The role of the CIO is undergoing the most transformation in decades.
The CIO is always responsible for the organization's IT operations, including the software and technology services the organization uses. However, with the dramatic rise of AI in modern organizations, the role of the CIO is expanding.
According to Chris Campbell, CIO at DeVry University, the CIO role is changing more rapidly than any other executive role as organizations move deeper into the AI era.
“AI is not just a wave of technology to be absorbed,” he said. “The way companies operate, the way they make decisions, and the way they serve customers is being reshaped.”
A case where the CIO becomes the chief AI officer.
The question of who should own the AI strategy largely depends on where data governance, security, and architecture are already in place within the organization.
Why CIOs need to lead AI strategy
CIOs are already at the intersection of data, infrastructure, and business operations.
“CIOs have a unique perspective across all departments,” said Saket Srivastava, CIO of Asana, a work management and team collaboration software company. “You can see where processes break, where data resides, and where friction is costing your business a lot of money.”
However, creating separate CAIO roles poses practical challenges. Sumit Johar, CIO at BlackLine, a cloud-based financial automation and accounting software provider, says the overlap in data, security and automation makes it difficult to separate this work from the traditional CIO organization. New executive-level roles also require a full-fledged team, resources, and budget.
AI is a data, architecture and governance challenge, which makes the CIO the natural owner, said Hrishikesh Pippadipalli, partner and CIO at accounting services firm Wiss. However, most organizations only need a standalone Chief AI Officer (CAIO) if AI is a core product or revenue driver.
When dedicated CAIO makes sense
Some organizations benefit from an independent CAIO role, especially if AI is central to their business strategy.
“The CAIO role can be a great catalyst for organizations that feel AI is central to their success and need a senior executive dedicated solely to advancing AI, or for organizations where the CIO is also driving other important changes,” said Fiona Mark, Principal Analyst at Forrester.
Campbell said the question of whether an organization needs a CAIO misses the point and reduces the discussion to a simple question.
“The issue isn't really about titles, it's about responsibility,” he said. “Every organization needs an AI strategy, accountable guardrails, and clear ownership for value realization.”
Every organization needs an AI strategy, accountable guardrails, and clear ownership for value realization.
chris campbellDeVry University CIO.
New responsibilities in the AI-enhanced CIO role
The CIO's responsibilities now extend into areas that didn't exist just a few years ago, from model lifecycle management to workforce transformation.
CIOs are responsible for four key new areas:
AI governance (model deployment, data use, ethics review, risk management, etc.).
Employee strategy including training programs, enablement, and change management.
Model lifecycle management, including AI model selection, deployment, monitoring, and retirement.
Cross-functional coordination, including coordination across IT, data, security, risk, and HR.
Skills and competencies for the next generation of CIOs
Leading an AI transformation requires a different skill set than traditional IT operations.
technical ability
The recruitment profile has changed. Pippadipalli said organizations don't need an army of researchers; they need AI-savvy problem solvers, strong process designers, and governance experts.
Technical requirements go beyond basic AI knowledge and leaders need to build overall AI literacy, Mark said. This includes how to effectively use AI tools, understand ethics and security issues, and use judgment and critical thinking to enhance the use of AI. As agent AI capabilities mature, leaders will also need skills in designing and orchestrating AI agents.
Focus on business results
Connecting AI to business outcomes has become important. According to Johar, the early stages of AI implementation at BlackLine were primarily focused on productivity.
“What has become clear is that productivity gains do not necessarily reflect actual business results,” he said.
change management skills
The educational component of the role has expanded. Rather than just training tools, CIOs should educate people to become AI managers who can direct AI efforts, review output, and improve instructions, Srivastava said.
Cross-functional governance model
Srivastava runs the AI Council at Asana with representatives from each department. “They're not just advisors,” he says. “They are co-owners who drive the needs of their teams forward and drive adoption into their departments.”
Campbell implemented the AI Lab model at DeVry, a cross-functional governance and steering group essential to moving quickly. This structure ensures alignment across the agency, speeds decision-making, and identifies high-value opportunities that can be expanded.
Extend AI capabilities across your organization
Many CIOs have found that a centralized AI team cannot keep up with demand. For example, BlackLine invested in a separate AI team in 2025 and integrated it with its existing automation team, Johar said. “It was working really well for a while until we realized we didn’t have enough AI capacity because the demand was going through the roof,” he said. BlackLine is currently setting AI-specific goals for every department in the organization.
Budget reallocation
According to Pippadipally, investment patterns need to change and budgets need to move from one-time tool purchases to ongoing investments in platforms, training, and model risk management.
Shadow AI and the compliance gap
Without clear direction, teams adopt any tool that promises to increase productivity, often without proper security reviews, vendor investigations or audit trails, Srivastava said. This creates “a real compliance risk by disconnecting dozens of agents and AI tools across an organization and obscuring visibility into what they are doing.”
strategic fragmentation
According to Campbell, without clear leadership, AI strategies risk becoming disparate and fragmented. This can lead to AI being used in ways that put the organization at risk or that are not clearly aligned with the organization's goals.
Decreased employee trust
Trust issues arise when AI authority is unclear. Srivastava said they will stop using AI if people can't understand where the decision-making authority lies in the AI, or if the output is unexplainable. Worse, they rely on it too much in high-stakes situations without proper human review.
Prepare for the next stage of technology leadership
CIOs should consider the following steps to prepare for the next phase of technology leadership in the modern AI era.
Establish a governance framework
Start with structure and accountability.
• Create an AI governance framework with clear decision-making authority.
• Define ownership of AI strategy, governance, and delivery.
• Formalize cross-functional steering to avoid siled experiments.
Assess your organization's readiness
Understand where your employees stand before building your program.
• Survey employees to assess AI maturity, enthusiasm, and barriers.
• Categorize teams as champions, early adopters, curious teams, or skeptics.
• Identify gaps in skills, tools, and support.
Focus on high-impact use cases
Start small and prove your value before scaling.
• Choose one or two specific use cases with measurable outcomes.
• Select large-scale processes such as customer support routing and IT ticket triage.
• Prioritize visible customer improvements over internal efficiency alone.
Build your organization's AI literacy
Cultural change requires sustained investment.
• Introduce new AI innovations to your workforce every quarter.
• Conduct hackathons to promote healthy competition and learning.
• Invest in regular training programs across all levels.
“Measuring the results of cultural change is not easy, but its impact is clear over the long term,” Dzhokhar said.
“The key is to move from talking about AI to actually using it in real-world workflows with the right guardrails, the right people involved, and clear ways to measure whether the AI is working,” he said.
This practical focus reflects a major evolution in the CIO role itself.
“AI leadership is no longer about deploying models or winning demos,” Campbell says. “It’s important to prepare organizations to use AI responsibly, confidently, and at scale. And that’s exactly where the CIO role is headed in 2026 and beyond.”
Sean Michael Kerner is an IT consultant, technology enthusiast, and tinkerer. He is known for pulling Token Ring, configuring NetWare, and compiling his own Linux kernel. He consults with industry and media organizations on technology issues.
