Automation doesn’t always look dramatic at first. Spot five subtle signs your work is shifting, from oversight to self-serve, and how it adds up over time.
The post 5 Subtle Signs Your Job Is Slowly Being Automated appeared first on TechRepublic.
Automation doesn’t always look dramatic at first. Spot five subtle signs your work is shifting, from oversight to self-serve, and how it adds up over time.
The post 5 Subtle Signs Your Job Is Slowly Being Automated appeared first on TechRepublic.
I remind CIOs, “You will always be transforming.” Every two years, new business drivers emerge, such as the pandemic from 2020-2022 and automation-driven efficiencies from 2023-2024. We’re now in the gen AI era, where most CIOs are under pressure to shift from driving broad experiments to delivering business value and ROI.
As a result, CIOs need to refocus their strategies and communicate an updated vision for transformation. My 2025 article on what’s in and out for digital transformation stressed the importance of developing transformational leaders and AI-ready employees while avoiding AI moonshots and ending lift-and-shift cloud migrations.
In 2026, experts suggest that CIOs must transform IT, transition AI to customer experience (CX) opportunities, and double down on data governance and security.
In 2025, I wrote about how AI is the end of IT as we know it and how CIOs are rethinking IT for the agentic AI era. World-class IT organizations are setting higher expectations, partnering with departments on AI change management, and committing to lifelong learning.
With all the AI innovations impacting IT, CIOs will need to refocus their digital operating models to deliver more capabilities faster, at lower cost, and with higher resiliency.
Sesh Tirumala, CIO at Western Digital, says, “Velocity gets us ahead, resilience keeps us steady, and adaptability ensures we stay ahead. Direction matters, and in 2026, velocity is the real currency of success.”
How can CIOs aim higher when CEOs and boards are demanding ROI from AI? Jay Upchurch, CIO at SAS, says the best and brightest CIOs will snap up commercial responsibilities. “Top CIOs will sell customers and their divisional peers on technology like CMOs, and answer the constant call to do more with less like CFOs.”
I expect many CIOs will reorganize IT in 2026. Some will be mandated to reduce costs and headcount, while others will drive efficient collaboration in their product management, agile, and DevOps practices. Top CIOs will seek opportunities to guide reorganization across the enterprise as agentic AI creates new workflow patterns and cross-department collaboration opportunities.
“CEOs will conclude that AI adoption is no longer a technology problem but a workforce and management problem,” says Florian Douetteau, co-founder and CEO of Dataiku. “Instead of selling cloud migrations and data platforms, consultants will start selling organizational rewiring to prepare for AI-run operations. This shift creates tension inside enterprises because it surfaces the real blocker: leadership culture, not technology.”
Raja Roy, senior managing partner in the office of technology excellence at Concentrix, adds, “The new priority: operating models that support rapid learning, collaboration, and real-time evolution, keeping the human/AI balance aligned to the right tasks, whether an interaction calls for a human touch or machine efficiency.”
Recommendation: CIOs should review IT’s structure and agile practices to increase the effectiveness of delivering AI innovations and improve operational resilience.
Data governance is a critical function in global regulated enterprises, where governance, risk, and compliance (GRC) are critical top-down mandates. Midsize organizations are catching up, as they evolve to data-driven organizations and centralize data for AI initiatives.
While governing relational databases and warehouses is a relatively mature process, deploying agentic AI capabilities requires new tools and practices to extend data governance to unstructured data sources.
“Unstructured data now moves too fast for manual oversight, and organizations can finally govern it as it’s created instead of cleaning it up later,” says Felix Van de Maele, CEO of Collibra. “In 2026, human judgment still matters, but AI-assisted systems, not spreadsheets or static controls, will carry the day-to-day load.”
Van de Maele suggests that AI-powered metadata generation for unstructured data, with integrated data practices for building reliable AI at scale is in, while CIOs should move away from manual tagging, siloed datasets, and one-time compliance efforts.
Additionally, many data governance leaders must get more granular controls on who gets access to what data. Authorizing users to full datasets and file systems is no longer sufficient as more organizations deploy AI agents on top of whatever data an employee can access.
“Many organizations do not know where their sensitive data lives, who can access it, or how much is exposed across cloud and SaaS systems,” says Yair Cohen, co-founder and VP of product at Sentra. “Leaders in 2026 will treat governance as an engineering practice by embedding classification, tagging, and access rules directly into data pipelines, warehouses, and AI workflows.”
Recommendation: CIOs should be paranoid about data risks, take a sponsorship role in data governance, and ensure that improving data quality is prioritized in every AI initiative.
In 2025, I warned CIOs about promoting AI as a driver of productivity and efficiency. Eventually, the CFO wants to see ROI, and this is one reason we saw significant technology layoffs in 2025.
I compiled over 50 expert predictions around 2026 on AI, from agentic workflows improving operations through gen AI embedded in customer experiences. I believe AI will have its Uber and Airbnb moment in 2026, as startups revolutionize customer experiences and disrupt slower-moving business-to-consumer (B2C) enterprises.
One easy way to embrace AI-enabled customer experiences is to upgrade call centers and chatbots without major infrastructure investments. Rob Scudiere, CTO at Verint, says, “Brands can layer an AI-powered chatbot onto their existing application instead of replacing an outdated telephony system and interactive voice response (IVR).”
When considering improving customer experiences, Pasquale DeMaio, VP of Amazon Connect, says to embrace systems that leverage AI and human strengths. “In customer support, agentic AI will manage routine requests while human agents will address complex issues with empathy and nuance, guided by AI insights and recommendations.”
CIOs should recognize a paradigm shift in UX, as data entry forms, customer journeys, and prescriptive reports get replaced with agentic AI capabilities. Focusing on AI in customer support is an easy entry point, as the entire customer experience, especially in ecommerce and SaaS tools, requires redesigning with AI capabilities.
“AI agents will become the frontend of the company as the primary starting point for any and all external contact,” says Antoine Nasr, head of AI at Forethought. “End-users will no longer have to try and navigate to the correct department and tool to get the help or information they need — they will simply interact with the company’s public AI agent in natural language. With that, agent design will become a key concern for several functions, not just customer support.”
Recommendation: Product-based IT organizations are a step ahead in anticipating how AI will evolve CX, and they should plan to segment and learn from early AI adopters.
Several research reports in 2025 highlighted how few AI experiments are being deployed into production and delivering business value. CEOs and boards will demand that CIOs narrow the portfolio of AI experiments and have real plans to deliver ROI from AI investments.
Conal Gallagher, CISO and CIO at Flexera, says in the next era of AI, execution matters more than experimentation. “CIOs will only continue to face bigger challenges and pressure to move beyond the AI experimentation phase and deliver clear, actionable, and measurable business outcomes.”
AI agents from top enterprise SaaS and security companies follow common patterns. These AI agents focus on a primary employee workflow, connect to multiple data sources, and aim to do more than complete tasks. CIOs will have to demonstrate the business value of how these AI agents guide employees in making smarter, faster decisions and the financial impacts of AI-revolutionized workflows.
“Agentic AI delivers measurable ROI in months, not years, because it replaces entire processes, not just parts of them,” says Luke Norris, co-founder and CEO of KamiwazaAI. “Each successful deployment accelerates the next, creating a self-funding innovation loop. More and more enterprises will be realizing this compounding ROI in the coming 6-12 months.”
Experts offer guidance on transitioning from an experimental to an outcome-based mindset. Kerry Brown, transformation evangelist at Celonis, says after years of big AI investment, it’s time to rethink end-to-end processes rather than just adding more automation on top.
“Leaders need to empower employees with visibility into how work really happens, and give them ownership in redesigning it,” says Brown. “When teams have that context and agency, they become true drivers of transformation and help create a faster, more direct path to ROI.”
Ed Frederici, CTO at Appfire, adds, “What’s out in 2026 is treating AI as a standalone, isolated initiative, and the next wave of digital transformation moves beyond scattered pilots to full operational integration. CIOs will treat AI as core business infrastructure rather than a special project — holding it to the same expectations for accuracy, security, and performance as every other critical system.”
Recommendation: Organizations with too many independently running AI experiments should revisit their AI governance strategy, communicate clear objectives, and prioritize where to build AI delivery plans.
Nearly every transformational technology started with a gold rush to deliver innovations, and bolting on security afterward. CIOs will face pressure to move last year’s AI experiments into production this year, and we’ll have to see to what extent security will be implemented in initial deployments.
Many experts chimed in on where CIO’s need to get ahead of the curve. Here are three recommendations:
Recommendation: CIOs must partner with CISOs, legal, and risk management to clearly define AI security non-negotiables, platforms, and implementation requirements.
CIOs should expect the unexpected in 2026, whether driven by volatile economic conditions, new AI capabilities, or headline-making security incidents. My back-to-basics recommendations for digital transformation in 2026 aim to guide CIOs toward growth opportunities while improving operational resiliency.
에너지 전환 가속, 규제 압박 확대, 글로벌 차원의 운영 효율성 제고 등 여러 요구 사항이 맞물리는 환경에서 디지털 트랜스포메이션은 엑솔룸과 같은 산업·물류 기업에게 전략적 핵심 축이 되고 있다. 현재 11개국에서 연 매출 10억 달러를 기록하는 엑솔룸은 경쟁력을 유지하기 위한 사업 전략을 모색하고 있다.
엑솔룸은 지금까지 휘발유와 디젤 운송, 탄화수소와 각종 화학물질 저장, 항공유 공급에 집중해 왔지만, 청정 연료로의 전환이 본격화되는 흐름 속에서 중대한 전환점을 맞이하고 있다. 엑솔룸 IT 디렉터 알폰소 알바레스는 “앞으로 등장할 새로운 에너지원에 맞춰 기존 인프라를 재활용하는 방법을 배워야 한다”라고 설명했다.
과제의 규모는 크지만, 엑솔룸의 디지털화는 새로운 전략이 아니다. 회사는 2000년대 초반부터 플랜트와 파이프라인에 운영 시스템을 도입하며 기술 활용 비중을 본격적으로 높였다. 이후에도 여러 차례 수립된 전략 계획을 통해 플랫폼을 교체하고 시스템을 현대화해 왔다. 알바레스는 “세기가 바뀐 이후 지금까지 지속적으로 이어져온 과정”이라고 언급했다.
현재 엑솔룸은 일상적인 운영의 상당 부분을 중앙에서 자동으로 관리하고 있다. 제품 운송 네트워크와 트럭 적재 터미널은 첨단 기술 시스템을 기반으로 소수 인력만으로 운영된다. 예를 들어 트럭 출입은 자동 번호판 인식 시스템을 통해 관리되며, 운전자는 별도의 인적 개입 없이 시스템 안내에 따라 사용할 호스를 전달받고 적재 완료 시점을 확인한다. 알바레스는 “기술은 엑솔룸에서 특히 사업 실행 측면에서 핵심적인 역할을 하고 있다”라고 말했다.
알바레스에 따르면 엑솔룸에서 활용 중인 기술이 직면한 다음 과제는 성장이다. 계속해서 사업을 확장하고 있는 엑솔룸에서는 기술이 단순히 성장을 따라가는 수준을 넘어 초기 단계부터 이를 뒷받침해야 한다는 설명이다. 그는 “이를 가능하게 하기 위한 의사결정을 내리고 관련 프로젝트를 실행하고 있다”라고 전했다.
가장 최근의 전환점은 2020년 팬데믹 이후였다. 영국과 미국을 비롯한 주요 시장에서의 인수합병을 계기로 엑솔룸의 국제 사업이 확대되면서, 회사는 글로벌 IT 조직을 신설하게 됐다. 약 4년 전 엑솔룸에 합류한 알바레스는 “모든 지역, 모든 사업을 포괄할 수 있도록 IT 접근 방식 역시 글로벌 수준으로 확장돼야 한다는 인식이 공유됐다”라고 설명했다.
현재 엑솔룸의 IT 운영 모델은 글로벌 계층과 로컬 계층이 병행되는 이중 구조를 기반으로 한다. 재무, 관리, 인사, 자산 유지보수와 같은 기업 공통 시스템은 글로벌 차원에서 통합 운영하고, 물리적 운영과 밀접하게 연관된 시스템은 현지에 유지해 유연성을 확보하는 방식이다. 알바레스는 이런 아키텍처를 통해 엑솔룸을 여러 개의 소규모 현지 조직이 아닌 하나의 통합된 기업으로 바라볼 수 있다고 설명했다.
IT 조직은 글로벌·로컬 팀을 포함해 약 70명 규모로 구성돼 있으며, 상시적으로 최대 150명의 외부 전문 인력이 이를 지원하고 있다. 스페인에서 자체 소프트웨어를 직접 개발하는 데 초점을 두기보다는, 프로젝트 관리와 기술 파트너가 요구되는 품질과 기능을 충족하는지 관리하는 역할에 집중하고 있다.
최근 몇 년간 엑솔룸은 첨단 기술, 특히 AI 활용을 한층 강화해 왔다. 현재 사내 업무 환경과 SAP 내에 코파일럿(Copilot)을 적용해 팀 생산성을 높이고 있다. 알바레스는 “사람들이 더 높은 부가가치 업무에 집중할 수 있도록 시간을 확보하는 것이 목표”라고 말했다.
아울러 AI는 파이프라인 네트워크 내 제품 이동 계획과 같은 핵심 사업 프로세스에도 직접 적용되고 있다. 이는 고도의 수학적 분석이 요구되는 매우 복잡한 시스템이다. 또한 엑솔룸은 자체 AI 거버넌스 체계와 AI옵스(AIOps) 솔루션, 코파일럿도 개발하고 있다.
전통적인 산업 분야에 속해 있음에도 불구하고, 엑솔룸 내부에서는 기술 변화에 대한 뚜렷한 저항이 크지 않다는 평가다. 엔지니어 출신 인력이 조직 전반에 두텁게 포진해 있고, 사업 부문 내부에서 자발적으로 추진되는 이니셔티브가 기술 도입을 자연스럽게 이끌어왔기 때문이다. 여기에 기존 IT 조직과는 별도로 디지털화를 전담하는 조직을 운영하며, 해커톤과 내부 교육 프로그램을 통해 새로운 사용례를 발굴하고 혁신을 확산하고 있다.
엑솔룸은 글로벌 전략의 일환으로 재무 관리 체계 고도화에도 나섰다. 회사는 컨설팅 기업 올CMS(All CMS)의 지원을 받아 키리바(Kyriba) 자금 관리 플랫폼을 도입하며 관련 시스템을 전면적으로 업데이트했다. 이번 프로젝트는 SAP R/3에서 S/4HANA로 전환하는 과정의 일부로, 사업의 국제화가 진전되는 환경에서 자금 운영을 보다 통합적이고 중앙에서 관리할 필요성에 대응하기 위한 조치다.
S/4HANA 도입 이후에도 알바레스와 IT 팀은 요구되는 수준의 통제와 가시성을 확보하는 데 한계가 있다고 판단했고, 이에 따라 보다 전문적인 솔루션을 검토하게 됐다. 키리바를 도입한 이후에는 자금 포지션 관리가 개선되고 프로세스 자동화가 확대되면서 수작업에 대한 의존도도 줄어들었다.
재무 부서는 해당 도구가 현금 흐름에 대한 가시성을 높이고 보안을 강화하는 동시에, 그룹의 향후 성장을 뒷받침하는 역할도 하고 있다고 평가했다. 알바레스는 “재무 부서 입장에서 개선 효과가 분명하게 나타나고 있어, 앞으로도 솔루션을 계속 발전시키고 더 많은 프로세스를 통합해 나갈 계획”이라고 설명했다.
엑솔룸이 앞으로 최우선으로 삼고 있는 과제는 자체 데이터센터를 단계적으로 종료하는 작업이다. 회사는 2026년 말까지 모든 IT 환경을 멀티클라우드 모델로 운영하는 것을 목표로 하고 있다.
알바레스는 “다국적 성장을 지원할 수 있는 확장성과 신속한 배포가 가능한 시스템이 필요하다”라며, 이번 전환이 그룹의 미래 성장을 지속하기 위한 또 하나의 중요한 단계가 될 것이라고 언급했다.
dl-ciokorea@foundryco.com
La distribución multimarca lleva años viviendo una profunda transformación marcada por la digitalización, la automatización de procesos y la necesidad de dar una respuesta más eficiente a los clientes. En este contexto opera Corver, compañía española con más de 30 años dedicada a la importación y distribución en exclusiva de productos, accesorios y recambios para motocicletas y motoristas de las principales marcas del mercado.
Al igual que muchas compañías del sector, el grupo, que opera a través de diversas empresas en España y Portugal, se enfrenta a un reto doble: mejorar su eficiencia interna y sostener un negocio cada vez más dinámico, apoyado en el comercio electrónico, la gestión inteligente del catálogo y la toma de decisiones basada en datos. Para ello, la organización ha emprendido un proyecto de transformación digital profundo y progresivo que está redefiniendo la manera en la que trabaja.
Según Marc Codina, IT project manager y CTO del grupo Corver, la compañía “se encuentra en una fase de consolidación y escalado”. Durante 2025 han iniciado la unificación del ERP a nivel de grupo, han puesto en marcha un PIM corporativo como fuente única de datos de producto y trabajan en la convergencia del SGA. “El foco ahora está en estabilizar, extraer eficiencias y extender estándares al resto de compañías del grupo”, apunta.
El proceso de transformación digital arrancó formalmente en el año 2023, con una hoja de ruta que fue activándose por fases entre 2024 y 2025. Codina recuerda que el programa nació con cuatro objetivos principales: “Disponer de un dato maestro único en clientes, precios y producto; homogeneizar procesos en toda la organización, desde order-to-cash hasta procure-to-pay; asegurar la escalabilidad del comercio electrónico; y facilitar un reporting directivo en tiempo casi real”. Este enfoque busca eliminar silos y crear un modelo operativo más coherente y medible.
Uno de los proyectos estrella acometidos por la compañía dentro de ese proceso de transformación digital es la estandarización del ERP a nivel de grupo, una iniciativa que ha permitido unificar procedimientos y mejorar el control operativo en distintas áreas, aprovechando todas las capacidades de la solución SAGE X3. En este despliegue, Corver ha colaborado con el integrador Aitana, cuya participación ha sido clave para acompañar el cambio cultural asociado al proyecto. “Su equipo nos ha acompañado con una alta profesionalidad y experiencia, ayudándonos a superar la resistencia natural al cambio que conlleva una transformación tecnológica de este alcance”, explica Codina.
Más del 95% de los pedidos ya se integran automáticamente en el ERP sin intervención manual, lo que ha permitido optimizar el proceso de compra, reducir errores y duplicidades, disminuir incidencias por recepciones incorrectas y aumentar la visibilidad en la trazabilidad de pedidos e información. Aunque la compañía no aporta cifras exactas de la inversión en este proyecto, según Marc Codina, “se sitúa en seis cifras, con un retorno estimado de entre 18 y 24 meses gracias a la eficiencia y el incremento de conversión en ventas”.
Marc Codina, IT Project Manager y CTO del grupo Corver.
Corver
Corver ha tenido que afrontar desafíos como la gobernanza del dato, la normalización de catálogos heredados, la gestión del cambio y la formación de usuarios
Todo ese proceso de transformación ha llevado a que, en la actualidad, la base tecnológica de la compañía se sustente en un ERP estandarizado que centraliza procesos financieros y operativos, en un PIM corporativo que actúa como repositorio único de información de producto en varios idiomas y en una plataforma eCommerce B2C y B2B integrada directamente con ambos sistemas. Todo ello se complementa con un sistema de BI que da soporte a modelos semánticos y cuadros de mando corporativos y con soluciones de ITSM para soporte, trazabilidad y gobierno de cambios. Además, según el CTO, la seguridad se ha convertido en un aspecto crítico, donde la organización ha reforzado capacidades con tecnologías como SSO, MFA, hardening y monitorización activa.
Sin embargo, el camino no ha sido sencillo. Corver ha tenido que afrontar una serie de desafíos que han acompañado a la implantación tecnológica, entre ellos la gobernanza del dato, la normalización de catálogos heredados, la gestión del cambio y la formación de usuarios. A esto se suman condicionantes propios del negocio, como la necesidad de ajustar los despliegues a períodos de alta actividad comercial, especialmente durante Black Month (extensión de las ofertas del Black Friday) y Navidad, y la integración con sistemas legacy, donde el SGA ha sido uno de los puntos más exigentes. Codina también subraya la importancia de una disciplina estricta en integración continua y aseguramiento de calidad para evitar retrabajos y mantener la estabilidad operativa.
De cara al futuro, el grupo español se ha fijado nuevos objetivos que consolidan y amplían la transformación ya iniciada. Entre ellos se encuentran la unificación del SGA/WMS, el desarrollo de una experiencia omnicanal real con stock unificado y reglas de precios coherentes, la automatización del ciclo de vida del producto de extremo a extremo, la mejora de las integraciones con herramientas más avanzadas y el refuerzo de la ciberseguridad y la gestión de identidades. En paralelo, Corver continúa incorporando tecnologías avanzadas allí donde tienen impacto directo, desde BI y analítica predictiva hasta IA y machine learning para enriquecimiento de atributos o traducciones asistidas, así como RPA en operaciones de back-office y traducción automática para acelerar la publicación de contenido multilingüe.
En definitiva, Codina define la visión del grupo como “pragmática y sostenida”, basada en seguir midiendo el valor de cada lanzamiento y extender herramientas y estándares a todas las empresas que forman parte del grupo. Todo ello con un objetivo claro: avanzar hacia una estandarización global que combine eficiencia operativa y la flexibilidad que exige el negocio.
CIOs are moving beyond AI copilots to autonomous agents. Learn where agents deliver value, how to govern them, and how to integrate with legacy tech.
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CIOs are moving beyond AI copilots to autonomous agents. Learn where agents deliver value, how to govern them, and how to integrate with legacy tech.
The post Why Autonomous AI Agents Will Redefine Enterprise IT Strategy appeared first on TechRepublic.
Indra ha comunicado haber firmado uno de los mayores contratos de su historia. Transport for London (TfL), que gestiona el servicio de una de las redes de transporte público más extensas y complejas del planeta, con más de 8,6 millones de desplazamientos diarios y más de 3.600 millones de viajes al año, le ha adjudicado el proyecto para operar el sistema de sistemas de venta y control de accesos (ticketing) de toda la red de transporte público de Londres y su área metropolitana. Se trata del “más avanzado, innovador y pionero del mundo occidental”, recalcan desde la consultora, que también se encargará de su mantenimiento y evolución hasta 2034. El importe del contrato asciende a 605 millones de euros, ampliable a más de 975 millones de euros y contempla posibles extensiones y opciones hasta 2039.
Tras un período de transición de casi dos años, Indra se convertirá en el proveedor único del sistema de ticketing de una red que cubre más de 8.500 autobuses, cerca de 400 estaciones de metro y otras casi 300 correspondientes al Overground, DLR, Elizabeth Line y servicios de tren suburbano, así como 4.000 puntos de venta de tarjetas Oyster, siete centros de atención al cliente y 24 puntos de embarque de ferry.
El proyecto abarca la operación, el mantenimiento y la evolución de una amplia gama de sistemas, como los pasos de control de acceso, validadores, máquinas expendedoras de títulos de transporte, terminales de venta en comercios minoristas, equipos portátiles de inspección, así como toda la infraestructura tecnológica y un back office que integra funcionalidades de ciberseguridad, gestión de datos, generación de informes y coordinación con terceros.
Desde la compañía recalcan que el proyecto contempla también la implementación, en colaboración con TfL, de nuevas tecnologías que permitan evolucionar el sistema, hacerlo más eficiente y automatizar procesos clave. “En definitiva, crear conjuntamente la próxima generación del sistema de ticketing para Londres”, dicen.
Un hito del proyecto será abordar, conjuntamente con TfL, la futura implementación del sistema de gestión de cuentas de viajero (Account Based Ticketing-ABT) sobre la Oyster Card, el billete de transporte electrónico en funcionamiento en Londres desde 2003.
Con este proyecto, añaden desde la consultora española, ésta refuerza su apuesta por Reino Unido como mercado estratégico, un país donde está presente desde hace 20 años y donde cuenta con un equipo de más de 200 profesionales locales que prevé aumentar hasta llegar a más de 1.000 empleados en los próximos años.
In a context marked by energy transition, regulatory pressure, and the need to operate with increasing efficiency on a global scale, digital transformation has become a strategic pillar for industrial and logistics companies like Exolum. Now present in 11 countries, with an annual turnover of over $1 billion, the company faces tough decisions on how to remain competitive.
Focusing on transporting gasoline and diesel, storing liquids like hydrocarbons and other chemicals, and supplying aviation fuel, the company is at a crucial juncture as the global push to cleaner sources of fuel gathers steam. “We have to learn to repurpose our infrastructure for the new energy sources that will emerge,” says Alfonso Álvarez, Exolum’s IT director.
Despite the substantial challenge, digitalization isn’t a new process for the company. Exolum began relying heavily on tech in the early 2000s, with implementation of operational systems in plants and pipelines. Since then, various strategic plans have been renewing platforms and modernizing systems. “It’s a process that’s been carried out throughout since the turn of the century,” Álvarez adds.
Today, much of the company’s daily operations are managed centrally and automatically. The product network and truck loading terminals operate with very small teams thanks to advanced technological systems. Truck access, for example, is managed through automatic license plate recognition, and the systems tell the driver which hose to use and when loading is complete, without direct human intervention. “Technology has a fundamental role at Exolum, especially in business execution,” says Álvarez.
According to Álvarez, the next big challenge for technology is growth since Exolum is an expanding company. “Technology must not only accompany that growth but support it from the beginning,” he says. “So we’re making decisions and executing projects that will allow us to do that.”
The most recent turning point came after the pandemic in 2020. Exolum’s international growth, driven by acquisitions in the UK, US, and other markets, led the company to create a global IT division. “It was understood that the approach had to be global, to provide coverage for all businesses across all geographies,” says Álvarez, who joined the company nearly four years ago with that objective.
The current model relies on a dual structure with a global layer and a local one. Corporate systems such as finance, control, HR, and asset maintenance are concentrated at the global level, while systems more closely tied to physical operations are maintained locally to preserve flexibility. According to Álvarez, this architecture allows the company to be viewed as a single entity, rather than as a series of small, local businesses.
The tech department itself currently has around 70 people, including global and local teams, and is supported on an ongoing basis by up to 150 external professionals. The focus isn’t on developing proprietary software in Spain, but rather on project management, and ensuring that tech partners meet quality and functionality requirements.
In recent years, Exolum has intensified its use of advanced technologies, especially AI, and already employs tools like Copilot in corporate environments and within SAP to improve team productivity. “The goal is to free up time so people can dedicate themselves to higher value-added tasks,” Álvarez says.
AI is also being applied directly to critical business processes, such as planning movement of products within the pipeline network, a highly complex system requiring advanced mathematical analysis. Plus, Exolum is developing its own AI governance framework, AIOps solutions, and specialized copilots.
Despite operating in a traditional sector, the company doesn’t perceive significant resistance to technological change. The strong presence of engineering professionals and the drive for initiatives from within the business have facilitated adoption. In addition, a dedicated digitalization area, separate from traditional IT, explores new use cases and promotes innovation through hackathons and internal training programs.
As part of this global strategy, Exolum has taken a big step to update its financial management by implementing the Kyriba treasury platform, supported by consulting firm All CMS. The project is part of the migration from SAP R/3 to S/4HANA, and addresses the need for a comprehensive and centralized view of treasury operations in an increasingly international environment.
After implementing S/4HANA, Álvarez and his team still identified limitations that prevented them from working with the level of control they needed, which led to seek a specific solution. So adopting Kyriba has improved treasury position control, automated processes, and reduced reliance on manual tasks.
The finance department emphasizes that the tool also offers greater visibility into cash flows, strengthens security, and supports the group’s future growth. “The improvement for the department is so clear that we want to continue evolving the solution and incorporating more processes,” says Álvarez.
Looking ahead still, one of the most important projects is the gradual phase-out of its own data centers. The goal is for Exolum to operate with a multicloud model for all its IT by the end of 2026.
“We need scalable and easily deployable systems that can support multinational growth,” Álvarez says. This change represents another key step to sustain the group’s future development.
Aunque la IA tiene el potencial de transformar la atención médica en todo el mundo, el progreso se está topando actualmente con un muro invisible. Los obstáculos son los sistemas de datos obsoletos. A esta conclusión llega el Foro Económico Mundial (FEM) en el informe publicado en vísperas de su reunión anual en Davos, llamado ‘La IA puede transformar la asistencia sanitaria si transformamos nuestra arquitectura de datos’.
Según el estudio, décadas de registros aislados, formatos incompatibles e infraestructuras rígidas frenan el progreso. Para que la IA no siga siendo solo una herramienta para tareas específicas, sino que se convierta en un sistema autónomo y capaz de aprender, el FEM considera que el sector sanitario debe replantearse desde cero su arquitectura de datos.
Hasta ahora, las estructuras se basaban a menudo en entradas manuales y actualizaciones diferidas. Sin embargo, el futuro pertenecerá a un canal de datos inteligente y unificado que limpie la información de los sensores y las fuentes automatizadas en tiempo real y la haga directamente legible para la IA. En lugar de almacenarse en rígidas bases de datos relacionales, la información se almacena cada vez más en bases de datos gráficas multidimensionales que permiten comprender inmediatamente el contexto y el significado.
El FEM considera que otro gran problema es la investigación médica. En este ámbito, muchos conocimientos valiosos permanecen ocultos en notas o imágenes complejas, ya que son difíciles de encontrar con una búsqueda convencional. Aquí es donde entra en juego la denominada vectorización: los datos multimodales, desde textos hasta secuencias genómicas y señales clínicas, se convierten en incrustaciones numéricas. Esto permite a la IA reconocer relaciones profundas, como comparar síntomas con casos anteriores o recuperar resultados de investigación relevantes con la máxima precisión.
En definitiva, según el FEM, un sistema sanitario moderno necesita un data lakehouse. Es decir, un lugar de almacenamiento centralizado en el que los datos de los laboratorios, los wearables y las aplicaciones de los pacientes confluyan de forma segura y estén disponibles para su análisis. Para que la protección de datos no se quede en el camino, una fábrica de datos inteligente debe garantizar que solo los usuarios autorizados tengan acceso y que la información sea coherente.
Para garantizar que las recomendaciones de IA para los médicos sean comprensibles y fiables, éstas deben basarse en conocimientos clínicos validados. Los denominados gráficos del conocimiento podrían servir como guías para garantizar que los resultados de la IA se ajusten a las directrices médicas.
Esta transformación de la IA es más que una simple renovación tecnológica. Según la valoración del FEM, para las naciones soberanas, la creación de una arquitectura de datos preparada para la IA significa considerar la sanidad como un recurso nacional. Y, desde el punto de vista del foro, esta transformación radical es indispensable. Solo así los países podrán garantizar una atención mejor y personalizada y aprovechar al máximo el potencial de una IA con capacidad de autoaprendizaje.
McKinsey está probando un nuevo enfoque de contratación en el que los recién graduados pueden utilizar el asistente de IA de la consultora, Lilli, durante las entrevistas de trabajo. El objetivo es reflejar cómo se espera que los consultores trabajen en el futuro con herramientas de IA, según informa el Financial Times.
En el programa piloto, los candidatos analizaron un caso con la ayuda de Lilli y fueron evaluados en función de su capacidad para formular preguntas, interpretar las respuestas y situarlas en el contexto del cliente. La prueba no es esencial para la contratación, sino que tiene por objeto medir la curiosidad y la capacidad de juicio.
Si la prueba tiene éxito, el elemento de IA se introducirá en todos los procesos de selección. La propia McKinsey se ha negado a comentar la información publicada por el Financial Times.
La escasez de trabajadores disponible, la alta rotación y la poca digitalización de los puestos de trabajo son, según Luis Zamora, director de personas (CHRO) de Grupo Iberostar, una de las grandes multinacionales españolas del sector turismo y especialmente fuerte en el negocio hotelero, los tres grandes desafíos que afronta este vertical en la actualidad desde el prisma laboral. Así lo indicó el directivo en un encuentro con prensa organizado por Workday, compañía con la que el grupo turístico selló un contrato en 2025 para implantar su tecnología de gestión de recursos humanos, en aras de unificar sus procesos de contratación, desarrollo de talento y administración de nóminas y beneficios laborales.
“En este momento —contó Zamora— nos hallamos en pleno proceso de implementación de Workday, que esperamos que esté a pleno uso el próximo mes de septiembre. Esto nos permitirá ofrecer formación a la carta a nuestros empleados, facilitarles realizar cambios de turno sin que esto impacte en el negocio y en función de la ocupación del hotel donde trabajen y mejorar así su experiencia y la de sus managers, facilitar el cumplimiento de la regulación, etc.”. El proyecto, añadió el responsable, implica un “gran cambio cultural”, pues “se trata de que el área de personas deje de realizar una gestión administrativa y de nómina a abordar una gestión del capital humano”.
La compañía, relató, confía en las posibilidades que trae consigo el software de la firma estadounidense, que se apalanca en la analítica de datos y la más reciente aplicación de agentes de inteligencia artificial, una tecnología que el directivo de Iberostar ve con buenos ojos incluso de cara a la captación de empleados. “Ya hemos realizado pilotos con IA que nos permiten realizar entrevistas de filtro con personas; esta tendencia, la de hablar con máquinas, que la gente más joven ya ha normalizado, entrará en los procesos de selección”, afirmó Zamora, convencido de que “la IA ayudará a quitar tareas administrativas a los gestores de forma que estos, en lugar de dedicar tanto tiempo a hacer informes, puedan centrarse en lo importante: en las personas y en sus equipos”.
“La IA entrará en los procesos de selección […] pero la máquina nunca va a tener la última decisión”
Con la IA, recalcó, “la mejora de la experiencia del empleado será importante”. Agregó que la naturalidad que traerá consigo a las conversaciones con las máquinas el uso de la IA generativa por voz será un factor determinante para ello; no obstante, subrayó, “hay que actuar con prudencia y tener en cuenta que en un proceso de selección de personal la máquina nunca va a tener la última decisión”. “En un sector donde la experiencia del cliente depende directamente de las personas, la IA no debe sustituir el criterio humano, sino potenciarlo”, recalcó.
La implantación de la tecnología de gestión de personal de Workday forma parte de una estrategia tecnológica a mayor escala que implica también el uso de soluciones de otras herramientas de gestión con las que estará integrada la primera, como una solución de planificación financiera de SAP u otra de Microsoft (Fabric) para hacer análisis avanzado de datos corporativos.
Zamora desgranó también otros proyectos del grupo en los que la inteligencia artificial tiene un rol relevante desde el punto de vista de la operación. Uno es el sistema Winnow, “basado en el machine learning de toda la vida” que ha implantado ya en decenas de sus hoteles y gracias al que ha conseguido ahorrar en estos establecimientos millones de comidas al año. “Nos preocupa el desperdicio alimentario”, apuntó el directivo. Este proyecto forma parte de su movimiento de sostenibilidad Wave of Change, que nació en 2022 con el objetivo de ahorrar 1.600 toneladas de residuos alimentarios al año, es decir, unos 5,3 millones de comidas.
El otro proyecto mencionado por Zamora fue BRAIAN, una inteligencia artificial diseñada para optimizar el consumo energético en los hoteles sin impactar en los huéspedes. La solución, desarrollada con la compañía Sener y que también forma parte del movimiento Wave of Change, tiene como objetivo reducir en un 35% el consumo energético y en un 85% las emisiones de alcance 1 y 2 para 2030.
Ya desde un punto de vista más general de transformación digital, Iberostar, bajo su proyecto Hotel Digital, utiliza la inteligencia artificial para mejorar la experiencia del cliente.
Modern CIOs and tech leaders carry responsibility not only for an organization’s technology but, as key partners, for its entire business success. So having access to readily transferable lessons is critical in order to solve real business challenges, and lead with clarity, confidence, and purpose.
As a jumping off point, I’ve distilled here some of my favourite maxims from different business functions.
You’re more interesting than you think. Try to be human. I realize this is a tough ask for us classic IT introvert types, but with many interactions now conducted remotely, it’s even more important to find opportunities to meet in person.
Letting people know what makes you tick personally is of more interest than you could probably imagine. Colleagues are interested in you as a whole person, not simply as the person they work with. So don’t be afraid to bring yourself to work, as the phrase goes. This allows others to do the same, and to talk about their own feelings and circumstances.
As an INTP (an introverted, intuitive, thinking, and perceiving type from the Myers-Briggs personality assessment), social events aren’t my natural environment. And we’ve probably all experienced how work and socializing sometimes don’t mix. Is an orchestrated corporate event all that comfortable for anyone? But try to show up and meet people, relax a bit, and have some fun.
IT people often prefer to vent about the technology-ignorant business rather than stand up and explain the tech. Instead of declaring something’s bad for the company or a dead-end, they shrug and say the business just doesn’t get it.
No matter how great your strategy is, your plans will fail without a company culture that encourages people to implement it. I know from speaking to other CIOs that a frequent role for them is standing up for IT and defending their teams in a culture where the business blames IT for its failures.
It’s therefore vital to coach your teams to deal on equal terms with their internal business customers. Key to this is talking in business terms, not IT jargon. The reason for not adopting a nonstandard piece of tech is it’ll inflate future company running costs, not that it doesn’t neatly fit the IT estate. So stand up and be counted on a matter of tech principle, and win the debate.
When IT projects fail, it’s often because of a lack of ownership by the business.
The entire purpose of your IT department is to move the organization forward. So any investment must deliver on quantifiable financial targets or defined business objectives. If it doesn’t, move on. This is fundamental. Forgetting to do so is easy when under pressure, as others press you with their own agendas, but dangerous for you and the business.
Everything I’ve learned and seen reinforces this. Without this focus, you’re just an IT supplier taking orders, not the executive IT partner of the business. Question any actions by your team that can’t be linked back to the company’s core objectives.
It all comes down to building relationships based on trust with your business colleagues who recognize that you understand what the business needs and can afford, so challenge projects not owned by the business leaders.
Be vocal about your team’s successes and be honest about your mistakes. As CIO, you’re the face of the IT function in your organization, and you set the tone for everyone in IT.
Try not to talk about the business and IT as separate entities. You and your team are just as integral to the company as sales, operations, or finance. Always talk about our business needs and what we should do.
Remember, you’re accountable for all the IT. These days, we talk about being authentic, so being honest about your slip-ups, and how you feel about them, is important in establishing your reputation, both internally and externally.
Explain a success to others in the organization and why it worked. Bring out how collaboration between their teams and IT, working to aligned plans and objectives, made good things happen for everyone involved.
Digital natives need to work together with old techies. Advances of the last decade have been delivered by fast-moving digital startups, financed by deep-pocketed investors. Unsurprisingly, this has spawned organizational impatience with the costs and time taken by traditional or legacy IT functions. This frustration can then translate into setting up a completely separate digital department under a CDO, charged with implementing the new and faster-moving business.
Your current business is built on long-established ways of working, and processes that remain necessary, unless you’re going to build them all a second time for the new digital channel. If not, then new components, including services and products, will have to interface with existing systems, as well as firmly established and mission-critical business processes. So with this dynamic, ensure that both traditional IT and new digital report to you.
AI is the most overhyped bandwagon in technology, more than bitcoin, big data, and augmented and virtual reality. Nevertheless, it’s the most far-reaching tech-driven change since the advent of the internet. In a matter of months, AI and AI agents are doing to white-collar jobs what production line robots did to blue-collar jobs 20 years ago.
AI is transforming the world and we’re just at the beginning of this revolution. So what are you doing about it?
Your challenge as CIO is that AI has cut through to your board and executive leadership like nothing before. Furthermore, all your partners and suppliers are building AI agents into their software and services. Plus, all your best digital innovators in the business, and definitely all your recent grad hires, are using Chat GPT and bespoke AI tools in their day jobs. As CIO, you hold the keys to AI working well by effectively wielding the data in your systems. After all, you and your team are the ones who best understand how the AI works as the means to achieve business value.
Hoy se ha inaugurado el Centro de Control de Infraestructuras Críticas (CCIC) que ha puesto en marcha el Gobierno de la Comunidad de Madrid para controlar, desde un solo lugar y de forma centralizada, los sistemas tecnológicos de la región. Esta iniciativa, aseguran desde la organización, permitirá “minimizar el impacto de cualquier incidencia y ofrecer una respuesta inmediata”.
Según el consejero de Digitalización de la Comunidad de Madrid, Miguel López-Valverde, “el centro estará operativo las 24 horas del día los 365 días del año y será el corazón digital de la región desde el que se reforzará la capacidad para que los madrileños puedan relacionarse con la Administración con plenas garantías”. Desde el Gobierno regional apuntan que esta infraestructura “innovadora y pionera en España, en cuanto a sus dimensiones, competencias y alcance”, permitirá gestionar y monitorizar en tiempo real todas las plataformas y aplicaciones esenciales para velar por su correcto rendimiento y proteger los datos que manejan.
El nuevo centro nace con una inversión cercana al millón de euros y está integrado por un jefe de operaciones y más de una veintena de profesionales técnicos, entre los que se encuentran ingenieros de infraestructuras críticas, analistas, consultores, especialistas en gestión de incidentes, expertos en ciberseguridad y en inteligencia artificial.
El nuevo recurso, explican desde el Gobierno regional, “tiene la capacidad de analizar al instante el funcionamiento de los sistemas informáticos, predecir posibles ciberataques o reaccionar con agilidad y de manera coordinada, en cuestión de segundos, ante cualquier incidente. También está provisto de entornos de respaldo eléctrico, grupos electrógenos y medios de alimentación con baterías, así como de conectividad con múltiples operadores para salvar contratiempos y asegurar su propia continuidad”.
Dentro del CCIC estará ubicada una representación del Centro regional de Operaciones de Ciberseguridad, que permitirá reforzar la protección de los sistemas críticos de la Administración autonómica.
La Consejería de Digitalización se encarga de proporcionar servicios digitales a todos los ciudadanos y empresas de la región y dirigir los recursos de las Tecnologías de la Información y la Comunicación (TIC) de 4.000 sedes administrativas, dar soporte a cerca de 200.000 empleados públicos o llevar todos los trámites tecnológicos de las consejerías. Asimismo, Madrid Digital realiza anualmente hasta 22.000 actuaciones para mejorar aplicaciones y otras herramientas y más de 12.000 cambios técnicos. Todo ello, recuerdan desde el Ejecutivo regional, se sustenta en más de 2.300 sistemas informáticos que se ubican en infraestructuras tecnológicas, “cuyo mantenimiento y seguimiento es esencial”.
What ever happened to Digital? The Cloud? Agile? Flattening IT’s org chart? ITIL/ITSM? Or whatever other transformational change was supposed to, well, transform IT but instead petered out into just another disappointing management fad?
There’s no one culprit. But here are a few of the more popular preventable reasons that IT change efforts die on the vine.
Sometimes, the change methodology is, not to put too fine a point on it, a chump’s game. Most reorganizations fall into this category.
Preventing reorganization failures is simple: Don’t reorganize. Recognize that if you want a more effective organization, redrawing the IT org chart is about as promising as the legendary Save-the-Titanic methodology of rearranging its deck chairs.
Sometimes the hoped-for change was underbudgeted. Understanding this one might take a history lesson.
Back in the late 1990s IT planners figured out that its data architects’ practice of saving money by only storing the last two digits of any date field had outlived its usefulness and had become lethal in the extremis. Remarkably, addressing this — the Y2K crisis — turned into what just might have been the most successful IT change effort in history.
Which led to the most colossal failure of appreciation in the history of the business world. In any event, in the months following the worldwide success of IT’s Y2K remediation efforts, various groups conducted post-non-mortem analyses to figure out what had, mystifyingly, gone right.
Among the critical success factors, one stood out: Around the world, Y2K remediation efforts weren’t starved for resources. And oh, by the way, the Y2K crisis was neither a hoax nor the result of incompetence. But given our species’ proclivity to assign blame whenever we have the opportunity, there’s little point trying to convince anyone.
But still, we might decide to learn from this success and give our change efforts a chance by giving them enough staff and budget.
Ready for another organizational change killer? Here’s a simple one: They became failed fads because the whole reason for trying them in the first place was that they were a trend someone influential had spotted and promoted. They became fads, that is, because they started out as fads.
The first three culprits are the easy ones. Or at least, they’re conceptually easy. Increasing project budgets, for example, certainly isn’t easy to do. It’s just easy to understand.
Now comes the hard one — the one where even if you do everything right the hill you’ll have to climb is steep. It’s like this:
Among the factors that make change hard is the need for all participants and stakeholders to have a deep and intuitive understanding of what the change will feel like when they’re living in it.
To understand the challenge, imagine that someone invented a flying car, and for some strange reason IT received the assignment of making a corporate fleet of airborne automotive vehicles real. What would that feel like. Pretty cool, right?
Well …
If you wanted flying cars to succeed, you’d need to give everyone who might drive one of the cars an intuitive feeling of what navigating through heavy traffic would be like.
“Terrifying” is the word that comes to mind. Spotting bikes, motorized scooters, other drivers, and the occasional fearless pedestrian is hard enough in a 2D driving environment. Your company’s drivers would have to spot vehicles above and below, and at all diagonal vectors, too. Even something as seemingly simple as a 3D turn signal gets complicated in a hurry.
Making this change successful would call for more than a souped-up drivers’ education course. You’re going to need future drivers to gain an intuitive sense of what driving in 3D traffic feels like. You’ll need photo- and haptically-realistic simulators.
Which gets us (finally!) to the 7x7x7 challenge.
Think about how you might describe how things are done right now in their pre-change state, as you would to train new employees. That might call for a PowerPoint slide with seven linked boxes on it, seven being the number of items viewers can easily grasp at a glance.
It’s a view that’s easy to grasp, but too superficial to be complete. To be useful, each of those boxes would need more explanation. So, figure you’d have to create explanatory PowerPoint slides for each box in the higher-level slides, with “explanatory” meaning that each of the seven boxes would need seven explanatory boxes of their own. That’s seven by seven: 49 boxes.
The 49-box view of things is more helpful but still oversimplifies the current state by quite a lot. It isn’t until you craft seven-box views for each of these seven boxes to provide enough information — 343 boxes worth in total — to fully describe how things happen now.
That’s the level of depth that the change’s stakeholders will need in order to understand what living inside the change will feel like — for it to be real.
Making a change sticky calls for an equivalent 343-box account of the future state.
And oh, by the way, this has little to do with the essential analysis required to make sure these new 343 boxes deliver the old results, and deliver them better. Living inside them doesn’t make them better.
And “better” won’t happen immediately either. The current way of doing things has, by now, been sanded and varnished to a shine. Even if the new way of doing things would theoretically be an improvement, it won’t be an actual improvement until it’s been sanded and varnished to its own shine.
The 343-box perspective isn’t limited to processes and practices. It describes the process optimization methodologies and frameworks organizations use to design the new 343 boxes; the new organizational chart (the real one, not the oversimplified version that shows only a couple of layers); not to mention the business culture a leader might want to change.
In the end, large-scale changes are hard to nail into place. Sometimes that’s because leaders make easy-to-avoid mistakes. But often it’s because of how difficult it is to help everyone feel what the result is supposed to feel like once the organization tries to make the change real.
See also:
지멘스와 엔비디아가 CES 2026 공동 기조연설에서 AI를 기반으로 한 제조 산업의 차세대 변화 청사진을 제시했다.
지멘스 CEO 롤란트 부시와 엔비디아 CEO 젠슨 황은 AI를 산업 분야의 새로운 운영체제로 활용하겠다는 비전을 제시했다. 이는 초기 설계부터 생산, 복잡한 공급망에 이르기까지 가치 사슬 전반을 근본적으로 변화시키는 것을 목표로 한다.
부시는 기조연설에서 “전기가 한때 세상을 혁신했듯, 산업계는 지금 깊은 변화를 겪고 있다”라고 말했다. 그는 이제 산업 혁신의 핵심이 개별 기능의 개선이 아니라, 문제가 발생하기 전에 이를 예측할 수 있는 ‘통합 지능’에 있다고 언급했다.
Der Digital Twin Composer von Siemens ermöglicht es Unternehmen, industrielle KI, Simulationen und physikalische Echtzeitdaten zu nutzen, um Entscheidungen virtuell zu treffen.
Siemens
이 기술 비전의 중심에는 ‘디지털 트윈 컴포저(Digital Twin Composer)가 있다. 이 솔루션은 올여름 지멘스 엑셀러레이터 마켓플레이스를 통해 제공될 예정이며, 산업용 메타버스를 대규모로 구현할 수 있는 현실적인 기반을 마련하는 것을 목표로 한다.
디지털 트윈 컴포저는 지멘스의 디지털 트윈 기술에 엔비디아 옴니버스 라이브러리와 실시간 엔지니어링 데이터를 결합한다. 사용자는 가상 3D 환경에서 시간을 되돌리거나 앞당기며, 기상 변화나 설비 조정이 시스템에 미치는 영향을 시뮬레이션할 수 있다. 이를 통해 공장 계획 과정에서 일종의 예측 도구를 확보할 수 있다. 엔비디아 옴니버스 및 시뮬레이션 기술 부문 부사장 레브 레바레디언은 “단 하나의 원자도 현실 세계에 투입되기 전에 가상 공간에서 오류를 바로잡을 수 있다”라고 설명했다.
이 같은 구상이 먼 미래의 이야기가 아니라는 점은 글로벌 식음료 기업 펩시코 사례를 통해 확인되고 있다. 펩시코는 이미 미국 내 공장에서 생산 시설과 물류·보관 거점을 디지털로 구현하기 위해 디지털 트윈 컴포저를 활용하고 있다. 펩시코는 그 성과에 대해 다음과 같이 밝혔다.
PepsiCo digitalisiert ausgewählte Produktionsstätten und Lagerhäuser in den USA mithilfe des Digital Twin Composer.
PepsiCo
지멘스는 여기서 한 걸음 더 나아가 제조 현장의 업무를 한층 더 지능화하기 위한 9개의 새로운 산업용 AI 코파일럿을 선보였다. 이 코파일럿은 제품 데이터 탐색을 최적화하고 시장 출시까지 걸리는 시간을 단축하는 역할을 맡는다. 기술은 웨어러블 형태로 확장될 예정이다. 지멘스는 메타, 레이밴과 협력해 공장 작업자의 시야에 실시간 음성 안내와 안전 정보를 직접 제공하는 AI 안경을 개발하고 있다.
지멘스는 이런 구상이 단순한 발표에 그치지 않는다는 점을 올해 안에 입증하겠다고 밝혔다. 이를 위해 독일 에를랑겐에 위치한 지멘스 디바이스 공장을 AI 제어 기반 적응형 제조 시설로 전환한다. 세계 첫 사례인 이 공장은 차세대 ‘AI 공장’을 위한 청사진 역할을 하게 된다.
산업용 AI의 영향력은 공장 현장에만 국한되지 않는다. 생명과학 분야에서는 연구 데이터 통합을 통해 혁신적인 치료제가 환자에게 전달되는 속도를 최대 50%까지 앞당길 수 있게 됐다. 커먼웰스 퓨전 시스템스는 지멘스와 협력해 상업용 핵융합을 현실화하기 위한 기반을 마련하고 있다.
dl-ciokorea@foundryco.com
As organizations accelerate their shift from traditional data center environments to hybrid and multi-cloud architectures, the scale and complexity of these initiatives demand a new caliber of project leadership. Having recently led a multi-year enterprise-wide data center transformation with global stakeholders, I’ve seen firsthand that technology alone is not what ensures success. Leadership is the key.
Even the most advanced platforms and tools can fall short without a project manager who brings the right mindset, adaptability and technical fluency. These programs are simultaneously technical undertakings and organizational-change journeys.
Based on lessons learned from managing one of the most ambitious transformations in my organization, here are the five skills essential for any project manager responsible for navigating cloud and data center modernization.
Data center transformations operate at an enterprise scale, where no system exists in isolation. Every application, integration point and data flow is part of a wider ecosystem and understanding that ecosystem is critical from day one. Systems thinking means looking beyond servers and environments to examine business processes, downstream dependencies, data protection needs and operational realities.
This requires asking targeted questions such as:
With these insights, project managers can make informed decisions about cutover sequencing and avoid grouping applications solely by physical infrastructure — an approach that often leads to outages or misplaced dependencies. Indeed, a recent empirical study of migrating legacy systems to cloud platforms identified a lack of architectural mapping and understanding of interdependencies as a key risk factor in migration failures.
Architectural awareness isn’t memorizing components; it’s understanding how a single change reverberates across the entire enterprise system.
Large-scale migrations rarely follow a predictable or linear path. They unfold in iterative phases, each introducing new variables, technical constraints and lessons learned. Because of this, a traditional waterfall approach quickly becomes a liability. What teams need instead is an elastic governance framework that provides structure while adapting to shifting realities.
Elastic governance means adjusting processes, decision models and approval flows as new insights surface. Each application and business unit often carries its own architecture, dependencies and constraints, so a one-size-fits-all model simply doesn’t work. During our migration, daily interactions with implementation teams, developers and product owners gave me real-time visibility into emerging issues and allowed us to refine our approach continuously.
This approach mirrors trends highlighted in the ISACA Journal’s 2023 article, “Redefining Enterprise Cloud Technology Governance.” ISACA argues that traditional governance frameworks are far too rigid for modern cloud environments. Instead, they advocate for adaptive, decentralized models that empower teams to respond quickly as new constraints and dependencies emerge.
Vendor-related challenges were especially common with aging legacy systems. Proactive engagement — rather than reactive firefighting — helped us avoid failures and maintain momentum.
Governance should guide, not grind. Flexibility is essential for managing uncertainty and sustaining progress in complex transformations.
In enterprise-wide transformation programs, stakeholder alignment is often the difference between controlled progress and project derailment. Every migration window, firewall rule adjustment, environment change or sequence shift requires close coordination across security, networking, infrastructure, operations, product teams and business leadership — all operating with their own priorities and pressures.
Research shows that stakeholders often have different “frames” of a digital transformation and successful programs actively manage these perspectives to create shared understanding and alignment. Similarly, a 2023 KPMG report highlights that building trust among stakeholders — particularly around risk, security and compliance — is essential for successful cloud adoption.
A critical part of this role is translation. The project manager must convert technical constraints into clear, business-friendly updates while also translating business expectations into actionable direction for engineering teams. This dual fluency reduces misunderstanding and accelerates decision-making.
To maintain alignment, structured communication becomes essential. I established predictable rhythms — daily standups, weekly product syncs, monthly executive briefings and shared dashboards — to ensure transparency, quick escalation and consistent visibility into progress and risks.
The stronger and more structured the communication, the smoother and more predictable the migration.
Modernization initiatives involve ongoing decisions about whether to re-host, re-platform or re-architect applications. While a project manager doesn’t need to be the most technical person in the room, they must understand the implications of each option well enough to facilitate informed decision-making.
Technical fluency builds credibility with developers, architects, vendors and deployment teams. It also enables the project manager to ask the right questions, challenge assumptions and guide discussions toward solutions. This is especially important given the “6 Rs” of cloud migration — re-host, re-platform, refactor (re-architect) and others — which are commonly used to rationalize workloads based on business goals and technical fit.
Technical fluency enables clarity, connection and better decisions.
Data center transformations are long, complex and filled with uncertainties. Unexpected technical issues, compliance demands and shifting business priorities can slow down momentum and strain teams. According to the KPMG report mentioned earlier, many organizations struggle with operational resilience — more than half experienced outages or compliance issues in their cloud operations over the past year. This reinforces the importance of proactive governance and risk management. In such environments, a resilient project manager provides clarity, maintains stability and ensures the team keeps moving forward.
During our project, an unexpected compliance mandate required rapid reprioritization and additional resources. With leadership support, we realigned the plan and still met the migration deadline. Maintaining team morale during such periods is just as important as technical delivery.
Resilient teams don’t resist change; they stay confident through it.
A data center transformation is more than a technical project — it reshapes processes, roles and behaviors across the organization. When these five skills come together, the project manager transitions from a delivery role into a true transformation leader.
The most effective transformation leaders balance discipline with flexibility.
Traditional success metrics such as reduced downtime, regulatory compliance and cost optimization are important. But true success becomes clear only when the organization demonstrates improved adaptability and stronger collaboration between IT and the business.
When a project manager embeds adaptability deep into the organization, the transformation continues long after the final cutover.
Looking ahead, managing a data center transformation a decade from now will be fundamentally different. The next generation of migrations will involve greater complexity, including advanced automation, AI-driven orchestration, multi-cloud environments and more sophisticated compliance and security requirements. Without continuous upskilling, project managers will struggle to lead confidently in this evolving landscape.
Future-ready leaders must be both technologically fluent and human-centered. They need to leverage data effectively, make decisions at the pace of AI and automation and understand emerging tools and methodologies. At the same time, they must maintain essential human leadership qualities — trust, accountability, resilience and the ability to inspire teams under pressure.
By balancing these technical and human skills, project managers remain indispensable. They not only ensure that migrations succeed technically but also guide teams and organizations with purpose, clarity and adaptability, enabling sustainable transformation that goes beyond the immediate project and strengthens the organization’s long-term capabilities.
Data center transformation was not an easy migration, as it was a complicated and most ambitious undertaking by the organization. Orchestrating more than a hundred stakeholders was not an easy feat and we accomplished it with meticulous planning and risk management. Hence, a project manager with those five skills doesn’t just lead, they become the transformation agents for the organization. As the saying goes: Real transformation happens when leadership turns complexity into clarity and uncertainty into forward motion.
This article is published as part of the Foundry Expert Contributor Network.
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El proceso de expansión de CaixaBank Tech avanza al ritmo esperado, según indican desde la propia compañía del Grupo CaixaBank, el banco con mayor base de clientes digitales del sector financiero español (12 millones de usuarios).
La filial, que agrupa a los equipos especializados en tecnología y sistemas del banco, cuyos profesionales se centran en diversos proyectos centrados en tecnología para mejorar los servicios bancarios, utilizando desde blockchain a computación cuántica, pasando por inteligencia artificial, big data o cloud computing, ha cerrado 2025 con 500 personas más en su plantilla, que ya suma 1.600 profesionales y que llegará a las 2.000 en 2027.
Durante este ejercicio, según indican desde la empresa, se ha puesto especial foco en el ‘hub tecnológico’ de la compañía en Sevilla, centrado en desarrollo de software; sólo allí se han sumado 100 personas. “El equipo en esta ciudad ha experimentado un crecimiento exponencial, pasando de 40 personas a 140 en 2025, lo que confirma el potencial de la región para atraer talento tecnológico y ofrecer oportunidades de desarrollo profesional dentro del Grupo CaixaBank”, explican desde la empresa.
Los 500 profesionales que ha contratado la filial tecnológica de Caixabank el pasado ejercicio son ingenieros de desarrollo, que no solo trabajan en el citado centro de Sevilla sino en los que también tiene la compañía en Barcelona y Madrid. Entre los profesionales más demandados, afirman desde la compañía, se encuentran expertos en IA, ingenieros de machine learning, científicos de datos, ingenieros de desarrollo de diferentes especializaciones (Backend, Python etc.), arquitectos cloud o expertos en seguridad, entre otros.
A finales del pasado año, el banco CaixaBank anunció la puesta en marcha de una Oficina de Inteligencia Artificial, dependiente de la Dirección de Medios y con alcance para todo el Grupo, que nace con “el objetivo de garantizar que todos los proyectos corporativos vinculados a la inteligencia artificial cumplen con la regulación, la ética y el aporte de valor real para el negocio”.
La oficina está compuesta por un equipo de nueva creación formado por profesionales con perfiles muy heterogéneos y transversales. Hasta ahora, entre los casos de uso más recientes implantados por CaixaBank se encuentra un asistente de soporte a la contratación remota, que utiliza la IA generativa para ayudar a gestores y clientes a reducir los tiempos de interacción e impulsar la contratación de productos desde los canales digitales (app de banca móvil y web). Por otra parte, ha implementado un agente basado en IA generativa que interactúa directamente con los clientes de la app de CaixaBank para ayudarles a explorar productos. El banco también aplica la inteligencia artificial a la automatización de los procesos de negocio y operaciones, para reducir la carga administrativa en las oficinas y mejorar los procesos de decisión de los empleados del banco.
Antes del anuncio de esta nueva oficina de IA, en noviembre, el banco desveló su hoja de ruta completa en procesos y tecnología, que se integra en el Plan Estratégico 2025-2027, que cuenta con una inversión global de 5.000 millones de euros.
La estrategia tecnológica de la entidad en los próximos años se articula en torno a cuatro objetivos: incrementar la agilidad y la capacidad comercial de sus áreas de Negocio; desarrollar nuevos servicios y simplificar procesos; potenciar la excelencia operativa mejorando la eficiencia y reforzar y evolucionar su actual plataforma tecnológica “con los mayores estándares de resiliencia y seguridad”.
Siemens’ Digital Twin Composer enables companies to use industrial AI, simulations, and real-time physical data to make decisions virtually.
Siemens
Siemens’ joint keynote with Nvidia at CES should make it clear that, with AI, the manufacturing industry is poised for its next major transformation.
Siemens CEO Roland Busch and Nvidia CEO Jensen Huang presented a vision in Las Vegas that would make AI the operating system for the industrial sector — one that would revolutionize the industrial value chain, from initial design through production to complex supply chains.
“Just as electricity once revolutionized the world, industry is now undergoing a profound transformation,” Roland Busch explained during his keynote address. It’s no longer just about individual functions, but about “integrated intelligence” that enables companies to anticipate problems before they arise.
The centerpiece of this technological vision is Digital Twin Composer, which is expected to be available in mid-2026 the Siemens Xcelerator Marketplace. The new offering aims to make the industrial metaverse feasible on a large scale.
The technology combines Siemens’ digital twins with photorealistic Nvidia Omniverse libraries and real-time engineering data. With Digital Twin Composer, users can rewind and fast-forward time in a virtual 3D environment, allowing them to simulate the effects of weather changes or technical adjustments to systems. In other words, the user gains a kind of crystal ball for factory planning, making it possible to correct errors in the virtual world before “even a single atom is brought into the real world,” according to Rev Lebaredian, vice president of Omniverse and simulation technology at Nvidia.
PepsiCo is digitizing selected production facilities and warehouses in the US using the Digital Twin Composer.
PepsiCo
That this isn’t a distant dream was demonstrated by beverage and food giant PepsiCo. The company is already using Digital Twin Composer in its US plants to digitally map production and storage locations. According to PepsiCo, the results speak for themselves:
• 90% of potential problems are identified before physical changes are made.
• Throughput was increased by 20% in the first implementation.
• Capital expenditures decreased by 10% to 15%.
But Siemens is going even further, offering nine new industrial copilots designed to make work in manufacturing smarter. They will help optimize navigation through product data and shorten time to market. And the technology is intended to become wearable. In cooperation with Meta and Ray-Ban, AI glasses are being developed that will deliver real-time audio guidance and safety information directly into the field of vision of factory workers.
Siemens intends to provide proof this year that this is more than just PowerPoint presentations. To this end, the Siemens device plant in Erlangen will be transformed into the world’s first fully AI-controlled, adaptive manufacturing facility. This will serve as a blueprint for the next generation of “AI factories.”
But industrial AI’s influence extends beyond the factory floor. In the life sciences sector, the integration of research data enables life-changing therapies to reach patients up to 50% faster. At the same time, Commonwealth Fusion Systems is working with Siemens to pave the way for commercial nuclear fusion.
As chief operating, information, and digital officer at TIAA, Sastry Durvasula oversees four interconnected pillars — technology, digital and client experience, operations, and shared services — powering one of the most trusted institutions in financial services. With a track record of leading transformation at big brand organizations, Durvasula is known for his ability to write new chapters for century-old companies. His leadership story is one of vision, reinvention, and impact, spanning 40-plus patents and AI-powered breakthroughs.
On a recent episode of the Tech Whisperers podcast, we unpacked Durvasula’s journey from engineer to Fortune 100 COO and the leadership playbook that has formed the foundation of his success. In a moment when AI disruption is overwhelming many organizations, he offers a blueprint for remaining focused, deliberate, and deeply human.
One of Durvasula’s operational principles is what he calls the historian’s advantage: the ability to learn and recognize patterns from the past and connect them to possibilities for the future. In a follow-up discussion after the show wrapped, we spent more time exploring his framework for anticipating what’s next and leading transformation at scale in the era of AI. What follows is that conversation, edited for length and clarity.
Dan Roberts: You’ve said one way leaders give their organizations an advantage is by being a ‘historian.’ What does that look like in practice for you, and how does it help you as a leader?
Sastry Durvasula: I’m a student of technology trends. I study a lot about companies. I study their annual reports, and it’s even easier now with AI. I look for industry trends. I have a folder on my phone called Geek It Out that has five or six apps that are following industry and technology trends.
The previous histories of technological revolutions — be it electricity or the internet or mobile or social media — are so useful to learn from because the change aspects of the AI revolution that we are going through now will be very similar to the change aspects of some of these big technology changes that happened in the past.
For instance, I was in Washington, D.C., recently for our Futurewise conference, and I was studying the history of lamplighters in the White House. Before electricity, the job of the lamplighter was to light the lamps in the White House, using natural gas. In 1891, electricity came into the White House and the lamplighter job got decommissioned. Essentially, they moved on to other crafts. Another interesting factoid is, adoption was a major issue, and in the early days staff were assigned to operate the switches due to fear of electrocution. Other crafts and roles went through massive changes. A master blacksmith had to reskill to become an effective manager of electric-powered metalworking equipment. The head baker role completely changed with electric mixers, ovens, and refrigeration.
Fast-forward to now, will AI replace jobs? Yes, it will replace jobs. Will we reskill? Yes, we will reskill. Will there be transient roles like prompt engineers as we all learn how to use it and get comfortable with it? Yes. In the end, will it give new opportunities for humans to perform in a different scale? One hundred percent.
Another story I often reference is BlackBerry as an example of a company that didn’t scale with the times. It grew from $3 billion in 2007 to close to $20 billion in 2011, despite the iPhone’s release, despite recession. And then, in five years it was down to $2 billion. Now it’s a historical study. There are other examples like AOL and Yahoo that were the pioneers in the past tech revolutions. So I think history matters a lot. Do I believe some of the pioneers of the current AI revolution will be the eventual BlackBerry, AOL, or Yahoo? Yes.
It also helps with managing your stakeholders, especially the board, when you have to present these big transformation programs. Sharing these anecdotes from history helps set the stage and motivate people when the complexity of transformation becomes so tough and dense. You can use it as a telescope as well, not just as a past. For example, as we think about the future, if AI were to be this way five years from now, what history are we going to set at this point as we tread the path?
What’s your advice for leaders who want to build teams and cultures that can telescope out and see around corners?
My general rule of thumb is to follow the customer. It’s one of the things that I guide my teams with and that I try to practice personally as well, because ultimately, where the customer is and where the customer will be is where your company will be, or what it will be driven by.
For example, the generations are shifting as we speak. In TIAA, we have millions of customers, which we call participants, and they encompass the retired population, people who are still investing, and Gen Zers coming into workforce. The Gen Z participants have a different set of needs and a different set of expectations from companies. A lot of them are leveraging AI in day-to-day life for so many things. If the customer is on the phone all the time, and they’re going through stores, and you’re a payment card company, you have to ask, are you going to be relevant on the phone or not? When I was with American Express, we made the decision to be relevant, and that’s how you tap the phone, and you can use American Express.
Or there’s the iconic currency of membership rewards points that American Express has, which was reserved for all these big, exotic cruises and vacations and stuff. We asked ourselves, are we going to be relevant and make this currency useful in the day-to-day life of our customers? The answer was yes, so if you go to Amazon or McDonalds or to pay for a taxi in New York City, you can use membership rewards points if you have an AmEx card.
So I try to use that as a guiding principle: Do you know where your customer is, and do you know where your customer will be? Especially with AI, now more than ever, we have to predict where the customer will be and be relevant in that area. I think a lot of companies, including TIAA, will have to go through that major transition as we go through this AI revolution.
Yogs Jayaprakasam, a former colleague of yours at American Express who is now the chief technology and digital officer of Deluxe, says one of the things that stuck with him is your ability to simplify how you articulate technology’s value. How do you articulate the value of AI to all stakeholders, not just the ones who understand it?
Half of my organization is operations. They’re not technologists. You have to explain the value of AI to non-technical folks and, frankly, even to technologists, because they have varying degrees of understanding of AI. Some just look at it as pure-play tech, some look at it as a little bit more than that, and some just look at it as a bunch of large language models that we’re going to use from different companies.
People compare AI with electricity, and I agree with that, because you don’t think about electricity as a technology, right? It’s just part of our life. We think of electricity in our daily life only when there’s a big power outage. All the change that happened during its early days got the world to the stage we are in now. So, if you apply that to AI, it’s not the technology you need to explain; it’s the change we are going through with AI and the power it brings as you go through this major transformation that needs to be made explainable. Here’s my hypothesis: Unlocking the true power of AI is one-third technology and two-thirds a change management challenge.
First, we have to look at it as a business rewiring opportunity. I use AI as a leapfrog opportunity. There are a lot of things that we could have done better in technology that we didn’t. AI comes in and levels the playing field, so you could almost become the very best in your craft despite having all this technical debt or other debts that we’ve been carrying in our processes and experiences, because it fundamentally rewires the company in a very different way.
The second thing is the workforce of the future. I think AI is a driving force to determine what the future workforce will be for any company. The comparison for that would be the pandemic.
I was at McKinsey back then, and you think about a global management consulting firm that spends all its time on travel and working in conjunction with the clients at the client’s site having to do that craft remotely at global scale. Digital collaboration was a thing, but it was not operating at scale in any company. And all of a sudden, we dropped everything and became the most digitally collaborative firm and digitally collaborative society. I believe AI is that forcing mechanism at this point to recraft and rewire the workforce of the future.
And third, I always say, you’ve got to solve the boring problems to get to scale, not just the sizzle side of the house. Because just giving a cool interface to clients or our colleagues is not going to cut it if you can’t build the underlying foundation. So those are the things I talk about when it comes to AI: rewiring the workflows, workforce of the future, and solving the boring problems of the company, where you have to fundamentally pay off your debt in data, in technology, in processes so that when you get to scale with AI, you have the whole company transitioning into that new scale, not just parts of the company.
You’ve said middle managers are pivotal to the success of any transformation and that they bear the greatest burden. Can you expand on that?
I’m a big advocate for elevating the learnings, accountability, and empowerment of middle management. Middle managers is where change either makes or breaks. Now, with AI, where there are all these hypotheses that the pyramid structure is going to be replaced by the diamond structure and the entry level is going to shrink, you wonder, how are you going to establish the new normal for middle management?
Elaborating further on change and adoption, I always say these things follow Newton’s Laws of motion in large companies. Everything continues to be in a state of rest of uniform motion unless it’s compelled by an external force. And every action has an equal and opposite reaction. I think AI change management will go through this. Top-down mandate and the bottom-up innovation will act as the forces, but it’s the middle management that will need to turn into actional outcomes.
When middle management takes charge in rewiring the workflows of the future, defining the workforce of the future, and solving the boring problems, innovating with the power of AI, it takes it to a different level.
Once you’ve set your vision in motion and communicated the why, how do you sustain momentum and keep stakeholders engaged over time?
The early years of any big transformation program is the honeymoon stage. You get a lot of visibility, you do these big partnerships, you release some early wins, and then the complexity sinks in. I often talk about ‘leadership stamina.’ I think it’s very important to have that level of stamina to execute through these complex initiatives because transformation is a lot about vision but a lot more about execution. Seeing through that execution with a level of operational focus is as important as being strategic and visionary at the beginning of transformation to activate the transformation. As you assemble the team, you have to pick and choose the players in a way that the team has visionaries and also great execution leaders. You have to have a team that collectively has that leadership stamina and an execution arm of the team that really gravitates to hardcore execution.
In terms of the organizational patience, every year you have to have an investment into this program. How do you ask for investment in a tough year? You have to have a strategy. And what happens if you don’t get all the investment? You have to have a contingency plan. And what happens if a partner you’re working with is not working out? Then you have to have another option. A lot of these lines of defense, whether it’s risk management or audit and control, legal and compliance, with all the general complexity that’s going to come in, you have to lead with a design in mind for these things so that when you hit the surprising aspects of a transformation, they’re knowables. Because we should predict the knowables and have an option that we will activate when we hit them, because every transformation will have complexity; we just have to design for it.
By studying the patterns of past revolutions, seeing around corners, simplifying the complex, and empowering the middle, Durvasula shows how great leaders prepare their organizations to shape the future, not simply survive today’s disruption. For more insights from Durvasula’s transformation playbook, tune in to the Tech Whisperers.
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