Why CIOs should look to newsrooms to govern AI

There are few professions where part of the job is literally to read the newspaper, watch television or continuously check what is happening across every channel. In a media organization, that is not confined to journalists. Keeping up with events is, in one way or another, part of the work of much of the organization. A newsroom is also one of the most paradoxical professional environments I know. Before moving into media, I was director of New Technologies at Spain’s leading franchise consultancy, a role that allowed me to work with companies of almost every size and across almost every sector.

After years as CIO of Canarias7, I have seen how some of the most senior journalists approach almost anything related to technology with suspicion while looking back fondly on an idealized newsroom of typewriters, cigarettes and whiskey. It may sound like a caricature, but there is truth behind the nostalgia. Few professions have gone through so many technological transformations while continuing to defend the essence of their craft.

That is precisely why I believe CIOs have a great deal to learn from newsrooms in the age of artificial intelligence. Not because journalism is an example of frictionless technology adoption, but because newsrooms have spent decades solving a problem that is now spreading across the enterprise. They absorb huge volumes of information, distinguish what matters from what does not, make decisions with incomplete data, change priorities within minutes, verify before acting and keep accountability for the outcome clear.

As AI evolves from assistants that generate information into agents that can recommend, decide and execute actions inside enterprise systems, the challenge is no longer purely technological. It is also an operating model problem. That is where the experience of a newsroom becomes particularly relevant to a CIO.

Human attention should be treated as a scarce resource

In a newsroom, it is impossible to subject every piece of information to the same level of review. The credibility of the source, the relevance of the story, the consequences of a possible error and the existence of conflicting accounts all influence how much effort is devoted to verification. Some routine decisions are made quickly, while others pass through several levels of checking before publication.

Companies are beginning to face the same problem with AI. The most common response has been to place a person at the end of the process, the familiar human-in-the-loop, to validate what the machine does. But if AI multiplies the number of analyses, recommendations and decisions, it can also multiply the workload of those expected to supervise them. Human attention, just as in a newsroom, has to be allocated according to risk.

CIOs should apply the same principle. A routine, reversible, low-impact action can be automated, while an anomalous or low-confidence decision involving sensitive information or consequences that are difficult to reverse should escalate to a higher level of supervision. A 2026 study of automation bias in computational pathology involving 28 specialists found that in 7% of cases, an initially correct assessment was overturned after the specialist received an incorrect AI recommendation. Simply adding a person to the process does not automatically remove AI risk.

The Associated Press offers a useful example of this logic. Its updated AI standards from July 2026 allow AI to assist with early-stage research, transcription, translation, document summaries and headline suggestions, while keeping editorial judgment, verification and accountability with journalists. The point is not to require human supervision for everything, but to reserve it for tasks where context, interpretation or the consequences of an error justify that control. For a CIO, that means determining which decisions can be automated and which should escalate to a person, based on risk, impact, and reversibility.

Governance must be built into the architecture, not the prompt

A newsroom does not control its work through instructions alone. Reporters, writers and editors have different responsibilities and decision rights and the workflow itself establishes points where a story can move forward, be sent back for further verification or be stopped. Control depends not only on each person remembering what they are allowed to do, but also on how far their authority extends.

The Replit incident in July 2025 showed why the same principle matters in AI systems. Jason Lemkin, founder of SaaStr, was using Replit’s coding agent to build an application when the agent deleted a production database despite explicit instructions not to make changes during a code freeze. The agent had direct access to the same database used by the live application. Replit subsequently introduced automatic separation between development and production databases so that changes made during development could not directly affect live customer data. What a written instruction in a prompt failed to prevent became constrained by the architecture itself.

For a CIO, the parallel with a newsroom is straightforward. A reporter can prepare a story without having the final say over whether it is published. In the same way, an agent can analyze data or recommend an action without necessarily having permission to execute it. The system that generates a transaction should not always be the same one that validates it. Separating functions, limiting permissions, distinguishing between read and write access, isolating environments and maintaining audit and rollback mechanisms translate into AI architecture a principle that newsrooms have applied for decades: the person or system proposing an action does not have to be the one with authority to approve and execute it.

Humans need power, not simply a place in the process

Human-in-the-loop has another limitation. A person can be inside the process without having meaningful control over it. Researcher Madeleine Clare Elish coined the term moral crumple zone to describe situations in which responsibility for the failure of an automated system is attributed to a human operator who had limited control over what happened.

An “Approve” button does not guarantee effective supervision if the person clicking it does not understand why the system is recommending an action, does not have time to review the evidence or lacks the authority to stop it. In a newsroom, an editor can challenge a source, demand additional verification, delay publication or stop it altogether. The editor is not simply part of the process but has authority over it. That is why I find it more useful to think in terms of a human-in-power model, where responsibility, context and veto authority remain connected.

This logic also applies to organizational design. When a major story breaks, a newsroom first tries to understand what is happening and then decides how to cover it on the website, in print or on social media. Something has changed in recent years: the story comes before the channel. Many companies still operate the other way around. Information and processes remain fragmented across CRM, ERP, finance, marketing, customer service and data platforms. If we introduce autonomous agents into each silo, we can increase speed without ensuring that all of them are acting on the same version of reality.

Data, identity, context, permissions and traceability should therefore become shared capabilities on which different AI systems operate. Otherwise, automating faster may simply mean producing inconsistencies faster.

There is one final characteristic of newsrooms that I find particularly relevant. A plan agreed first thing in the morning can be obsolete ten minutes later. At Canarias7, we hold daily meetings with the different teams involved, from journalists and homepage editors to SEO, analytics, multimedia and other support functions, to review what is happening, what has changed since the last decision, which stories require more attention and where resources should be concentrated. When something important happens, priorities change, teams reorganize and decisions are made again. This is not a failure of planning. It is a normal condition of the job.

AI is taking companies toward the same environment of continuous decision-making. As systems absorb more information and agents gain more autonomy, they will need to update their context and reconsider actions when reality changes. The operating model has to make that possible without losing accountability along the way.

After years of working between journalism and technology, I still find it paradoxical that a profession that so often greets each new tool with skepticism can now offer some of the best clues for organizing the AI-driven enterprise. Artificial intelligence can accelerate our ability to observe and act, but the advantage will lie in what happens between those two points: how we interpret information, set priorities, verify what matters and determine who has the authority to decide. In that sense, the CIO in the AI era is becoming the editor of how the organization observes the world, makes decisions and acts.