By Cheval John | Vallano Media
This article was created with the assistance of artificial intelligence.
The final version was reviewed, edited and fact-checked by the author

Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.

Artificial intelligence can make journalism faster.

That is precisely why journalists need to be careful with it.

AI can help organize research, compare documents, identify questions, summarize complicated material and assist with drafting.

Tasks that once consumed hours can sometimes be completed much more efficiently.

But speed is not the same thing as accuracy.

An AI system can produce an answer that sounds authoritative while overlooking context, confusing chronology, relying on weak sources or presenting an interpretation as fact.

The technology can assist with journalism, but it cannot assume responsibility for what a journalist ultimately publishes.

That distinction became increasingly clear to Vallano Media while researching its two-part series examining the FIFA Forward Enterprise proposal and its aftermath.

What began as a sports story also became a real-world test of how artificial intelligence could fit inside an existing journalistic process without replacing the principles that made that process reliable.

The central question was simple:

What does responsible AI-assisted journalism look like when the journalist remains responsible for the work?

The Story Came First

The project did not begin as an experiment with artificial intelligence.

It began with a story.

The FIFA Forward Enterprise proposal raised questions that could not be answered simply by reading one article or accepting one organization’s explanation.

The reporting required multiple sources, comparisons between claims and documents, attention to chronology and a clear distinction between what could be verified and what remained uncertain.

AI became useful because it could assist with parts of that process.

It could help organize information.

It could surface potential inconsistencies for further investigation.

It could compare material from different sources.

It could generate additional questions.

And it could help structure complicated research before that research became an article.

But throughout the project there was one important boundary:

AI could help investigate the story.

It could not decide what was true.

That still required verification.

Using AI Is Not the Same as Depending on AI

There is an important difference between using artificial intelligence in journalism and handing journalism over to artificial intelligence.

A simplified AI-first approach might look like this:

Choose a topic → ask AI to research it → ask AI to write the article → publish.

That process may be fast.

It is also risky.

A more responsible workflow looks very different:

Question → research → source evaluation → verification → AI-assisted analysis → additional research → drafting → fact-checking → editorial judgment → publication.

Video Courtesy of NBA YouTube Channel

In that model, AI operates inside the journalistic process rather than replacing it.

The purpose of this project is not to argue that journalists should avoid artificial intelligence.

Nor is it to suggest that AI can independently perform journalism simply because it can produce convincing prose.

The more useful question is how journalists can use the technology while preserving the sourcing, verification, skepticism and accountability that journalism already requires.

That is what the FIFA reporting project allowed Vallano Media to examine in practice.

Journalism Already Had a Workflow

Artificial intelligence did not invent the journalistic process.

Long before generative AI became widely available, reporters already had a basic system for determining whether something was ready to publish.

A reporter receives information.

The reporter asks where it came from.

A source is identified.

Additional sources are consulted.

Claims are compared with documents, statistics, direct statements or other evidence.

Contradictions are investigated.

Context is added.

The reporter determines what can be stated as fact, what needs attribution and what cannot yet be supported.

Then the story is edited and reviewed before publication.

The tools have changed repeatedly over journalism’s history.

Reporters moved from notebooks and typewriters to computers, databases, search engines, digital archives, social media and now artificial intelligence.

But the fundamental responsibility did not change with each technological shift.

The journalist still had to determine whether the information was reliable enough to publish.

AI should be treated the same way.

It is another tool inside the workflow.

It can make portions of that workflow dramatically faster, but it does not eliminate the workflow itself.

In fact, because an AI system can generate polished explanations so quickly, the temptation to skip traditional verification may become greater.

A confident answer can look like a researched answer.

Those are not necessarily the same thing.

The Professional Has to Know the Field

This leads to another important lesson from the FIFA project.

AI becomes more useful when the person using it already understands the fundamentals of the work.

A journalist familiar with sourcing can recognize when an answer depends too heavily on secondary reporting.

A journalist who understands attribution can identify when an AI response has turned somebody’s claim into an apparent fact.

A journalist familiar with chronology can notice when separate events have been incorrectly combined.

And a journalist who understands the subject can recognize when an explanation simply does not make sense.

This is why AI literacy alone is not enough.

Someone can become highly skilled at prompting an AI system and still produce unreliable journalism if they do not understand reporting.

The technology does not remove the need for professional knowledge.

It increases the value of that knowledge because the professional must evaluate what the technology produces.

In that sense, one of the most important AI skills may not be knowing what to ask.

It may be knowing when the answer is wrong.

AI Can Play More Than One Role

Another lesson from the reporting process was that AI did not perform one fixed job.

Its role changed depending on what the story required.

At one stage, it could function like a research assistant, helping organize information that had already been gathered.

At another, it could act like a brainstorming partner by suggesting questions that had not yet been considered.

Later, it could help compare claims from different sources.

And during review, AI could be deliberately prompted to challenge the assumptions behind the reporting.

That shift is important.

AI does not always have to help a journalist strengthen an argument.

Sometimes one of its most useful roles is helping expose weaknesses in that argument.

Rather than treating the first AI response as an answer, the response can become the beginning of another round of questioning.

What evidence supports this?

What is missing?

What assumptions are being made?

What would contradict this conclusion?

Is this statement supported by a primary source?

Those questions deliberately slow down a technology whose greatest appeal is speed.

The objective is not to use AI as quickly as possible.

The objective is to use it without allowing speed to outrun verification.

Accountability Never Moved

There was one responsibility that could not be delegated anywhere in the process.

Publication.

AI could recommend wording.

It could summarize research.

It could challenge assumptions.

It could help identify information that deserved further examination.

But when Vallano Media published the FIFA articles, responsibility for those articles belonged to Vallano Media.

If a claim had been wrong, saying that an AI system generated it would not have been an adequate explanation.

That may be the simplest principle in this entire project:

AI can participate in the process.

Accountability stays with the publisher.

Why This Matters Beyond One FIFA Story

The FIFA case is useful because it turns an abstract debate about artificial intelligence into something practical.

Much of the conversation around AI and journalism tends to swing between two extremes.

One side treats AI primarily as a threat to journalism.

The other presents it as a replacement for large parts of the reporting and writing process.

The experience of using AI inside an actual reporting project was more complicated than either position suggests.

AI was useful.

It made research easier to organize.

It helped surface questions.

It helped test assumptions.

It reduced the time required for some tasks.

But none of those benefits removed the need for reporting judgment.

The technology worked best when combined with an existing professional process.

That lesson extends beyond journalism.

Public relations professionals still have to understand audiences, messaging and reputation.

Researchers still have to understand methodology.

Business owners still have to understand their markets.

Analysts still have to understand the information they are interpreting.

AI can increase a professional’s capacity, but it does not automatically create expertise.

Someone who understands the work is in a much better position to use AI effectively because that person knows what should happen before, during and after AI becomes involved.

The Goal Is Not Automation for Its Own Sake

It is tempting to measure AI adoption by how much work can be automated.

That may be the wrong measurement for journalism.

A better question is whether AI improves the quality, speed or depth of the reporting without weakening verification.

If the technology helps organize dozens of sources more efficiently, that can be useful.

If it surfaces a contradiction that deserves further investigation, that can be useful.

If it helps produce a clearer outline, that can be useful.

But if it encourages a reporter to stop checking sources because an answer sounds convincing, the technology has made the workflow worse rather than better.

Efficiency only matters if the finished work remains trustworthy.

That principle became one of the most important lessons of the FIFA project.

The objective was not to remove the journalist from the process.

It was to determine where AI could make the journalist more effective.

A Working System, Not a Perfect One

The workflow examined in this series should not be treated as a universal formula.

Different journalists, publications and subject areas will require different safeguards.

Breaking news will operate differently from long-form analysis.

Investigative reporting will require different levels of documentation from a game recap.

A local newsroom may use AI differently from an independent digital publication.

And the technology itself will continue to change.

What matters is the underlying principle.

AI should be incorporated into a system where its output can be questioned, checked and rejected.

The journalist needs the ability to say:

That answer is useful.

That answer needs verification.

That answer is incomplete.

Or simply:

That answer is wrong.

The process documented here developed through actual reporting rather than theory.

It evolved as problems appeared.

Weak answers led to stronger verification steps.

Overconfident conclusions led to more adversarial questioning.

Gaps in research led to additional sourcing.

In other words, the workflow improved because the technology was not assumed to be reliable by default.

It had to earn its place in the process.

What Comes Next

The next stage is where the discussion becomes more practical.

Before AI could help interpret the FIFA story, the reporting needed a research foundation strong enough to support that analysis.

That meant identifying credible sources, separating primary evidence from secondary reporting, establishing chronology and recognizing where the evidence remained incomplete.

Only then could AI become useful without becoming the source itself.

Chapter 2 will examine how that research foundation was built — and why source quality had to come before AI analysis next Tuesday.

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