For decades, the Chief Information Officer has been the person who keeps the lights on. Infrastructure, security, vendor management, and the annual budget cycle for laptops and licenses. It is a critical role, and most CIOs do it well.
But “doing it well” no longer means what it used to. The arrival of AI has not just added a new line item to the CIO’s portfolio. It has fundamentally broken the assumptions the role was built on. And if organizations do not rethink what they expect from their CIO, they will find themselves governed by a job description that was written for a world that no longer exists.
Here is what has changed, and why it matters.
Governance Is No Longer an IT Problem
AI governance is one of the most urgent challenges facing any organization today, and almost no one is structured to handle it correctly.
Historically, IT governance meant controlling access, managing risk, and ensuring compliance with known frameworks. The CIO owned it. Legal reviewed contracts. The boundaries were clear.
AI obliterates those boundaries. When a large language model is processing customer data, generating content that could create liability, or making recommendations that touch regulatory domains, governance becomes a shared problem between IT, legal, compliance, and increasingly, every department that touches a model. The CIO cannot own AI governance alone, but neither can legal. It requires a dance between the two, and right now, most organizations have not even agreed on the music.
This is not a theoretical concern. Organizations that fail to build cross-functional AI governance frameworks are not just accepting risk. They are accepting risk they cannot even see.
The Stack Changes Without Your Permission
Here is something that would have been unthinkable five years ago: your core technology platform can change its capabilities overnight, with zero changes on any user device, and with no change request from your team.
When OpenAI, Anthropic, or Google pushes a model update, the capabilities available to every employee using those tools shift instantly. Features appear. Behaviors change. Guardrails move. And the CIO, who has spent a career managing controlled change through structured release cycles, finds out the same way everyone else does.
This is a fundamentally different relationship with technology. CIOs have always operated under the assumption that they control the stack. That assumption is gone. The question is no longer “what did we deploy?” It is “what can our tools do today that they could not do yesterday, and do we even know?”
The CIO who is not monitoring model updates, reading release notes from AI providers, and stress-testing changed capabilities is flying blind. And flying blind at the speed these models evolve is a recipe for organizational surprise.
Everyone Is a Builder Now
For most of IT history, the ratio has been roughly 85% buy and 15% build. Companies bought software and customized it. The build work happened inside IT, managed by IT, governed by IT.
AI has broken that model in two ways.
First, the ratio is shifting. More organizations are building custom workflows, fine-tuning models, and creating internal tools that did not exist as products twelve months ago. Second, and more importantly, the builders have changed. A marketing analyst with access to Claude or Cursor can build an internal tool in an afternoon. A finance team can automate a reporting workflow without filing a single IT ticket. The CIO’s monopoly on creation is over.
This is not inherently bad. Democratized building can accelerate an organization dramatically. But it requires a completely different governance model. The CIO is no longer the gatekeeper of what gets built. The CIO is the architect of the framework within which everyone builds safely. That is a fundamentally different job.
You Are Now Competing With Your Vendors for Compute
The budget model for IT used to be predictable. A laptop depreciates on a known schedule. A SaaS license renews annually. A server has a capacity plan. For decades, resource planning has been one of the more stable functions in any organization.
AI breaks that predictability in two ways, and the second one is the one nobody is talking about.
The first is straightforward: the core unit of consumption has shifted from devices and seats to tokens. Token consumption is driven not by headcount but by adoption and creativity. The more your people use AI, and the better they get at using it, the more it costs. Success and expense scale together in a way that has no precedent in traditional IT budgeting.
The second is structural, and it is more concerning. IT departments are now buying compute and intelligence from the same companies that are their direct competitors for those resources. Google is not just your cloud provider. Google is consuming its own compute capacity to run Gemini. Microsoft is not just selling you Azure. Microsoft is prioritizing its own Copilot workloads on that same infrastructure. Amazon is doing the same with its own AI ambitions.
This is a new kind of vendor risk. For years, compute was abundant and getting cheaper. That era is over. Look at the price of HBM and DRAM. Look at the capacity constraints on GPU clusters. The underlying resources that power AI are finite and increasingly contested, and your supplier is also your biggest competitor for them.
CIOs have never had to think about their vendor relationship this way. But they need to now.
The Burstability of Intelligence
There is a concept that captures the new resource challenge better than “unpredictable spend.” It is the burstability of intelligence.
Traditional compute was burstable in a familiar way. You could spin up more virtual machines, add more storage, scale horizontally. The supply side was elastic. If you needed more, you could get more, and the planning cycles were measured in weeks or months.
Intelligence does not work that way. When a team discovers a transformative use case for AI and adoption spikes, the demand for tokens can surge overnight. But the supply side is constrained by GPU availability, model capacity limits, rate limits, and the competitive dynamics described above. You cannot just order more intelligence the way you once ordered more servers.
This means CIOs need to think about intelligence the way utility companies think about peak load. There will be bursts. There will be capacity ceilings. There will be moments when demand for AI outstrips what the organization can access or afford. Planning for the burstability of intelligence, not just the average consumption of it, is a new competency that did not exist two years ago.
Authentication Is Solved. Authorization Is Not. AI Exposes the Gap.
Over the past decade, IT departments have gotten very good at authentication. The BYOD era forced the issue. Single sign-on, multi-factor authentication, zero trust architectures, these are mature capabilities in most organizations. The question of “are you who you say you are?” is largely solved.
Authorization, the question of “what are you allowed to see and do?”, has improved but remains far more complex and far more neglected. Systems have controls for it, but those controls are scoped to individual applications. A person might have access to an HR system but never think to query it for executive compensation data. The data is technically accessible, but practically obscure. Nobody goes looking for what they do not know exists.
AI changes that equation entirely. An AI agent or assistant with broad system access does not respect the practical obscurity that has been quietly doing the work of authorization for years. It does not distinguish between “data you are allowed to access” and “data you would never think to ask for.” It surfaces everything within its permission scope, and most permission scopes were never designed for an entity that can query everything simultaneously.
I know of a company that deployed AI tools and got authentication right. Every employee was properly verified. But they neglected authorization. The result? Every employee in the company, a private company, could suddenly see the compensation of the owner. Not because anyone hacked anything. Not because there was a breach. Because the AI had access, and someone asked.
This is the new frontier. Authentication tells you who is at the door. Authorization tells you which rooms they can enter. AI is the entity that walks through every unlocked door in the building simultaneously. If your authorization model was built for humans who wander slowly and rarely ask uncomfortable questions, it is not built for AI.
Shadow AI Is the New Shadow IT, But the Blast Radius Is Different
A decade ago, shadow IT meant someone signed up for Dropbox or Slack without telling IT. Annoying, occasionally a compliance issue, but manageable. The data exposure was limited, and the tools were generally benign.
Shadow AI is a different animal entirely. When an employee pastes proprietary customer data into ChatGPT, when a team fine-tunes a model on confidential financial data using a personal account, when someone builds an automated workflow that makes decisions with no human review, the blast radius is categorically larger. Intellectual property leakage, regulatory violations, and decisions made by systems no one approved are not edge cases. They are the default outcome when AI adoption outpaces governance.
Most CIOs are not structured to detect shadow AI, let alone manage it. The tooling does not exist yet in most organizations, and the cultural norms around acceptable AI use are still forming. This is an area where the CIO must lead, not follow.
What the Evolved CIO Looks Like
The thread connecting all of these shifts is this: the CIO role was designed around control. Control of infrastructure, control of the build process, control of the budget, control of the technology stack. AI systematically removes that control.
The CIO who thrives in this environment will look less like an infrastructure manager and more like an organizational intelligence architect. That means being the person who builds the governance frameworks, not the one who approves every decision. It means understanding not just what the technology can do, but what the organization should do with it. It means navigating vendor relationships where your supplier is also your competitor, planning for the burstability of intelligence rather than the predictability of hardware, and rebuilding authorization models that were never designed for an entity that asks every question at once.
It also means the CIO needs a seat at a different table. AI strategy is not an IT strategy. It is a business strategy with technology implications. The CIO who is still reporting into a structure that treats technology as a cost center will be structurally unable to lead the way the organization needs.
The CIO role is not dying. But the CIO role as it has been defined for the past thirty years? That role is already dead. The organizations that recognize this first will have a significant advantage. The ones that do not will discover the cost of governing AI with a job description written for managing servers.