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: Reengineering IT’s digital operating model

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.

Out: Underinvesting in data governance

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: Targeting AI for growth and UX

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.  

Out: AI experimentation without paths to short-term business value

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. 

In: Implementing security before AI deployments

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:

• Implement agentic AI observability and trust verification frameworks. “2026 marks a major shift in the threat landscape as agentic commerce takes hold, and in turn, AI-driven deception accelerates,” says Gavin Reid, CISO at HUMAN Security. “CIOs need visibility into how and what AI agents operate across their environments and deploy trust verification frameworks that continuously validate identity, intent, and behavior in real-time.”
• Establish security by design, especially around identity. “A unified identity layer is now a prerequisite for effective AI security implementation and is an urgent priority for any organization making AI investments,” says Ev Kontsevoy, CEO and co-founder of Teleport. “Organizations that embed these secure-by-design practices across development, delivery, and operations, and treat infrastructure security as a necessary mandate, will be best prepared for the transformational changes that AI will introduce.”
• Extend data loss prevention to AI-powered browsers. “AI-powered browsers like OpenAI’s Atlas and Perplexity’s Comet are one of the biggest blind spots in enterprise security,” said Rohan Sathe, co-founder and CEO at Nightfall. “Employees use them to research deals, draft customer outreach, and summarize strategy docs, giving agents with memory and sync direct access to logged-in Gmail, CRM, and code repos. Legacy data loss prevention cannot see this, since it was built for files, not browser-level activity, prompts, or clipboard moves.”

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.

수십 년 동안 IT 운영 매뉴얼은 대개 50페이지 분량의 빽빽한 PDF 문서였다. 사람이 만들고 사람이 읽도록 설계된 문서는, 감사가 필요해질 때까지 디지털 저장소 어딘가에서 방치되는 경우가 대부분이었다. 그러나 2026년에 접어든 지금, 전통적인 SOP는 사실상 수명을 다한 상태다. 이제 이 매뉴얼의 주된 사용자가 사람이 아니기 때문이다.

시스템은 점점 더 에이전트 기반으로 진화하고 있다. 단순히 대시보드를 감시하는 수준을 넘어, 스스로 사고하고 계획하며 인프라 내 변경을 실행하는 자율형 에이전트가 배치되고 있다. 이들 에이전트는 PDF 문서를 읽을 수 없고, 법률 용어로 작성된 보안 정책의 취지를 해석하지도 못한다. 자율형 IT 시대에 통제력을 유지하려면 고정된 규칙에 머무르지 않고 ‘에이전트 헌법’, 즉 앤트로픽이 제시한 ‘헌법 중심 AI(Constitutional AI)’를 기업 환경에 적용해야 한다. 이는 AI의 문제점을 AI가 스스로 검증하고 고치기 위한 시스템을 의미한다.

문서 속 정책에서 코드로 구현된 정책으로

과거 IT 거버넌스는 사후 대응만 가능한 ‘체크리스트’ 방식이었다. 그러나 오늘날 기업은 정책을 코드로 구현하는 ‘PaC(Policy as Code)’로의 전환이 필요하다.

• 전전두엽 역할: 에이전트 헌법은 자율 시스템 기계가 읽을 수 있는 기본 원칙 집합이다.
• 운영 경계: 에이전트가 수행할 수 있는 작업 범위와 절대 넘지 말아야 할 윤리적 한계를 규정한다.
• 실행 가능한 규칙: 코드로 구현된 강제 규칙의 예로는 ‘피크 시간대에는 인간 개입 토큰 없이는 운영 데이터베이스를 수정하지 않는다’와 같은 원칙이 있다.
• LLM 이해 가능성: 이러한 규칙은 실행 가능하며, 오케스트레이션을 담당하는 LLM이 이해하고 활용할 수 있다.

이런 전환은 근본적인 변화를 의미한다. IT 전문가의 역할은 ‘운영자’에서 ‘의도 설계자’로 변화하고 있다. IT 직원은 더 이상 시스템을 직접 조작하는 사람이 아니라, 자율 시스템이 따라야 할 행동 규칙을 설계하는 주체가 되고 있다.

IT 운영을 위한 자율성 계층 구조

기업이 ‘킬 스위치’에 대한 통제권을 유지하면서 AI 역량을 확장하려면, ‘자율성의 계층 구조’에 초점을 맞출 필요가 있다. 이는 1978년 연구자 토머스 셰리던와 윌리엄 버플랭크의 기초 연구에서 제시된 프레임워크에 기반한 개념이다.

1단계: 완전 자율화 영역(가장 쉽게 도입할 수 있는 영역)

• 이는 사람이 개입하는 비용이 해당 작업의 가치보다 더 큰 업무를 의미한다.
• 사례
  • 자동 확장
  • 로그 로테이션
  • 기본 티켓 라우팅
  • 캐시 정리
• 거버넌스: 사전에 정의된 임계값 조건에 따라 동작하는 통제된 자동화 영역(sandbox of trust)에서 관리된다.

2단계: 감독형 자율화 영역(사전 확인 구간)

• 에이전트가 데이터 수집과 문제 원인 분석, 해결 방안 도출 등 핵심 작업을 수행하지만, 최종 실행 단계에서는 사람의 승인, 즉 확인 절차가 필요한 단계다.
• 사례
  • 시스템 패치
  • 사용자 계정 프로비저닝
  • 비중요 설정 변경
• 거버넌스: 에이전트는 해당 조치를 왜 수행하려는지에 대한 판단 근거, 즉 추론 과정을 관리자에게 제시해야 한다.

3단계: 사람 전용 영역

• 어떤 상황에서도 에이전트가 자율적으로 수행해서는 안 되는, 시스템의 존립과 직결된 핵심 작업을 의미한다.
• 사례
  • 데이터베이스 삭제
  • 핵심 보안 설정 우회
  • 에이전트 헌법 자체에 대한 수정
• 거버넌스: 다단계 인증(MFA) 또는 복수 인원의 이중 승인과 같은 강력한 통제 절차를 적용한다.

숨겨진 공격 표면 줄이기

중앙화된 헌법 체계를 구현하면, 중앙 IT의 관리 및 감독 없이 배포되는 섀도우 AI 에이전트로 인한 리스크를 완화할 수 있다.

• 통합 API: 모든 에이전트는 핵심 인프라와 상호작용하기 전에 해당 운영 원칙 체계에 따라 인증을 거쳐야 한다.
• 컴플라이언스 이력: 이를 통해 SOC2나 EU AI 법과 같은 규제 대응에 활용할 수 있는 중앙화된 감사 이력이 생성된다.
• 검증 가능한 의사결정: 자율적으로 내려진 판단과 실행에 대한 검증 가능한 기록을 축적할 수 있다.

기계 중심 세계 속 사람의 목소리

이른바 ‘헌법’은 코드가 아니라, 엔지니어의 경험과 판단이 집약된 사람의 문서다. 따라서 사람의 역할은 여전히 중요하다.

• 의도 설계자: IT 전문가의 역할은 ‘운영자’에서 ‘의도의 설계자’로 변화하고 있다.
• 문화적 전환: IT 팀은 개인이 나서서 문제를 해결하는 방식에서 벗어나, 시스템 중심의 거버넌스 문화로 전환해야 한다.

‘헌법 제정 회의’를 시작할 때

2020년대 후반에도 PDF 형식의 기존 SOP에 의존한다면, IT 운영은 비즈니스의 발목을 잡는 병목으로 전락할 가능성이 크다.

지금 바로 취해야 할 단계는 다음과 같다.

• 레드라인 정의: 수석 아키텍트를 중심으로 3단계 영역의 경계를 명확히 설정한다.
• 자동화 성과 도출: 즉시 자동화가 가능한 1단계 업무를 식별한다.
• 전략에 집중: 사람은 봇을 감시하는 데 시간을 쓰는 대신, 전략과 혁신에 집중하도록 방향을 전환한다.

dl-ciokorea@foundryco.com

For decades, the IT operations manual was a dense, 50-page PDF — a document designed by humans, for humans, and usually destined to gather digital dust until an audit required its retrieval. But as we enter 2026, the traditional standard operating procedure (SOP) is officially on life support. Humans are no longer the primary users of their own manuals.

Our systems are becoming agentic, deploying autonomous agents that don’t just monitor dashboards but actively “think,” plan, and execute changes within our infrastructure. These agents cannot read a PDF, nor can they “interpret the spirit” of a security policy written in legalese. If you want to maintain control in an era of autonomous IT, you must move beyond static guardrails and adopt an Agentic Constitution, which is the enterprise application of Constitutional AI, a term pioneered by Anthropic.

From policy on paper to policy as code 

In the past, IT governance was a reactive “check-the-box” exercise. The modern enterprise must shift toward Policy as Code (PaC).

• The pre-frontal cortex: An Agentic Constitution is a machine-readable set of foundational principles for your autonomous systems.
• Operational boundaries: They define what an agent can do and the ethical boundaries it must never cross.
• Actionable rules: An example of an encoded hard rule is: “Never modify production data during peak hours without a human-in-the-loop token”.
• Understandable by LLMs: These rules are actionable and understandable by the models powering your orchestration.

This shift represents a fundamental transformation: the role of the IT professional is moving from “Operator” to “Architect of Intent”. IT professionals are no longer the ones turning the wrenches; they are the ones writing the rules of engagement.

The hierarchy of autonomy: A framework for IT ops 

To scale AI capabilities without ceding total control of the “kill switch”, enterprises should adopt a hierarchy of autonomy, a framework credited to the foundational work of Thomas Sheridan & William Verplank (1978).

Tier 1: Full autonomy (the low-hanging fruit) 

• Description: Tasks where the cost of human intervention exceeds the value of the task.
• Examples: 
  • Auto-scaling 
  • Log rotation 
  • Basic ticket routing 
  • Cache clearing 
• Governance: Defined by threshold-based triggers within a “sandbox of trust”.

Tier 2: Supervised autonomy (the ‘check-back’ zone) 

• Description: Agents perform heavy lifting — gathering data and identifying fixes — but require a “human nod” before final execution.
• Examples: 
  • System patching 
  • User provisioning 
  • Non-critical configuration changes 
• Governance: Agents must present a “reasoning trace” to the admin explaining why the action is being taken.

Tier 3: Human-only (the red line) 

• Description: “Existential” actions that no agent should ever perform autonomously.
• Examples: 
  • Database deletions 
  • Critical security overrides 
  • Modifications to the Agentic Constitution itself 
• Governance: Multi-factor authentication (MFA) or multi-person “dual-key” approvals.

Reducing the ‘hidden attack surface’ 

Implementing a centralized constitution helps mitigate the risks of shadow AI agents — autonomous tools deployed without central IT oversight.

• Unified API: Any agent must “authenticate” against the constitution before it can interact with core infrastructure.
• Compliance history: This creates a centralized audit trail invaluable for compliance frameworks like SOC2 or the EU AI Act.
• Verifiable decision-making: You are building a verifiable history of autonomous decision-making.

The human voice in a machine world 

The “Constitution” is a human document representing the collective wisdom of your engineers.

• Architects of intent: The role of the IT professional shifts from “Operator” to “Architect of Intent”.
• Cultural shift: IT teams must move away from “hero culture” firefighting toward a culture of systemic governance.

Conclusion: Starting your constitutional convention 

If you rely on human-readable SOPs in the second half of the decade, your IT operations will become a bottleneck for the business.

Steps to take this quarter:

• Identify red lines: Gather lead architects to define your Tier 3 boundaries.
• Map automated wins: Identify Tier 1 tasks for immediate automation.
• Focus on strategy: Ensure humans focus on strategy and innovation, not babysitting a bot.

This article is published as part of the Foundry Expert Contributor Network.
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Project managers are doing exactly what they were taught to do. They build plans, chase team members for updates, and report status. Despite all the activity, your leadership team is wondering why projects take so long and cost so much.

When projects don’t seem to move fast enough or deliver the ROI you expected, it usually has less to do with effort and more with a set of common mistakes your project managers make because of how they were trained, and what that training left out. Most project teams operate like order takers instead of the business-focused leaders you need to deliver your organization’s strategy.

To accelerate strategy delivery in your organization, something has to change. The way projects are led needs to shift, and traditional project management approaches and mindsets won’t get you there.

Here are the most common project management mistakes we see holding teams back, and what you can do to help your project leaders shift from being order takers to drivers of IMPACT: instilling focus, measuring outcomes, performing, adapting, communicating, and transforming.

Mistake #1: Solving project problems instead of business problems

Project managers are trained to solve project problems. Scope creep. Missed deadlines. Resource bottlenecks. They spend their days managing tasks and chasing status updates, but most of them have no idea whether the work they manage is solving a real business problem.

That’s not their fault. They’ve been taught to stay in their lane in formal training and by many executives. Keep the project moving. Don’t ask questions. Focus on delivery.

But no one is talking to them about the purpose of these projects and what success looks like from a business perspective, so how can they help you achieve it?

You don’t need another project checked off the list. You need the business problem solved.

IMPACT driver mindset: Instill focus

Start by helping your teams understand the business context behind the work. What problem are we trying to solve? Why does this project matter to the organization? What outcome are we aiming for?

Your teams can’t answer those questions unless you bring them into the strategy conversation. When they understand the business goals, not just the project goals, they can start making decisions differently. Their conversations change to ensure everyone knows why their work matters. The entire team begins choosing priorities, tradeoffs, and solutions that are aligned with solving that business problem instead of just checking tasks off the list.

Mistake #2: Tracking progress instead of measuring business value

Your teams are taught to track progress toward delivering outputs. On time, on scope, and on budget are the metrics they hear repeatedly. But those metrics only tell you if deliverables will be created as planned, not if that work will deliver the results the business expects.

Most project managers are taught to measure how busy the team is. Everyone walks around wearing their busy badge of honor as if that proves value. They give updates about what’s done, what’s in progress, and what’s late. But the metrics they use show how busy everyone is at creating outputs, not how they’re tracking toward achieving outcomes.

All of that busyness can look impressive on paper, but it’s not the same as being productive. In fact, busy gets in the way of being productive.

IMPACT driver mindset: Measure outcomes

Now that the team understands what they’re doing and why, the next question to answer is how will we know we’re successful.

Right from the start of the project, you need to define not just the business goal but how you’ll measure it was successful in business terms. Did the project reduce cost, increase revenue, improve the customer experience? That’s what you and your peers care about, but often that’s not the focus you ask the project people to drive toward.

Think about a project that’s intended to drive revenue but ends up costing you twice as much to deliver. If the revenue target stays the same, the project may no longer make sense. Or they might come up with a way to drive even higher revenue because they understood the way you measure success.

Shift how you measure project success from outputs to outcomes and watch how quickly your projects start creating real business value.

Mistake #3: Perfecting process instead of streamlining it

If your teams spend more time tweaking templates, building frameworks, or debating methodology than actually delivering results, processes become inefficient.

Often project managers are hired for their certifications, which leads many of them to believe their value is tied to how much of and how perfectly they create and follow that process. They work hard to make sure every box is checked, every template is filled out, and every report is delivered on time. But if the process becomes the goal, they’re missing the point.

You invested in project management to get business results, not build a deliverable machine, and the faster you achieve those results, the higher your return on your project investments.

IMPACT driver mindset: Perform relentlessly

With a clear plan to drive business value, now we need to show them how to accelerate. That means relentlessly evaluating, streamlining, and optimizing the delivery process so it helps the team achieve the project goals faster.

Give them permission to simplify. When the process slows them down or adds work that doesn’t add value, they should be able to call it out.

This isn’t an excuse to have no process or claim you’re being agile just to skip the necessary steps. It’s about right-sizing the process, simplifying where you can, and being thoughtful about what’s truly needed to deliver the outcome. Do you really need a 30-page document no one will read, or would two pages that people actually use be enough? You don’t need perfection. You need progress.

Mistake #4: Blaming people instead of leading them through change

A lot of leaders start from the belief that people are naturally resistant to change. When projects stall or results fall short, it’s easy to assume someone just didn’t want to change. Project teams blame people, then layer on more governance, more process, and more pressure. Most of the time, it’s not a people problem. It’s how the changes are being done to people instead of with them.

People don’t resist because they’re lazy or difficult. They resist because they don’t understand why it’s happening or what it means for them. And no amount of process will fix that.

IMPACT driver mindset: Adapt to thrive

With an accelerated delivery plan designed to drive business value, your project teams can now turn their attention to bringing people with them through the change process.

Change management is everyone’s job, not something you outsource to HR or a change team. Projects fail without good change management and everyone needs to be involved. Your teams must understand that people aren’t resistant to change. They’re resistant to having change done to them. You have to teach them how to bring others through the change process instead of pushing change at them.

Teach your project teams how to engage stakeholders early and often so they feel part of the change journey. When people are included, feel heard, and involved in shaping the solution, resistance starts to fade and you create a united force that supports your accelerated delivery plan.

Mistake #5: Communicating for compliance instead of engagement

The reason most project communication fails is because it’s treated like a one-way path. Status reports people don’t understand. Steering committee slides read to a room full of executives who aren’t engaged. Unread emails. The information goes out because it’s required, not because it’s helping people make better decisions or take the right action.

But that kind of communication doesn’t create clarity, build engagement, or drive alignment. And it doesn’t inspire anyone to lean in and help solve the real problems.

IMPACT driver mindset: Communicate with purpose

To keep people engaged in the project and help it keep accelerating toward business goals, you need purpose-driven communication designed to drive actions and decisions. Your teams shouldn’t just push information but enable action. That means getting the right people and the right message at the right time, with a clear next step.

If you want your projects to move faster, communication can’t be a formality. When teams, sponsors, and stakeholders know what’s happening and why it matters, they make decisions faster. You don’t need more status reports. You need communication that drives actions and decisions.

Mistake #6: Driving project goals instead of business outcomes

Most organizations still define the project leadership role around task-focused delivery. Get the project done. Hit the date. Stay on budget. Project managers have been trained to believe that finishing the project as planned is the definition of success. But that’s not how you define project success.

If you keep project managers out of the conversations about strategy and business goals, they’ll naturally focus on project outputs instead of business outcomes. This leaves you in the same place you are today. Projects are completed, outputs are delivered, but the business doesn’t always see the impact expected.

IMPACT driver mindset: Transform mindset

When you help your teams instill focus, measure outcomes, perform relentlessly, adapt to thrive, and communicate with purpose, you do more than improve project delivery. You build the foundation for a different kind of leadership.

Shift how you and your organization see the project leadership role. Your project managers are no longer just running projects. You’re developing strategy navigators who partner with you to guide how strategy gets delivered, and help you see around corners, connect initiatives, and decide where to invest next.

When project managers are trusted to think this way and given visibility into the strategy, they learn how the business really works. They stop chasing project success and start driving business success.

More on project management:

• Project management guide: Tips, strategies, best practices
• What is a project manager? The lead role for project success
• 5 early warning signs of project failure
• 10 project management myths to avoid
• 16 tips for a smooth switch to agile project management
• The 22 best project management tools for business
• Scrum vs. Lean vs. Kanban: Comparing agile project management frameworks
• How to pick the right project management methodology for success
• Agile project management: A comprehensive guide
• 8 common project management mistakes — and how to avoid them
• Top 11 project management certifications for 2017
• 10 traits of highly effective project managers
• 8 goals every project manager should aspire to achieve
• Project management: 7 steps to on-time, on-budget, goal-based delivery

IBM은 기업 및 정부가 클라우드 업체의 데이터센터 위치에 의존하지 않고도 소버린 클라우드 배포에 대한 운영 통제권을 확보할 수 있도록 설계된 소프트웨어 스택 ‘소버린 코어(Sovereign Core)’를 출시했다. 이를 통해 CIO가 강화되는 규제 심사에 대응하고 컴플라이언스를 자동화하며, 데이터의 엄격한 위치 조건 아래에서 민감한 AI 워크로드를 실제 운영 환경에 배치할 수 있도록 지원하는 것을 목표로 하고 있다.

소버린 클라우드는 일반적으로 클라우드의 효율성을 활용하면서도 데이터와 IT 운영에 대한 통제권을 유지하는 데 초점을 맞춘다. 이는 데이터 위치 규제와 같은 현지 법규를 준수하는 동시에, 데이터와 운영, 보안에 대해 국가 또는 조직 차원의 완전한 통제를 보장하기 위해 대부분 특정 지역에 구축된다. 이상적으로는 격리된 클라우드 환경에서 운영되는 IT 인프라를 의미한다.

마이크로소프트나 구글의 소버린 클라우드가 전용 데이터센터를 기반으로 설계되는 것과 달리, IBM은 기업이나 정부가 배포하려는 모든 소프트웨어와 애플리케이션에 주권을 기본적으로 탑재하겠다는 입장이다. IBM은 오는 2월 기술 프리뷰 공개가 예정된 소버린 코어를 통해, 고객이 자체 하드웨어는 물론 지역 클라우드 업체나 다른 클라우드 환경에서도 워크로드를 실행할 수 있다고 밝혔다.

퓨처럼 그룹(Futurum Group)의 CIO 실무 책임자 디온 힌치클리프는 “이는 전통적인 소버린 클라우드라기보다는, 각 조직이 자체적으로 클라우드를 구축할 수 있도록 하는 소프트웨어 스택에 가깝다”라고 설명했다. 그는 소버린 코어가 온프레미스 데이터센터, 지역 내에서 지원되는 클라우드 인프라, IT 서비스 업체를 통한 환경 등 다양한 운영 환경에서 활용될 수 있다고 분석했다.

벤더 종속성 제거

분석가들은 이러한 접근 방식이 소버린 클라우드 관리 방식을 재정의하고, 벤더 종속성을 피하는 데 도움이 될 수 있다고 진단했다.

힌치클리프는 기존 소버린 클라우드 환경에서는 클라우드 업체가 업데이트나 접근 권한과 같은 핵심 운영 요소를 계속 통제하는 경우가 많다고 언급했다. 이로 인해 규제 리스크가 커질 뿐 아니라, 고객이 특정 업체의 아키텍처와 API, 컴플라이언스 도구에 종속되는 구조가 형성될 수 있다는 것이다.

또한 워크로드를 다른 환경으로 이전할 경우, 기존 업체의 신원 관리 체계와 암호화 키, 감사 추적 정보가 매끄럽게 이전되지 않는 문제가 발생할 수 있다. 힌치클리프는 이로 인해 CIO가 새로운 환경에서도 규제 요건을 충족하기 위해 거버넌스 체계를 다시 구축해야 하는 부담을 떠안게 된다고 지적했다.

반면 IBM의 소버린 코어는 암호화 키와 신원 관리, 운영 권한을 각 조직의 관할 영역 안에 유지할 수 있도록 함으로써 CIO에게 더 많은 통제권을 부여할 수 있다. 이런 구조로 인해 CIO는 거버넌스 체계를 다시 구축하지 않고도 클라우드 업체를 전환할 수 있다.

하이퍼프레임 리서치(HyperFRAME Research)의 AI 스택 총괄인 스테파니 월터는 규제 기관 주도의 감사가 점점 더 빈번해지고, 요구 수준도 강화되고 있다고 진단했다. 특히 유럽연합(EU)의 규제 당국은 기업의 규제 준수 약속만으로는 충분하지 않다고 보고, 실제 준수 여부를 입증할 수 있는 증거와 감사 기록, 상시적인 컴플라이언스 보고를 요구하고 있다.

힌치클리프는 소버린 코어가 자동화된 증거 수집과 지속적인 모니터링을 통해 이런 요구에 대응할 수 있다고 분석했다. 이를 통해 은행과 정부 기관, 방위 산업과 연관된 분야에서 발생하는 운영 부담을 줄이는 데도 도움이 될 수 있다고 평가했다.

소버린 AI 파일럿의 실제 배포 지원

분석가들은 소버린 코어가 기업의 AI 파일럿 프로그램을 실제 운영 환경에 배포하는 데도 힘을 실어줄 수 있다고 봤다. 특히 엄격한 데이터 위치 조건과 컴플라이언스 통제가 요구되는 AI 프로젝트에서 효과가 클 것이라는 분석이다.

HFS 리서치(HFS Research)의 CEO 필 퍼슈트는 대부분의 기업과 조직이 자체 데이터를 범용 AI 모델에 전달하는 데 여전히 부담을 느끼고 있다고 진단하면서, 동시에 GPU 기반 추론을 완전히 자체 주권 경계 안에서만 실행하는 것도 현실적으로 제약이 많은 상황이라고 설명했다.

이에 비해 소버린 코어의 기능과 역량은 기업 및 정부 조직이 내부 환경에서 AI 추론을 실행할 수 있도록 지원한다. 이를 통해 처리되는 데이터뿐 아니라 AI 모델 자체도 주권 요구사항을 충족할 수 있으며, 결과적으로 CIO가 주권을 확보하면서 AI를 파일럿 단계에서 운영 단계로 옮길 수 있는 기반을 제공한다고 퍼슈트는 설명했다.

시장 환경의 변화

소버린 코어는 IBM이 향후 AI 규제 강화 흐름을 염두에 두고 소버린 클라우드 시장 공략을 본격화하려는 전략으로 풀이된다. 동시에 마이크로소프트와 AWS, 구글 등 주요 클라우드 업체보다 한발 앞서 주도권을 잡으려는 의도도 담겨 있다.

힌치클리프는 “유럽이 규제를 강화하고 아시아태평양(APAC) 지역도 이를 뒤따르는 상황에서, IBM은 주권 문제가 기업의 AI 도입 여부를 가르는 핵심 요인이 될 것으로 보고 있다. 일부 기업에서는 비용이나 성능보다도 훨씬 더 중요한 요소가 될 수 있다”라고 설명했다.

특히 EU는 주요 클라우드 업체 대부분이 미국에 본사를 두고 있다는 점에서, 외국 기업이 데이터에 접근하거나 핵심 IT 시스템을 통제하는 것을 엄격하게 규제하고 있다.

EU 규제를 충족하기 위해 클라우드 업체는 보통 지역 통합 업체나 관리형 서비스 업체와 협력한다. 다만 힌치클리프에 따르면, 이 경우에도 기본 플랫폼에 대한 운영 통제권은 클라우드 업체가 유지하고, 파트너는 그 위에서 서비스 구축과 운영을 맡는 경우가 대부분이다.

IBM의 소버린 코어는 파트너가 고객을 대신해 전체 환경을 직접 운영할 수 있고, IBM은 운영 과정에 전혀 개입하지 않는 구조다. 힌치클리프는 이러한 접근이 규제 준수 측면에서 더 높은 신뢰성을 제공한다고 분석했다.

이와 관련해 IBM은 독일의 컴퓨타센터(Computacenter) 및 유럽 지역을 시작으로 전 세계 IT 서비스 업체와 협력을 확대할 계획이라고 밝혔다. IBM은 소버린 코어에 추가 기능을 더해 2026년 중반 정식 출시할 계획이다.
dl-ciokorea@foundryco.com

지난 한 해 기업 시장에서 AI 에이전트는 ‘대세’로 떠올랐지만, 현실은 과열된 기대와 실패한 실험이 뒤섞인 모습이다. 하지만 전문가들은 CIO가 신뢰성과 거버넌스, 성과라는 기본기를 얼마나 탄탄히 갖추느냐에 따라 2026년 에이전틱 AI의 확산 속도도 달라질 것으로 전망한다.

가트너는 비용 증가, 불명확한 비즈니스 가치, 미흡한 리스크 통제 등을 이유로 2027년까지 에이전틱 AI 프로젝트의 40%가 취소될 것이라고 내다봤다. 실제 도입 성과를 두고도 지표는 엇갈린다. 회계·컨설팅업체 PwC의 5월 설문에서는 응답 기업의 79%가 어떤 형태로든 에이전트를 도입했다고 답했지만, 기업용 검색 솔루션 업체 루시드웍스(Lucidworks)는 이커머스 사이트 1,100곳을 분석한 결과 복수의 에이전틱 솔루션을 운영하는 곳이 6%에 그쳤다고 밝혔다.

AI 의사결정 및 워크플로우 자동화 솔루션 업체 페가(Pega)의 CTO 돈 슈어먼은 2026년에 에이전틱 AI 도입이 증가하겠지만, 이 기술이 곧바로 ‘주류’가 되기는 어렵다고 본다. 에이전트를 구동하는 LLM의 환각 문제가 남아 있다는 이유다. 슈어먼은 “2026년은 성공한 접근법과 실패한 접근법이 본격적으로 갈리기 시작하는 해가 될 것”이라며, “에이전트가 모든 것을 완전히 대체하는 단계는 더 장기적인 변화가 될 것”이라고 설명했다.

과도한 기대 탓에 ‘전망은 흐림’

많은 조직이 에이전틱 AI와 관련해 직면하게 될 과제는 여러 문제에 대응하는 추론 기능을 기대하며 LLM을 배치했다는 것이다. 슈어먼은 이 때문에 결과물에 대한 기대치가 부풀려졌다며, “LLM은 추론 기계가 아니라 텍스트 예측 기계”라고 지적했다.

슈어먼은 당장 성과를 내기 위해 ‘추론이 필요 없는’ 예측 가능한 워크플로우부터 공략하는 전략이 유효하다고 본다. 다만 에이전틱 AI가 진짜 성과를 내려면, 설계 단계에서부터 에이전트 업무에 추론 요소를 녹여야 한다고 강조했다. 또한, “에이전트가 하는 일을 비즈니스 프로세스와 워크플로우에 단단히 고정해야 한다. 기업이 원하는 것은 매번 일관되게 실행되고, 예측 가능성과 일관성, 감사 가능성이 높은 ‘결정론적 워크플로우’인 경우가 대부분”이라고 설명했다.

‘수천 개의 에이전트를 무작정 배치해 알아서 굴린다’는 상상은 신화에 가깝다는 주장도 내놨다. 슈어먼은 “에이전트를 마구 배포하는 대신, 에이전트를 활용해 우리가 필요한 워크플로우를 정의하고 설계해 과거보다 훨씬 빠르고 완성도 높게 만드는 방향이 현실적”이라고 말했다.

세일즈포스의 CIO 댄 쉬밋도 2026년 에이전틱 AI의 남은 과제로 ‘로드맵 부재’를 꼽았다. 시작 방법, 확장 방식, 성공 기준을 명확히 정리하지 못한 조직이 많고, 고품질 데이터와 통합 거버넌스 모델이 없으면 에이전트가 불안정한 결과를 낼 수 있다는 설명이다. 다만 쉬밋은 “조직 전반에 완전 자율 시스템이 보편적으로 배치되지는 않을 것”이라며, “사람과 다른 에이전트 옆에서 협업하며 일상 프로세스에서 생산성과 의사결정을 보강하는 형태로 확산될 것”이라고 전망했다.

슈어먼은 여기서도 CIO가 ‘IT 배포의 기본’으로 돌아가야 한다고 강조했다. 슈어먼은 “에이전트 같은 신기술이 등장했다고 해서 워크플로우와 데이터, 성과 정의 같은 기초 작업을 건너뛸 수는 없다. 어떤 방식으로 성과를 만들어내는지 이해하고, 에이전트를 데이터에 제대로 연결해야 한다”고 조언했다.

에이전틱 AI의 무한한 가능성

IBM CIO 맷 라이트슨은 2026년에 에이전틱 AI의 ‘성공적인’ 배포가 더 늘어날 것으로 내다봤다. 다만 전제 조건은 분명하다. 에이전트를 배포할 때 목표 성과를 더 구체적으로 설정하고 데이터 보안과 통제를 강화하며, 에이전트가 다른 IT 시스템과 어떻게 상호작용하는지 이해해야 한다는 것이다.

라이트슨은 “더 많은 사용례로 에이전트를 확장해 조직에 가치를 가져오려면, 원하는 성과와 에이전트에 제공해야 할 데이터, 그리고 이를 관리·통제하는 방법을 명확히 해야 한다”라며, “조직이 그 수준까지 해내면 도입도 성공도 훨씬 늘어날 것”이라고 말했다.

배포를 가로막는 대표 요인으로는 기존 시스템 및 데이터와의 통합 난이도를 꼽았다. 목표 성과를 충분히 정의하지 않은 채 에이전트를 얹는 접근도 문제라고 봤다. 라이트슨은 “IT는 보통 프로세스부터 생각해 ‘무엇을 하게 만들지’를 설계해 왔지만, 에이전트 시대에는 ‘어떤 성과를 얻을지’부터 생각해야 한다”라며, “이 지점에서 준비가 부족하면 쉽게 발목이 잡힌다”라고 지적했다.

IBM은 현재 엔터프라이즈 워크플로우 AI 에이전트를 수백 개, 개인 생산성 에이전트를 수천 개 배포했다고 밝혔다. 예를 들어, IT 지원 티켓을 분류하고, 단순·저난도 지원 요청을 처리하는 업무에 에이전트를 활용하고 있다.

한편, 파일럿에서 기대만큼 성과가 나오지 않았더라도 가능성을 닫지 말라고도 조언했다. 라이트슨은 “매일, 매주 새로운 것을 배우고 있다”라며, “이렇게 배운 것을 비즈니스 성과로 바꿀 수 있는 호기심과 실행이 쌓이면, 가능성은 무한대다”라고 강조했다.

라이프사이클 관리가 과제

아사나(Asana)의 CIO 사켓 스리바스타바도 2026년 에이전트 배포가 확대될 것으로 봤다. 동시에 CIO가 풀어야 할 숙제로 ‘사람의 저항’과 ‘신뢰·신빙성’ 문제를 꼽았다.

특히 스리바스타바는 에이전트 라이프사이클 관리가 곧 핵심 이슈가 될 것으로 전망했다. 직원들이 현업에서 만들어내는 에이전트를 추적하고, 성과가 없는 에이전트를 언제 퇴역시킬지 결정하는 체계가 필요하다는 것. 스리바스타바는 “곧 직원 수보다 에이전트 수가 많은 환경을 맞이할 것”이라며, 효과 측정과 모니터링의 중요성을 강조했다.

신뢰는 구조와 맥락에서 나온다는 점도 짚었다. 스리바스타바는 “신뢰는 권한과 가시성, 의사결정이 어떻게 이뤄지는지, 워크플로우가 어떻게 진행되는지에서 나온다”라며, “AI에 대한 과열된 기대 속에서 명확성 없이 이것저것 시도했던 ‘파일럿 모드’가 있었고, 그 과정에서 ‘데이터가 맞는지, 프로세스가 맞는지’ 같은 기본 질문이 뒤로 밀린 경우가 있다”라고 진단했다.

“나쁜 프로세스를 자동화해봐야 소용없다”는 과거 자동화 시대의 교훈도 다시 꺼냈다. 스리바스타바는 “결함 있는 워크플로우에 에이전트를 적용하는 사례도 있다. 프로세스를 더 깊이 들여다보고 재구상한 뒤 AI를 적용하는 흐름이 새해에 본격화될 것”이라고 내다봤다.

하지만, 실험 자체를 멈춰서는 안된다. 스리바스타바는 “천 개의 꽃을 피우듯 무작정 확장하는 접근은 최선이 아닐 수 있다”면서도 “실험이 꽃필 수 있는 환경을 만들되, 동시에 AI가 그 문제를 풀 준비가 됐다는 더 높은 확신을 확보해야 한다”고 말했다. 이어 “올바른 문제를 올바른 방식으로 풀고, 성과를 측정한 뒤 다음 문제로 넘어가야 한다”라고 덧붙였다.
dl-ciokorea@foundryco.com

IBM has launched Sovereign Core, a software stack that aims to offer enterprises and governments full operational control over sovereign cloud deployments without relying on hyperscaler-managed regions.

Sovereign deployments, typically, try to combine cloud benefits with strategic autonomy. They are IT infrastructures that have been set up locally, ideally in isolated cloud environments, to ensure complete national or organizational control over data, operations, and security, while ensuring compliance with local laws, such as data residency regulations.

Unlike traditional sovereign clouds from Microsoft or Google that hinge on dedicated data center locations, IBM’s Sovereign Core, expected to be available in tech preview in February, is trying to make sovereignty an inherent property of any software or application that an enterprise or government wants to deploy, enabling customers to run workloads on their own hardware, local providers, or even other clouds.

“It’s less a sovereign cloud and more of a software stack to build your own sovereign cloud,” Dion Hinchcliffe, lead of the CIO practice at the Futurum Group, said, adding that Core can be used across environments, such as on-premises data centers, supported in-region cloud infrastructure, or through IT service providers.

Avoiding vendor lock-in

That shift in approach, according to analysts, could redefine how CIOs manage sovereign deployments and help them avoid vendor lock-in.

In traditional sovereign cloud deployments, hyperscalers retain control over critical operations like updates and access, creating regulatory risk and locking customers into provider-specific architectures, APIs, and compliance tools, Hinchcliffe said.

When workloads move, identity management, encryption keys, and audit trails tied to the old provider don’t transfer seamlessly, forcing CIOs to rebuild governance frameworks to meet regulatory requirements in the new environment, Hinchcliffe added.

In contrast, Sovereign Core is trying to offer more control to CIOs by allowing them to keep encryption keys, identity management, and operational authority within their jurisdiction, which should enable them to switch providers without rebuilding governance frameworks, Hinchcliffe pointed out.

Seconding Hinchcliffe, HyperFRAME Research’s leader of AI stack Stephanie Walter noted that the frequency and stringency of regulator-driven audits were increasing, specifically the EU: Regulators are no longer satisfied with promises of compliance but are seeking more evidence, audit trails, and continuous compliance reporting.

Sovereign Core, according to Hinchcliffe, could also help CIOs tackle these demands with automated evidence collection and continuous monitoring, reducing overhead for banks, government agencies, and defense-adjacent industries.

Boost for moving sovereign AI pilots to production

Analysts say Sovereign Core could help CIOs and their enterprises push their AI pilots into production, especially the ones that require strict data residency and compliance controls.

Most enterprises and organizations are hesitant to send proprietary data to a public AI model, and at the same can’t run GPU-backed inference completely inside their own sovereign boundary, said Phil Fersht, CEO of HFS Research.

Sovereign Core’s functionalities and capabilities, in contrast, will allow enterprises to run local AI inference inside their own four walls, ensuring the AI model is as “sovereign” as the data it’s processing, in turn providing CIOs with a credible landing zone to move AI from pilots into production under sovereign conditions, Fersht added.

Changing market dynamics

Sovereign Core could be a strategic move by IBM to double down on the sovereignty market ahead of broader AI regulation and surge ahead of hyperscalers such as Microsoft, AWS, and Google.

“With Europe tightening controls and APAC following, IBM is betting that sovereignty will be a major gating factor for enterprise AI adoption. For some companies, much more even than cost or performance,” Hinchcliffe said.

More so in Europe because regulations restrict foreign entities, such as the hyperscalers, which are all headquartered in the US, from having access to data or control over critical IT systems.

To comply with European regulations, hyperscalers typically work with local integrators and managed service providers, but retain operational control of the underlying platform while partners build and manage services on top, Hinchcliffe said.

IBM’s Sovereign Core takes a different approach: partners can operate the entire environment on behalf of the customer, with IBM stepping out of the operational loop altogether, ensuring more compliance with regulations, Hinchcliffe added.

To that extent, IBM said that it is planning to collaborate with IT service providers globally, starting with an initial rollout in Europe with Computacenter in Germany.

IBM plans to make Sovereign Core generally available around the middle of 2026 with additional capabilities, which are likely to be disclosed soon.

The past year generated major hype about AI agents, with tons of experimentation and lots of failure, and some AI experts see only limited improvements in 2026.

Research firm Gartner, for example, has predicted that 40% of agentic AI projects will be cancelled by 2027, because of escalating costs, unclear business value, or inadequate risk controls.

Meanwhile, data about how many enterprises have actually deployed agents successfully is conflicting. A May survey by PwC found that 79% of companies represented had adopted agents in some capacity. But enterprise search vendors Lucidworks, which developed an agent to assess the AI capabilities of ecommerce sites, found that only 6% of the 1,100 sites it analyzed had deployed more than one agentic solution.

While some growth in agent deployments will happen this year, the technology may not yet hit the mainstream, says Don Schuerman, CTO of AI decisioning and workflow automation vendor Pega, in part due to hallucination problems with the LLMs that power agents.

“2026 will be the year that starts to separate the winning approaches from the failed approaches,” he says. “I don’t know if it’s the year where we actually see agents fully take over everything — that’s going to be potentially a little bit longer term of a transformation that maybe people estimate.”

Cloudy outlook

Part of the uphill battle many organizations face with agentic AI is that they have deployed LLMs expecting them to apply reasoning functionality to problems, creating an overinflated expectation of results, Schuerman says. “The LLMs aren’t reasoning machines, they’re just text prediction machines,” he adds.

Schuerman sees many organizations designing agents for predictable workflows where they don’t need to reason but can save employees time by taking over routine tasks. Truly succeeding with agentic AI, however, requires building reasoning into agent tasks at the design phase, he says.

“Really anchor what your agents are doing in those business processes, in those workflows, because most of what the enterprise is trying to do wants to run as a pretty deterministic workflow with a prescribed series of steps that you want to get performed in a consistent way every time, with high degrees of predictability, consistency, and audit,” he explains.

Schuerman argues that expectations about how agents should be used have been skewed by early rollouts. Instead, AI should be used to help redefine the business workflows that agents will take over, he says.

“What is a little bit of a myth is this idea that we’re just going to randomly deploy thousands of agents across our business and just let them go,” he says. “Instead, what we’re going to do is we’re going to use agents to define and design a lot of the workflows that we need in our business and do that with much more speed and completeness than we ever had before.”

Salesforce CIO Dan Shmitt agrees that hurdles remain for agentic AI’s outlook in 2026. For example, many organizations still lack clear roadmaps for how to start, scale, and define success, he says, adding that without high-quality data and a unified governance model, agents can produce unreliable results.

Still, Shmitt sees agents becoming more widely used as the year goes on, if not in the fully autonomous way some AI experts have predicted.

“We’re not likely to see fully autonomous systems deployed universally across organizations,” he says. “Instead, organizations will start to adopt agents as collaborative systems that work alongside people and other agents in day-to-day processes to augment employee productivity and decision-making.”

To get there, Pega’s Schuerman stresses CIOs’ need to stick to the basics of IT deployments when rolling out agents.

“We are seeing more and more of a realization that having new technology like agents doesn’t mean you get to forget about the important foundational work, like having the workflow right, having the data right, having the outcomes defined,” he says. “You’ve got to understand how they deliver outcomes. You’ve got to make sure agents are connected to the data.”

‘The sky’s the limit’

IBM CIO Matt Lyteson also expects more successful deployments of agentic AI in 2026, especially if IT leaders can focus on targeted outcomes when rolling out agents. CIOs also need to pay attention to data security and controls and better understand how agents interact with other IT systems, he says.

“Our focus is, how do we scale agents across more and more use cases to bring value to the organization, and how do I really understand the outcomes, the data that I’m going to need to give the agents, and then how to manage and control them?” he explains. “If organizations can do that, we’re going to see a lot more adoption and a lot more success.”

One impediment to deploying agents has been integrating them with existing systems and data, Lyteson says. Deploying agents without fully defining the targeted outcome is another, he adds.

“We’re used to, as an IT business, thinking about the process first and having that process accomplish something, instead of thinking about the outcome first and what I want an agent to achieve,” he says. “There are some things that start to get in our way and are tricky areas, and if you aren’t thinking through them in the right way, they can really result in stumbling.”

IBM has deployed hundreds of enterprise workflow AI agents and thousands of personal productivity agents, Lyteson says. For example, the company is using agents to triage IT support tickets and handle low-level support requests.

Lyteson recommends that CIOs stay open to the potential of agents, even if some pilot projects haven’t yielded good results.

“Every day, every week, we’re learning something new,” he says, adding that CIOs need to apply those learnings toward maximizing value for the organization. “We need to be continuously curious and to translate that into business outcomes. If myself and my peer CIOs are able to do more of that, then the sky’s the limit.”

Thinking about agent lifecycle

Like Lyteson, Asana CIO Saket Srivastava sees AI agent deployments growing in 2026, even as CIOs face several challenges.

Among those challenges is human resistance to using agents, but Srivastava also believes CIOs need to get a better handle on agent lifecycles, including tracking agents spun up by employees and deciding when to retire ineffective agents. Many CIOs will soon have to navigate a work environment with more agents than employees, and monitoring agent effectiveness will be essential.

In the meantime, reliability and trust concerns may still limit the number of agents deployed in the near term, he says.

“Trust comes from structure, trust comes from context, from the permissions, the visibility of how decisions are being made, how workflows are being progressed,” Srivastava adds. “And there certainly might have been a little bit of us all getting overexcited with all things AI and being in this pilot mode where we were trying out a bunch of things without having clarity. Do we have the right data; is it the right process?”

In some cases, organizations have added AI agents to workflows and processes that were flawed to begin with, he adds.

“In the simple days of automation, we used to keep saying that there’s no point in automating a bad process,” Srivastava says. “Are you looking at your processes more deeply? Are you looking at reimagining those processes and then applying AI to that? That’s what I see coming into the new year.”

While some agent pilot projects may have been premature, CIOs also need to keep experimenting and balance solid results with innovation, Srivastava says.

“Perhaps letting a thousand flowers bloom might not be the best approach, but create the right environment for experimentation to flourish, yet at the same time, you have higher confidence that AI is more ready to go solve for those problems,” he says. “Make sure that you’re solving the right problems, you’re solving it the right way, you’re measuring the outcomes, and then moving on to the next problem.”

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.

Maxim 2: Try to be human

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.

Maxim 6: Beware the IT cultural cringe

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.

Maxim 8: There are no IT projects, only business projects.

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.

Maxim 10: The CIO as the personification of IT

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.

Maxim 36: Join up digital and IT

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.

Maxim 56: AI is a tech-driven business revolution

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.

Waiting on replacement parts can be more than just an inconvenience. It can be a matter of sharp loss of income and opportunity. This is especially true for those who depend on industrial tools and equipment for agriculture and construction. So to keep things run as efficiently as possible, Parts ASAP CIO John Fraser makes sure end customer satisfaction is the highest motivation to get the tech implementation and distribution right.

“What it comes down to, in order to achieve that, is the team,” he says. “I came into this organization because of the culture, and the listen first, act later mentality. It’s something I believe in and I’m going to continue that culture.”

Bringing in talent and new products has been instrumental in creating a stable e-commerce model, so Fraser and his team can help digitally advertise to customers, establish the right partnerships to drive traffic, and provide the right amount of data.

“Once you’re a customer of ours, we have to make sure we’re a needs-based business,” he says. “We have to be the first thing that sticks in their mind because it’s not about a track on a Bobcat that just broke. It’s $1,000 a day someone’s not going to make due to a piece of equipment that’s down.”

Ultimately, this strategy helps and supports customers with a collection of highly-integrated tools to create an immersive experience. But the biggest challenge, says Fraser, is the variety of marketplace channels customers are on.

“Some people prefer our website,” he says. “But some are on Walmart or about 20 other commercial channels we sell on. Each has unique requirements, ways to purchase, and product descriptions. On a single product, we might have 20 variations to meet the character limits of eBay, for instance, or the brand limitations of Amazon. So we’ve built out our own product information management platform. It takes the right talent to use that technology and a feedback loop to refine the process.”

Of course, AI is always in the conversation since people can’t write updated descriptions for 250,000 SKUs.

“AI will fundamentally change what everybody’s job is,” he says. “I know I have to prepare for it and be forward thinking. We have to embrace it. If you don’t, you’re going to get left behind.”

Fraser also details practical AI adoption in terms of pricing, product data enhancement, and customer experience, while stressing experimentation without over-dependence. Watch the full video below for more insights, and be sure to subscribe to the monthly Center Stage newsletter by clicking here.

On consolidating disparate systems: You certainly run into challenges. People are on the same ERP system so they have some familiarity. But even within that, you have massive amounts of customization. Sometimes that’s very purpose-built for the type of process an organization is running, or that unique sales process, or whatever. But in other cases, it’s very hard. We’ve acquired companies with their own custom built ERP platform, where they spent 20 years curating it down to eliminate every button click. Those don’t go quite as well, but you start with a good culture, and being transparent with employees and customers about what’s happening, and you work through it together. The good news is it starts with putting the customer first and doing it in a consistent way. Tell people change is coming and build a rapport before you bring in massive changes. There are some quick wins and efficiencies, and so people begin to trust. Then, you’re not just dragging them along but bringing them along on the journey.

On AI: Everybody’s talking it, but there’s a danger to that, just like there was a danger with blockchain and other kinds of immersive technologies. You have to make sure you know why you’re going after AI. You can’t just use it because it’s a buzzword. You have to bake it into your strategy and existing use cases, and then leverage it. We’re doing it in a way that allows us to augment our existing strategy rather than completely and fundamentally change it. So for example, we’re going to use AI to help influence what our product pricing should be. We have great competitive data, and a great idea of what our margins need to be and where the market is for pricing. Some companies are in the news because they’ve gone all in on AI, and AI is doing some things that are maybe not so appropriate in terms of automation. But if you can go in and have it be a contributing factor to a human still deciding on pricing, that’s where we are rather than completely handing everything over to AI.

On pooling data: We have a 360-degree view of all of our customers. We know when they’re buying online and in person. If they’re buying construction equipment and material handling equipment, we’ll see that. But when somebody’s buying a custom fork for a forklift, that’s very different than someone needing a new water pump for a John Deere tractor. And having a manufacturing platform that allows us to predict a two and a half day lead time on that custom fork is a different system to making sure that water pump is at your door the next day. Trying to do all that in one platform just hasn’t been successful in my experience in the past. So we’ve chosen to take a bit of a hybrid approach where you combine the data but still have best in breed operational platforms for different segments of the business.

On scaling IT systems: The key is we’re not afraid to have more than one operational platform. Today, in our ecosystem of 23 different companies, we’re manufacturing parts in our material handling business, and that’s a very different operational platform than, say, purchasing overseas parts, bringing them in, and finding a way to sell them to people in need, where you need to be able to distribute them fast. It’s an entirely different model. So we’re not establishing one core platform in that case, but the right amount of platforms. It’s not 23, but it’s also not one. So as we think about being able to scale, it’s also saying that if you try to be all things to all people, you’re going to be a jack of all trades and an expert in none. So we want to make sure when we have disparate segments that have some operational efficiency in the back end — same finance team, same IT teams — we’ll have more than one operational platform. Then through different technologies, including AI, ensure we have one view of the customer, even if they’re purchasing out of two or three different systems.

On tech deployment: Experiment early and then make certain not to be too dependent on it immediately. We have 250,000 SKUs, and more than two million parts that we can special order for our customers, and you can’t possibly augment that data with a world-class description with humans. So we selectively choose how to make the best product listing for something on Amazon or eBay. But we’re using AI to build enhanced product descriptions for us, and instead of having, say, 10 people curating and creating custom descriptions for these products, we’re leveraging AI and using agents in a way that allow people to build the content. Now humans are simply approving, rejecting, or editing that content, so we’re leveraging them for the knowledge they need to have, and if this going to be a good product listing or not. We know there are thousands of AI companies, and for us to be able to pick a winner or loser is a gamble. Our approach is to make it a bit of a commoditized service. But we’re also pulling in that data and putting it back into our core operational platform, and there it rests. So if we’re with the wrong partner, or they get acquired, or go out of business, we can switch quickly without having to rewrite our entire set of systems because we take it in, use it a bit as a commoditized service, get the data, set it at rest, and then we can exchange that AI engine. We’ve already changed it five times and we’re okay to change it another five until we find the best possible partner so we can stay bleeding edge without having all the expense of building it too deeply into our core platforms.

No es nada raro que los profesionales de TI se sientan en los grupos empresariales como poco más que “receptores de pedidos”: se les indica qué sistemas implementar o solucionar, en lugar de preguntar cómo la tecnología puede resolver problemas empresariales más importantes. No obstante, dar el salto de técnico de soporte a asesor estratégico lleva su tiempo. Quienes lo hacen bien no se centran sólo en solucionar problemas, sino que aprenden el negocio, hablan en un lenguaje sencillo, se enfocan en resultados en lugar de tareas, y miran hacia el futuro para prevenir problemas en lugar de reaccionar únicamente ante ellos.

Poro, a continuación detallamos siete pasos para pasar de ser personal de soporte reactivo a asesor de confianza.

1. Dejar de esperar a que nos digan qué hacer

Eric Johnson, director de TI de PagerDuty, proveedor de software de gestión de operaciones digitales, considera que el mayor obstáculo que frena a los profesionales de TI es la mentalidad pasiva. Quedarse sentado esperando instrucciones impide que los equipos de TI alcancen el nivel de asociación estratégica que desean.

De ahí que señales que “muchas organizaciones de TI caen en esa trampa. Es como decir: ‘Bueno, no podemos hacer nada a menos que nos digan qué hacer’. Y yo les pregunto: ‘¿Han propuesto algo? ¿Han dado ideas sobre lo que se podría hacer?’. Y muchas veces la respuesta es no. Nunca se alcanzará el nivel deseado si no se aporta algo”.

Mientras, Bill Young, director de tecnología y socio operativo de la empresa de selección de personal de TI RightClick, prefiere ser más directo: los profesionales de TI deben pasar de pensar “mi trabajo es arreglar cosas” a “mi trabajo es mejorar el negocio”.

Por su parte, Dana Stocking, directora de TI de la startup de selección de personal de IA Mercor, destaca que uno de los mayores errores que cometen los responsables de TI es tratar las solicitudes de la empresa como casillas a marcar en lugar de oportunidades para identificar causas fundamentales. Y dice: “Cuando alguien solicita acceso a un software o quiere comprar una nueva herramienta, no se limite a satisfacer la solicitud. Indague en el “porqué”. ¿Qué problema subyacente intentan resolver? A menudo descubrirá que la solución propuesta no es la más adecuada y, al comprender la causa raíz, podrá resolver problemas organizativos más amplios”.

Noe Ramos, vicepresidenta de operaciones de IA de Agiloft, destaca que los buenos líderes de TI ven su trabajo como parte de un ecosistema más amplio, que funciona mejor cuando las personas son abiertas, comparten información y colaboran. De ahí que afirme: “Pasar de “mi sistema” a “nuestro resultado” es un cambio sutil, pero transformador”.

2. Comprender el negocio, no sólo los problemas

Para Johson, los profesionales de TI deben mostrarse como socios comprendiendo realmente lo que ocurre en el negocio, en lugar de esperar a que las partes interesadas acudan a ellos con problemas que resolver. Por eso considera que “cuando interactúas con tus socios comerciales, aportas ideas y soluciones proactivas. No esperas a que te expongan sus problemas y los pongan en tu lista de tareas pendientes; de lo contrario, te conviertes en nada más que una organización que se limita a tomar nota”.

¿Cómo desarrollar esa comprensión? Muy sencillo: asistiendo a reuniones de negocio, haciendo preguntas y escuchando. Por eso Joe Locandro, vicepresidente ejecutivo y director de informática global de Rimini Street, anima a su equipo a asistir regularmente a reuniones departamentales. “Para adquirir ese lenguaje empresarial hay que asistir a reuniones de ejecutivos y ser invitado, ya sea una vez al mes o al trimestre. Eso permite comprender la jerga empresarial y lo que realmente importa”.

Adrienne DeTray, vicepresidenta sénior y directora de informática de Universal Technical Institute, coincide en el argumento: “Acérquese al negocio, tanto física como estratégicamente. Asista a revisiones de la cadena de valor, comprenda la estrategia y los objetivos, participe en reuniones operativas fuera del ámbito técnico. No se pueden alinear los objetivos si no se comprende de dónde viene el negocio y hacia dónde se dirige”.

Para profesionales que dan soporte a departamentos específicos, esto significa pasar tiempo con esos equipos a diario. Por ejemplo, si da soporte a ventas, comprenda cómo funciona el proceso, los puntos débiles a los que se enfrentan los vendedores y cómo miden su éxito trimestral.

3. Liderar con el objetivo, no con las peticiones

En lugar de mantener una mentalidad de “tomar nota”, los profesionales de TI deben hacer preguntas profundas sobre lo que necesitan sus socios y qué impulsa esa necesidad, lo que fomenta la resolución de problemas y el enfoque en resultados en lugar de limitarse a implementar soluciones, afirma DeTray.

El equipo de Johnson sigue un enfoque estructurado. Cada mes realizan reuniones de alineación de cartera, donde discuten iniciativas clave con los socios comerciales. “De hecho, hacemos esas preguntas del estilo cuál es el estado objetivo (es decir, el resultado deseado). Y si conocemos ese estado objetivo, podemos mantener conversaciones con ellos sobre lo que se puede hacer de forma proactiva para ayudarles a alcanzarlo”, explica.

Para un equipo financiero que quiere cerrar los libros más rápido, esto podría significar explorar oportunidades de automatización. Para equipos de ventas que luchan con trabajo administrativo, podría implicar crear herramientas que eliminen la introducción repetitiva de datos. La clave es comprender los objetivos empresariales antes de proponer soluciones técnicas. ¿Cómo se define el éxito para este equipo este trimestre? ¿Cómo miden el progreso? ¿Qué obstáculos se interponen en su camino?

El equipo de datos de Johnson lo ha demostrado al pasar de un enfoque reactivo a proactivo. Al mantener distintas conversaciones con la empresa sobre sus objetivos, el equipo comenzó a sugerir programas para atraer nuevos clientes, identificar oportunidades de expansión y crear copilotos de IA que redujeran el trabajo administrativo del equipo de ventas.

4. Centrarse en los resultados, no en las tareas

Uno de los cambios más efectivos que pueden hacer los profesionales de TI para pasar de “tomadores de pedidos” a asesores estratégicos es modificar la forma en que comunican su trabajo. En lugar de describir lo que hicieron, deben describir lo que ese trabajo permitió lograr.

Locandro, de Rimini Street, observa de manera repetida el antiguo estilo de comunicación basado en tareas en su organización. “Muchos de los informes que se enviaban a la empresa se centraban en actividades: lo ocupados que estaban todos, o “hemos actualizado esto”, o “hemos entregado aquello”… Nadie informaba: “Este es el valor que se ha generado como resultado de esta tarea. Este es el resultado que se ha entregado o se ha podido lograr gracias a esta tarea”. ¿La solución? Pasar de informes basados en actividades a informes basados en valor y resultados. Eliminar jerga técnica y siglas.

“Uno de los cambios más eficaces es pasar de describir la actividad a describir el impacto. Por ejemplo, en lugar de decir “hemos automatizado este proceso”, diga “hemos liberado 200 horas al mes para que el equipo de ventas se concentre en trabajo que genera ingresos”. Este cambio de enfoque, de los insumos a los resultados, replantea el papel de TI como facilitador de la estrategia vinculada a los objetivos empresariales”, afirma Ramos, de Agiloft.

A los líderes de la empresa no les preocupan los detalles técnicos, afirma Christopher Daden, director tecnológico de Criteria Corp. Quieren comprender el propósito del trabajo y el impacto que tendrá. “Los profesionales de TI deben enmarcar cada iniciativa en términos del problema empresarial que resuelve, el riesgo que reduce o la oportunidad que abre”, añade. “Un lenguaje enfocado en resultados, como cómo una solución mejora la experiencia del cliente, acelera los ingresos o refuerza la escalabilidad, ofrece a los ejecutivos una visión clara del valor”.

5. Buscar pequeñas victorias que sumen

Los profesionales de TI no siempre necesitan grandes proyectos de transformación para marcar una diferencia real. A veces, la decisión más inteligente es centrarse en unas pocas victorias rápidas que ofrezcan resultados inmediatos. “Las pequeñas cosas pueden sumar algo grande. Si identificas cosas pequeñas, victorias rápidas que puedan beneficiar a la organización con la que colaboran, a veces se trata de un proceso que podría automatizarse. Si haces esto para varios pasos de un proceso, de repente habrás automatizado una buena parte del mismo”.

Johnson advierte contra la búsqueda constante de grandes logros. En su opinión, “son más difíciles de encontrar y más difíciles de ejecutar. En un plazo de 30 a 60 días, los profesionales de TI pueden comprender las métricas y los estados objetivo, y luego buscar oportunidades para ayudar, aunque empiecen con cosas pequeñas”.

Ese mismo enfoque de pequeñas victorias también se aplica cuando los equipos de TI miran más allá de los procesos y centran su atención en las herramientas y licencias cotidianas de las que dependen los empleados. Meg Donovan, directora de personal de Nexthink, empresa que desarrolla software de gestión de la experiencia digital del empleado, explica cómo su equipo de TI proporciona datos sobre el uso real de las herramientas de software en la empresa —y cuáles no se utilizan. “Creemos que necesitamos esta herramienta. Pero, ¿realmente la necesitamos? ¿Y necesitamos 2.500 licencias? Porque puede mostrarme que sólo están utilizando 10”, admite.

6. Medir el impacto, no sólo la finalización

Demasiados profesionales de TI implementan un proyecto y siguen adelante sin comprobar si realmente ha funcionado. Los profesionales de TI estratégicos se aseguran de hacer un seguimiento completo. Después de implementar una solución, los equipos de TI deben medir la diferencia entre el estado “antes” y “después” para determinar el impacto real, afirma Johnson. “Veo que muchas empresas no hacen todo lo que deberían en este sentido. Son muy rápidas a la hora de identificar el problema y poner en marcha la solución, pero no dan el paso final, que probablemente es el más importante: ¿ha tenido el impacto que esperaban y están [realmente] midiendo eso?”, añade.

Cuando el impacto no alcanza lo esperado, los profesionales de TI estratégicos no se dan por vencidos. Investigan por qué no se han materializado los resultados esperados y realizan correcciones. A veces, esto significa refinar la solución. Otras veces, significa encontrar un enfoque completamente distinto.

7. Utilizar la IA para liberar capacidad estratégica

La IA no es la estrella del cambio estratégico de TI, pero sin duda puede impulsar el trabajo. El valor real proviene del uso de la IA para manejar tareas repetitivas, de modo que los profesionales de TI tengan más tiempo para abordar problemas de mayor valor. Según Johnson, muchas áreas de TI están implementando soluciones basadas en agentes para gestionar el soporte de nivel uno, lo que libera tiempo del personal para proyectos que la empresa consideraría de mayor impacto que responder tickets de soporte.

El equipo de Daden en Criteria ha logrado resultados sorprendentes: “Más del 80% de nuestro código de producción ahora es creado por sistemas de IA, lo que ha aumentado la productividad de ingeniería en al menos un 30%. “Al rediseñar los flujos de trabajo e integrar la automatización inteligente, el equipo logró una desviación de tickets superior al 94%, manteniendo e incluso mejorando la satisfacción de los candidatos”. Los tiempos de respuesta y resolución han mejorado, y el propio trabajo evolucionado, con varios miembros del equipo pasando a desempeñar roles de mayor valor en otras partes de la empresa. “Los que se quedaron pasaron del soporte transaccional a crear activos de alto rendimiento que hacen que nuestros modelos de IA sean más precisos y más sensibles al contexto”, dice Daden, para añadir: “Pasaron de responder tickets a mejorar la inteligencia del propio sistema, lo que supone una contribución fundamentalmente más estratégica”.

Young, de RightClick, señala un ejemplo de cómo las herramientas de toma de notas con IA pueden facilitar el trabajo del equipo de TI y hacerlo más estratégico. Estas herramientas liberan a los profesionales de TI de la distracción de capturar cada detalle durante las reuniones. En lugar de detenerse a escribir, pueden mantenerse concentrados en la conversación y en las decisiones que se toman. Esto significa que dedican menos tiempo al trabajo administrativo y más tiempo a pensar, resolver problemas y planificar los siguientes pasos, el tipo de trabajo que realmente impulsa el negocio.

“No hay nada peor que estar concentrado y tener que interrumpir ese flujo para tomar notas sobre lo que se está diciendo”, afirma, para concluir: “Estas herramientas de toma de notas con IA permiten que las reuniones fluyan a un ritmo normal y pueden proporcionar al equipo un resumen de acciones y puntos de debate. Son muy valiosas”.

Los directores de sistemas de información o CIO actuales se enfrentan a expectativas cada vez mayores en múltiples frentes: impulsan la estrategia operativa y empresarial al mismo tiempo que dirigen iniciativas de IA y equilibran las cuestiones relacionadas con el cumplimiento normativo y la gobernanza. Además, Ranjit Rajan, vicepresidente y director de investigación de IDC, afirma que los CIO tendrán que justificar las inversiones realizadas en automatización a la vez que gestionan los costes relacionados con esta. “Los CIO tendrán la tarea de crear manuales de estrategias de valor de la IA empresarial, con modelos de ROI [retorno de inversión] ampliados para definir, medir y mostrar el impacto en la eficiencia, el crecimiento y la innovación”, afirma el analista.

Mientras tanto, los líderes tecnológicos que han pasado la última década o más centrados en la transformación digital están impulsando ahora un cambio cultural dentro de sus organizaciones. Los CIO hacen hincapié en que la transformación en 2026 requiere centrarse tanto en las personas como en la tecnología.

Así es como los propios CIO aseguran estar preparándose para abordar y superar estos y otros retos en 2026.

Brecha de talento y formación

El reto más citado por los CIO es la escasez constante y creciente de talento tecnológico. Dado que es imposible alcanzar sus objetivos sin las personas adecuadas para ejecutarlos, los líderes tecnológicos están formando internamente y explorando vías no tradicionales para contratar nuevos empleados.

En la última encuesta State of the CIO 2025 realizada por esta cabecera, más de la mitad de los encuestados afirmaron que la escasez de personal y de habilidades “les restaba tiempo para dedicarse a actividades más estratégicas y de innovación”. Los líderes tecnológicos esperan que esta tendencia continúe en 2026.

“Al analizar nuestra hoja de ruta de talento desde una perspectiva de TI, creemos que la IA, la nube y la ciberseguridad son las tres áreas que van a ser extremadamente importantes para nuestra estrategia organizativa”, afirma Josh Hamit, CIO de Altra Federal Credit Union. Este afirma que la empresa abordará esta necesidad incorporando talento especializado, cuando sea necesario, y ayudando al personal actual a ampliar sus competencias. “Por ejemplo, los profesionales tradicionales de la ciberseguridad necesitarán mejorar sus competencias para evaluar adecuadamente los riesgos de la IA y comprender los diferentes vectores de ataque”, relata.

El CIO de Pegasystems, David Vidoni, ha tenido éxito identificando a empleados que combinan competencias tecnológicas y empresariales y uniéndolos con expertos en IA que pueden actuar como mentores. “Hemos descubierto que los tecnólogos con conocimientos empresariales y mentalidad creativa son los más indicados para aplicar eficazmente la IA a situaciones empresariales con la orientación adecuada”, señala. “Después de unos cuantos proyectos, los nuevos empleados pueden alcanzar rápidamente la autosuficiencia y tener un mayor impacto en la organización”.

Daryl Clark, CIO de Washington Trust, afirma que la empresa de servicios financieros ha dejado de exigir títulos universitarios y se centra en las competencias demostradas. Dice que han tenido suerte al asociarse con Year Up United, una organización sin ánimo de lucro que ofrece formación laboral a los jóvenes. “Actualmente contamos con siete empleados a tiempo completo en nuestro departamento de TI que comenzaron con nosotros como becarios de Year Up United. Uno de ellos es ahora vicepresidente adjunto de seguridad de la información. Es una vía probada para que los talentos que están empezando su carrera profesional accedan a puestos tecnológicos, obtengan orientación y se conviertan en futuros colaboradores de gran impacto”, dice.

Integración coordinada de la IA

Los directores de TI afirman que, en 2026, la IA debe pasar de la experimentación y los proyectos piloto a un enfoque unificado que muestre resultados medibles. En concreto, estos líderes afirman que un plan integral de IA debe aunar datos, flujos de trabajo y gobernanza, en lugar de basarse en iniciativas dispersas que tienen más probabilidades de fracasar.

Para 2026, el 40% de las organizaciones no alcanzarán sus objetivos de IA, afirma Rajan, de IDC. ¿Por qué? “Por la complejidad de la implementación, la fragmentación de las herramientas y la mala integración del ciclo de vida”, argumenta, lo que está llevando a los directores de sistemas de información a aumentar la inversión en plataformas y flujos de trabajo unificados.

“No podemos permitirnos más inversiones en IA que operen en la oscuridad”, sentencia el director de TI de Flexera, Conal Gallagher. “El éxito de la IA hoy en día depende de la disciplina, la transparencia y la capacidad de conectar cada dólar gastado con un resultado empresarial”.

Trevor Schulze, CIO de Genesys, sostiene que los programas piloto de IA no son en vano, siempre y cuando proporcionen lecciones que puedan aplicarse en el futuro para impulsar el valor empresarial. “Esos primeros esfuerzos proporcionan a los directores de TI una visión crítica de lo que se necesita para sentar las bases adecuadas para la siguiente fase de madurez de la IA. Las organizaciones que apliquen rápidamente esas lecciones estarán en la mejor posición para obtener un retorno de la inversión real”.

Gobernanza para la rápida expansión de los esfuerzos en IA

Rajan, de IDC, afirma que, a finales de la década, las organizaciones se enfrentarán a demandas, multas y despidos de directores de informática debido a las perturbaciones causadas por controles inadecuados de la IA. Como resultado, según los CIO, la gobernanza se ha convertido en una preocupación urgente, y no en una cuestión secundaria. “El mayor reto para el que me estoy preparando en 2026 es ampliar la IA a toda la empresa sin perder el control”, afirma Siroui Mushegian, CIO de Barracuda. “Las peticiones de IA llegan en masa desde todos los departamentos. Sin una gobernanza adecuada, las organizaciones corren el riesgo de sufrir conflictos en los flujos de datos, arquitecturas incoherentes y lagunas de cumplimiento que socavan toda la pila tecnológica”.

Para estar al día con estas demandas, Mushegian creó un consejo de IA que prioriza los proyectos, determina el valor empresarial y garantiza el cumplimiento. “La clave es crear una gobernanza que fomente la experimentación en lugar de obstaculizarla”, afirma. “Los CIO necesitan marcos que les proporcionen visibilidad y control a medida que se amplían, especialmente en sectores como las finanzas y la sanidad, donde las presiones normativas son cada vez mayores”.

Morgan Watts, vicepresidente de TI y sistemas empresariales de la empresa de VoIP basada en la nube 8×8, afirma que el código generado por la IA ha acelerado la productividad y ha liberado a los equipos de TI para que se dediquen a otras tareas importantes, como mejorar la experiencia del usuario. Pero esas ventajas conllevan riesgos. “Las principales organizaciones de TI están adaptando las medidas de protección existentes en torno al uso de modelos, la revisión de códigos, la validación de la seguridad y la integridad de los datos”, afirma. “Ampliar la IA sin gobernanza invita a sobrecostes, problemas de confianza y deuda técnica, por lo que es esencial incorporar medidas de protección desde el principio”.

Alineación de las personas y la cultura

Los CIO afirman que uno de sus principales retos es alinear a las personas y la cultura de su organización con el rápido ritmo del cambio. La tecnología, siempre en constante evolución, está superando la capacidad de los equipos para mantenerse al día. La IA, en particular, requiere personal que trabaje de forma responsable y segura.

Maria Cardow, CIO de la empresa de ciberseguridad LevelBlue, afirma que las organizaciones suelen creer erróneamente que la tecnología puede resolver cualquier problema si se elige la herramienta adecuada. Esto conduce a una falta de atención e inversión en las personas. “La clave es crear sistemas y personas resilientes”, indica. “Eso significa invertir en el aprendizaje continuo, integrar la seguridad desde el principio en todos los proyectos y fomentar una cultura que promueva el pensamiento diverso”.

Rishi Kaushal, CIO de la empresa de servicios de identidad digital y protección de datos Entrust, afirma que se está preparando para 2026 centrándose en la preparación cultural, el aprendizaje continuo y la preparación de las personas y la pila tecnológica para los rápidos cambios impulsados por la inteligencia artificial. “La función del director de TI ha ido más allá de la gestión de aplicaciones e infraestructura. Ahora se trata de dar forma al futuro. A medida que la IA remodela los ecosistemas empresariales, acelerar la adopción sin alineación conlleva riesgos de deuda técnica, brechas de habilidades y mayores vulnerabilidades cibernéticas. En última instancia, la verdadera medida de un director de informática moderno no es la rapidez con la que implementamos nuevas aplicaciones o IA, sino la eficacia con la que preparamos a nuestro personal y a nuestras empresas para lo que está por venir”, señala.

Equilibrio entre coste y agilidad

Los CIO afirman que en 2026 se pondrá fin al gasto descontrolado en proyectos de IA, y que la disciplina de costes deberá ir de la mano de la estrategia y la innovación. “Nos centramos en aplicaciones prácticas de IA que aumentan nuestra plantilla y optimizan las operaciones”, afirma Vidoni, de Pegasystems. “Toda inversión en tecnología debe estar alineada con los objetivos empresariales y la disciplina financiera”.

Vidoni sostiene que, a la hora de modernizar las aplicaciones, los equipos deben centrarse en los resultados e introducir gradualmente mejoras que respalden directamente sus objetivos. “Esto significa que las iniciativas de modernización de aplicaciones y optimización de costes en la nube son necesarias para seguir siendo competitivos y relevantes. El reto consiste en modernizarse y ser más ágil sin que los costes se disparen. Al capacitar a una organización para desarrollar aplicaciones de forma más rápida y eficiente, podemos acelerar los esfuerzos de modernización, responder más rápidamente al ritmo de los cambios tecnológicos y mantener el control sobre los gastos en la nube”.

Los líderes tecnológicos también se enfrentan a retos a la hora de impulsar la eficiencia mediante la IA, mientras que los proveedores están aumentando los precios para cubrir sus propias inversiones en tecnología, afirma Mark Troller, CIO de Tangoe. “Equilibrar estas expectativas contrapuestas —ofrecer más valor impulsado por la IA, absorber el aumento de los costes y proteger los datos de los clientes— será un reto determinante para los directores de informática en el próximo año”, asegura. “Para complicar aún más las cosas, muchos de mis compañeros de nuestra base de clientes están adoptando la IA internamente, pero, como es lógico, establecen el límite de que sus datos no pueden utilizarse en modelos de formación o automatización para mejorar los servicios y aplicaciones de terceros que utilizan”.

Ciberseguridad

Marc Rubbinaccio, vicepresidente de seguridad de la información de Secureframe, prevé un cambio drástico en la sofisticación de los ataques de seguridad, que no se parecerán en nada a los actuales intentos de phishing. “En 2026, veremos ataques de ingeniería social impulsados por la IA que serán indistinguibles de las comunicaciones legítimas”, afirma. “Dado que la ingeniería social está relacionada con casi todos los ciberataques exitosos, los autores de las amenazas ya están utilizando la IA para clonar voces, copiar estilos de redacción y generar vídeos deepfake de ejecutivos”.

Rubbinaccio afirma que estos ataques requerirán una detección adaptativa basada en el comportamiento y la verificación de la identidad, junto con simulaciones adaptadas a las amenazas impulsadas por la IA.

En la última encuesta sobre el estado de los directores de informática, aproximadamente un tercio de los encuestados afirmó que preveía dificultades para encontrar talentos en ciberseguridad capaces de hacer frente a los ataques modernos. “Creemos que es extremadamente importante que nuestro equipo busque formación y certificaciones que profundicen en estas áreas”, afirma Hamit, de Altra. Sugiere certificaciones como ISACA Advanced in AI Security Management (AAISM) y la próxima ISACA Advanced in AI Risk (AAIR).

Gestión de la carga de trabajo y las crecientes exigencias a los CIO

Vidoni, de Pegasystems, afirma que es un momento emocionante, ya que la IA impulsa a los CIO a resolver problemas de nuevas formas. El puesto requiere combinar estrategia, conocimientos empresariales y operaciones diarias. Al mismo tiempo, el ritmo de la transformación puede provocar un aumento de la carga de trabajo y el estrés. “Mi enfoque es sencillo: centrarse en las iniciativas de mayor prioridad que impulsarán mejores resultados a través de la automatización, la escala y la experiencia del usuario final. Al automatizar las tareas manuales y repetitivas, liberamos a nuestros equipos para que se centren en trabajos de mayor valor y más interesantes”, afirma. “En última instancia, el CIO de 2026 debe ser primero un líder empresarial y luego un tecnólogo. El reto consiste en guiar a las organizaciones a través de un cambio cultural y operativo, utilizando la IA no solo para mejorar la eficiencia, sino también para crear una empresa más ágil, inteligente y centrada en las personas”.

Every product starts with a spark: a big idea, a bold vision, a belief that your team can build something transformative. But between vision and launch lies the messy middle: translating strategy into a roadmap to get real work done.

In How strategy and alignment can make or break your product launches, I explored how a clear, unified strategy sets the foundation for success. The next step is turning this strategy into a tangible, time-bound plan the team can rally around. This is where a roadmap comes in. It’s the blueprint that turns ambition into outcomes.

A roadmap isn’t just a scheduling exercise. It’s an act of translation, connecting the why of a product strategy with the how of execution. When done well, it inspires confidence, motivates teams and ensures everyone — from engineers to executives — is rowing in the same direction.

Why roadmaps matter

The pace of technological change has never been faster. In an era defined by AI, automation and continuous delivery, product teams can’t afford to drift. A roadmap provides the anchor to keep everyone aligned amid constant flux.

Yet many organizations still treat roadmaps as static artifacts — a one-and-done exercise intended to appease executives or investors. That’s a mistake. The most effective roadmaps are living documents evolving with the product and market realities.

According to a Gartner analysis of product roadmapping tools, organizations that maintain flexible, continuously updated roadmaps see up to a 25% improvement in release predictability and stakeholder satisfaction. That’s because teams focus less on prediction and more on iteration; mapping not just what will happen, but how they’ll adapt when things inevitably change.

A roadmap gives shape to uncertainty. It doesn’t eliminate ambiguity; it transforms uncertainty into structured possibility.

From vision to roadmap: Where the rubber meets the road

At this stage, excitement gives way to reality. After articulating the vision comes the hard part: making trade-offs, defining milestones and turning “someday” into “next sprint.”

Here’s how to build a roadmap that actually works:

1. Start with near-term accuracy. Focus on what can be confidently delivered in the next 3–6 months. Early precision matters more than long-term speculation. Anything beyond a year should remain directional, not definitive.
2. Create collaborative ownership. Don’t dictate the roadmap; instead, build it with the team. Involve engineers, designers, project managers and documentation writers from day one. The most successful teams I’ve led — at Splunk, SentinelOne, Chronosphere and elsewhere — emerged from collective input, not top-down mandates.
3. Establish delivery milestones. Early milestones should be small, celebratory and achievable, like the first working prototype, the first dataset successfully processed and the first customer beta. These moments build excitement, energize people and create visible momentum.
4. Plan the process. Roadmap creation isn’t something to be squeezed into an hour-long Zoom call. Schedule dedicated time — ideally 1–2 days in person — for structured collaboration. If the team is remote, leverage digital whiteboards like Miro or Notion to simulate the in-person energy virtually. If possible, establish a clear set of outcomes, customer profiles to target and key deliverables. While this may not be your final roadmap, it gives everyone time to digest and react to what’s being proposed rather than starting from scratch.
5. Capture decisions in a system of record. Once the roadmap is drafted, commit it to a tool the whole organization can access. Regardless of the tool — Jira, Productboard or another specialized platform — consistency is key.

A roadmap built in isolation is a roadmap doomed to fail. The point isn’t to get everyone to agree on everything, it’s to surface disagreements early and align on the next concrete steps forward.

Milestones: The secret to sustainable momentum

If strategy defines direction, milestones are the engine that keeps the train moving. Too often, teams treat milestones as arbitrary checkpoints or internal deadlines. Done right, these can become powerful tools for motivation, alignment and storytelling.

Start small. The first milestone might be as simple as “ingest and store sample data.” That’s enough to celebrate! Over time, milestones should evolve in ambition, but not necessarily in complexity. The key is proximity: Each milestone should be achievable in 1–3 months, not six.

Why does this matter? Because long, nebulous timelines erode morale. Frequent, visible progress sustains it. Teams that regularly celebrate small wins see measurable boosts in productivity and engagement.

And don’t keep those wins quiet. Market your team’s successes across the company. Share demos, post updates in Slack and host show-and-tells. Visibility breeds trust. When executives see tangible progress, they’re far more likely to protect a product roadmap from shifting priorities.

Perfection isn’t the goal, consistency is. A product that ships 80% of what’s planned every quarter will outperform one that promises the moon but delivers sporadically.

What not to do

Building a roadmap is as much about what to avoid as what to include:

• Don’t plan a year out with false precision. The further out the project, the less accurate the assumptions will become.
• Don’t assign GA dates prematurely. It’s better to deliver great products later than mediocre ones on time.
• Don’t roadmap in fragments. Avoid 1:1 syncs or piecemeal updates; they create drift and confusion.
• Don’t mistake alignment for consensus. Disagreement is healthy; it means the team cares enough to challenge ideas.

Above all, don’t boil the ocean. Focus on small, meaningful steps that collectively build momentum. Vision is the north star, but execution is the path to get you there.

Roadmaps are team sports

The best roadmaps aren’t written by PMs — they’re co-authored by teams. That’s why I advocate for bottom-up collaboration anchored in executive alignment. Before any roadmap offsite, sync with the CEO or leadership team. Understand what they care about and why. If they disagree with priorities, resolve those conflicts early. Then bring that context into a team workshop.

During the session, identify technical leads — those trusted voices who can translate into action. Encourage them to pre-think tradeoffs and dependencies before the group session. That preparation pays off when tough calls need to be made in the room.

The roadmap meeting itself should feel like a design sprint: energetic, creative, grounded in shared accountability. Use a flexible medium — whiteboards, sticky notes, shared docs — to keep ideas flowing. Once decisions are made, codify them. The goal is alignment, not perfection.

After the team has shaped the roadmap, close the loop with your executive stakeholders. Play the plan back to them, clearly, confidently and with evidence. Show how the proposed roadmap unlocks parts of the market the company couldn’t access before, strengthens your ability to win against specific competitors or opens meaningful new revenue or customer segments. When possible, quantify impact. Executives don’t just want to know what you’re building, they need to see why it matters and the scale of the opportunity it creates.

AI: the invisible ingredient

Until now, I’ve intentionally avoided calling out AI. Instead of a feature on the roadmap, AI must now be part of the product’s DNA.

The best teams don’t ask, “Where can we add AI?” They ask, “Where can AI make our users’ lives easier or our processes more efficient?” Whether it’s automating documentation, accelerating anomaly detection or enhancing decision making, AI should serve as an enabler, not an add-on.

In practice, this means incorporating AI into the ideation process. During roadmap planning, prompt team leads to identify repetitive pain points or data-rich opportunities where AI can augment human capability. Over time, this mindset will become second nature — a quiet but transformative force that compounds with every release.

A roadmap and a promise for success

The perfect roadmap doesn’t exist and that’s the point. Remember, the goal isn’t to build a flawless plan, but a resilient one. As President Dwight D. Eisenhower said, “Plans are useless, but planning is indispensable.”

Every successful product I’ve launched followed the same rhythm:

• Start with clarity of purpose.
• Translate that purpose into concrete, time-bound steps.
• Align the team through collaboration and visibility.
• Celebrate progress relentlessly.

Vision without execution is hallucination. But execution without vision is chaos. The magic of product leadership lies in balancing both: crafting a roadmap that’s both inspiring and achievable.

As you build yours, remember a roadmap is a promise. It says: we know where we’re going and we’ll get there — one milestone at a time.

This article is published as part of the Foundry Expert Contributor Network.
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Legacy manufacturing environments are inherently complex. Deep technical expertise, global operations, and precision processes create a level of interdependence that makes transformation challenging to orchestrate. For CIOs, the task isn’t just about deploying new technologies, but untangling that complexity and evolving from old and deeply embedded ways of working.

When Aroon Sehgal joined Videojet Technologies as CIO last year, he became part of an organization with decades of technical excellence and a proud engineering culture. Videojet, a global leader in coding, marking, and printing solutions for product traceability, had long operated as part of healthcare company Danaher. Now, as a key business within Veralto, a $5 billion global tech leader focused on environmental and product quality solutions, Sehgal saw an opportunity to position technology as a source of differentiation and growth.

“When we were part of Danaher, Videojet was a rounding error,” Sehgal says. “Now under Veralto, we’re a meaningful part of the portfolio. That creates both visibility and accountability, and leadership is laser-focused on using technology to drive business outcomes.”

Tech moves to the center of strategy

Following Videojet’s most recent strategic planning cycle, one of the company’s top enterprise-wide initiatives focused on commercial excellence is being led by Sehgal himself. It marks the first time in company history that a technology executive has been chosen to lead one of its most critical strategic programs.

“Historically, these initiatives were owned by product or operations leaders,” Sehgal says. “The fact that technology is now seen as a primary driver of growth says everything about how the organization’s mindset has shifted.”

When he arrived, IT was viewed largely as a service provider. His first move was to rebrand the organization, both in name and purpose. IT became digital and technology solutions, or DTS, a deliberate signal that the function would no longer operate in the background. “We needed to recast technology,” he adds. “That meant aligning to our three most important outcomes: growth, margin expansion, and productivity.”

Embedding tech in the business

To make that shift real, Sehgal restructured how technology partners with the business. His team introduced geography-based business engagement leads, each embedded with regional leadership to ensure direct input into business decisions instead of hearing technology needs second or third hand. He also elevated leaders to run new centers of excellence around Videojet’s most strategic capabilities, including data and AI, e-commerce and web, and ERP transformation.

“It’s about being deliberate,” Sehgal says. “You can’t extract long-term value from AI or automation without first fixing your data strategy and governance. We’re laying the foundation for what I call the multi-agentic future, where workflows are increasingly autonomous.”

Laying the foundation for AI and automation

That foundation is already producing results. In partnership with Sehgal’s team, Videojet is piloting AI and ML applications across multiple fronts. In operations, they’re deploying ML to optimize production scheduling, and improve inventory forecasting and planning. The goal is to digitize their sales and operations planning process using a unified data set.

On the commercial side, Videojet has implemented AI-powered translation tools to create marketing content at scale across global markets, and is working with a startup to design an AI-first ERP system that automates order intake. At the same time, tools like Microsoft Copilot and ChatGPT Enterprise are being deployed widely to improve productivity across the organization.

“We’re not limiting experimentation,” Sehgal says. “Teams across R&D and operations are exploring large language models, and our job is to make sure they have the right data and governance in place to scale.”

Speaking the language of business

Still, Sehgal knows that even the most elegant technology story won’t land unless it’s translated into business terms. “You can’t walk into a leadership meeting and talk about APIs and architectures,” he said. “You have to talk about how technology contributes to growth and profitability.”

Every initiative under his watch is evaluated through a commercial lens, with clear visibility into how it supports both the customer and the company’s strategic and financial goals. Sehgal and his team also forecast how their programs will translate to earnings per share, giving leadership a tangible measure of technology’s targeted contribution to enterprise value. “When we model the impact of our initiatives, we express that impact in business terms that everyone in the organization understands,” he says. “That’s how technology earns its credibility.”

Lessons for tech leaders in legacy industries

For Sehgal, Videojet’s vision for technology holds lessons for every CIO navigating a legacy environment. His advice, shaped by leadership roles held at manufacturing giants Terex, ESAB, and ITT Inc., begins with identifying the business pain points where tech can drive the greatest impact. “In manufacturing, you have to know what holds the business back: labor intensity, asset dependency, supply chain complexity,” he says. “Then, pinpoint where technology can make a difference.”

Building credibility early is equally essential. “The business has to see you as a peer, not a service provider,” he adds. “And you can’t have your CFO reading about a breakthrough before you do.”

Above all, Sehgal believes technology leaders have to be willing to take risks. “In manufacturing or any legacy organization, you have to put skin in the game,” he says. “If you want to drive change, you need to be willing to take on the tough initiatives, own them, and deliver results.” In an industry where efficiency often surpasses innovation, Sehgal is positioning technology to be at the core of a strategy that blends Videojet’s track record of operational rigor with forward-looking ambition, grounded in the language of the business, and aimed squarely at customer growth and innovation. “Ultimately, our success will be measured not by how digital we are, but by how much we move the business forward,” he says.

While there may be a lot of “hysteria” surrounding AI right now, Jim Palermo, for one, isn’t too concerned about a potential AI bubble.

Palermo, CIO of Trimble, a $3.7 billion platform company serving the engineering, construction, and transportation industries, says that despite the noise, Trimble will continue investing in the technology to drive innovation and improve productivity.

Palermo is among the CIOs who think an AI bubble is not unrealistic, but are taking a measured approach to adopting the technology. The level of concern, Palermo says, “depends on how much you’ve drank the Kool-Aid.”

Other IT leaders say an AI bubble wouldn’t indicate the technology has no future but would be more about inflated expectations colliding with operational reality. The real risk isn’t investing in AI, they contend, but in betting on unproven models, vendors, or single-use platforms.

In light of concerns over inflated company valuations, IT leaders advise their colleagues to make more disciplined and informed decisions, and consider shorter contracts to hedge their bets in case the technology or market shifts. They also counsel peers to tighten governance and get small proofs of concept (PoCs) under their belts before committing to full-scale initiatives.

De-risking AI commitments

As for the AI industry’s future, Palermo believes there will be “a lot of point AI solutions that are going to disappear … over the next year or two.”

“I think that the larger platform ecosystems are really starting to have a strong AI foundation, and they’re … better able to articulate how they can leverage AI,” Palermo adds.

“Any CIO is trying to take advantage of those types of ecosystems because we’re spending millions of dollars on that software,” says Palermo, who sees governance as a key priority in ensuring the appropriate rigor around security and privacy for AI rollouts. “So, I think you’re going to see CIOs double down on that.”

One strategy CIOs are using to hedge against a potential AI bubble without stalling progress is to separate capability from hype, says Shawn Jahromi, founder and principal advisor at Alpharay Consulting.

“CIOs are funding narrowly scoped AI use cases tied to operational metrics like cycle time reduction, error rates, and cost containment,” says Jahromi, who is also a doctoral researcher in digital transformation and AI governance. “This limits exposure if valuations or vendor viability shift.”

CIOs are also treating AI as an operating model change rather than a technology purchase, he says. “This includes governance, accountability, and human override structures. CIOs who do this are less vulnerable to bubbles because value creation is embedded in workflow design, not tools.”

The CIOs Jahromi works with “are not slowing AI adoption. They are de-risking it structurally.”

Staying resilient is a core strategy

Another strategy Jahromi sees CIOs implementing is retaining architectural control by prioritizing data ownership, model portability, and vendor exit options. “The goal is resilience,” he says. “If an AI vendor fails or pricing collapses, the institution does not lose decision rights or operational continuity.”

Bread Financial CTO Allegra Driscoll is taking “a measured, super pragmatic approach” to AI investments, and says leadership is not interested in “chasing the best tool or being first to market.” It’s important to build capabilities that create value while maintaining resiliency, she says.

“We’re focused on high-value, proven use cases,” Driscoll says, adding that she spends a lot of time “evaluating a full set of risks” with all Bread Financial tech investments. “So, I feel really confident that those high-value, proven use cases we’ve moved forward on and put into production are going to continue to provide value for [us] and our customers.”

Staying the course with AI starts with the outcomes CIOs are trying to drive, Trimble’s Palermo says, as well as working with the business on solving pain points. “That protects you from complete chaos,” he says. “Cultivate that spirit of innovation, but there should be some rigor when you go from innovation and ideas to actual production. There’s got to be some governance around that.”

Further, CIOs shouldn’t hedge against AI “as much as being intentional and designing for resilience,” says Anurag Sharma, CTO of VyStar Credit Union. This is especially important for financial services firms, he says, “where trust and stability matter as much as innovation.”

To that end, VyStar’s approach is to use AI where it clearly solves a business problem, improves outcomes for their members, and enhances operational efficiency “with a deliberate focus on strengthening fundamentals that will outlast any hype cycle,” Sharma says.

This requires clean and well-governed data, modular and interoperable architecture, and people who understand both the technology and the business, he says.

“If the AI bubble cools, these fundamentals and investments will still compound value and improve safety and efficiency; if it accelerates, we will be positioned to scale responsibly without compromising compliance or member experience,” Sharma says. “The goal shouldn’t be to chase the shiny AI at all costs, rather, to remain adaptable, financially disciplined, and be able to pivot with confidence.”

Reigning in tool sprawl

A lot of enterprises struggle with tool sprawl, and in an uncertain economy, CIOs are doubling down on reining in tool overload and spending. Palermo says Trimble is beginning to rationalize and create metadata around the software they have and whether there are 10 tools that do the same thing. This is particularly true of AI tools, so Palermo is working on tightening up their source to pay process “so we get more rigor around ensuring we’ve got everything that’s coming in registered.” That way, if someone is looking for an AI tool IT will have vetted it and can make a recommendation.

“We want to drive innovation [using] groups of tools that satisfy particular needs in the AI space,” he says.

“Buying up a lot of tools can create an architecture that’s very complicated, and if you’re not sure that’s going to produce lot of value, then the risk of having one link in the chain fail on a critical process is probably not worth it,” agrees Driscoll.

Anchoring low risk with high value

Bread Financial will “continue to invest in high-value use cases going into 2026,” such as a knowledge management capability IT built for the company’s customer care agents, Driscoll says. At the same time, “we approach all new technology in a similar way — we try to slow down to go fast.”

Like Sharma, she says that consumer trust is paramount, “so we tend to spend a good amount of time building out a robust, controlled environment and make sure we understand the full scope of risks. Then we’ll chose a use case or use cases that are low-risk, high-value, to start to build experience across the team.”

That approach will continue, especially as they build out knowledge management use cases and start to use agentic AI.

Sharma also anticipates continued investments in AI in 2026. “The percentage of our IT budget dedicated to AI investments will depend on various factors, including the evolving landscape and specific business needs,” he says. “However, we remain committed to leveraging AI where it makes a meaningful impact on our operations and member experience.”

Vendor consolidation may heighten risk exposure

Benjamin Hori, cofounder and CSO of Spotlite, an online booking platform connecting models with fashion brands and agencies, believes the signs of an AI correction are already here, and startups are being impacted, which creates exposure to risk.

“When dominant players begin bundling capabilities that eliminate the need for entire categories of startups, we see immediate fallout — rapid consolidation, abrupt pivots, and smaller vendors disappearing overnight,” Hori says. “That instability directly impacts security teams that rely on those tools.”

One of the clearest indicators of what’s real versus hype is whether a company is training its own models “or simply wrapping someone else’s API,” he says. “From a CSO perspective, that distinction matters because it affects data control, attack surface, long-term viability, and ultimately, risk. A vendor without proprietary models or rights-cleared datasets has no defensible foundation, and that becomes our risk exposure.”

To hedge against AI volatility, Hori says Spotlite prioritizes partners with distinct data advantages, strong governance practices, and architectures resilient to market shifts. “We also build flexibility into our stack so we’re not dependent on any one model provider,” he says, “especially in a climate where startups can vanish quickly.”

Sviluppare una roadmap, nel mondo dei CIO (Chief Information Officer), significava pensare a cinque o dieci anni avanti riguardo alle tendenze tecnologiche e poi pianificare e prepararsi per esse.

Ma con tecnologie impreviste e immediatamente dirompenti che diventano un fatto dell’IT di oggi, inclusa la necessità di difendersi da esse in un batter d’occhio, sviluppare roadmap tecnologiche diventa molto più che pianificare aggiornamenti a tecnologie e sistemi obsoleti. La complessità e la lungimiranza coinvolte riducono notevolmente l’orizzonte delle aspettative del CIO, rendendo una sfida stabilire anche un orizzonte temporale IT di tre anni [in inglese].

Cosa comporta esattamente creare una roadmap IT oggi, e come possono i CIO garantire che le roadmap che realizzano rimangano rilevanti? Ecco come ripensare il vostro approccio data la strada accidentata che vi attende.

Prepararsi alla disruption (interruzione/sconvolgimento)

La pianificazione della roadmap IT dipende ancora dalla comprensione dell’attuale panorama tecnico e dalla proiezione delle implicazioni a lungo termine dei cambiamenti previsti negli anni a venire. Al momento, l’AI (Intelligenza Artificiale) appare come la forza più impattante sui sistemi IT e sulle operazioni aziendali nei prossimi 10 anni. La sua continua evoluzione risulterà in una maggiore automazione e cambiamenti nell’interfaccia uomo-macchina che faranno sembrare le operazioni aziendali, anche tra soli cinque anni, piuttosto diverse da come sono oggi. L’intelligenza artificiale in sé è un importante elemento di disturbo per le operazioni e i sistemi per cui bisognerà pianificare.

Come afferma la società di consulenza tecnologica West Monroe [in inglese]: “Non avete bisogno di piani più grandi, avete bisogno di mosse più veloci”. Questo è un mantra appropriato per lo sviluppo della roadmap IT oggi.

I CIO dovrebbero chiedersi da dove arriveranno i più probabili elementi di disturbo per i piani aziendali e tecnologici. Ecco alcuni dei principali candidati:

Resilienza organizzativa e gestione del rischio: l’azienda è preparata per lo spostamento di posti di lavoro e le ridefinizioni dei ruoli [in inglese] che si verificheranno man mano che verranno implementate più automazione e AI? I dipendenti saranno adeguatamente formati ed equipaggiati con le competenze e le tecnologie che dovranno essere utilizzate in un nuovo ambiente aziendale? E i sistemi? Quali sistemi probabilmente terranno il passo con il tasso di cambiamento tecnologico e quali no [in inglese]? Qual è il Piano B se un sistema viene improvvisamente reso obsoleto o inoperativo?

Sicurezza: l’AI sarà utilizzata sia da attori buoni che cattivi, ma mentre i cattivi attori iniziano a colpire le organizzazioni con attacchi assistiti dall’intelligenza artificiale [in inglese], l’IT interno ha gli strumenti e le competenze giuste per respingere questi attacchi e rispondere? O l’IT può sviluppare un approccio più preventivo per rilevare, anticipare e prepararsi a nuove minacce alla sicurezza basate sull’AI? Il vostro team di sicurezza possiede gli ultimi strumenti e competenze di sicurezza AI per fare questo lavoro? E da un’altra prospettiva: Avete la strategia, le competenze e la tecnologia per difendere adeguatamente la vostra stessa infrastruttura di intelligenza artificiale [in inglese] quando sorgono attacchi contro di essa?

Catene di approvvigionamento: il panorama geopolitico sta cambiando rapidamente. L’azienda, incluso l’IT, è pronta a passare a fornitori alternativi e rotte della catena di approvvigionamento se i fornitori attuali o le rotte della catena di approvvigionamento subiscono un impatto negativo? E i sistemi possono tenere il passo con questi cambiamenti?

Failover (Garantire la continuità operativa): avete sistemi ridondanti in atto se si verifica un evento disastroso in una particolare geozona e dovete eseguire il failover? E se i vostri sistemi, l’AI e l’automazione diventano totalmente inoperativi, l’azienda ha in organico dipendenti che possono tornare ai processi manuali se necessario?

Sviluppare una roadmap IT resiliente

Comprensibilmente, i CIO possono sviluppare roadmap tecnologiche rivolte al futuro solo con ciò che vedono in un momento presente nel tempo. Tuttavia, hanno la capacità di migliorare la qualità delle loro roadmap rivedendo e revisionando questi piani più spesso.

Oggi, la carenza in molte aziende è che la leadership scrive piani strategici solo come esercizio annuale. Dato il tasso di cambiamento della tecnologia, mettere via una roadmap IT per 12 mesi senza revisioni periodiche e modifiche per adattarsi ai cambiamenti dirompenti non è più fattibile. I CIO dovrebbero rivedere le roadmap IT almeno trimestralmente. Se queste ultime devono essere alterate, i CIO dovrebbero comunicare ai loro CEO, ai consigli di amministrazione e ai colleghi di livello C cosa sta succedendo e perché. In questo modo, nessuno sarà sorpreso quando dovranno essere apportate modifiche.

Man mano che i CIO si impegnano maggiormente con le linee di business, possono anche mostrare come i cambiamenti tecnologici influenzeranno le operazioni e le finanze aziendali prima che questi cambiamenti avvengano. Possono avvisare il consiglio e la direzione di nuovi fattori di rischio che probabilmente sorgeranno dall’IA e da altre tecnologie dirompenti, e garantire che queste interruzioni e rischi siano considerati nel piano di gestione del rischio aziendale. In questo modo, i CIO possono mantenere l’allineamento del piano strategico IT e della roadmap con la strategia aziendale.

Ugualmente importante è sottolineare che un cambiamento sismico nella direzione della roadmap tecnologica potrebbe avere un impatto sui budget.

Ad esempio, se le minacce alla sicurezza guidate dall’IA iniziano a colpire l’IA aziendale e i sistemi generali, l’IT avrà bisogno di strumenti e competenze pronti per l’IA per difendere e mitigare queste minacce. È possibile che debba essere fatta un’eccezione di budget o una riallocazione di fondi affinché le giuste tecnologie e formazione possano essere acquisite. Problemi finanziari possono sorgere anche sulle catene di approvvigionamento aziendali o IT se un particolare fornitore è improvvisamente non disponibile e/o devono essere trovate rotte di fornitura alternative.

Infine, la formazione del personale IT dovrebbe diventare un elemento standard nelle roadmap IT, e non solo un’opzione. Le roadmap IT passate avevano la tendenza a soffermarsi solo sulle previsioni tecnologiche e di sistema, omettendo spesso elementi come la riqualificazione della forza lavoro.

Con il rapido cambiamento tecnologico, la riqualificazione del personale dovrebbe essere una loro componente obbligatoria perché è l’unico modo per pianificare e garantire che l’IT rimanga all’altezza del compito di lavorare con le nuove tecnologie. La riqualificazione dovrebbe includere anche piani di formazione trasversale per i membri del personale IT in modo che siano in grado di lavorare in più ruoli se l’IT deve reindirizzare rapidamente le risorse.

Ripensare – o rimpiangere

Come disse una volta Benjamin Franklin: “Fallendo nel prepararsi, ci si sta preparando a fallire”.

Ora è il momento per i CIO di trasformare la roadmap IT in un documento più malleabile e reattivo che possa accogliere i cambiamenti dirompenti nel business e nella tecnologia che le aziende probabilmente sperimenteranno.

Digital transformation has its own physics. Three bodies, three gravities, one shared orbit.

• The Organization in its transformational journey, pulling for control and efficiency.
• The Partners — the force that can accelerate or destabilise the efforts.
• The Talent — orbiting with its own velocity, shaped by ambition, life stage and opportunity.

In Part 1 of this three-part series, we explored the body that sets this orbit: the transforming organization and its internal turbulence.

This piece looks at the second body in the orbit — the transformation partners. Consulting firms, domain specialists, engineering houses and capability builders who walk in from the outside and try (heroically, often thanklessly) to bring order to a system still searching for its rhythm.

Their gravity doesn’t just support the orbit. It actively reshapes it.

The hidden pressure points

Transformation partners operate under a silent pressure that the transforming organization can rarely see, let alone acknowledge.

They carry the wage bill while the client holds most of the power. Their best people are often halfway toward their next opportunity. Margins are thin, expectations unforgiving and contracts unpredictable. They are frequently asked to provide direction when even the destination itself is still being debated.

They also navigate a constant continuity deficit: Talent keeps rotating, handovers are rarely complete, knowledge falls through the cracks between projects and cost pressures often force partners to rebalance teams mid-flight. This is not incompetence; it is the structural reality of partner economics.

Yet, they are still expected to act as stabilizers — injecting structure, absorbing emotional turbulence and keeping the program on course.

McKinsey’s survey of global health-system leaders notes that while nearly 90 % rank digital transformation as top priority, 75 % admit they’re not yet able to deliver—highlighting the very real readiness and culture gap that transformation partners frequently have to step into.

A partner’s gravity shapes the trajectory just as much as the transforming organization’s does. Healthy orbits require a balance of forces, and that balance is far more fragile than most assume.

The strategic culture fit

Transformations wobble quickly when partners arrive with a standardized playbook that conveniently ignores the organization’s actual readiness, culture and decision rhythms.

Every transforming organization has an emotional atmosphere: anxious, hesitant, optimistic but chaotic, burnt by previous initiatives, politically sensitive or simply unsure about its own readiness.

A transformation partner’s first responsibility is not delivery — but diagnosis.

One interesting piece of research from McKinsey clearly brings out that risk-aversion and siloed mindsets remain top cultural blockers in digital transformations—meaning a partner who arrives with just a methodology, without diagnosing culture first, is walking into turbulence.

Partners need to take a step back and get a real sense of how things actually move inside the organization — who decides, who influences, how fast teams can really go and how much discomfort the culture can tolerate without melting down.

A culturally attuned partner becomes a complement to the organization’s gaps: offering clarity where there is hesitation, cohesion where there is fragmentation, scaffolding where there is chaos and neutrality where the environment is politically charged.

Partners who fail to account for the cultural disposition of the transforming organization often become yet another problem added to the mix. Partners who get it right become catalysts — absorbing turbulence, not amplifying it.

When the cultural match is wrong, turbulence amplifies. When it’s right, the relationship produces resonance — the kind of mutual stability that keeps the orbit intact.

The rule organizations forget

Often, there is one principle that most transforming organizations forget at their own peril:

Externalise capacity — not intelligence.

Partners can supercharge execution — support ops, infra, delivery velocity, all the heavy lifting.
But only the organization’s own internal team is truly positioned to understand how the systems actually behave, once they are out in the wild. They’re the ones who know where the data is buried, where the cracks in the process sit, which shortcuts have potential to explode later, and how business rules can collide with real humans at their workplace.

Partners, for all their strengths, live with a reality many transforming organizations overlook: their talent is inherently in motion. People roll off, benches shift, project pressure triggers mid-stream swaps. Continuity is fragile — not as a flaw, but as a built-in feature of their operating model.

The transforming organization, therefore, needs a strong in-house spine — the axis of continuity that survives partner churn, incomplete handovers, knowledge leaks and periodic dips in talent quality driven by cost or timeline pressures.

A groundbreaking Forrester study clearly reveals that leading digital teams engage service-providers extensively, yet stress the need for knowledge transfer and building internal capability alongside external delivery.

A good partner acknowledges this reality and helps the organization build this core capability at the right moment — strengthening the centre rather than creating dependency that will eventually collapse under its own weight. When too much contextual intelligence is handed over, the organization weakens its own gravitational pull. It becomes dependent on partners for understanding, not just execution.

Transformations thrive when intelligence stays inside, execution flexes outside and partners complement rather than replace the centre.

If a partner knows your terrain better than your own team, the orbit is already wobbling.

The archetypes — and when they truly belong

Transformation partners fall into archetypes — each with its own orbit and personality, not a value judgment.

• Large consulting houses are brilliant when the task is big: industry benchmarking, aligning leaders, sorting out the operating model, stitching cross-functional chaos into something coherent. But if the transforming organization is still figuring out who decides what, or the culture is fragile, their presence can feel like adding a capstone before the foundation has had time to set.
• Vertical or domain specialists shine when the organization needs someone who knows how things actually work, not how they’re presumed to work. They cut cleanly, sharpen the design and push the work closer to reality. But when leadership is still debating direction—or when the transformation needs wider coordination more than precision—they’re probably a wrong fit.
• Execution-focused engineering shops fit when solutions have matured, adoption has stabilized, governance is functioning and velocity is the priority. They thrive when the backlog is clear and the path is defined. But when brought in too early, they multiply noise instead of providing clarity.

Engaging the right partner at the wrong time may still turn out to be the wrong partner.

The quiet gravity of pricing

If culture shapes the emotional gravity of the relationship, pricing shapes the contractual gravity.

Clients want predictability.
Partners want protection.
Talent wants to be compensated fairly without getting burned out.

And amidst all this, transformation keeps changing shape — shifting priorities, hidden dependencies, messy data, people-related friction, all the lovely chaos that no proposal ever fully captures.

Rigid, risk-free pricing models choke momentum before anything meaningful even begins. A healthier approach is a risk-balanced construct — where both sides share some risk and some upside. Where Transformation outcomes unlock incentives, rate cards are transparent instead of ambiguous. Change requests are co-defined instead of weaponized, and effort estimation is collaborative rather than adversarial.

Partners who design pricing with shared risk signal commitment. Partners who avoid all risk signal distance from the mission. Pricing reveals whether both sides are truly building together or simply protecting themselves from each other.

Fear or shared purpose — whichever sits at the foundation of the contract decides whether the orbit steadies or shakes apart.

When empathy runs into reality

Transformation partners constantly walk a thin line between being human and staying commercially solvent. Their talent often spends more time inside the client’s world than in their own. They live by another team’s rituals, absorb another organization’s chaos and still carry the expectation of growth, loyalty and performance.

The firms that hold together over the long arc usually do one thing well: they strengthen their own centre, not just their capacity. They build learning ecosystems where people genuinely grow, not just get certified. They build real career growth charts instead of billing pyramids. They treat people as part of a continuing relationship, and when talent moves on, the place doesn’t lose its memory. The culture stays intact; the centre doesn’t crack. Continuity isn’t an accident; it’s a design choice.

This isn’t softness. It’s structural durability.

Treat people like interchangeable components and the whole system becomes brittle. Treat them as humans in motion — learning, evolving, occasionally drifting — and talent becomes an engine of continuity, not a rotating cost line.

When intent aligns, the orbit stabilizes

Eventually, everything comes back to intent.

If the transforming organization and the transformation partner share true intent — a belief in the envisaged outcome, a willingness to be transparent and a commitment to be fair and just — the orbit stabilises. Decisions flow. Conflicts reduce. Teams breathe. Momentum compounds.

Transformation becomes lighter.

But when intent fragments — when distrust creeps in, when transparency evaporates, when both sides focus more on self-preservation than shared progress — everything breaks: timelines, morale, culture, outcomes.

Orbit goes kaput!

The quiet truth beneath it all

Transformation partners live in a strange space — close enough to influence the arc, far enough to be blamed when things slip. But the ones who truly shift the arc are the ones who bring steadiness, not theatrics.

In a system defined by push and pull, stability is not an accident. It comes from shared intent, honest conversations, transparent effort and a commitment to something larger than the contract.

When both sides move with that posture, the orbit holds. When they don’t, no methodology in the galaxy has the power to save the trajectory.

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According to recent research by Freshworks, organizational complexity accounts for 7% of total annual revenue loss for an average business. Software is a major contributor, with companies estimating a loss of $1 out of every $5 spent on total software due to complexity, including IT complexity. That’s time, talent, and money that could instead be fueling innovation, improving customer experience, and driving expansion.

IT asset management (ITAM) solutions are one of the most effective ways to combat software complexity. They provide tools for recording, categorizing, and organizing technology assets, giving IT leaders visibility across hardware, software, and cloud resources. Beyond reducing cost and operational complexity, ITAM mitigates business risk: unused or poorly tracked software can create security vulnerabilities, increase points of failure, and expose organizations to audit and compliance issues—especially in regulated industries. Acting as a central hub, ITAM ensures accurate data, supports better decisions, streamlines operations, and maintains compliance.

How should CIOs go about choosing the right ITAM platform?

The best ITAM software does more than track inventory. It provides unified visibility across all assets, automates lifecycle processes, and integrates tightly with IT service management so asset data drives operational decisions. The right platform depends on asset scale, regulatory requirements, and ITSM maturity. CIOs should prioritize solutions that turn asset data into actionable intelligence rather than static records.

At a minimum, leading ITAM platforms should deliver:

• Unified asset visibility across hardware, software, cloud, and SaaS
• End-to-end lifecycle automation, from procurement to retirement
• Native ITSM integration that connects assets to incidents, changes, and SLAs

What capabilities define full-lifecycle IT asset management?

• Acquisition & procurement to onboard vendors and capture cost data
• Deployment to assign assets, update inventory, and validate configurations
• Usage & operation to monitor performance, usage, incidents, and changes
• Maintenance to manage patching, warranty tracking, and scheduled service
• Retirement & disposal to decommission assets, perform secure wipes, manage disposition, and maintain audit trails

The most effective platforms manage all of these stages within a single workflow, rather than across disconnected tools.

How do leading ITAM platforms support ITSM workflows?

The strongest IT asset lifecycle platforms are deeply integrated with IT service management systems. Assets are not static records, but rather operational entities tied to SLAs, incidents, and changes.

For example, when an incident is logged, the service desk can immediately identify the affected device, its dependencies, warranty status, and change history. Automated workflows can trigger asset reassignment during employee offboarding or initiate refresh cycles when hardware reaches end of life.

This tight coupling between ITAM and ITSM improves resolution times, supports root-cause analysis, and enables proactive service delivery.

What capabilities should CIOs look for in ITAM solutions?

The best ITAM solutions support end-to-end visibility and automation at scale. Key features to look for include: 

1. Centralized hardware & software inventory to track laptops, servers, mobile devices, SaaS applications, licenses, and virtual assets in a single system of record
2. Automated discovery & dependency mapping to identify assets using agent-based and agentless scanning, network discovery, and configuration management database (CMDB) relationships, and to understand how assets interact
3. SaaS license & software management to reduce spend through license utilization insights, unused seat reclamation, automated renewal visibility, and contract linkage
4. Lifecycle tracking from procurement to disposal to effectively monitor every aspect of assets, including purchase cost, warranties, depreciation, maintenance events, assignment history, and end-of-life milestones 
5. Workflow automation to automate lifecycle activities such as onboarding and device assignment, patching and maintenance, asset return workflows, and retirement approvals.
6. Contract, vendor & compliance governance to maintain operational efficiency and compliance through renewal reminders, contract metadata, vendor performance reviews, audit readiness, and lifecycle governance
7. Integrations & API support to connect with HRIS (for onboarding and offboarding), procurement systems, SSO/SaaS management tools, MDM solutions, service desks, and related platforms

Freshservice: An integrated approach to ITAM and ITSM in practice

These capabilities come together in practice when ITAM and ITSM are designed as a single, integrated system, an approach embodied by Freshservice from Freshworks. With asset lifecycle management and an integrated CMDB, organizations track resources across every stage and gain visibility into how assets interact. By unifying ITAM and ITSM, teams can automate workflows, maintain compliance, and make faster, data-driven decisions.

To find out more, click here.

The warning signs were subtle at first — an unexpected shift in customer recommendations, a spike in credit anomalies, a supply chain model that seemed unusually confident, or a workforce scheduling system that made decisions no one could fully explain. Executives chalked these moments up to “analytics behavior” or “algorithmic quirks,” but board directors began to sense something deeper. By late 2025, it became clear: Artificial Intelligence was no longer merely supporting the business. It was quietly steering it.

This is the threshold the enterprise has now crossed. AI is not waiting for permission. It is already shaping financial outcomes, operational decisions and customer experiences in ways that even seasoned technologists sometimes struggle to articulate. And by 2026, boards around the world will enter their meetings with a new level of urgency. They fear the risk of governing an enterprise whose intelligence layer is distributed, dynamic, partially invisible and capable of generating consequences at machine speed.

The question has shifted from “How do we use AI for growth?” to “How do we govern the intelligence that is already defining our destiny?” This is the moment when CIOs must lead with a new authority, because in 2026, AI is not a technology agenda. It is a governance mandate.

Why AI has become an immediate boardroom mandate

Directors are not reacting to hype cycles or vendor marketing. They are responding to structural forces reshaping the enterprise environment. First, they recognize that AI has already infiltrated nearly every decision-making surface, including credit scoring, pricing optimization, ESG reporting, claims adjudication, inventory forecasting, customer segmentation and fraud detection. Even when executives believe they are not “doing AI,” vendor systems and cloud platforms often embed intelligence that influences core workflows.

Second, global regulatory bodies have moved decisively. The EU AI Act is establishing the world’s most comprehensive AI governance regime, focusing on high-risk systems, documentation and lifecycle monitoring. The NIST AI Risk Management Framework has become the de facto U.S. standard for trust, traceability and risk classification. And ISO/IEC 42001 is the first global management system standard dedicated specifically to AI governance. These frameworks do not merely request oversight, they require it.

And third, investors have evolved from curiosity to scrutiny. Analyses from institutions such as Morgan Stanley and BlackRock emphasize that AI governance maturity now affects valuation. Organizations that demonstrate reliable, transparent AI behavior outperform peers, while those operating opaque or unmonitored models invite uncertainty and market penalties.

Board members understand the stakes. They have seen examples of AI-driven failures that created regulatory intervention, reputational damage, or unexpected operational shocks. They know the organization cannot rely on intuition, incomplete inventories, or siloed data science teams. They need the CIO to provide a coherent, strategic, enterprise-wide narrative of how AI behaves today, tomorrow and under stress.

This is the new AI mandate for modern CIOs.

The new boardroom reality

As directors begin discussing AI in 2026, they find themselves navigating unfamiliar territory. Unlike prior transformations, AI does not arrive as a controlled program. It emerges everywhere simultaneously, and sometimes in sanctioned initiatives, sometimes in “shadow AI” projects built by teams experimenting with tools, and sometimes through vendor systems whose embedded algorithms have quietly grown more powerful.

Boards grapple with new questions that cut to the heart of enterprise integrity: Where is AI operating today? How does it make decisions? Who monitors it? How fast does it change? How do we know it is reliable? Could it drift without our knowledge? Could a hidden dependency trigger cascading failures? How does this influence our financial statements, our workforce, our customers and our regulatory posture?

The CIO must answer these questions not as a technologist, but as a strategic interpreter: as the one executive who understands that AI is no longer a technology system but a cognitive layer shaping enterprise judgment. Directors want context, clarity and confidence. They want narrative, not dashboards. They want fluency, not feature lists. And they want to understand AI as a governance system, not an innovation engine.

This is where the modern CIO must lead.

The demand for visibility

Boards quickly discover the first major gap: visibility. They cannot govern what they cannot see. And in most organizations, AI is far more pervasive than executives initially acknowledge. Models operate in risk functions, marketing automation, underwriting engines, fraud systems, supply-chain optimization tools and workforce routing platforms. Meanwhile, acquisitions bring unfamiliar models. Vendors evolve their products without transparency. And employees increasingly rely on open-source or lightweight AI tools without disclosing them.

The enterprise intelligence layer becomes a patchwork — powerful, distributed and often undocumented. Boards recognize that this is untenable. They press the CIO to articulate the entire AI footprint in narrative terms: where intelligence exists, what purpose it serves, how it behaves and where it intersects with key decisions.

CIOs must help directors understand that unknown AI is unmanaged AI, and unmanaged AI is now considered a fiduciary risk. Visibility becomes the foundation of enterprise trust not because it prevents all harm, but because it enables governance.

The rise of cognitive risk

Once visibility is established, boards confront a deeper revelation: AI introduces a form of risk that traditional frameworks cannot detect. Unlike legacy systems, AI learns and adapts. This adaptability is its power and its danger. When data shifts, models can drift. When upstream inputs change, downstream systems can misalign. When vendor tools evolve, behavior shifts silently. And when bias enters the system, it may emerge through proxies no one recognizes.

Boards begin to see cognitive risk not as an extension of operational risk, but as a fundamentally new category. A pricing model that drifts slightly may distort millions in revenue. A workforce scheduling engine that misinterprets patterns may overwork certain groups. A credit model influenced by an external data shift may misclassify risk profiles at scale. These failures are not mechanical, but they are behavioral.

The CIO must therefore narrate cognitive risk in a way that directors can govern. They must explain how AI systems behave over time, where the enterprise is most exposed, and how cascading failures could unfold. They must provide not merely the existence of risk, but the enterprise storyline of how risk manifests.

Trust as a board-level metric

After visibility and risk, boards inevitably ask the most consequential question: “Can we trust our AI?” This is not a technical query — it is a strategic, ethical and financial one. AI systems may produce accurate outputs today while drifting tomorrow. They may behave well under normal conditions yet collapse under edge cases. They may generalize incorrectly when exposed to unfamiliar patterns.

Trust must be quantified. Boards insist on understanding how each model earns its trust through explainability, fairness, resilience, auditability and human intervention. CIOs must describe trust not as a vague concept, but as a measurable, evidence-based characteristic, one that evolves, strengthens, or weakens depending on how the enterprise maintains oversight.

The work of researchers at MIT’s Trustworthy AI initiative reinforces this: trust cannot be assumed or promised. It must be demonstrated continually.

Directors adopt this mindset quickly. They understand that they will be held accountable for AI failures and that trust metrics provide the only defensible foundation for oversight.

The economic reframing of AI

Once boards understand the governance requirements, they shift toward the financial implications. AI alters the economics of the enterprise, including its decision velocity, cost curves, workforce structure, risk exposure, margin potential and reinvestment capacity. But these impacts are uneven across industries and inconsistent across implementations.

Directors want to know how AI changes the financial architecture of the organization. They want to see how intelligence compresses cycle times, enables revenue acceleration, improves yield, sharpens pricing, enhances predictive accuracy and reduces waste. They want to understand how AI influences cash flow timing, reduces operational drag and alters the cost of decision-making.

CIOs must therefore articulate AI’s financial narrative. This requires not generic ROI estimates, but a coherent explanation of how AI affects capital velocity: the speed at which the enterprise can convert information into economic advantage. Research from McKinsey reinforces this point: AI’s greatest value arises not from automation, but from decision acceleration.

Boards quickly realize that AI economics are not optional, but they are an essential lens for evaluating competitiveness.

Continuous oversight and the duty of care

As boards grasp the economic significance of AI, they reach the final realization: AI requires continuous oversight. Unlike traditional systems, which behave consistently unless updated, AI behaves dynamically as data shifts. A single change in an upstream data pipeline can cause a downstream model to drift rapidly. A vendor update can modify behavior overnight. A new customer segment can break assumptions quietly.

CIOs must present a story of lifecycle governance that includes how the enterprise monitors models, detects anomalies, responds to variance, manages dependencies, escalates issues and documents interventions. Continuous oversight becomes the modern duty of care. It is the standard upon which regulators, investors and customers will judge enterprise responsibility.

Boards expect the CIO to operationalize this discipline not as a project, but as an operating model.

The fiscal architecture CIOs must redesign

By the end of these discussions, directors recognize that AI governance cannot fit inside legacy budgeting models. AI requires ongoing investment in monitoring systems, lineage tools, explainability technologies, adversarial testing, risk instrumentation, documentation automation and workforce upskilling.

CIOs must redesign the enterprise’s fiscal architecture to support this. They must translate AI consumption patterns into CFO-friendly terms, which is inclusive of cost per inference, cost of drift, cost of model decay, cost of compliance exposure and cost of control. They must manage vendor relationships to secure transparency, predictability and performance guarantees. They must articulate multi-year governance roadmaps that reveal how maturity will evolve.

The board is not simply approving budget now, they are approving an enterprise-wide governance posture.

A new compact between boards and CIOs

This is the new compact: boards will demand visibility, clarity, financial intelligence, ethical measurability and continuous reinvention. CIOs must deliver a unified narrative that integrates AI governance, economics, ethics and reliability. The board will govern strategy; the CIO will govern intelligence.

Directors do not want to understand every technical detail. They want to understand the story of how AI makes decisions, why it behaves the way it does, how it affects economics and how the organization ensures integrity.

The CIO must be the new enterprise’s chief intelligence narrator.

The defining question of 2026

In 2026, enterprises will separate into two categories. The first are the AI-trusted organizations whose intelligence systems are visible, monitored, explainable, reliable and financially articulated. They earn investor confidence, regulatory goodwill and customer loyalty. They scale advantage predictably and defensibly.

The second are the AI-opaque enterprises operating with drifting models, vendor black boxes, misaligned decisions, undocumented behavior and unclear economics. They invite scrutiny, volatility, financial penalties and reputational erosion.

The distinction is not who adopts AI the fastest. It is who governs AI the best.

A global call to action for CIOs

This is the moment for CIOs to step into a new definition of leadership, one grounded in intelligence stewardship. The world does not need more AI pilots, more automated workflows, or more isolated proofs of concept. It needs enterprise leaders who can see the intelligence layer clearly, govern it decisively, measure it rigorously and articulate it with the fluency directors require.

CIOs must champion visibility when others resist it.
They must expose risks that others overlook.
They must quantify trust when others assume it.
They must translate economics when others simplify it.
They must enforce oversight when others prefer speed.
And above all, they must preserve enterprise integrity when AI becomes the engine of competitive advantage.

The next decade will be shaped by how well organizations govern their intelligence, and not how quickly they deploy it.

And the leaders who rise to this moment will not simply run technology, but rather, they will define the enterprise’s legacy.

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Everything is changing, from data pipelines and technology platforms, to vendor selection and employee training — even core business processes — and CIOs are in the middle of it to guide their companies into the future.

In 2024, tech leaders asked themselves if this AI thing even works and how do you do it. Last year, the big question was what the best use cases are for the new technology. This year will be all about scaling up and starting to use AI to fundamentally transform how employees, business units, or even entire companies actually function.

So whatever IT was thought of before, it’s now a driver of restructuring. Here are seven ways the CIO role will change in the next 12 months.

Enough experimenting

The role of the CIO will change for the better in 2026, says Eric Johnson, CIO at incident management company PagerDuty, with a lot of business benefit and opportunity in AI.

“It’s like having a mine of very valuable minerals and gold, and you’re not quite sure how to extract it and get full value out of it,” he says. Now, he and his peers are being asked to do just that: move out of experimentation and into extraction.

“We’re being asked to take everything we’ve learned over the past couple of years and find meaningful value with AI,” he says.

What makes this extra challenging is the pace of change is so much faster now than before.

“What generative AI was 12 months ago is completely different to what it is today,” he says. “And the business folks watching that transformation occur are starting to hear of use cases they never heard of months ago.”

From IT manager to business strategist

The traditional role of a company’s IT department has been to provide technology support to other business units.

“You tell me what the requirements are, and I’ll build you your thing,” says Marcus Murph, partner and head of technology consulting at KPMG US.

But the role is changing from back-office order taker to full business partner working alongside business leaders to leverage innovation.

“My instincts tell me that for at least the next decade, we’ll see such drastic change in technology that they won’t go back to the back office,” he says. “We’re probably in the most rapid hyper cycle of change at least since the internet or mobile phones, but almost certainly more than that.”

Change management

As AI transforms how people do their jobs, CIOs will be expected to step up and help lead the effort.

“A lot of the conversations are about implementing AI solutions, how to make solutions work, and how they add value,” says Ryan Downing, VP and CIO of enterprise business solutions at Principal Financial Group. “But the reality is with the transformation AI is bringing into the workplace right now, there’s a fundamental change in how everyone will be working.”

This transformation will challenge everyone, he says, in terms of roles, value proposition of what’s been done for years, and expertise.

“The technology we’re starting to bring into the workplace is really shaping the future of work, and we need to be agents of change beyond the tech,” he says.

That change management starts within the IT organization itself, adds Matt Kropp, MD and senior partner and CTO at Boston Consulting Group.

“There’s quite a lot of focus on AI for software development because it’s maybe the most advanced, and the tools have been around for a while,” he says. “There’s a very clear impact using AI agents for software developers.”

The lessons that CIOs learn from managing this transformation can be applied in other business units, too, he says.

“What we see happening with AI for software development is a canary in the coal mine,” he adds. And it’s an opportunity to ensure the company is getting the productivity gains it’s looking for, but also to create change management systems that can be used in other parts of the enterprise. And it starts with the CIO.

“You want the top of the organization saying they expect everyone to use AI because they use it, and can demonstrate how they use it as part of their work,” he says. Leaders need to lead by example that the use of AI is allowed, accepted, and expected.

CIOs and other executives can use AI to create first drafts of memos, organize meeting notes, and help them think through strategy. And any major technology initiative will include a change management component, yet few technologies have had as dramatic an impact on work as AI is having, and is expected to have.

Deploying AI at scale in an enterprise, however, is a very contentious issue, says Ari Lightman, a professor at Carnegie Mellon University. Companies have spent a lot of time focusing on understanding the customer experience, he says, but few focus on the employee experience.

“When you roll out enterprise-wide AI systems, you’re going to have people who are supportive and interested, and people who just want to blow it up,” he says. Without addressing the issues that employees have, AI projects can grind to a halt.

Cleaning up the data

As AI projects scale up, so will their data requirements. Instead of limited, curated data sets, enterprises will need to modernize their data stacks if they haven’t already, and make the data ready and accessible for AI systems while ensuring security and compliance.

“We’re thinking about data foundations and making sure we have the infrastructure in place so AI is something we can leverage and get value out of,” says Aaron Rucker, VP of data at Warner Music.

The security aspect is particularly important as AI agents gain the ability to autonomously seek out and query data sources. This was much less of a concern with small pilot projects or RAG embedding, where developers carefully curated the data that was used to augment AI prompts. And before gen AI, data scientists, analysts, and data engineers were the ones accessing data, which offered a layer of human control that might diminish or completely vanish in the agentic age. That means the controls will need to move closer to the data itself.

“With AI, sometimes you want to move fast, but you still want to make sure you’re setting up data sources with proper permissions so someone can’t just type in a chatbot and get all the family jewels,” says Rucker.

Make build vs buy decisions

This year, the build or buy decisions for AI will have dramatically bigger impacts than they did before. In many cases, vendors can build AI systems better, quicker, and cheaper than a company can do it themselves. And if a better option comes along, switching is a lot easier than when you’ve built something internally from scratch. On the other hand, some business processes represent core business value and competitive advantage, says Rucker.

“HR isn’t a competitive advantage for us because Workday is going to be better positioned to build something that’s compliant” he says. “It wouldn’t make sense for us to build that.”

But then there are areas where Warner Music can gain a strategic advantage, he says, and it’s going to be important to figure out what this advantage is going to be when it comes to AI.

“We shouldn’t be doing AI for AI’s sake,” says Rucker. “We should attach it to some business value as a reflection of our company strategy.”

If a company uses outside vendors for important business processes, there’s a risk the vendor will come to understand an industry better than the existing players.

Digitizing a business process creates behavioral capital, network capital, and cognitive capital, says John Sviokla, executive fellow at the Harvard Business School and co-founder of GAI Insights. It unlocks something that used to be exclusively inside the minds of employees.

Companies have already traded their behavioral capital to Google and Facebook, and network capital to Facebook and LinkedIn.

“Trading your cognitive capital for cheap inference or cheap access to technology is a very bad idea,” says Sviokla. Even if the AI company or hyperscaler isn’t currently in a particular line of business, this gives them the starter kit to understand that business. “Once they see a massive opportunity, they can put billions of dollars behind it,” he says.

Platform selection

As AI moves from one-off POCs and pilot projects to deployments at scale, companies will have to come to grips with choosing an AI platform, or platforms.

“With things changing so fast, we still don’t know who’s going to be the leaders in the long term,” says Principal’s Downing. “We’re going to start making some meaningful bets, but I don’t think the industry is at the point where we pick one and say that’s going to be it.”

The key is to pick platforms that have the ability to scale, but are decoupled, he says, so enterprises can pivot quickly, but still get business value. “Right now, I’m prioritizing flexibility,” he says.

Bret Greenstein, chief AI officer at management consulting firm West Monroe Partners, recommends CIOs identify aspects of AI that are stable, and those that change rapidly, and make their platform selections accordingly.

“Keep your AI close to the cloud because the cloud is going to be stable,” he says. “But the AI agent frameworks will change in six months, so build to be agnostic in order to integrate with any agent frameworks.”

Progressive CIOs are building the enterprise infrastructure of tomorrow and have to be thoughtful and deliberate, he adds, especially around building governance models.

Revenue generation

AI is poised to massively transform business models across every industry. This is a threat to many companies, but also an opportunity for others. By helping to create new AI-powered products and services, CIOs can make IT a revenue generator instead of just a cost center.

“You’re going to see this notion of most IT organizations directly building tech products that enable value in the marketplace, and change how you do manufacturing, provide services, and how you sell a product in a store,” says KPMG’s Murph.

That puts IT much closer to the customer than it had been before, raising its profile and significance in the organization, he says.

“In the past, IT was one level away from the customer,” he says. “They enabled the technology to help business functions sell products and services. Now with AI, CIOs and IT build the products, because everything is enabled by technology. They go from the notion of being services-oriented to product-oriented.”

One CIO already doing this is Amith Nair at Vituity, a national physician group serving 13.8 million patients.

“We’re building products internally and providing them back to the hospital system, and to external customers,” he says.

For example, doctors spend hours a day transcribing conversations with patients, which is something AI can help with. “When a patient comes in, they can just have a conversation,” he says. “Instead of looking at the computer and typing, they look at and listen to the patient. Then all of their charting, medical decision processes, and discharge summaries are developed using a multi-agent AI platform.”

The tool was developed in-house, custom-built on top of the Microsoft Azure platform, and is now a startup running on its own, he says.

“We’ve become a revenue generator,” he says.