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.

Before artificial intelligence could help analyze the FIFA story, there was a more basic problem to solve.

What information could actually be trusted?

That question matters in any reporting project, but it becomes especially important when AI enters the workflow.

Artificial intelligence can organize information quickly.

It can compare documents, summarize reporting, identify differences between accounts and generate additional questions.

But if the information going into that process is weak, incomplete or incorrectly understood, faster analysis does not solve the problem.

It can simply produce a more polished version of the wrong answer.

For Vallano Media’s reporting on the FIFA Forward Enterprise proposal, the research therefore had to begin somewhere AI could not replace: the sources themselves.

Primary Sources and Secondary Reporting Serve Different Purposes

One of the first steps was separating primary sources from secondary reporting.

Primary sources included material originating from organizations or people directly involved in the story, including official statements and other first-hand material relevant to the proposal.

Those sources were important because they could establish what an organization had actually said, proposed or published.

But being a primary source does not automatically make a source neutral or complete.

An organization speaks from its own perspective.

An official statement may explain a proposal, defend a decision or respond to criticism without answering every question surrounding the issue.

That is where independent reporting becomes important.

Secondary reporting can provide additional context, outside sourcing and information that may not appear in an organization’s own communications.

The goal was not to declare one category universally superior to the other.

It was to understand what each source could legitimately establish.

An official statement might establish what FIFA said.

Independent reporting might establish what journalists learned through additional sourcing.

Analysis might help explain why those developments mattered.

Those are different functions.

Treating them as interchangeable creates problems.

Video Courtesy of CrashCourse YouTube Channel

AI Was Not the Source

That distinction helped establish one of the most important rules of the reporting process: An AI response was not treated as evidence.

Generative AI can make that boundary surprisingly easy to blur.

Ask a system a complicated question and it may produce a detailed answer containing names, dates, chronology and explanations within seconds.

It can look like research.

But the response itself does not establish that those claims are true.

AI could help locate material that deserved examination, organize information that had already been gathered, compare accounts and generate additional questions.

But when a factual claim mattered to the story, the important question remained: Where did this information actually come from?

The underlying source still had to be identified and evaluated.

AI could participate in source discovery.

It could not substitute for the source.

Source Quality Matters More Than Source Quantity

Having more links does not necessarily mean having better reporting.

A dozen websites can repeat the same original claim.

Twenty articles can ultimately trace back to one source.

Search results can therefore create the appearance of widespread confirmation when several publications are simply repeating information from one another.

That makes source quality more important than source volume.

The important questions were familiar journalistic ones.

Who originally reported the information?

Was there a primary source?

Was another credible organization independently confirming it?

Was an article contributing new reporting or repeating somebody else’s work?

Did the source clearly distinguish verified information from allegation or interpretation?

How close was the source to the event being described?

None of those questions were invented by artificial intelligence.

The value of the workflow was not that it created a new form of journalism.

It applied familiar reporting standards to a new tool.

Building the Timeline

Chronology became another important part of the research foundation.

Complex stories rarely arrive in perfect order.

One report may describe something that happened several days earlier.

An official response may appear after independent reporting.

A later development may change how an earlier event should be understood.

If those events are combined incorrectly, the resulting narrative can imply relationships the evidence does not support.

For the FIFA project, establishing the timeline helped separate: what happened, when it happened and what was known at each stage.

That distinction matters because hindsight can distort reporting.

Information that appears obvious after several developments may not have been available when an earlier decision was made.

Likewise, two events that occurred close together are not automatically connected.

AI could help organize dates and compare accounts, but chronology still had to be checked against the underlying reporting.

AI-assisted summaries can collapse events from different moments into a single narrative if that chronology is not checked carefully.

A reliable timeline became one safeguard against that problem.

Facts, Allegations, Analysis and Speculation Are Not the Same Thing

Another step was determining what level of certainty the evidence actually supported.

Not every sentence in a complicated story carries the same weight.

Some information can be stated as established fact.

Some must be attributed because it represents what an organization or person claims.

Some material comes from reported allegations.

Some conclusions are analysis.

And some possibilities remain speculation.

Those categories cannot be casually blended together.

There is a significant difference between saying: An organization did something.

and saying: A news organization reported that sources said the organization was considering doing something.

Removing that distinction might make a sentence shorter.

It can also make the sentence inaccurate.

AI systems can be useful for condensing information, but condensation can erase important qualifications.

Words such as according to, reported, alleged, proposed, and could may appear minor.

They are not.

Sometimes they are the difference between accurately representing the evidence and overstating it.

Contradictions Are Signals, Not Answers

When two credible sources appeared to conflict, the goal was not to immediately decide that one of them was wrong.

The contradiction itself became a reporting question.

Were the sources describing different time periods?

Were they answering different questions?

Did one report contain newer information?

Was one describing an official position while another relied on independent sourcing?

Could both accounts be accurate within different contexts?

This is an area where AI can be particularly useful.

It can place statements next to one another.

It can surface apparent differences.

It can help formulate questions about those differences.

But an apparent contradiction identified by AI is not automatically a contradiction in reality.

It is a signal to investigate further.

AI can surface the problem.

Reporting still has to resolve it — or acknowledge that the available evidence does not yet allow it to be resolved.

Sometimes the Gap Is the Finding

One of the easiest mistakes in research is feeling compelled to fill every blank.

Journalists naturally want answers.

AI systems are also designed to produce answers.

That combination requires caution.

Sometimes the evidence simply does not answer a question.

Responsible reporting does not require constructing the most plausible explanation.

Sometimes the responsible conclusion is simply: The evidence is incomplete.

During the FIFA research process, gaps in available information were treated as gaps rather than invitations for AI to construct connections between them.

This matters because generative AI can produce plausible explanations.

But plausibility is not evidence.

If two verified facts sit on opposite sides of an information gap, AI may be able to suggest several possible explanations connecting them.

Those possibilities can become questions for further research.

They cannot automatically become facts in the story.

Research Became an Iterative Process

The research did not move neatly from beginning to end.

One source raised another question.

A secondary report led back to primary material.

A timeline exposed something that needed additional research.

An AI comparison surfaced a potential inconsistency.

Verification produced new questions.

The process became cyclical: Find information → evaluate the source → verify the claim → compare the evidence → identify questions → research again.

AI could accelerate parts of that process.

It could not eliminate it.

In fact, some of the most useful AI interactions produced more reporting rather than less.

That may sound counterintuitive.

Much of the promise surrounding AI focuses on efficiency: fewer steps, less time and faster output.

But in journalism, a tool can be valuable precisely because it reveals something that deserves another round of research.

Efficiency does not always mean finishing sooner.

Sometimes it means discovering sooner that the work is not finished.

The Foundation Comes Before the Analysis

By the time AI became useful for deeper analysis of the FIFA story, the basic research structure was already taking shape.

Sources had been separated according to their role.

Important claims could be traced back to evidence.

Chronology had been established.

Potential contradictions had been identified for further examination.

Facts had been distinguished from allegations, reporting and interpretation.

And gaps were allowed to remain gaps when the evidence did not justify filling them.

Only then could AI provide meaningful assistance.

That sequence matters.

If AI analysis comes first, there is a risk that the journalist begins building the research around the system’s initial explanation.

If the evidence comes first, AI can instead be asked to work against a foundation the journalist has already examined.

The difference is subtle but important.

One approach asks AI: What happened?

The other begins with evidence and asks: What should we examine next?

That is a much safer place to begin.

What Comes Next

Building a strong research foundation did not eliminate the risks associated with artificial intelligence.

It made those risks easier to recognize and manage.

The next challenge was deciding how much confidence to place in what AI produced once it began analyzing that material.

That required another principle: Verification does not end when AI enters the workflow.

If anything, it becomes more important.

Next Tuesday, chapter 3 will examine verification before generation — including how Vallano Media challenged AI-assisted conclusions, checked confident answers and prevented analysis from outrunning the evidence.