-driven,” said Faisal Kawoosa, founder and chief analyst at Techarc. “So if they have to expedite adoption, because that is where the real money is, they will have to align with the existing framework and go-to-market model of enterprises.”

Kawoosa said AI companies are currently at the top of the value chain and want to remain “in the driver’s seat” rather than become just another IT vendor.

Deepika Giri, head of research for AI, analytics, and data for Asia Pacific at IDC, said the shift could point to a broader restructuring of enterprise AI.

“AI model providers are moving beyond being platform vendors to actively shaping the entire AI value chain,” Giri said. “By expanding into implementation, consulting, and managed services, they are positioning themselves closer to enterprise outcomes rather than just supplying underlying technology.”

Kawoosa added that some IT services companies may be cautious about AI because the technology is still unreliable, and because wider adoption could weaken their role in enterprise IT projects. “With this change in go-to-market strategy, AI players are taking charge,” he said.

Lower deployment risk, but deeper lock-in

Buying AI services directly from model providers could make early deployments easier.

The process could reduce deployment risk in the short term because enterprises get tighter integration and access to specialized expertise, said Tulika Sheel, senior vice president at Kadence International.

But that convenience could come with a longer-term trade-off.

“It also creates deeper dependency across the stack, from models to data pipelines and workflows,” Sheel said. “Over time, this could increase lock-in, making it harder to switch vendors without significant disruption.”

AI model providers are trying to become a “one-stop shop” for enterprises by tying AI applications and services more closely to their usage-driven business models, according to Neil Shah, VP for research and partner at Counterpoint.

“Controlling the application and services layer allows them to lock in enterprises and also benefit from optimizing the model better by understanding the enterprise needs, pain points, and way of working firsthand,” Shah said.

Lock-in is not inevitable, according to Giri, but avoiding it requires CIOs to make deliberate architecture choices early.

“While the model layer can increasingly be abstracted through modular architectures, avoiding lock-in requires deliberate design choices,” Giri said. “Without that, organizations risk becoming dependent not just on a model, but on the entire stack: data pipelines, workflows, and governance frameworks tied to a specific provider.”

The move also shows why enterprise AI requires so much hands-on implementation work, according to Sheel. Generative AI platforms may be powerful, but they still require significant enterprise adaptation before they can support real business processes.

“Enterprise AI isn’t plug-and-play because it needs deep integration with internal data, workflows, and governance systems,” Sheel said. “This highlights a gap between model capability and real-world deployment.”

That might prompt CIOs to consider not only which AI model performs best, but also who will control the implementation path once those models are embedded in enterprise systems.

How UKG puts AI to work for frontline employees

As organizations rebrand themselves as AI companies, most of the conversation is focused on knowledge workers rather than the people in retail, manufacturing, and healthcare who can benefit from AI just as much. Prakash Kota, CIO of UKG, one of the largest HR tech platforms in the market, which delivers a workforce operating platform utilized by 80,000 organizations in 150 countries, explains how his company uses agentic AI, voice agents, and a democratized innovation framework to transform the frontline worker experience, and why the CIO-CHRO partnership is critical to making it stick.

How do you leverage AI for growth and transformation at UKG?

UKG is one of the largest HR, pay, and workforce management tech platforms in the market, and our expertise is in creating solutions for frontline workers, which account for 80% of the world’s workforce. This is important because when companies rebrand themselves as AI for knowledge workers, they’re not talking about frontline workers. But people in retail, manufacturing, healthcare, and so on also benefit from AI capabilities.

So the richness of our data sets, and our long history with the frontline workforce, positions us well for AI driven workforce transformation. 

What are some examples?

We use agentic AI for dynamic workforce operations, which shows us real-time labor demand. Our customers employ thousands of frontline workers, and the timely market insights and suggested actions we give them are new and valuable.

We also provide voice agents. Traditionally, when a frontline worker requests a shift, managers would review availability, fill out paperwork or update scheduling software, and eventually offer an appropriate job. With voice agents, AI works directly with the frontline worker, going through background and skills validation, communication, and even workflow execution. The worker can also ask if they can swap shifts or even get advice on how to make more money in a particular month. This is where AI changes the entire frontline worker experience.

We also launched People Assist, an autonomous employee support agent. Typically, when an employee is onboarded, IT and HR need to trigger and approve workflows. People Assist  not only tracks workflows, but also performs those necessary IT and HR onboarding activities so new employees are productive from day one.

What framework do you use to create these new capabilities?

For internal AI usage for our own employee experience, we use an idea-to-implementation framework, which involves a community of UKG power users who are subject matter experts in their area. Ideas can come from anybody, and since we started nine months ago, more than 800 ideas have been submitted. The power users set our priorities by choosing the ideas that will make the most impact.

Rather than funneling ideas through a small central team — a linear process that kills momentum — we’ve democratized innovation across the business. We give teams the governance frameworks, change models, and risk guardrails they need to move quickly.  With AI, the most important thing isn’t to launch, but to land.

But before we adopted the framework, we defined internal personas so we could collaborate with different employee groups across the company, from sales to finance.

With the personas and the framework, we can prioritize ideas by persona, which also facilitates crowd sourcing. You’re asking an entire persona which of these 10 ideas will make their lives better, rather than senior leaders making those decisions for them.

Why do so many CIOs focus on personas for their AI engine?

Across the enterprise, every function has a role to play. We hire marketing, sales, and finance for a particular purpose. Before AI, we gave generic packaged tools to everyone. AI allows us to build capabilities to make a specific job more effective. Even our generic AI tools are delivered by persona. Its impact on specific roles is the reason personas are so important right now. Our focus is on the actual jobs, the people who do them, the skills and tasks needed, and the outcomes they want to achieve.

We know our framework and persona focus work from employee data. In our most recent global employee engagement survey, 90% said they’re getting the right AI tools to be effective. For the AI tools we’ve launched broadly across the company, eight out of 10 employees use them. For me, AI isn’t about launching 10,000 tools, because if no one uses them, it’s just additional cost for the CIO and the company.

Is the build or buy question more challenging in this nascent stage of AI?

The lifecycle of technology has moved from three years to three hours, so whenever we build at UKG, we use an open architecture, which allows us to build with a commercial product if one comes on the market.

Given the speed of innovation, we lean toward augmentation rather than build. There are areas, like our own native products, where a dedicated engineering team makes sense. But for most of our AI capabilities — customer support and voice agents, for example — we work with our vendor partners. We test and learn with multiple vendors, and decide on one usually within two weeks.

This is what AI is giving all CIOs: flexibility, rapid adoption, interoperability, and the ability to quickly switch vendors. It’s IT that’s very different from what it used to be.

Given the shift to augmentation, how will the role of the software engineer change?

For software builders, business acumen — the ability to understand context — is no longer optional. In the past, the business user would own the business context, and the developer, who owns the technology, brings that business idea to life. Going forward, the builder has the business context to create the right prompts to let AI do the building, and the human in the loop is no longer the technology builder, but the provider of context, prompts, and validation of the work. So the engineer doesn’t go away, however they now finish a three-week scope of work in hours. With AI, engineers operate at a different altitude. The SDLC stays, but agility increases where a two-week concept compresses into two days.

At UKG, you’re directly connected to the CHRO community. What should they be thinking about how the workforce is changing with AI?

The best CHROs are thinking about the skills they’ll need for the future, and how to train existing talent to be ready. They’re not questioning whether we’ll need people, but how to sharpen our teams for new roles. The runbooks for both IT and HR are evolving, which is why the CIO-CHRO partnership has never been more critical to create the right culture for AI transformation.

CIOs can deliver a wealth of employee data like roles, skillsets, and how people spend their time. And as HR leaders help business leaders think through their roadmap for talent —  both human and AI — IT leaders can equip them with exactly that intelligence.

What advice would you give to CIOs driving AI adoption?

Invest in AI fluency, not just AI tools. Your people don’t need to become data scientists, but they do need a new kind of literacy — the ability to work alongside AI, question its outputs, and know when to override it. That’s a training and culture investment, not a software investment.

And redesign work before you redeploy people. Don’t just drop AI into existing workflows. Use this moment to ask what work really matters. AI is forcing us to have the job design conversations we should’ve had years ago, so it’s important to be transparent about the journey. What’s killing workforce trust now is ambiguity. Your people can handle hard truths but not silence. Leaders who communicate openly about where AI is taking the organization will retain the talent they need to get there.

The AI assessment gap: Why your hiring process can’t find the talent you need

The next time someone on your team says, ‘hire an AI engineer,’ stop the conversation.

That title is too vague because it fails to account for critical differences in engineering strengths. Instead, companies need to decide specifically what they need. Is it someone to rapidly prototype AI solutions? Or someone to build the solution that makes it ready for production? Or someone to design the supporting capabilities and infrastructure to scale it? These are all different skills and require different assessments during the hiring process.

But here’s where companies also fall short. Assessing skills is hard and assessments, as we know them, are broken when it comes to AI. They’re misaligned with what AI roles actually demand. That misalignment is what I call the AI assessment gap.

Where the gap lives

Most technical assessments were built for a pre-AI world: Coding proficiency, algorithms, deterministic system design. These are skills tests. They confirm that an engineer can do the work. But they don’t tell you whether that engineer has the technical taste to make the right decisions when building, scaling or deploying AI systems in production.

In conversations with enterprise engineering leaders, we’re hearing that candidates are now running AI agents during live interviews, getting textbook-perfect answers fed to them in real time. If your assessment can be passed by an AI whispering in someone’s ear, it was never testing for the right thing. Skills can be faked or augmented. Taste can’t.

To see what this looks like, consider this scenario: An enterprise needs someone with deep experience in a specific data platform. A candidate passes the data engineering assessment. They get to the client interview, and the hiring manager says: ‘Tell me about a time you had to make a hard tradeoff in designing a streaming architecture.’ The candidate defines every concept involved. They don’t have the taste to explain why one approach would be dramatically better than another for a specific context. They’re out.

This happens because most assessment pipelines only test for skills: Can they code, understand the fundamentals? Nobody is systematically testing for technical taste: Can this person make better-than-default decisions about architecture, tooling and approach? That question only surfaces once someone with real experience asks it. By then, everyone has wasted time and the role is still open.

Traditional job postings compound the problem by filtering for ‘5+ years of AI experience,’ which screens out strong candidates because the category itself is only a few years old. What matters at the AI layer isn’t tenure. It’s the depth and specificity of what someone has built, deployed or scaled in production. Meanwhile, seniority at the foundational role level still matters in the ways it always has: A senior engineer brings architectural judgment that can’t be shortcut. The mistake is applying years-of-experience filters to the AI layers, where the work hasn’t existed long enough for tenure to be a meaningful signal.

One of the most telling signals of a broken assessment process: When stakeholders simultaneously complain that assessments are too hard and too easy. That’s not a calibration problem. It means the assessments aren’t measuring the right things in the first place. They’re testing for skills when they should be testing for taste.

Start with the work, not the title

To close the AI assessment gap, decompose the problem before you assess and decompose the need across the dimensions that actually determine whether someone can do the job. For example:

DimensionThe QuestionWhat It DeterminesHow You Evaluate
RoleWhat technical domain does the work live in?Foundational skills and stack (e.g., backend engineer, Python, PostgreSQL)Skills assessment: Project-based or simulation-based filter that confirms core engineering competency
SeniorityWhat level of judgment and autonomy does this work require?Engineering maturity, depth of technical taste, ability to operate under ambiguityExperience depth at the role level: Years of practice in the domain, complexity of systems designed and shipped
AI Engagement PatternHow will this person engage with AI systems?The specific technical taste required (e.g., Prototyper needs taste for sensing value; Builder needs taste for architecture and integration decisions; Scaler needs taste for performance, governance and risk tradeoffs)Applied assessments: Not ‘define RAG’ but ‘given this use case, which approach would you choose and why?’ Testing for tradeoff reasoning, not terminology

This decomposition replaces the single job description with a structured picture of what you actually need. It also immediately reveals whether you’re looking for one person or a team. If the project requires rapid prototyping to find value and then a production build, you probably need engineers with different profiles–not one ‘AI engineer’ who’s supposed to do both.

Three things most enterprises get wrong

  1. They test for skills when they should test for taste. Most assessments confirm that an engineer can write code and define concepts. They don’t test whether that engineer can make the architectural and tooling decisions that actually determine project success. An engineer who knows what agentic search is and an engineer who knows when agentic search is the right choice for a specific problem are two completely different hires. The first passes your skills test. The second delivers in production.
  1. They conflate skills with experience. A skills assessment tells you if someone can do the work. An experience validation tells you if someone has done the work in the specific context the job demands. These require completely different evaluation methods. When companies try to test both with a single instrument, they get the ‘too hard and too easy’ paradox: The assessment is simultaneously screening out competent people and letting through candidates who can’t perform. Seniority and years of experience are meaningful at the role level, where 10 years of backend engineering builds real architectural judgment and compounds technical taste. They’re much less meaningful at the AI engagement layer, where the work itself is only a few years old and depth of hands-on exposure matters more than calendar time.
  1. They treat assessment as a snapshot. The traditional model is a one-time gate: Pass or fail, in or out. In an AI world where skills are evolving monthly, that approach breaks down fast. Six months ago, almost nobody was shipping production code with agentic tools like Claude Code. Model Context Protocol, which lets AI systems plug into enterprise tools and data sources, was barely on anyone’s radar. Now enterprises are hiring for these skills specifically. Six months from now, the list will change again.

That means an assessment built in January is already partially stale by June. Companies that treat assessment as a living system, continuously updated by performance signals from real engagements, will consistently field better talent than those running the same tests they built a year ago.

The reskilling imperative

The reality is, there is no way to close this gap through hiring alone. The supply of engineers who already have the technical taste for AI work is a tiny fraction of what the market demands.  For example, since the launch of ChatGPT in 2022, demand for roles that require more analytical, technical or creative work has increased by 20%.

Which means enterprises have to reskill and upskill existing workforces. And without a targeted approach mapped to actual needs, AI upskilling efforts often fail, leaving employees unsupported and initiatives stalled.

This is where the multi-dimensional model pays off beyond hiring. The same framework that powers your talent acquisition also powers your training strategy. Assessment results don’t just filter candidates in or out. They generate a heat map of where your workforce is strong and where it’s thin, across every dimension: Role competency, seniority depth and the specific technical taste required for prototyping, building or scaling AI systems. That heat map becomes your training roadmap.

Most companies skip this entirely and jump straight to ‘let’s buy an AI training program.’ Without that foundation, even the best training program is solving the wrong problem.

Ever ready

In the world of AI, the most critical skill might be knowing that you don’t and can’t possibly know everything. Or even what’s coming next. The roles we need today will look different in six months. The skills taxonomies we build now will need constant revision. The assessments we deploy this quarter will need recalibration by next quarter.

Companies that accept this reality and build nimble, multi-dimensional approaches to talent assessment will find something valuable: The technical taste they need already exists in their workforce, hiding behind outdated job descriptions and misaligned tests. CIOs must actively audit these descriptions to eliminate the traditional experience filters that mask the latent talent already sitting on their teams.  The others will keep posting for ‘AI engineers’ and wondering why nobody who gets hired can actually do the job.

This article is published as part of the Foundry Expert Contributor Network.
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중국 법원 “AI 도입은 해고 사유 아니다”…기업 인력 전략에 경고

중국 법원이 기업이 단순히 인공지능(AI)으로 인력을 대체하기 위해 직원을 해고하는 것은 허용되지 않는다고 결론 내렸다. 이에 따라 자동화 기반 해고를 정당화하려는 기업의 접근 방식에도 제약이 불가피해질 전망이다.

법원 문서에 따르면, 해당 사건은 일부 업무가 자동화된 직원이 급여가 크게 삭감된 이후 재배치를 거부했고, 결국 해고에 이르게 된 사례다.

사건에 대한 중국 법원 입장을 인용한 블룸버그는 “회사가 제시한 해고 사유는 사업 축소나 운영상의 어려움과 같은 부정적 상황에 해당하지 않았으며, ‘고용 계약을 지속할 수 없는’ 법적 요건도 충족하지 못했다”라고 전했다.

항저우 중급인민법원은 AI 도입이 중국 노동법상 고용 계약 해지를 정당화하는 ‘객관적 상황의 중대한 변화’에 해당하지 않는다고 판단했다. 또한 기업이 제시한 해고 사유 역시 법적 기준에 미치지 못했다고 결론 내렸다.

이번 판결은 그동안 기업들이 운영 문제로만 여겨온 사안, 즉 AI로 인한 효율성 향상이 추가적인 의무 없이 곧바로 인력 감축으로 이어질 수 있는지에 대해 법적 관점에서 재조명했다.

AI는 ‘법적 사유’ 아닌 ‘경영 판단’

이번 판결의 핵심은 법원이 AI 도입을 해석하는 방식의 변화에 있다. 법원은 자동화를 외부 충격으로 보지 않고, 기업의 경영 판단으로 규정했다. 이에 따라 변화에 따른 부담을 직원에게 자동으로 전가할 수 없다는 입장을 분명히 했다.

그레이하운드 리서치의 수석 애널리스트 산치트 비르 고기아는 “법원이 자발적인 자동화 선택을 예측 불가능한 외부 충격처럼 간주해 해고를 정당화하던 기업의 관행을 제한했다”고 설명했다.

이 같은 구분은 상당한 의미를 가질 수 있다. 고기아는 “AI가 통제 불가능한 사건이 아닌 전략적 선택으로 간주될 경우, 기업은 직무를 없애기 전에 협의, 재교육, 합리적인 재배치 등 적법한 절차를 입증해야 할 필요가 있다”고 분석했다.

이번 항저우 판결은 중국 외 지역에서 법적 선례로 적용되지는 않지만, 그 논리는 다른 시장으로 확산될 가능성이 높다. 고기아는 “인도, 영국, 미국 등에서는 판결이 직접적인 구속력을 갖지 않지만, 노동자 측에서는 AI 도입이 기업의 선택이라는 점을 강조하며, 기업이 그에 따른 책임을 입증해야 한다는 논리를 보다 명확하게 제기할 수 있게 됐다”고 말했다.

예를 들어 인도에서는 이미 기술 변화에 따른 구조조정 시 사전 통지와 보상이 요구된다. 이번 중국 판결은 이러한 구조조정이 법적 절차를 따라야 한다는 해석을 더욱 강화할 수 있다.

유럽과 영국 역시 사전 협의 의무와 자동화된 의사결정에 대한 제한이 존재하는 만큼, 기업이 해고를 정당화하기 위해 제시해야 할 입증 책임은 한층 높아질 것으로 보인다.

비용 절감 수단 넘어 ‘거버넌스 이슈’로

이번 판결은 CIO에게 중요한 변화를 시사한다. AI 중심 혁신이 더 이상 기술이나 효율성 논의에 그치지 않고, 거버넌스 영역으로 확장되고 있다는 점이다.

시장조사업체 카운터포인트 리서치의 부사장이자 파트너인 닐 샤는 “이번 판결은 글로벌 시장에서 AI 기반 해고에 이의를 제기할 수 있는 보다 합리적인 기준이 될 수 있다”고 평가했다.

샤는 이어 기업들이 AI 도입과 인력 전략 간의 정합성을 재검토해야 할 필요가 있다고 강조했다. 그는 “AI 시대에 인사 부서는 채용과 해고 중심에서 벗어나, 재배치와 교육, 재숙련에 더 집중하는 방향으로 전환해야 한다”고 말했다.

이 같은 변화는 기업들이 인력 구조조정과 병행해 AI 투자를 가속화하는 흐름 속에서 나타나고 있다. 오라클, 마이크로소프트(MS), 타타컨설턴시서비스(TCS) 등 주요 기업들의 최근 행보는 AI 중심 운영 모델에 맞춰 인력을 조정하는 전반적인 추세를 보여준다.

새로운 리스크 ‘문서화와 메시지 간 괴리’

이번 판결은 법리적 판단을 넘어, 기업이 직면할 수 있는 보다 현실적인 리스크로 ‘일관성’ 문제를 부각시켰다.

산치트 비르 고기아는 “이 같은 사례에서 발생하는 부담은 비용보다 거버넌스에 가깝다”며 “특히 AI 도입과 연계된 인력 의사결정을 어떻게 문서화하느냐가 핵심”이라고 지적했다.

실무적으로 기업은 특정 직무가 왜 폐지됐는지, 어떤 대안이 검토됐는지, 재배치나 재교육이 실제로 충분히 검토됐는지를 명확히 입증해야 할 가능성이 커졌다. 동시에 내부 의사결정 논리와 외부 커뮤니케이션 메시지 간의 정합성도 확보해야 한다.

이러한 정합성은 향후 주요 검증 대상이 될 수 있다. 투자자나 대외 발표에서는 AI로 인한 생산성 향상을 강조하면서, 내부적으로는 구조조정이나 역량 불일치를 해고 사유로 설명할 경우, 분쟁 발생 시 해당 서사가 문제로 지적될 수 있기 때문이다.

샤는 “기업들이 인력 변화에서 AI의 역할을 축소하거나 모호하게 표현하려 할 수 있지만, 규제 당국의 관심은 더욱 높아질 것”이라며 “근로자에 대한 경제적 영향을 반영하고 충분한 보상을 보장하기 위해 규제는 지속적으로 강화될 것”이라고 말했다.

달라지는 것과 변하지 않는 것

이번 판결이 기업의 AI 도입 속도를 늦출 가능성은 크지 않다. 자동화, 생성형 AI, 지능형 워크플로우에 대한 투자는 산업 전반에서 계속 확대되는 추세다.

다만 변화가 예상되는 부분은 이러한 의사결정이 실행되고 전달되는 방식이다.

기업들은 해고와 AI의 직접적인 연관성을 명시적으로 드러내기보다, 구조조정이나 역량 전환과 같은 표현으로 인력 변화를 설명하는 데 보다 신중해질 수 있다. 그러나 내부 문서와 대외 메시지 간 불일치가 발생할 경우, 이러한 접근은 또 다른 리스크로 이어질 수 있다.

CIO에게 주는 시사점은 분명하다. 이제 AI 이니셔티브는 단순한 효율성 향상만으로 평가할 수 없다. 법적 리스크, 인력 전략, 기업 거버넌스와 직접적으로 맞물리는 영역으로 확대되고 있다.

고기아는 “향후 분쟁의 초점은 AI의 경제적 가치 여부가 아니라, 기업이 AI를 도입하고 그에 따른 인력 영향을 관리하는 과정이 공정했는지를 입증할 수 있는지에 맞춰질 것”이라고 분석했다.
dl-ciokorea@foundryco.com

‘AI is more efficient’ is not enough reason to lay off staff, says Chinese court

Enterprises cannot terminate employees solely to replace them with artificial intelligence, a court in China has ruled, complicating how enterprises seek to justify automation-driven layoffs.

The case involved an employee whose role was partly automated, leading to a significant pay cut and their eventual dismissal after they refused reassignment, the court document said.

“The termination grounds cited by the company did not fall under negative circumstances such as business downsizing or operational difficulties, nor did they meet the legal condition that made it ‘impossible to continue the employment contract,’” according to a Bloomberg News translation of the court’s statement about the case.

The Hangzhou Intermediate People’s Court found that AI adoption does not constitute a “major change in objective circumstances” required under Chinese labor law to end an employment contract, and that the employer’s justification failed to meet the legal threshold for termination.

The decision casts a legal light on a question many enterprises have so far treated as operational: whether AI-driven efficiency gains can directly translate into workforce reductions without additional obligations.

AI as a business case, not a legal case

At the center of the ruling is a shift in how courts may interpret AI adoption.

Rather than treating automation as an external disruption, the court framed it as a management decision, one that does not automatically transfer the burden of change onto employees.

“The court has narrowed an increasingly popular corporate shortcut—treating a voluntary automation choice as if it were an unforeseeable external shock that automatically justifies dismissal,” said Greyhound Research chief analyst Sanchit Vir Gogia.

That distinction could prove significant. If AI is seen as a strategic choice rather than an uncontrollable event, employers may need to demonstrate due process — consultation, retraining, and reasonable redeployment — before eliminating roles, he said.

The Hangzhou ruling does not set legal precedent outside China, but its logic is likely to travel.

“The outcomes will not bind courts in markets like India, the UK, or the US,” Gogia said. “But worker-side counsel now have a sharper way to frame the argument—that AI adoption is a choice, and companies must show their work before passing the cost to employees.”

In India, for example, existing labor frameworks already require notice and compensation in cases of cut-backs linked to technological change. The Chinese ruling may reinforce interpretations that such restructuring must follow established statutory processes.

In Europe and the UK, where consultation requirements and limits on automated decision-making already exist, the evidentiary burden on employers could also increase.

A governance issue, not just a cost lever

For CIOs, the ruling underscores a broader shift: AI-led transformation is moving beyond a technology and efficiency discussion into governance.

“This ruling could set precedent as a more logical framework for global markets to challenge AI-driven layoffs,” said Neil Shah, VP for research and partner at Counterpoint Research.

He added that enterprises may need to rethink how workforce strategies align with AI adoption.

“The role of human resources will have to pivot from hiring and firing to more focus on reassigning, training and reskilling in the AI era,” Shah said.

That shift comes as enterprises continue to accelerate AI investments, often alongside workforce restructuring. Recent moves by companies such as Oracle, Microsoft, and Tata Consultancy Services reflect a broader trend of aligning headcount with AI-led operating models, developments that CIO audiences have been tracking closely.

The emerging risk: documentation and narrative gaps

Beyond legal doctrine, the ruling highlights a more immediate enterprise risk—consistency.

Gogia said the burden created by such cases is less about economics and more about governance, particularly around how companies document workforce decisions tied to AI adoption.

In practice, that means employers may need to clearly demonstrate why a role was eliminated, what alternatives were considered, and whether efforts such as redeployment or retraining were meaningfully explored before termination. Just as importantly, companies may need to ensure that their internal reasoning aligns with external messaging.

That alignment could become a point of scrutiny. Organizations that emphasize AI-driven productivity gains in investor or public communications while attributing layoffs internally to restructuring or skill mismatch may find those narratives challenged if disputes arise.

Shah said that while companies may attempt to soften or obscure AI’s role in workforce changes, regulatory attention is likely to increase.

“There will always be loopholes… but regulators will have to stay ahead to factor in the economic impact on workers and ensure robust severance,” he said.

What changes and what doesn’t

The ruling is unlikely to slow enterprise AI adoption. Investment in automation, generative AI, and intelligent workflows continues to accelerate across industries.

What may change is how those decisions are executed and communicated.

Enterprises could become more cautious about explicitly linking layoffs to AI, instead framing workforce changes in terms of restructuring or capability shifts. But that approach carries its own risks if documentation and public messaging diverge.

For CIOs, the implication is clear: AI initiatives can no longer be evaluated solely on efficiency gains. They now intersect directly with legal exposure, workforce strategy, and corporate governance.

As Gogia put it, the next phase of disputes will not focus on whether AI has economic value, but on whether companies can demonstrate that their process for adopting it and managing its workforce impact was fair.

The rise of the double agent CIO

CIOs of B2B SaaS companies are just as responsible to represent technology as they are to run it. In an environment where the buyer is often another CIO, however, the role becomes something fundamentally different. It’s no longer confined to internal execution. It extends into the market, customer conversations, and the moments that ultimately shape revenue, trust, and long-term relationships. So the modern SaaS CIO operates as a true double agent, running the business from within while representing it to the market.

Box CIO Ravi Malick sits squarely in that duality. After serving as CIO of Vistra Energy, a company defined by legacy systems and industrial scale, he stepped into a digitally native, founder-led SaaS business in 2021 where technology is inseparable from the business itself. He now leads internal tech while engaging directly with CIOs of companies evaluating Box, bringing a perspective shaped by both worlds. What stands out in Malick’s perspective isn’t how different the role is, but how much more expansive it’s become.

What stays the same, what evolves

The core tension of the CIO role hasn’t changed. “There’s always more demand than you have the capacity or funding for,” Malick says. Prioritization, alignment to business strategy, and the constant need to modernize while operating at scale still define the job. The difference, however, is the environment in which those challenges now exist.

At Box, Malick operates inside a workforce where technology fluency is high and expectations are even higher. “I partner with 3,000 technologists who love to solve problems with technology,” he says. That creates a powerful advantage, but also a new kind of pressure. Demand for tools, platforms, and innovation is constant, and AI has only accelerated it.

That dynamic is further shaped by Box’s leadership. As a founder-led company, technology conversations extend well beyond the CIO’s organization. “It’s a different dynamic when your CEO is a founder and a technologist,” Malick says. “You’re as much a steward of incoming ideas as you are a generator of them.” That relationship creates both pace and perspective, requiring the CIO to operate as both orchestrator and partner in shaping how technology evolves across the business.

In that context, the CIO is leading within a highly informed, highly engaged organization where expectations for speed and innovation are constant. The challenge isn’t modernization as a one-time effort, but ensuring the tech stack continuously evolves and scales with the business.

Balancing the internal mandate with external pull

What truly differentiates the role in SaaS is what happens outside the enterprise, and the pressure that comes with it. The CIO is still accountable for running IT, ensuring security, and maintaining operational excellence. At the same time, there’s growing expectation to show up externally, engage customers, and directly support revenue.

Malick doesn’t present that balance as seamless. “It’s a daily challenge,” he says. “But sometimes not balanced so well.” There’s a constant push and pull between internal priorities and external demands, and in many cases, revenue pulls hard. The opportunity to influence deals, build relationships, and contribute to growth elevates the strategic importance of the role, but it doesn’t remove the responsibility for the day job.

What allows Malick to operate effectively in both worlds is the strength of the foundation behind him. He points to the maturity of his leadership team, operating model, and internal processes as critical enablers. With clear structures, strong leaders, and disciplined execution in place, he has the bandwidth to spend meaningful time externally. It isn’t always a perfect balance, but it’s a deliberate one.

From operator to peer in the market

Through Box’s customer zero program, Box on Box, Malick operates as both CIO and practitioner, bringing firsthand experience into customer conversations. “I can take how we build at Box to customer conversations,” he says. That perspective shifts the dialogue away from product positioning, and toward the realities of execution.

In a market where CIOs are constantly being pitched, that distinction carries weight. “They want to know how it works from the perspective of someone managing it,” he says, adding he leans into that by being transparent about both successes and missteps. “We share the challenges and false starts we’ve managed through.”

That candor builds credibility, and credibility builds trust. After all, people buy from people they trust, and in enterprise technology, says Malick, peer-to-peer conversations are a faster path to trust than demos. 

The external dimension of the role also holds a symbiotic relationship with internal responsibilities. Malick brings customer conversations back into Box, using them to inform how he thinks about technology decisions and broader strategy. He describes the CIO community as uniquely open, even therapeutic, where leaders candidly share challenges and exchange ideas. That openness creates a feedback loop where external insights sharpen internal execution, and internal experience strengthens external credibility.

What this means for the CIO role

What makes Malick’s perspective especially relevant is that the lesson isn’t limited to SaaS. As technology becomes more central to growth, customer experience, and business model change, CIOs in every industry are being pulled closer to the front office. The shift is about becoming more fluent in how technology translates into trust, speed, and commercial impact, not just becoming more visible.

For Malick, one of the biggest lessons is the role now demands a different kind of leadership than many CIOs were originally trained for. “Don’t make assumptions, and don’t assume something’s easy or intuitive,” Malick says. In a world where technology is reshaping how people work in real time, communication becomes a strategic discipline. CIOs have to explain change, absorb feedback, and keep translating between technical possibility and business reality.

The rise of AI adds another dimension to the double agent role. CIOs are building the content foundation that AI needs to be effective, and ensuring the organization can experiment with AI without sacrificing compliance or control. In a fast-paced technology company, ideas, opinions, and new technologies come from every direction. So the CIO isn’t simply the expert with the answers but often the one managing velocity itself, deciding where to push and where to hold.

“You have to figure out when you need to be in the fast lane and when you don’t,” Malick says. That kind of judgment is becoming more critical as technology moves to the center of the business, and it’s another reason CIOs are stepping into CEO and COO roles.

As AI accelerates the pace of change and creates the potential to decouple revenue growth from headcount growth, that ability to manage speed, scale, and tradeoffs becomes a defining leadership capability. That’s why the SaaS CIO should matter to leaders far beyond software. With AI transforming every industry, the role is becoming a preview of where the profession is headed — not just to run technology, but help shape how the company grows, how it shows up in the market, and how it earns trust. The double agent CIO may sound like a SaaS phenomenon. Increasingly, though, it looks more like the future of the job.

AI won’t fix tech talent gaps — but YOU can

Every CIO I talk to — and I talk to a lot of them — agrees that skills-first hiring makes sense. And with AI now embedded in nearly every stage of the hiring process, from resume screening to candidate matching, many assume the technology will finally make it happen at scale.

It won’t. AI can accelerate hiring decisions, but it can’t fix the underlying systems that power those decisions.

Despite initial progress in removing college degree requirements from job postings, many organizations are still getting it wrong — and AI is giving them new ways to get it wrong faster. Agreeing on a principle isn’t the same as operationalizing one. Even when there’s a skills-first hiring strategy in place, execution fails if IT, HR and business leaders aren’t aligned on outcomes, accountability and measurement. When AI screening tools are layered on top of misaligned systems, the result isn’t smarter hiring; it’s automated bias with a veneer of objectivity.

Why skills-first hiring became a buzzword

The idea of prioritizing skills in hiring decisions isn’t new. Competency-based hiring has been discussed in talent management circles since the 1970s. Over the last two decades, the growing technology skills gap, the explosion of non-traditional learning pathways and the broader recognition of “degree inflation” pushed skills-based hiring into the mainstream. Large employers publicly dropped degree requirements. States followed. Everyone was buzzing about skills-first hiring.

But buzz doesn’t change systems. Data from Indeed showed a decrease in job postings with college degree requirements between 2019 to 2024, but by November 2025, the number swung back up, nearly erasing the gains of the previous five years. 

Meanwhile, the skills that matter most now — prompt engineering, AI tool fluency, the ability to scope and complete AI-augmented projects — are being developed outside traditional degree programs. That makes degrees an even worse proxy for career readiness.

Announcing that an organization is “skills-first” without redesigning the infrastructure that surrounds hiring — job descriptions, applicant screening, recruiter training, interview rubrics and onboarding frameworks — doesn’t change practices. A recent survey by the University of Phoenix found that 69% of hiring decision makers believe there’s still too much focus on college degrees, with little clarity on what they should evaluate instead.

The 3 most common failure points

From my experience in working with CIOs on their entry-level talent pipelines, skills-first initiatives tend to break down in one or more of these places: job descriptions, screening tools and internal skepticism.

First, take job descriptions. Hiring managers tend to default to historical templates — pasting in degree requirements and years-of-experience thresholds that were never validated against actual job performance and are even less relevant with AI in the mix.

Second, screening tools. Nearly 90 percent of companies are using some form of AI to screen candidates, expecting greater efficiency and less bias. But AI screening tools learn from existing hiring data — which, if biased, just means that bias is now automated. Patterns in successful candidates’ backgrounds get baked into future decisions, except now, these decisions appear “data-driven” and neutral instead of more obviously predicated on certain hiring managers’ preference for college graduates.

The third failure point is internal skepticism — and the training gap that feeds it. According to the survey by the University of Phoenix, one in four non-HR managers receives no training before interviewing job candidates, even when the final hiring decision is theirs. Without shared definitions of what “skills-first” means and clear accountability, the initiative collapses under the weight of individual discretion.

What CIOs see that others miss

CIOs are often closer to the consequences of a bad hire than anyone else in the C-suite. When a cybersecurity analyst freezes during an incident response, the CIO gets a front-row view of the damage a skills gap can cause.

CIOs are also watching AI redefine what “qualified” looks like in real time. The engineer who deploys an agentic AI system to automate monitoring, or the analyst who chains multiple AI tools into a custom workflow — these people deliver outsized value, and their qualifications often look nothing like what a traditional job description demands.

And CIOs have a keen understanding of issues with tech talent pipelines. Projects slip while niche technical roles sit open for six months or more, even as candidates from rigorous programs — people with the specific skills for the job — are filtered out.

How successful CIOs operationalize skills-first hiring

Successful CIOs get specific. They work with their teams to define exactly which skills matter for each role — and they validate those definitions against the performance of current, thriving employees.

Should that taxonomy include demonstrated experience with AI tools and platforms: Has the candidate built or deployed an AI agent? Can they work across multiple AI tools? Have they completed projects requiring AI-augmented problem-solving? These concrete, observable skills predict performance far more than a degree ever could.

Second, they establish shared metrics across IT and HR. Organizations that get this right track 90-day performance reviews, first-year retention and promotion velocity alongside traditional recruiting metrics. In its New Collar Program with Sentara Healthcare, TEKsystems worked with company leaders to fill open big data positions through a skills-based cohort model and achieved 80% retention one-year post-training.

Third, these CIOs build direct relationships with employer-aligned training pipelines before a role opens. Bank of America invested nearly $40 million in workforce development initiatives in 2025 alone and partnered with more than 600 nonprofits across the US.

At Per Scholas, our head of IT, Tyrone Washington, makes it clear that while technical skills might get you through the door, it’s “smart skills” — discernment, emotional intelligence, complex problem-solving and agility — that build a career in an AI-driven landscape.

What the data shows

Skills-first hiring, when paired with structured onboarding and development pathways, is not just a talent acquisition strategy — it’s a retention strategy. And higher retention contributes directly to the bottom line, as the fully loaded cost of replacing a technical employee ranges from one to two times their annual salary.

In one employer partnership deploying skills-trained talent, a TEKsystems client came out $238,000 ahead in the first year after accounting for program costs, with a projected annual return of over $1.2 million. IT leaders reported that skills-trained talent becomes productive measurably faster than early-career hires from conventional pipelines.

How CIOs can lead the Shift — even without owning HR

CIOs who are moving the needle are piloting skills-based hiring for one or two roles, tracking outcomes rigorously and using that data to make the case for broader adoption. They’re building external partnerships before they need them. Bank of America, a Per Scholas long-term partner, knows that our graduates are team players, lifelong learners and motivated employees; our graduates’ certifications (through CompTIA and Google) validates that they have the technical know-how.

Every quarter that technical roles sit open has a measurable impact on project delivery, team capacity and competitive positioning. Surfacing these costs — backed by data — is something CIOs are uniquely positioned to do.

The bottom line

Skills-first hiring will remain a well-intentioned abstraction unless CIOs treat it as an operating model — one that reflects how AI is reshaping the skills organizations need.

The candidates who can demonstrate hands-on experience in building and deploying AI agents, integrating multiple AI tools into a workflow or evaluating when AI can help are the ones who will drive value. But they’ll keep getting filtered out unless CIOs get specific about skill definitions, align IT and HR around shared metrics, and build employer-aligned pipelines. Bank of America and TEKsystems didn’t achieve their results by endorsing a principle — they achieved them by building systems.

Luckily, building systems is something that CIOs know how to do well.

This article is published as part of the Foundry Expert Contributor Network.
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You can’t train your way out of the AI skills gap

Most enterprises I talk to say they have an AI skills gap. That sounds plausible right up until you look at what companies are doing. They are spending millions on copilots, launching AI academies, hiring chief AI officers and rolling out internal training at scale. Yet for all that activity, most organizations still do not move faster, decide better or operate in fundamentally new ways. That is the real tension at the center of enterprise AI right now: Companies think they have a skills problem, but what they really have is a work design problem. I have seen this pattern repeatedly. The organizations that get real value from AI are usually not the ones that train the fastest. They are the ones who redesign work sooner.

The AI skills gap is real, but it is not the whole story. In many enterprises, the bigger failure is that AI is being layered onto jobs, workflows and operating models built for a pre-AI world. People are learning new tools, then being sent back into the same meetings, approvals, handoffs and reporting structures that made work slow in the first place. Training may improve local productivity. It does not automatically redesign how the business runs.

That is why so many AI programs feel busy without becoming transformative. The organization can point to courses completed, licenses deployed and pilots launched, but the underlying system still behaves the same way. Decision latency stays high. Bottlenecks remain intact. Managers absorb more complexity, not less. Employees become faster in small ways while the enterprise remains slow in all the ways that matter.

This gap is showing up clearly in the research. Deloitte’s 2026 State of AI in the Enterprise report says insufficient worker skills are the biggest barrier to integrating AI into existing workflows. Yet the most common organizational response is education and reskilling, not role or workflow redesign. In fact, Deloitte explicitly notes that companies are much more focused on AI fluency than on re-architecting how work is done. The same tension appears in Wharton’s 2025 AI Adoption Report: Executive sponsorship is rising; chief AI officer roles are now present in 6 out of 10 enterprises and capability building is still falling short of ambition. The signal is hard to miss. Enterprises know AI matters. Many are investing. But they are still treating adoption as a learning problem when it is really an operating model problem.

Training creates users. Redesign creates advantage

Training matters. Every enterprise needs a baseline level of AI fluency. People need to understand where AI is strong, where it is weak and how to use it responsibly. They need to know how to challenge outputs, apply judgment and separate acceleration from automation. None of that is optional anymore.

But training alone does not create an enterprise advantage. At best, it creates pockets of local efficiency.

An individual contributor may draft faster. A manager may synthesize information faster. An analyst may produce a first pass in less time. Those gains are real, but they do not automatically translate into better operating performance. In many organizations, the efficiency never reaches the P&L. It gets trapped inside legacy workflows, approval layers, meeting culture and fragmented decision rights.

That is the real issue. AI may already be improving work at the individual level, while the enterprise itself remains structurally unchanged. The Writer enterprise AI adoption survey found that executives see AI super-users as at least five times more productive than their peers, yet only 29% of organizations report significant ROI from generative AI. The contrast is telling. The constraint is no longer whether employees can use AI. The constraint is whether the organization is designed to convert those gains into faster decisions, shorter cycle times, higher throughput and better business outcomes.

This is where many AI initiatives quietly stall. Leaders can point to adoption metrics, training completion rates and growing license counts. Employees can honestly say they are using the tools. But the business still does not feel materially more responsive. Revenue does not move faster. Product cycles do not compress enough. Decision latency remains high. Management complexity increases instead of falling.

That is why I believe the wrong question is, “How do we train our people on AI?”

The better question is, “Which work should humans continue to own, which work should AI accelerate and which workflows should be redesigned entirely now that AI exists?”

That is the shift that matters. It moves the conversation from individual capability to institutional performance. It moves AI from a training initiative to an operating model decision.

And that is where CIOs must lead. The organizations creating advantages with AI are not simply teaching employees new tools. They are redesigning roles, workflows and management systems so that individual productivity gains become enterprise-level outcomes. Companies do not fall behind because AI arrived. They fall behind because they kept the same work design after it did.

The real AI shift is separating judgment from execution

The most important AI transformations I have seen do not start with tools. They start with a harder leadership discipline: Separating judgment work from execution work.

Once AI can reliably handle portions of execution, the role itself must be reconsidered. Not eliminated. Reconsidered. The question is no longer just how to make people faster inside the job as it exists. The question is whether the job was designed correctly in the first place.

That is where the real work begins. Leaders must deconstruct work below the level of titles and org charts. This is a much harder challenge than deploying a copilot. It forces decisions about spans of control, management layers, performance expectations and career paths. It changes what excellence looks like across the enterprise. And it changes what companies should reward.

If AI takes on more drafting, synthesis, retrieval and coordination, then the value of human work moves up the stack. The ability to frame the problem, define quality and make accountable decisions becomes more valuable than manually producing every intermediate step.

This is also why so many employees feel uneasy even when leaders talk about AI in optimistic terms. They are being told to use new tools, but not what the organization will still need uniquely from them. They are hearing about productivity, but not about role evolution. Training without redesign does not feel like empowerment. It feels like a shot across the bow of their career.

That is why the most important workforce conversation in AI is not about tool usage. It is about role clarity. People need to understand where human judgment still creates value, where AI should accelerate execution and how their path to relevance and mastery changes as a result.

That is not an HR side discussion. It is one of the central leadership tasks of the AI era.

CIOs must lead the redesign, not just the rollout

This is where CIOs have a larger role than many companies still recognize.

AI adoption is often framed as a cross-functional initiative, and of course it is. But when AI moves from experimentation into execution, the CIO is often the only executive with a clear view of the full system: Workflows, dependencies, security, data architecture, control points, operational friction and how work moves across the enterprise. That perspective matters because the next phase of AI is not about individual productivity. It is about institutional redesign.

That means CIOs cannot limit their role to rollout, enablement and tool selection. They must help redesign how the business operates.

In my view, that starts with three questions:

  1. Where is AI simply making existing work cheaper or faster, and where could it allow the business to operate differently? Those are not the same thing.
  2. Which roles need to be rebuilt? Not renamed. Rebuilt. If analysts spend less time gathering information, what should the organization expect more of in return? If leadership cannot answer that, it is not redesigning work. It is just hoping individual productivity turns into enterprise value on its own.
  3. What new management disciplines does AI require? As AI becomes part of execution, leaders need clearer standards for validation, accountability and quality control. AI can compress execution, but it can also multiply errors at scale. That raises the premium on operating discipline, not lowers it.

This is why I think the skills-gap narrative can be misleading. It invites leaders to believe the problem is mostly educational, as if enough courses, certifications and training hours will somehow carry the organization into the future. They will not. They are necessary, but they are nowhere near sufficient.

The companies that pull ahead will treat AI as a redesign moment. They will rethink work at the level of tasks, decisions, teams and operating models. They will create roles with more judgment and less administrative drag. They will redesign career paths, so people are not just trained on AI, but advanced through the responsible use of it. And they will measure success not only through adoption, but through decision velocity, throughput, exception rates and business outcomes.

Most of all, they will stop asking employees to bolt AI onto broken systems.

That is the real opportunity in front of CIOs. Not just to deploy the tools. Not just to sponsor training. It is to help redesign the enterprise around a new division of labor between humans and machines.

The AI skills gap is real. But education alone will not close it.

Only better work design will.

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You’ve got the F1 car. Now where’s your driver?

For years, the AI conversation has centered on tools — what can this technology do, and how fast can we deploy it? That chapter is closing. Nearly every organization now has access to powerful AI, and executives across industries say the bigger constraint is finding people who know how to turn those tools into outcomes. 

Think of it this way: An F1 car is fast, impressive and loaded with potential. But without a skilled driver behind the wheel, it’s just an expensive machine sitting on the track. You need someone who understands the course, who knows when to brake and when to accelerate and who can navigate risk at high speed. 

The playing field has leveled. Talent is the new edge. 

In KPMG’s Q4 2025 AI Quarterly Pulse Survey, leaders identified AI prompt engineers (71%), AI performance analysts (59%), and AI trainers/data curators as the most anticipated emerging roles for 2026 — a clear signal that the real bottleneck is talent, not technology. 

That gap is only widening. Access to AI tools is no longer a differentiator. Neither is implementation. What separates the leaders from the rest is something harder to scale — the expertise, curiosity and hands-on engagement of the people putting those tools to work every day. 

Consider a senior corporate tax planning adviser who started out skeptical about AI. A year ago, her team would manually reconcile large data sets at quarter end, spending late nights cleaning and checking numbers. Today, she and a small group of “AI hobbyists” on her team have built and refined prompts, models and workflows that automate much of that effort. Her role is less about grinding through spreadsheets and more about reviewing anomalies flagged by AI, probing the “why” behind the patterns and translating those insights into client-ready strategies. She’s still doing tax — but the value she creates now comes from judgment and interpretation, not manual effort. 

The organizations pulling ahead aren’t the ones with the biggest tech budgets — they’re the ones whose people have become hobbyists like her. They’re tinkering. They’re documenting what works and what doesn’t. Building repeatable patterns, sharing prompts and staying relentlessly curious. 

This matters because the technology isn’t standing still. Models are improving at a pace that makes static skills obsolete almost immediately. The professionals who treat AI fluency as a one-time training exercise will fall behind. The ones who approach it as a lifelong practice — fingers on keys, constantly experimenting — will compound their advantage over time. They’ll be the ones who can quickly evaluate new models, identify where they fit and redesign processes to capture value before competitors do. 

Leaders are starting to price that curiosity into the market: 76% say they would pay up to 10% more for candidates with strong AI skills, and 22% would pay 11-15% more. 

Now get out of the chat window

Those premiums don’t reward people who dabble — they reward people who go deep. And going deep means moving beyond surface-level use. It’s not enough to ask a chatbot questions and review its answers. The real value creation starts when you break out of that box — when you connect AI to your data, automate the insights that used to take days and build systems that flag anomalies before they become problems. It’s when AI moves from being a novelty in someone’s browser to being embedded in workflows, controls and decision-making. 

That’s where platform thinking comes in. The next wave of value won’t come from isolated tools; it will come from integrated systems that bring together data, AI capabilities, governance and human expertise in one place. When organizations unify their data, models and workflows on a common platform, they make it easier for people to experiment, share what works and scale good ideas quickly and safely across the enterprise. At KPMG, we’re building that kind of environment through Digital Gateway — but the principle applies universally. The platform is only as good as the people on it. 

We see the same shift inside major corporations. An early-career analyst at a large financial services firm used to spend most of his time copying data between systems, preparing slide decks and running one-off analyses requested by senior leaders. After his organization rolled out an AI-enabled platform, his day looks very different. He now configures AI agents to monitor risk indicators across portfolios, designs prompt templates that business users can reuse and works with compliance to ensure outputs meet regulatory expectations. He hasn’t left the world of analysis — he’s moved up the value chain, from producing numbers to orchestrating the system that produces and explains them. 

The bottom line 

If you’re wondering where to start, the answer is simple: Get behind the wheel. Encourage your teams to interact with every model, every tool, every capability they can access. Give them permission to experiment within guardrails and reward the behaviors that lead to better client service, sharper insights and smarter risk management. Foster a culture where curiosity isn’t a nice-to-have — it’s a performance expectation. 

Because at the end of the day, it’s not the car that wins the race. It’s the driver. And in the AI era, talent — curious, empowered and in the driver’s seat — is the only sustainable advantage. 

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칼럼 | 멀티 벤더 프로젝트 실패, 대부분은 ‘거버넌스’에서 시작된다

벤더가 프로그램을 스스로 바로잡아주기를 기다리는 것은 전략이 아니다. 이는 조용히 누적되는 비용일 뿐이며, 회의실 안의 모두가 프로세스가 정상적으로 작동하고 있다는 착각을 유지하는 동안 그 부담은 계속 커진다.

필자는 두 가지 상황을 모두 경험했다. 하나는 고객이 이미 문제가 있음을 인지하고 행동에 나설 근거와 표현을 필요로 하는 경우, 다른 하나는 아직 문제를 인식하지 못한 상태다. 후자의 경우 프로그램은 관리 가능한 수준으로 보이고, 벤더는 전문적으로 보이며, 운영위원회 회의도 제시간에 진행된다. 그러나 경고 신호는 이미 곳곳에 드러나 있으며, 누군가 이를 짚어내기만을 기다리고 있다.

이 두 번째 상황이 더 중요하다. 아직 대응할 수 있는 시간이 남아 있기 때문이다. 하지만 대부분의 기업은 그 기회가 줄어들기 시작할 때까지 움직이지 않는다.

대부분의 기업이 놓치는 경고 신호는 설계 단계에서 나타난다

초기의 신호는 일정 지연이나 산출물 실패가 아니다. 오히려 ‘언어’에서 드러난다. 상태 보고서나 운영위원회 자료에 ‘프로젝트 정상화 방안(path to green)’이라는 표현이 등장하기 시작하면, 이는 이미 프로젝트가 정상 상태가 아님을 스스로 인정한 것이다. 실행을 관리하는 단계에서 ‘서사를 관리하는 단계’로 전환된 셈이다.

운영위원회가 실제로 무엇을 하고 있는지도 살펴봐야 한다. 회의가 다음 달 전망이 아닌 지난달 결과 보고에 집중된다면, 리더십은 의사결정 주체가 아니라 단순한 청중으로 전락한 것이다. 이 경우 벤더가 의제 설정, 메시지 구성, 정보 공개 주기를 사실상 통제하게 된다.

가장 심각한 신호는 프로그램 스폰서가 벤더가 아닌 내부 보고를 통해 주요 문제를 인지하는 경우다. 이는 단순한 커뮤니케이션 문제를 넘어, 어떤 정보를 공유할지에 대한 의도적인 선택이다. 이러한 패턴이 SAP, 오라클, 세일즈포스 기반 프로젝트에서 나타난다면, 거버넌스의 핵심인 신뢰는 이미 무너진 상태다.

이러한 신호가 보이면 다음 운영위원회를 기다릴 이유가 없다. 독립적으로 검증 가능한 데이터를 요구해야 하며, 벤더에게 ‘보고’가 아닌 ‘예측’을 요구해야 한다. 만약 60일 후 프로젝트 상태를 설명하지 못한다면, 벤더는 프로젝트가 아니라 고객의 인식을 관리하고 있는 것이다.

총괄 조정자 역할, 해결되지 않은 이해충돌 문제

액센추어, 딜로이트, PwC 등 다수의 글로벌 컨설팅 기업이 참여하는 멀티 벤더 프로젝트에서 반복적으로 나타나는 패턴이 있다. 총괄 조정자, 즉 프로그램 통합 코디네이터는 고객의 문제, 다른 벤더의 한계, 외부 의존성 지연 등을 빠르게 지적한다. 그러나 정작 자사 문제에 대해서는 같은 수준의 직설적인 언급을 거의 하지 않는다.

이는 개인의 성향 문제가 아니라 구조적인 이해충돌이다. 총괄 조정 역할을 맡은 기업은 소위 업무 범위 정의서로 불리는 자체 업무 범위(Statement Of Work, SOW)를 수행 책임도 동시에 지고 있다. 이 과정에서 거버넌스 권한으로 확보한 정보 접근성과 보고 권한을 바탕으로, 자사 리스크를 방어하는 방향으로 움직일 가능성이 크다.

이 때문에 총괄 조정자 역할은 반드시 벤더의 수행 역할과 구조적으로 분리해야 한다. 가장 이상적인 방식은 결과에 이해관계가 없는 독립적인 통합 관리 조직을 두는 것이다. 현실적으로는 기존 벤더 내에서 별도 조직이나 인력을 지정하되, 해당 조직이 자사 리더십이 아닌 고객에게 직접 보고하고 운영위원회에 책임을 지도록 해야 한다.

이 구조에는 완벽한 방화벽이 존재하지 않는다. 대신 행동으로 판단할 수 있는 기준이 있다. 해당 역할이나 팀이 자사에 불리한 정보를 어떻게 다루는지 살펴보는 것이다. 고객의 문제를 제기할 때와 동일한 긴급도로 이를 공유하거나 상향 보고하는지, 혹은 자사 이슈는 숨기고 타 조직의 문제만 부각하는지를 확인해야 한다.

자사 전달 조직의 실패까지도 적극적으로 공개하고 에스컬레이션하는 총괄 조정자라면 제 역할을 수행하고 있는 것이다. 반대로 고객과 타 벤더의 문제만 지적한다면, 이는 프로젝트가 아니라 자사 계약을 방어하고 있는 것에 가깝다.

다음 SOW 체결 이전에 이러한 구조를 명확히 해야 한다. 총괄 조정자 역할을 수행 역할과 분리해 정의하고, 담당 인력이나 조직을 명확히 지정해야 한다. 또한 보고 체계를 고객 직속으로 설정하고, 실제로 해당 역할이 수행되고 있는지 행동 기준을 통해 검증해야 한다.

기다림은 중립이 아니다

대응을 미루는 데 따른 비용은 많은 기업이 생각하는 것보다 훨씬 구체적이다. 여러 시스템 통합업체가 동시에 참여하는 멀티 벤더 환경에서는 일정이 한 달만 지연돼도 기업별로 수백만 달러에서 수천만 달러에 이르는 비용이 발생할 수 있다. 이는 범위 확장이 아니라, 거버넌스가 일정 관리를 제대로 하지 못한 결과다.

상업적 리스크는 더 이른 시점부터 나타난다. 범위가 명확하지 않고 통합 계획이 불안정하면, 벤더는 가격 산정의 기준점을 확보할 수 없다. 그 결과 동일 범위에도 시간·자재 기반 견적과 고정가 견적 간 큰 차이가 발생한다. 이는 단순한 가격 차이가 아니라, 거버넌스 불확실성을 계약 조건으로 전가한 것이다. 결국 그 부담은 고객이 떠안게 된다.

문제가 쉽게 드러나지 않는 이유는 현장 팀이 대체로 전문적이고 성실하게 일하고 있기 때문이다. 하지만 핵심은 노력의 문제가 아니라 권한과 인센티브 구조에 있다. 프로젝트를 운영하는 프로그램 매니저는 추가 자원 투입이나 조직 간 비용 집행을 승인할 권한이 없다. 이들의 역할은 관계를 유지하고 수익성을 관리하는 것이며, 고객 프로젝트를 근본적으로 해결하는 것과는 다르다.

효과적인 개입은 ‘리더십’에서 시작된다

문제가 명확해지고 고객이 대응을 결정했다면, 실제 변화를 이끄는 것은 거버넌스 문서나 정기 회의가 아니라 고객과 벤더 최고 경영진 간의 직접적인 대화다. 이는 일상 운영을 담당하지 않지만, 결과에 책임을 지는 임원들이 참여해야 한다.

이러한 대화가 효과적인 이유는 인센티브 구조를 바꾸기 때문이다. 벤더의 산업 책임자나 파트너는 해당 프로젝트를 성공 사례로 만들어야 하며, 실패는 조직 내 부담으로 이어진다. 이들은 실행 조직이 갖지 못한 권한을 보유하고 있다. 최우수 인력 투입, 계약 범위를 넘어선 비용 부담, 신속한 인력 재배치 등 프로젝트의 방향을 바꿀 수 있는 결정이 가능하다.

또한 개입은 구조적으로 설계돼야 한다. 양측 고위 임원이 참여하고, 신규 인력 투입을 통해 벤더의 투자 의지를 명확히 보여야 한다. 동시에 프로젝트가 정상 궤도에 오를 때까지 일정 주기로 점검을 이어가고, 양측 모두에 시간 기반 책임을 부여해야 한다. 이 과정에서 단순히 프로젝트뿐 아니라 파트너십 자체도 평가 대상임을 명확히 해야 한다.

이는 일회성 회의가 아니라 지속적인 리더십 개입이며, 기존 거버넌스를 대체하는 것이 아니라 이를 실제로 작동하게 만드는 역할을 한다.

신뢰할 수 있는 유일한 회복 신호

리더십 간 논의가 효과를 발휘했다면, 그 결과는 벤더의 대응 방식에서 드러난다. 단순한 낙관적 계획이나 약속이 아니라, 실패 지점과 개선 방안, 그리고 고객 측의 성과 격차까지 구체적으로 제시해야 한다.

자사 책임만 인정하는 벤더는 여전히 관계 관리에 머무른다. 반면 자사 실패와 고객의 개선 필요사항을 동시에 명확히 제시하는 벤더는 공동 책임 구조를 구축하고 있는 것이다. 이것이 진정한 신뢰의 기준이다.

프로젝트 실패는 대부분 양측 요인이 결합해 발생한다. 지연된 의사결정, 부족한 내부 자원, 설계 이후 변경된 요구사항 등이 대표적이다. 이러한 요소를 자사 문제와 함께 제시하는 벤더는 책임을 회피하는 것이 아니라 결과 개선에 투자하고 있는 것이다.

만약 경영진 회의 결과가 단순한 약속과 형식적인 의지 표명에 그친다면, 지속적으로 압박을 유지해야 한다. 실제 개입은 구체적인 문제 인정, 명확한 자원 배정, 그리고 양측 모두를 향한 냉정한 평가에서 드러난다.

최종 책임은 고객에게 있다…‘중간에서 판단하는 역할’ 필요

모든 과정에서 최종 책임은 고객에게 있다. 총괄 조정자는 실행과 통합을 책임지지만, 판단 자체를 외부에 맡길 수는 없다. 이는 단순한 역할 구분이 아니라, 거버넌스의 본질이다.

벤더의 상태 보고서는 중립적인 데이터가 아니다. 이는 보상, 향후 계약, 개인의 평판이 걸린 사람들이 구성한 ‘의도된 서사’다. 보고서에 담긴 내용도 중요하지만, 빠져 있는 내용이 더 많은 것을 말해준다.

따라서 고객은 ‘중간에서 판단하는 역할’을 수행해야 한다. 데이터를 검증하고, 교차 확인하며, 보고서가 답하지 않은 질문을 던져야 한다. 무엇이 포함됐는지뿐 아니라 무엇이 빠졌는지도 살펴야 한다.

운영위원회가 좋은 소식만 듣고 있다면, 이는 프로젝트가 잘 진행되고 있다는 의미가 아니라, 누군가 리더십이 듣고 싶어 하는 내용만 선택하고 있다는 신호일 수 있다.

고객은 상태 보고가 아닌 예측을 요구해야 한다. 독립적으로 검증 가능한 근거를 확보하고, 벤더가 자사 문제와 고객 문제를 함께 제시할 때 이를 गंभीर하게 받아들여야 한다. 이는 책임 회피가 아니라, 거버넌스가 제대로 작동하고 있다는 신호다.

경고 신호는 항상 명확하지 않을 수 있다. 그러나 대응할 수 있는 시간은 제한적이다. 기다림은 결코 전략이 아니다.
dl-ciokorea@foundryco.com

The inference bill nobody budgeted for

Picture this. Thursday morning. The CFO’s assistant just sent you a calendar invite for Q3 AI Infrastructure Spend at 2:00 pm. No agenda. Just that number from last month’s cloud bill, 40 percent above forecast. You have five hours. Do you own the narrative, or does finance own it for you?

Those who escaped that conversation had a governance architecture in place before the bill arrived.

The training budget was the wrong number all along

Training is a project. Inference is a utility. When an AI agent is embedded in a workflow, it runs every time that workflow runs, around the clock, at scale, with no natural stopping point.

Inference workloads are set to overtake training revenue in 2026, with Deloitte Tech Trends 2026 estimating inference will account for two-thirds of all AI compute this year. Public cloud API pricing has fallen nearly 80 percent year over year, yet Gartner places AI spending at $2.52 trillion in 2026. A volume problem, not a unit cost problem. PwC’s 29th Global CEO Survey of 4,454 chief executives finds 56 percent report AI has produced neither increased revenue nor decreased costs; only 12 percent have achieved both. The differentiator is governance architecture, not model choice.

The triple convergence: 3 forces you cannot fight separately

The inference cost crisis would be manageable in isolation. What makes it genuinely difficult is that it arrives simultaneously with two other structural forces, each carrying its own financial and legal consequences.

Convergence #1: The agentic cost amplifier

The FinOps Foundation’s State of FinOps 2026 report, covering 1,192 organizations and $83 billion in cloud spend, finds AI workloads account for 18 percent of cloud spend at AI-forward enterprises, up from 4 percent in 2023. A three-hour recursive loop generates approximately $3,700 in unplanned compute before any guardrail activates; at ten agents simultaneously, $37,000 per incident. Analytics Week’s March 2026 analysis documents an estimated $400 million absorbed annually from recursive loop failures alone. McKinsey’s 2024 Global Survey on the State of AI finds 78 percent of knowledge workers use unsanctioned AI tools, generating inference costs and compliance obligations FinOps teams cannot see.

Convergence #2: The compliance architecture you cannot defer

Article 5 prohibited practices have been enforceable since February 2025, with penalties up to 7 percent of global annual turnover. The Digital Omnibus on AI, approved in committee on March 18, 2026, extends the compliance window for new Annex III high-risk system deployments under Articles 9-15, covering risk management (Article 9), audit logging (Article 12), and human oversight (Article 14), to December 2027. Building that architecture takes 12 to 18 months, and at 3 percent of global annual turnover, the maximum Annex III exposure for a $10 billion enterprise is $300 million. A credit decision through a public cloud endpoint may simultaneously violate GDPR Articles 44-49 (international transfers), Article 22 (automated decisions), and EU AI Act lineage requirements. The US CLOUD Act compounds this: choosing Frankfurt over Virginia does not solve your sovereignty problem if your provider is headquartered on California Avenue.

Convergence #3: The data gravity reversal

AI follows data. When egress costs plus transfer restrictions plus sovereignty exposure exceed owned inference capacity costs, the placement decision has been made for you.

Infrastructure is a placement discipline, not a platform choice

Five questions classify every workload before any platform is selected: Where should this run? How fast must it respond? Who owns its cost and compliance trajectory? What regulations govern where inference can legally execute? And at what volume does owned capacity beat pay-as-you-go cloud?

Ask those five questions, and three tiers emerge naturally. Public cloud for variable, burst and experimental workloads. Private on-premises for predictable high-volume production inference, where owned capacity consistently delivers 4x to 8x lower cost per token on Hopper-generation or later GPU hardware (H100 or equivalent at 75 to 85 percent utilization, GPT-4-class model, production batch sizes above 32). Edge for latency-critical and sovereignty-constrained decisions, where round-trip latency is a disqualifier. Some workloads will stay in the public cloud indefinitely. The goal is to stop letting infrastructure decisions make themselves.

Placement in practice

Those five questions are not theoretical. A single case shows how they play out under real compliance pressure.

One Tier 1 North American financial institution, processing more than 1 million credit decisions per month, saw cloud bills exceed forecast by 3x. A compliance audit identified two exposure points: the CLOUD Act made EU customer data accessible to US law enforcement, and audit logging failed to capture the lineage trail required under Annex III, Point 5(b), of the EU AI Act (AI systems assessing the creditworthiness of natural persons).

Applying the five questions took two hours. At 1.2 million decisions per month, on-premises was the obvious tier (cloud latency 340ms versus 22ms, same GPT-4-class model, both environments). Both compliance exposure points required moving inference to an EU-headquartered private stack. The workload migrated in 83 days. Monthly spend fell from $85,000 to $35,000. CLOUD Act exposure was eliminated. EU AI Act lineage requirements were met. The cost reduction per decision, combining compute, compliance overhead and latency cost, was 59 percent (GPT-4-class model at production batch sizes).

What to put in front of the CFO

The CFO’s question is whether the investment is working, provable in a number that someone is accountable for. The answer is a different denominator: cost per unit of business output. Four numbers: (1) compute cost per decision (the institution above: $0.071 on public cloud, $0.029 on private infrastructure, GPT-4-class model); (2) compliance overhead per decision (audit logging and regulatory evidence management, fixed regardless of tier); (3) latency cost per decision (340ms versus 22ms is measurable in abandoned transactions and SLA penalties); (4) human-equivalent benchmark (if your loaded analyst rate puts a human decision at $1.80 to $3.20, the CFO needs to be shown how to scale it).

Resilience is a cost line, not a design philosophy

In building the cloud outage database at whencloudsfail.opey.org, I have tracked more than 400 enterprise-impacting AI platform incidents since 2023. Duration of disruption correlates more strongly with provider dependency concentration than with incident severity. Claude AI experienced three major incidents in the first two weeks of March 2026, peaking at 4,700 down detector reports. Azure OpenAI logged a confirmed 20-hour degradation across seven regions on March 9 and 10. The difference between those bills is not a resilience philosophy. It is a number. Build resilience into the architecture or pay for it in the incident.

Resilience and governance are the same problem. The architecture question and the ownership question have the same answer.

Stop the organizational blame game

Deloitte’s 2026 State of AI in the Enterprise, across 3,235 senior leaders, finds only 1 in 5 companies has a mature governance model for autonomous AI agents. Three fixes: (1) a cross-functional governance body meeting quarterly, per-decision cost by workload class as its single agenda; (2) a named owner for every inference endpoint accountable for cost and the Article 14 model card; (3) real-time guardrails with automated kill switches. Gartner reports only 44 percent of organizations have adopted financial guardrails for AI.

Leaders who act vs. leaders who react

Gartner’s 2026 CIO Agenda finds 94 percent of CIOs expect major changes within 24 months, yet only 48 percent of digital initiatives meet their targets. Score yourself:

QuestionActReact
Cost per inference call named for top 3 workloads?YesNo
Named owner per production AI endpoint?YesNo
Cloud DPAs reviewed for EU AI Act data lineage?YesNo
Hard budget guardrails auto-stopping agents?YesNo
All AI workloads classified by 5-dimension frame?YesNo
Per-decision cost with compliance overhead to CFO?YesNo
Cross-functional AI governance body (quarterly)?YesNo
Shadow AI deployments inventoried across all BUs?YesNo

Predominantly NO: the 90-day sprint below is your path forward.

Your 90-day reckoning

Execute these phases sequentially. You cannot move a workload to the correct tier in Phase 3 if you have not classified it in Phase 1.

PhaseFocusOwnerDeliverable
Days 1-30Expose the billFinOps leadInventory all workloads; name a cost owner for each; calculate cost per decision for top 10; audit DPAs for EU AI Act class.
Days 31-60Wire the guardrailsAI Eng leadDeploy cost monitoring; set hard budget limits per agent; activate zombie alerts; first governance meeting; present dashboard to CFO.
Days 61-90Move first workloadCIOMigrate highest-cost predictable workload; complete EU AI Act gap assessment; brief board on per-decision cost; publish placement policy.                         

The competitive consequence of waiting

McKinsey’s Global Tech Agenda 2026, surveying 632 business leaders, finds that nearly two-thirds of top performers have technology leaders deeply involved in enterprise strategy, compared with 52 percent at others. PwC’s AI Vanguard, the 12 percent achieving both revenue and cost gains, carries nearly four percentage points higher profit margins. The separation is governance architecture, not model choice. The CIOs who navigate this most effectively are not managing AI as a technology initiative. They are managing it as a financial and regulatory obligation. The CIO who built this architecture before Thursday’s meeting does not dread that calendar invite. They sent it.

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You selected the right vendors. Now govern them like you mean it.

Waiting for your vendor to fix a program isn’t a strategy. It’s a cost, accumulating quietly while everyone in the room maintains the fiction that the process is working.

I’ve been in both rooms. The room where the client already knows something is wrong and needs the language and the evidence to act, and the room where the client doesn’t know yet. The program feels manageable, the vendor is professional, the steering committee meetings run on time, and the warning signs are sitting in plain sight waiting for someone to name them.

That second room is the more important one. Because the window to act is still open. And most clients don’t move until it’s started to close.

Warning signs most clients miss appear in design

The earliest signal is rarely a missed milestone or a failed deliverable. It appears in language. When the phrase “path to green” starts appearing in status reports and steering committee decks, the program has already accepted it’s not green. It’s shifted from managing execution to managing the narrative.

Watch what the steering committee is actually doing. If it’s consistently hearing about what happened last month rather than what’s forecast for next month, leadership has been converted from a decision-making body into an audience. The vendor controls the agenda, the framing, and the cadence of what gets surfaced.

The most serious signal is when a program sponsor hears about material issues from their own direct reports that the vendor hasn’t raised in the room. That’s not a communication gap but a calculated decision about what leadership is ready to hear. When that pattern appears in SAP, Oracle, or Salesforce programs, the trust that makes the governance model function has already eroded.

When you see these signals, don’t wait for the next steering committee. Start demanding data that can be independently corroborated. Ask the vendor to forecast, not report. If they can’t tell you where the program will be in 60 days, they’re managing your perception, not your program.

Your master conductor has a conflict of interest you’re not addressing

A pattern I’ve seen consistently across multi-vendor programs involving Accenture, Deloitte, PwC, and others is the master conductor, or program integration coordinator, is quick to name client’s gaps, other vendors’ shortcomings, and third-party dependencies running behind. What they almost never do is name their own firm’s failures with the same directness in the same room.

That’s not a personality issue but a structural conflict. The firm serving as master conductor is delivering against its own statement of work (SOW), and the governance position gives them access to information, reporting authority, and narrative control they’ll use to, consciously or not, protect their own delivery track.

This is why I advise clients to treat the master conductor and program integration coordinator role as structurally separate from the vendor delivery role. That means a, entirely separate firm, an independent integrator with no delivery stake in the outcome. In practice, it’s more often a designated individual or a group within the project management or transformation office carved from one of the existing vendors, reporting directly to the client and accountable to the steering committee, not to their own firm’s engagement leadership.

There’s no true firewall in that model, but there’s a behavioral test. Watch what that role or team does with information that reflects badly on their own firm. Do they surface it or escalate it with the same urgency they bring to client gaps? Do they forecast problems on their own track, or only on everyone else’s?

A master conductor who’ll escalate failures that implicate their own delivery team is doing the job. One who only calls out the client and the other vendors is protecting the engagement.

Before the next SOW is signed, make it structural. Define the master conductor role separately from the delivery role, name the individual or team, set the reporting line directly to the client, and use the behavioral test to determine whether the role is being performed or merely filled.

Waiting isn’t neutral

The financial cost of waiting is more specific than most clients realize. In a multi-vendor environment where two or three system integrators are billing against active SOWs, every month of schedule extension carries a material cost, potentially millions to tens of millions of dollars per firm, not because scope expanded, but because governance didn’t hold the timeline.

The commercial exposure appears even earlier. When scope boundaries are unclear and the integrated plan is unstable, vendors have no reliable baseline to price against. The result is predictable: a significant spread between a time-and-materials estimate and a fixed fee quote for the same scope. That spread is not a pricing difference. It’s the vendor converting your governance uncertainty into their contract protection. The client absorbs it either way.

What makes the waiting feel reasonable is the vendor’s day-to-day team is usually professional and working hard. So the problem is authority and incentive, not effort. The program manager running the engagement can’t authorize additional resources nor commit spend across organizational lines. Their job is to manage the relationship, protect their firm’s margin, and keep the engagement profitable. Fixing your program isn’t the same job.

The window to act is real and short. A senior executive at the vendor can absorb costs, bring new talent, and make commitments the delivery team has no authority to make. But that authority diminishes as the program ages. The more that’s been billed and the more scope has shifted, the harder it is for even a motivated senior executive to make the client whole. Clients who act in design or early build have options that clients who wait until three months before go-live don’t.

The intervention that works is a leadership one

When the signals are clear and the client is ready to act, the intervention that moves the needle isn’t a governance document or a scorecard meeting but a top-to-top conversation between client and vendor senior leadership. This includes execs who aren’t running the day-to-day program but have something personal at stake in the outcome.

That conversation works because it activates a different set of incentives. The vendor’s senior executive, the sector partner and industry leader whose name is on the relationship, needs your program to be referenceable. They don’t want a PR failure on a flagship engagement, nor do they want to explain to their firm’s leadership why a major client program collapsed. They have authority their delivery team doesn’t: power to assign their best resources, ability to absorb costs the SOW or change order doesn’t cover, and they can accelerate staffing decisions and make commitments that change what the program can do. They have skin in the game their team doesn’t.

Also, structure the engagement deliberately. Have senior executives on both sides and new talent brought in as a visible signal of vendor investment. And have a cadence that continues until the data shows the program is back on track, with time-bound accountability on both sides. And have explicit understanding that the relationship itself is under review, not just the program.

This is sustained leadership engagement, not a one-time meeting, and it doesn’t replace the governance model. It enforces it.

The only recovery signal worth trusting

When the top-to-top works, you’ll know it by what the vendor brings back to the table. Not reassurances or a revised plan with optimistic milestone dates, but facts about where they failed, what they’re changing, and, most critically, where the client has performance gaps that also need to close.

A vendor who comes back and accepts blame still manages the relationship. A vendor who says we failed here and here, these are the specific changes we’re making, and you have a gap here we need you to address, that vendor is engaged and mutually accountable. That’s the integrity test.

It runs both ways because program failure almost always does. Slow client decisions. Unavailable business resources. Requirements that shifted after design was locked. A vendor who names those things alongside their own failures isn’t deflecting, they’re investing in an outcome. That’s the signal the recovery is real.

If the executive meeting produces only promises and general commitment, keep the pressure on. Real engagement looks like specific admissions, named resources, and a willingness to hold the mirror up to both sides of the table.

You hold the accountability. Be the human in the middle.

Through all of it, the client holds the ultimate accountability. The master conductor holds the responsibility for execution and integration across the vendor ecosystem. That distinction isn’t administrative. It means the client can’t outsource their judgment, regardless of how rigorous the governance model looks on paper.

Think of it like the vendor can hallucinate. Not out of malice, but because every status report is a curated narrative produced by people whose compensation, future work, and professional reputation depend on how that narrative lands. The program deck isn’t neutral data, it’s information filtered through interests. What’s present tells you something. What’s absent, however, tells you more.

Be the human in the middle. Verify, cross-reference, ask questions the deck didn’t answer, and notice what’s missing as much as what’s there. If the steering committee is only hearing good news, that’s a sign someone is deciding what leadership is ready to hear, not that the program is running well.

Demand forecasts, not status reports. Look for hard evidence that can be independently corroborated. When the vendor names a client performance gap alongside their own, take it seriously. That’s the accountability model working the way it’s supposed to, not a deflection.

The warning signs may not always be apparent, though. The window is open, but won’t stay that way, so waiting isn’t a strategy.

Shadow AI is already inside your organization. Now what?

For most CIOs, AI adoption is no longer a question of if. It is a question of how fast. While many organizations are actively rolling out approved tools and building roadmaps, a second reality is unfolding in parallel. AI is already being used across the enterprise without formal approval, without governance and often without visibility.

This is the rise of shadow AI. Unlike previous waves of shadow IT, this is not just about unsanctioned tools. It is about employees using AI to influence decisions, generate content and interact with sensitive data in ways that extend beyond traditional controls. The risk is not simply that it exists. The risk is that it exists without oversight

Why shadow AI is spreading faster than you think

In most organizations, the growth of shadow AI is not driven by negligence. It is driven by a set of understandable and often rational decisions.

One factor is short-term cost avoidance. Employees can access powerful AI tools for little or no cost, delivering immediate productivity gains. At the same time, enterprise-grade solutions require licensing, integration and security investments. In the absence of a clear mandate, many organizations tolerate the tradeoff.

Cultural dynamics also play a significant role. Leaders are hesitant to introduce restrictions that could be perceived as limiting innovation or frustrating high-performing employees. In a competitive talent market, access to modern tools is often seen as part of the employee experience, not just a technology decision.

Governance gaps further complicate the issue. In many cases, ownership of AI is unclear. Security teams focus on risk, legal teams focus on compliance, HR considers ethical implications, and IT is expected to enable productivity. Without clear accountability, decisions stall while usage continues to grow.

The pace of change adds another layer of complexity. The AI landscape is evolving so quickly that leaders are understandably cautious about investing in governance models or platforms that may be outdated within months. This often leads to analysis paralysis, where organizations delay action while waiting for standards to mature.

At the same time, the benefits of AI are becoming increasingly visible. Employees are working faster, automating repetitive tasks and in some cases delivering higher-quality outputs. This creates what I would describe as a productivity paradox. Leaders recognize the risks but are reluctant to slow down tools that are clearly improving performance.

Finally, practical constraints cannot be ignored. IT teams are already stretched across multiple priorities, and building an AI governance model requires both time and investment. Funding for governance tools, monitoring capabilities and cross-functional oversight is not always readily available, especially when the return is framed as risk mitigation rather than revenue generation.

The result is a growing disconnect between how AI is actually being used and how it is formally managed.

The real risk is not usage. It’s invisibility.

It is important to be clear. Shadow AI is not inherently negative. In many cases, it reflects a workforce that is curious, resourceful and motivated to improve how work gets done.

The challenge is not the presence of AI. The challenge is the lack of visibility into how it is being used.

When AI operates outside of governance, several risks emerge. Sensitive data may be entered into external models without proper safeguards. Outputs may be inaccurate or biased, yet still influence decisions. Intellectual property may inadvertently be shared. Over time, these risks compound, especially as usage scales.

Industry research reinforces this concern. According to IBM, ungoverned AI systems are more likely to be breached and more costly when they are. Similarly, frameworks such as the National Institute of Standards and Technology AI Risk Management Framework emphasize the importance of governance, transparency and accountability as foundational elements of responsible AI adoption.

At the same time, the idea of banning AI is increasingly unrealistic. Employees will continue to experiment with tools that make them more effective. The question for leaders is not whether AI will be used. It is whether its use will be visible, guided and aligned to enterprise priorities.

From shadow to strategy

The goal is not to eliminate shadow AI. The goal is to bring it into the light and channel it productively.

This begins with acknowledging that employees are often ahead of formal policies. Rather than responding with strict controls, organizations should focus on creating safe and supported pathways for adoption. Providing approved, enterprise-grade tools gives employees an alternative to external platforms. Clear guidelines help define acceptable use without creating confusion. Education builds awareness of both the benefits and the risks.

Monitoring also plays a role, but it must be implemented thoughtfully. The objective is not to create a culture of surveillance. It is to understand usage patterns, identify risks early and guide behavior in a way that builds trust rather than fear.

Organizations that take this approach are better positioned to move quickly without losing control. They are shaping how AI is used, rather than reacting after issues emerge.

What CIOs should do next

Addressing shadow AI does not require a perfect or fully mature strategy from day one. It requires momentum and clarity.

Start by making the invisible visible. Even a lightweight assessment can provide valuable insight into where AI is being used and for what purposes. This does not need to be complex. The goal is to understand reality before defining policy.

Next, establish clear ownership. Whether governance sits within IT, security or a cross-functional team, accountability must be defined. Without it, progress will remain slow and fragmented.

It is also important to invest in enablement, not just enforcement. Employees will adopt the tools that help them work more effectively. If the organization provides secure, approved options, adoption will naturally shift in that direction.

Finally, communicate openly. Employees are far more likely to follow guidelines when they understand the reasoning behind them. Transparency builds alignment and reduces the perception that governance is simply a barrier to productivity.

If you are looking for practical frameworks, resources like the National Institute of Standards and Technology AI Risk Management Framework and World Economic Forum guidance on responsible AI adoption provide helpful starting points.

The bottom line

Shadow AI is not a future concern. It is already embedded in how work gets done across most organizations. Ignoring it does not reduce the risk. It simply makes the risk harder to see.

The organizations that succeed will not be the ones that attempt to shut it down. They will be the ones who recognize it early, bring it into the open and align it with their broader strategy.

In doing so, they will turn what appears to be a risk into a source of advantage.

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The AI workplace paradox: Higher productivity, higher anxiety

Workers are facing a conundrum: They worry about the potential for their displacement by AI even as it dramatically speeds up their own productivity.

According to a new survey from Anthropic, workers in roles most likely to be taken over by AI (developers or IT workers, for instance) recognize their precarious position. Yet, perhaps naturally, they readily adopt the tools that could take their jobs, and see first-hand how well they work.

This measurement is fundamentally different from the way others are gauging AI job displacement, noted Thomas Randall, research director at Info-Tech Research Group.

While macro reports, such as those from Goldman Sachs, the International Monetary Fund (IMF), or the World Economic Forum (WEF), are asking what share of existing job tasks AI could theoretically perform in the future, “Anthropic is measuring qualitative experiences of workers in the present,” he pointed out. This “tells us how people are navigating this landscape in real time.”

The paradox of AI in the workforce

Anthropic’s survey of 81,000 Claude users gauged peoples’ “visions and fears” around advances in AI, and weighed these findings against the company’s own measurement of jobs most vulnerable to AI displacement. This was based on Claude usage data; jobs are identified as more exposed when associated tasks are significantly performed on the platform, in work-related contexts, and take up a larger share of a role.

Some occupations at risk include computer programmers, data entry keyers, market researchers, software quality assurance analysts and testers, information security analysts, and computer user support specialists.

Overall, one-fifth of respondents expressed concern about displacement, noting that their job, or at least aspects of it, is being taken over by automation. Those in jobs identified as most exposed readily recognized that fact, voicing worry three times as often as those in less at-risk positions. One software engineer remarked: “like anyone who has a white collar job these days, I’m 100% concerned, pretty much 24/7 concerned, about losing my job eventually to AI.”

Early-career respondents were also more nervous than others.

At the same time, those in the highest-paid occupations reported the largest productivity gains when using AI. This is most notably in terms of their ability to perform new tasks, which was cited by 48% of users. In addition, 40% of workers said the technology helped speed up their work, and a little more than 10% said it improved the quality of their work.

In general, enterprise usage of AI is “actually quite consistent,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. Teams are using the technology “where information is abundant and time is limited,” such as in drafting documents and code, summarizing content, responding to customer queries, navigating internal systems.

Is AI actually creating more work?

Still, not everyone thinks AI makes their jobs easier or faster. In some cases, people felt it made their work harder; for instance, project managers are assigning tickets for issues that are much more difficult to solve, Anthropic noted.

Gogia agreed that, even when tasks become easier, scope and responsibilities expand, and roles can absorb adjacent tasks. This results in a “redistribution of effort,” rather than a reduction of effort.

“Faster generation means higher expectations on quality,” he said. More output feeds into decision pipelines that are already constrained. “In some cases, the system becomes heavier, not lighter.”

Delayed impact on enterprises

 The market is rewarding those who can integrate AI into complex workflows to do more, faster, and often with better outcomes, Gogia noted. However, the most exposed tasks, including  documentation, basic coding, routine analysis, and structured support work, often “sit at the base of the experience ladder.”

These very tasks traditionally have given entry-level workers a way in, and the automation of them reduces the urgency for companies to hire them. “What you begin to lose is not the job,” said Gogia. “It is the path into the job.”

This can have a delayed impact; enterprises may not realize until years later that they do not have enough mid-level experts because they didn’t bring enough people in at lower levels. As AI plays a greater role in the workplace, there must be a “conscious effort” to rethink how people enter and grow, Gogia said. “New pathways need to be created, and they need to be deliberate.”

How enterprise leaders should adjust

As is often the case, sentiment moves faster than structural change, Gogia pointed out. Workers feel the shift almost immediately, but organizations take longer to adjust hiring, redesign roles, and rethink workforce structures.

“This is why expectations can become misaligned,” he noted. The reality is that most enterprises have introduced AI into existing ways of working without fundamentally changing them. Acceleration occurs in unchanged systems that still carry the same dependencies, approval chains, and coordination challenges.

Ultimately, Gogia advised, leaders must approach the shift with “intentional design.” This requires clarity, he emphasized; people need to understand how their work is expected to change. What will be enhanced? What will reduce? Where should they focus their development?

Baselines are moving: Roles may begin to look “oversized” as what used to be considered a full day’s work begins to look like half a day’s work, or what used to be considered efficient begins to look average. “AI is changing how work is done, but more importantly, it is changing what work expects from people,” said Gogia.

As well, Info-Tech’s Randall pointed out that workers who experience AI expanding what they can do by performing tasks previously outside their competence appear to relate to AI more positively than those who experience it as doing their existing job faster. So, he advised, “tech leaders should design AI deployment around capability extensions.”

Along with goal setting, managers must have support, Gogia emphasized. They set expectations and interpret strategy, and when they’re not properly equipped, “even the best tools will fall short,” he said. Measurement must also evolve; enterprises need to look at quality, sustainability, and capability development over time.

“What we are witnessing right now is not a sudden disruption,” said Gogia. “It is a gradual shift that is becoming impossible to ignore.”

This article originally appeared on Computerworld.

Your AI coding agent isn’t a tool. It’s a junior developer. Treat it like one

Yet that is precisely how most organizations are deploying AI coding agents today. The prevailing narrative around “AI-powered development” frames these systems as productivity tools. Vibe-coding and agentic coding are considered something closer to a faster autocomplete or a more sophisticated IDE plugin. Flip the switch, the story goes, and suddenly your engineering organization becomes dramatically more efficient. Everyone is “all in” on the first hand of cyber-Texas Hold ’Em. That mental model is wrong.

AI coding agents are not tools. They behave far more like junior developers: Capable, energetic, sometimes brilliant, but absolutely capable of causing catastrophic damage if given autonomy before they understand and respect the environment they’re operating in.

The organizations that treat AI coding agents like tools will create and accumulate technical debt at unprecedented speed. The organizations that treat them like junior engineers by onboarding them as talent, pairing with them and teaching them context will unlock the productivity gains everyone is chasing. The difference between those outcomes is not the technology. It is the management model.

The lesson every engineer learns early

Midway through the DevOps phase of my career, I worked at the CME Group, where the exchange operates one of the most critical financial infrastructures on the planet. The CME processes roughly a quadrillion dollars’ worth of contracts annually and, at the time, ran across five datacenters with more than 10,000 servers, including racks of Oracle Exadata systems costing hundreds of thousands of dollars each. The biggest SIFI of SIFIs.

You did not get root access to that environment on day one.

Instead, you were paired with a mentor. Your mentor was part of a buddy system for onboarding new hires and was effectively a docent for the infrastructure. My mentor was a deeply technical manager named Matt, one of the most capable engineers I have ever worked with. His job wasn’t simply to show me which commands to run or where to find documentation. His job was to teach me how to ask the platform, a system of systems, meaningful questions.

When you’re managing infrastructure at that scale, every question returns thousands of answers.

  • Are the matching engines pinned correctly to CPU cores?
  • Are cgroups configured properly for workload isolation?
  • Which RAID arrays are starting to show drive failures?
  • Are firmware and BIOS versions aligned across production and QA?

None of this can be learned through a quick tutorial or a training video. You learn by doing. You learn by working through the ticket queue, performing dry runs, preparing rollback plans and executing changes within narrow maintenance windows (a few minutes per week).

The lesson wasn’t simply technical. It was epistemological. Engineering expertise is not about knowing commands. It is about knowing which questions matter and how to understand the response. And that knowledge only develops through mentorship, iteration and experience.

Why the pair-programming model matters

The software industry already solved this problem decades ago through a practice called pair programming. In agile teams, a senior developer pairs with a junior one. They work together on the same problem in real time. The junior developer contributes energy and fresh thinking, while the senior developer contributes experience and judgment. The result is faster capability development without sacrificing quality. At first, it might seem an expensive allocation of resources, but when you think it through, it is really a strong knowledge management technique.

AI coding tools are like a super smart baby, a nascent intelligence that is as eager as any recent college graduate, but without much in the way of real-life experience solving real-world problems because it cannot rely on a body of lived experience and hard-won lessons in software development, release engineering and debugging. That description should sound familiar. It is essentially the profile of a junior developer.

The implication is obvious once you see it: the most effective deployment model for AI coding agents is the same pairing model that works for human developers. Human plus agent.

Not a human supervising an agent after the fact. Not just a human reviewing pull requests from an automated pipeline. But genuine co-development, with contextual education on why the vulnerability should not be introduced in the first place. When that pairing works, the productivity gains are real. When it doesn’t, you ship vulnerabilities faster than your security team can ever hope to triage them.

What the agent gets wrong first

The first time I worked alongside a coding model on a real security problem, the mistake it made was subtle but revealing. I was experimenting with ways to harden an API without introducing latency or complexity on the client side. The goal was to produce a transparent security uplift that improved the API’s defensive posture without forcing developers to substantially change how they interacted with the service.

The model generated plausible suggestions quickly. Too quickly. Some of the techniques it proposed were technically correct but operationally obsolete. Others referenced security mechanisms that had been deprecated. Still others ignored non-functional requirements around compliance or performance. In other words, the model surfaced relevant information but lacked the judgment to distinguish wheat from chaff. 

There is also a tendency to accept the legitimacy of the ask rather than questioning the assumptions and baseline parameters of the situation. The agent is not going to think outside the box (unless it is hallucinating a nonexistent function or package/library that solves the problem). It assumes that the question being asked for it to try to solve is a legitimate and valid question or problem to be solved.

Humans develop that discernment over time. It’s part of how we move from data to information to knowledge to wisdom. What information scientists have called the DIKW pyramid.

Models don’t struggle their way up that pyramid. They jump directly to conclusions. The struggle, however, is a messy process of trial, failure and iteration, but it is where human experience and knowledge form. That knowledge is then further refined and distilled into wisdom. When that process is skipped, real expertise never develops. This is why treating AI coding agents as tools is dangerous. Tools don’t need to exercise judgment. Junior developers do.

How trust actually develops

Think about the best junior engineer you ever worked with. How long did it take before you trusted them to work independently? Rarely less than months. Oftentimes a year or more.

Trust emerges gradually. It grows from observing how someone works through problems: how they document changes, how they write tests, how they think about rollback procedures and anticipating edge cases and race conditions. In my own teams, I’ve always preferred a management philosophy of 100% freedom and 100% responsibility (Netflix Manifesto circa 2001).

Engineers on my teams are expected to behave like owners of the company. They are indoctrinated to commit infrastructure changes as code. They document their reasoning. They attach testing artifacts to their pull requests. We track progress not just by time spent but by contributions: Commits, documentation, testing evidence and operational discipline.

That process shapes junior engineers into reliable junior engineers. The exact same logic applies to AI coding agents. Trust should expand progressively.

  • At first, the agent proposes little code snippets and stanzas.
  • Then it drafts functions and packages libraries.
  • Eventually, it might implement entire features, but only after proving it understands the environment and the risk appetite of the company.

Skip those steps, and you aren’t accelerating development. You’re accelerating chaos being driven by FOMO and FUD.

Learning from more than one chef

Over the course of my career, I’ve worked across a wide range of industries: dot-com era web development in San Francisco, trading infrastructure in European financial markets, cloud transformations for legacy enterprises and large-scale infrastructure engineering.

Each environment changed how I thought about software and security. The dot-com era taught speed and experimentation. European financial institutions taught rigorous project governance (PRINCE2 anyone?). Large-scale options and commodity exchanges taught what real operational resilience looks like.

Those experiences fundamentally reshaped how I approach engineering problems. AI agents will benefit from the same diversity. Pairing them with multiple engineers and rotating pairings over time will expose them to different coding styles, architectural philosophies and security techniques. Best practices, but not monolithic best practices aggregated and homogenized by token prediction algorithms trained on millions and billions of lines of code. Just as aspiring chefs learn from multiple masters, agents improve faster when exposed to varied expertise.

A warning for CISOs

Many security leaders today are under pressure to reduce developer headcount because executives believe AI can absorb the workload. This assumption misunderstands both security and AI. If an organization already has strong security discipline with well-documented architectures, clear coding standards and mature review processes then AI agents will amplify that core mindset and culture.

But if the organization has weak security habits, AI will amplify those weaknesses even faster. Human knowledge is like sunlight. Large language models are more like moonlight. A mere reflection of that knowledge. You cannot build a thriving ecosystem entirely under moonlight. Sooner or later, you need the sun, despite what the vampires and werewolves howling at the moon might lead you to believe.

The real promise of AI development

None of this is an argument against AI coding tools. Used properly, they are extraordinary collaborators. They can surface patterns across massive codebases, accelerate documentation and help engineers explore alternative designs more quickly than ever before.

But unlocking that potential requires the right mental model. Not as a tool, but as a junior developer. Onboard them. Pair with them. Teach them your systems, regale them with your stories of isolating a bug or race condition that took weeks to pinpoint. Rotate them across your teams. Expand their responsibilities gradually as trust develops.

That investment phase is what transforms AI from a novelty into a genuine multiplier. And like every good mentorship relationship in engineering, the payoff compounds over time. Treat your AI coding agent like a disposable tool and you’ll get disposable code (aka slop).

Treat it like a junior developer and you might just raise up the best engineering partner you’ve ever had.

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Why hiring ‘AI engineers’ won’t work

Practically every company today is posting roles to hire an “AI engineer.”  They’re likely assuming that an “AI engineer” can handle everything from product development to infrastructure to data integration. Most of the time, though, they’re going to be disappointed.

That’s because assessing the competency of engineers has always been hard–and adding AI to the mix makes it even harder–and companies are often testing for the wrong thing. Under the umbrella of “AI engineer,” they’re collapsing at least three different types of technical work into a single job description, then wondering why the person they hired can’t do the job they need done.

At Andela, where we assess, train and vet software engineering talent as the core of our business, we’re finding that basic AI skills assessments produce an almost 75% fail rate.

My first reaction when I saw the 75% fail rate was that we had an assessment problem. But the more I dug in, the clearer it became that the problem was upstream. Those candidates weren’t failing because they lacked skills. Many of them are exceptional engineers. They were failing because the entire industry is based on assessment frameworks that can’t distinguish between the types of AI work that need to be done.

Consider this situation. I was recently reviewing assessment results for a batch of AI engineering candidates. One candidate stood out: strong resume, passed the coding assessment and defined every concept we threw at them: RAG architectures, agentic search, vector databases, prompt chaining. On paper, this person had the skills.

Then we got to design. We presented a real enterprise scenario and asked which approach they’d use and why. The candidate described a RAG implementation. The solution was technically correct and valid. But for this use case, a RAG implementation would have required significantly more engineering while producing less complete results than an agentic search approach. (The problem required dynamic reasoning across multiple data sources rather than retrieval from a fixed index.) The candidate knew the concepts but lacked the judgment to know which solution was dramatically better for the specific problem.

I’d call that a gap in technical taste: the ability to choose between valid options and find the one that’s right for a specific context. And it’s the gap our assessments, and almost every assessment pipeline in the industry, weren’t built to catch.

And it’s costing real money. Enterprises are burning months on mismatched hires, misaligned teams and AI initiatives that stall, not because the technology failed, but because the people doing the work were the wrong people for that particular work. Highlighting this difficulty is ManpowerGroup’s 2026 Talent Shortage Survey, which found that AI skills have surpassed all others to become the most difficult for employers to find globally, with 72% of employers reporting hiring difficulty.

Digging deeper

In my previous article, I spoke about how enterprises should seek to hire Forward Deployed Engineers (FDEs) who can bridge engineering, architecture and business strategy, to push AI past the ‘integration wall’ and into production. FDEs are the expedition leaders. No company has enough of them. No company can afford to hire enough of them for all the work ahead.

So, what do you do below the FDE layer? You have to dig deeper. For every one FDE, teams will need three or four engineers operating in more specific modes of work. In our experience, the AI work that enterprises need done falls along a spectrum defined by three archetypes.

  • Prototypers. These are the rapid experimenters. They are engineers, product managers or designers who use AI tools to quickly test ideas, find value and throw away what doesn’t work. In a previous era, validating a new product concept meant scoping a project, building a team and committing to a six-month build cycle. Now one person with the right tools and good instincts can shortcut that entire process, testing and discarding dozens of ideas to find the ones worth investing in. The prototyper’s technical taste is about sensing what’s valuable before an organization commits real resources.
  • Builders. The engineers who turn validated ideas into production systems. A builder needs to do more than ‘vibe code.’ They need to operate as agentic engineers: architecting the system, orchestrating the agents to build it, verifying the output and shipping with confidence. Critically, building in an AI context means building the full stack, including the data pipelines that organize content from disparate systems, the access controls that govern what the AI can reach and the integration layer that connects AI to the messy reality of enterprise data and infrastructure. Without this end-to-end capability, AI stays trapped in sandboxes. The builder’s technical taste is about choosing the right architecture and integration approach when multiple valid options exist and knowing which one will be dramatically better for a specific production context.
  • Scalers. The engineers responsible for reliability, governance, observability and production AI operations. These professionals were, in a previous era, DevOps engineers. They know how to deploy LLMs and manage the liability of model output at enterprise scale. The scaler’s technical taste is about tradeoffs: performance versus cost, governance rigor versus development velocity and risk tolerance versus time to market.

These aren’t rigid job categories. They’re patterns of AI engagement. In practice, they blend. A backend engineer on a given project might spend 60% of their time doing builder work and 40% on scaling. The point isn’t to put people into boxes. It’s to give enterprises a vocabulary for decomposing what they need, so they stop collapsing fundamentally different work into a single job posting.

These patterns have different toolchains, different skill profiles and different hiring criteria. Companies that treat them as interchangeable will end up building subpar teams. Understanding where your AI initiatives fall along this spectrum is one of the most important change-management decisions enterprises face in an AI-first world, and it’s why companies that identify their specific location on this spectrum move dramatically faster than those hiring generically.

How to think about AI talent

Here’s where it gets practical. Prototypers, builders and scalers are not job titles. They’re lenses that sit on top of the domain roles enterprises have always hired for: frontend engineers, backend engineers, data engineers, DevOps/SRE and so on. To move from the vague ‘AI engineer’ to a structured picture of what you need, you have to think across three dimensions.

  • Role is the foundation: what technical domain does this person work in? Backend, data engineering, DevOps/SRE, full stack? These are the roles enterprises have always hired for. They come with foundational skills like API design, database architecture and CI/CD pipelines. And they come in specific flavors: a Python backend engineer is not a Java backend engineer. This layer hasn’t changed because of AI.
  • Seniority determines the level of judgment and autonomy you can expect. A senior backend engineer with 10 years of experience brings architectural instincts and decision-making under ambiguity that a two-year engineer doesn’t. Seniority is also where technical taste compounds. An engineer with deep experience has seen more tradeoffs, made more wrong calls and developed the pattern recognition that allows them to make better-than-default decisions. Not every role on an AI initiative requires a senior engineer, but the roles that involve system design decisions, risk trade-offs and client-facing judgment absolutely do.
  • AI engagement pattern is how this person engages with AI systems. This is the archetype layer, and it’s what’s new. A backend engineer doing builder work (designing the orchestration logic for an agentic workflow and integrating it with enterprise data) needs fundamentally different technical tastes than that same backend engineer doing scaler work (deploying LLM infrastructure and building observability for model performance). The role is the noun. The archetype is the adjective. And it changes what you need to test for.

In practice, certain role families map naturally to certain AI engagement patterns. Prototypers can come from anywhere; engineering, product, design and are often already on your team. They’re the person who’s always building side projects and testing ideas. Builders tend to draw from full-stack, frontend, backend, data engineering and AI/ML talent. Scalers tend to draw from DevOps/SRE, security, backend and infrastructure engineering. Forward-deployed engineers span all the above with business acumen and stakeholder fluency.

Hiring with precision

This multi-dimensional view is what allows enterprises to stop hiring for a vague ‘AI engineer’ and start composing teams with precision. It’s also what makes a credible assessment strategy possible, because now you know what you’re testing for at each level.

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The 4 disciplines of delivery — and why conflating them silently breaks your teams

A mid-size bank has three core applications: a loan origination system, an account servicing platform and a branch operations system. They’ve served the domestic market for over a decade —patched, extended and held together through years of incremental fixes. They work, but just barely.

Here’s the deeper problem that nobody talks about: business leadership sees three applications. What they don’t see is that distinct product capabilities are buried inside each one, never identified as standalone products. The loan origination system doesn’t just handle lending — credit decisioning, pricing, document generation, compliance workflows and customer identity verification are all tightly coupled within it. The account servicing platform has transaction processing, fee management, dispute handling, statements and regulatory reporting tangled together. The branch system has customer onboarding, relationship management and product cross—sell logic embedded as features rather than recognized as products.

Nobody ever drew the line between these capabilities because they didn’t need to. It all worked well enough for one country.

Then leadership announces: “We’re going global.”

And immediately, every assumption baked into the original design — single currency, one regulatory regime, one set of payment rails — needs to be rethought. The existing products must keep running and improving for domestic customers while the platform evolves for new markets. Some capabilities are foundational and must come first because everything else depends on them — a multi—currency ledger, a compliance engine that supports jurisdiction—specific Know Your Customer (KYC) and Anti-Money Laundering (AML) rules. Others, like localizing the domestic customer experience or designing regulatory reporting frameworks for new markets, can be built in parallel. And some, like account origination in new markets or integration with local payment networks, are blocked until the foundational layers are ready.

The sequencing is critical. Getting it wrong means months of wasted engineering effort — teams building capabilities that can’t function because the dependencies underneath them don’t exist yet.

If this sounds familiar, it should. Replace “bank” with “insurance company,” “healthcare platform” or “logistics provider” and the pattern holds. A product built for one context, patched over time, now being asked to scale beyond what it was ever designed for. The technology differs, but the organizational challenge is identical.

This is where four distinct disciplines must show up clearly — product management, technical architecture, program management and release management. Each answers a fundamentally different question. Each requires different expertise. And in my experience across enterprise environments, this is exactly where most organizations stumble. Not because they lack talent, but because these disciplines get conflated, left undefined or silently absorbed by people who were never meant to own them.

4 disciplines, 4 different questions

The confusion starts when organizations treat these disciplines as interchangeable — or worse, assume one person or team can own all of them. They can’t. Each discipline exists because it answers a question that the others are not equipped to answer.

  1. Product management answers: What are we building, for whom and why? In our banking example, this means deciding which markets to enter first, which product capabilities to extract from the monoliths and rebuild versus extend, and what customers in Germany need that differs from customers in the US. Product management owns the vision, the principles that guide decision-making and the strategy that translates vision into priorities. Without it, every other discipline is guessing.
  2. Technical architecture answers: How do we build it, so it meets the quality attributes the business requires? This is where scalability, compliance, performance and maintainability get translated into structural decisions. In our example, the architect determines that the multi-currency ledger is foundational and must be built first, that data residency constraints require region-specific deployments and that the monoliths need to be decomposed along product boundaries rather than along application boundaries. Architecture defines what is technically feasible, what constraints exist and what sequence the technical dependencies demand.
  3. Program management answers: How do we coordinate across teams, dependencies and constraints to deliver? The program manager takes the product priorities and the architectural constraints and maps the critical path. In the banking scenario, this means identifying that the compliance engine team and the ledger team are on the critical path, that three other teams are blocked until those foundations land and that two workstreams can run in parallel. Program management orchestrates — sequencing work, managing cross—team dependencies, surfacing risks and making sure everyone is working from the same timeline.
  4. Release management answers: When and how does each capability get to production safely? This is not the same as program management. In a global expansion, release management plans phased rollouts market by market, manages regulatory approval gates that differ by jurisdiction, defines rollback strategies when a release works in one region but fails compliance in another and coordinates environment strategy across multiple deployment targets. Release management is the discipline that ensures what was built reaches customers without breaking what already works.

These four disciplines form a chain. Product management defines the what and why. Architecture translates that into the how. Program management orchestrates the when and in what order. Release management executes the delivery. Remove any link and the chain breaks.

What happens when the lines blur

Back to our bank. Leadership has declared the global expansion. The vision is clear at the highest level — go global. But product management hasn’t defined which markets come first — is it Germany because of market size, or Singapore because of regulatory ease of entry? It hasn’t clarified which product capabilities to prioritize — does the bank lead with lending, which has the highest revenue potential, or with account origination, which is a prerequisite for everything else? And nobody has addressed how the domestic experience evolves alongside the expansion — do existing US customers get a redesigned platform that shares the new global architecture, or does the domestic product stay frozen while all investment goes toward new markets?

There’s energy and ambition, but no product strategy to channel it.

This is where the blur begins.

The technical architects, facing a vacuum of product direction, start making decisions that aren’t theirs to make. Without knowing which markets come first, the team builds a fully abstracted integration layer capable of connecting to any payment network globally when the first wave of expansion only required support for two European markets. Without knowing which product capabilities to prioritize, the architects begin decomposing all three monoliths simultaneously — lending, origination and servicing — spreading the team thin across parallel efforts when a clear product priority would have focused them on one decomposition at a time. And without knowing whether the domestic experience will converge with the new global platform or remain separate, they hedge by designing a multi—tenant architecture that can support both models — doubling the complexity of every design decision when a clear product direction would have eliminated one path entirely.

These aren’t bad engineering decisions in isolation — they’re rational responses to an absence of product clarity. But they result in an over-engineered foundation that takes twice as long to deliver and delays everything downstream.

The problem deepens when product management does exist but is fragmented. The bank has several product managers —one for lending, one for account origination, one for servicing, one for payments. Each is convinced their product capability is the priority. The lending product manager pushes to go first because of revenue impact. The account origination product manager argues that lending can’t function without accounts being opened first. The servicing product manager insists that launching origination without servicing in place means onboarding customers the bank can’t support. Each of them is right within their own scope — but nobody owns the cross—product view that determines the sequence. And layered on top of this is a constraint that nobody wants to own: these product capabilities are running in production today for domestic customers. Any refactoring or decomposition must happen without breaking what already works. The interdependencies aren’t just technical — they’re live business operations with real customers on the other end.

Meanwhile, program management sees the timeline slipping before a single line of code has shipped. Lacking clear product priorities, the program manager fills the gap by making market sequencing decisions based on what they can control — team availability, existing vendor relationships, which teams have capacity. The first market becomes whichever one is easiest to staff for, not the one with the highest strategic value. The sequencing is now driven by operational convenience rather than product strategy. Well—intentioned, but the wrong lens.

On the other side, architecture gets bypassed entirely when leadership pushes for speed. “Just make it work for Germany” becomes the directive. Instead of waiting for the decomposed platform the architects were designing, the team clones the domestic codebase, hardcodes German-specific fee structures and product rules directly into the application logic and stands up a separate database instance with locale-specific columns bolted onto the original schema. It works for Germany. But when the second market launches, none of it is reusable — the fee logic can’t be swapped, the product rules can’t be reconfigured, the schema can’t be extended and the organization now has two diverging codebases to maintain instead of one platform to scale.

And release management? It gets collapsed into deployment. Nobody plans for the fact that regulatory approval cycles in the EU take weeks longer than domestic release cycles. Nobody defines what happens if a release passes validation in one market but fails in another. The first production incident in a new market catches everyone off guard — not because the team was incompetent, but because the discipline that would have anticipated it was never given a seat at the table.

None of these failures are caused by a lack of talent. Every person in this scenario is skilled at their actual role. The architect is a strong architect. The program manager is an experienced coordinator. The problem is that each one is being pulled into a role they weren’t meant to play, filling gaps with their best judgment but without the expertise that role demands. An architect making product prioritization decisions will optimize for technical elegance over market fit. A program manager making market sequencing decisions will optimize for execution efficiency over strategic value. The decisions get made, but with blind spots that compound over time.

The cost of dysfunction and silence

The blur described above isn’t just an organizational inconvenience. It has measurable costs — both to the business and to the people inside it.

When product direction is absent and architects over-engineer to compensate, the cost shows up as delayed time to market. The bank’s competitors enter new markets while the engineering team is still building abstractions for markets that may never materialize. When program management sequences work by team availability rather than strategic value, the cost shows up as wasted effort — entire teams delivering capabilities that can’t be used because the dependencies they sit on top of were deprioritized. When architecture gets bypassed in the name of speed, the cost shows up months later as rework. That cloned codebase that got the first market live? It now needs to be untangled and rebuilt properly before the second market can launch. The shortcut didn’t save time. It borrowed it.

These costs compound. Every decision made in the wrong discipline creates downstream consequences that the right discipline would have anticipated. An architect would have known that the cloned codebase wouldn’t scale. A product manager would have known that the third market had more strategic value than the first. A program manager would have flagged the dependency that nobody planned for. A release manager would have built regulatory approval time into the timeline. The knowledge existed in the organization — it just wasn’t in the room when the decision was made.

Then there is the cost of silence.

In many organizations, the people who feel the absence of these disciplines most acutely are the ones least empowered to demand them. An engineer who needs product direction but doesn’t receive it will quietly make assumptions rather than escalate — because escalating feels like admitting they can’t do their job. An architect who sees foundational problems but stays silent because “that’s a product decision, not mine to make.” A program manager who builds timelines around undefined scope and hopes it clarifies later because raising the ambiguity feels like slowing the team down.

Over time, this silence becomes cultural. People stop asking for what they need and start working around its absence. They fill the gaps themselves — not because they want to, but because the work must move forward. And so, the architect becomes a part—time product strategist. The program manager becomes a part—time architect. The engineer becomes a part—time everything. The gaps get filled, but by people operating outside their expertise, accumulating blind spots with every decision.

The human toll is real. The people filling these gaps are often the most capable and committed people in the organization. They stretch because they care. But over time, the constant gap—filling leads to fatigue, frustration and eventually disengagement. Some burn out. Others stop stretching and start going through the motions — learned helplessness born from repeatedly solving problems that shouldn’t have been theirs to solve. And others simply delegate and walk away, not out of apathy but out of exhaustion from carrying accountability without authority.

This isn’t a story about bad people or bad intentions. It’s a story about a structural problem that manifests as individual pain.

Getting it right: Collaboration with clear accountability

The fix isn’t adding more process. It’s removing confusion about who owns what — and then building the habit of asking for what you need rather than silently filling the gap.

It starts with product management. In our banking example, this means someone explicitly owns the answers to: which markets first and why? Which product capabilities get extracted from the monoliths and in what priority? What does the customer in Germany need that the domestic customer doesn’t? What is the minimum viable product per market? These aren’t questions that can be deferred or delegated to architecture or engineering. They are product decisions, and until they are made, every downstream discipline is working with incomplete information. When multiple product managers each own a piece of the portfolio — lending, origination, servicing, payments — someone must own the cross—product prioritization. That means resolving the interdependencies: origination before lending, servicing alongside origination, payments integrated at the right point. Without this sequencing at the product level, program management is left trying to orchestrate work that hasn’t been prioritized, and architecture is left designing for a scope that nobody has bounded.

But product management setting direction is only half the equation. That direction must be translated into work that engineering teams can build. In most organizations, this translation is the responsibility of product owners — the people who take the product strategy and break it down into refined backlogs, epics and user stories with clear acceptance criteria. When this translation doesn’t happen, engineering teams are left staring at a high—level product vision with no actionable path forward. They can’t estimate, they can’t sequence their own work, they can’t identify technical risks early enough to mitigate them. The result looks like inaction from the outside, but from the inside it’s paralysis — teams that want to deliver but don’t have enough definition to start. Product management can set the right direction and architecture can define the right structure, but if product owners aren’t refining and defining the work at the level engineering needs, the delivery pipeline stalls before it begins.

Once product direction exists and is translated into actionable work, architecture can do what it’s meant to do — translate that direction into technical decisions. If product management says, “Germany and France first, with account origination and servicing as priority capabilities,” the architect can now make bounded decisions: what foundational components must be in place, how to decompose the relevant parts of the monolith, what can be reused and what needs to be rebuilt. Architecture stops being speculative and starts being purposeful. The architect also feeds back into product — surfacing technical constraints that affect feasibility, identifying dependencies that influence sequencing and flagging where product assumptions don’t survive contact with the existing codebase. This feedback loop is critical. Product direction without architectural reality—checking leads to roadmaps that look good on slides but fall apart in execution.

Program management takes the product priorities and the architectural constraints and builds the execution plan. The program manager maps which teams are on the critical path, which workstreams can run in parallel, where cross—team handoffs create risk and what decisions are blocking progress. In our example, the program manager identifies that the ledger team and the integration layer team are sequential dependencies for account origination — and that the servicing team can begin designing their decomposition in parallel. Program management doesn’t make product decisions or architecture decisions. It orchestrates the decisions that have already been made, sequences the work and surfaces risks early enough to act on them.

Release management takes the execution plan and ensures safe delivery. In a global expansion, this means planning phased rollouts market by market — not just deploying code but validating that each release meets market—specific requirements before it reaches customers. It means defining what happens when a release works in one market but encounters issues in another. It means coordinating environment strategy, managing feature toggles by region and building rollback plans that account for the fact that rolling back in one market can’t break another. Release management is the discipline that bridges the gap between “it works in our environment” and “it works for our customers.”

The key insight is that these disciplines are collaborative, not siloed. Architecture informs product about what’s technically feasible. Program management surfaces risks back to product and architecture. Release management feeds production learnings back upstream. The information flows bidirectionally. But accountability does not. Product management owns the what and why. Architecture owns the how. Program management owns the orchestration. Release management owns the delivery. When these accountabilities are clear, people stop filling gaps they shouldn’t be filling and start demanding the inputs they need to do their own job well.

The shift isn’t about hierarchy or blame. It’s about giving every discipline the clarity to operate at its best — and the permission to push back when that clarity is missing.

The goal? Make the invisible visible

Product management, technical architecture, program management and release management are not interchangeable. They are not different labels for the same work. They are distinct disciplines, each with its own expertise, its own accountability and its own contribution to getting complex products built and delivered.

The banking example in this article is specific, but the pattern is universal. Any organization scaling a product beyond what it was originally designed for — whether expanding globally, modernizing a legacy platform or decomposing monoliths into products — will hit the same inflection point. The disciplines described here either show up clearly or they don’t. When they do, teams move with purpose. When they don’t, talented people burn energy compensating for structural confusion.

The goal isn’t to add bureaucracy. It’s to make the invisible visible. Name the disciplines. Define who owns each one. Make it safe to say, “I need product direction before I can make this architecture decision” or “I need the architectural constraints before I can build this timeline.” The moment people stop silently filling gaps and start openly asking for what they need, the system begins to self-correct.

These four disciplines aren’t overhead. They’re the operating system of delivery. And like any operating system, they work best when each component does its job and stays out of the way of the others.

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