AI in the Research Room: Finding, Testing and Verifying Information
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.
Research used to mean opening tabs.
A lot of tabs.
Search results.
PDFs.
Old articles.
Statistics.
Official websites.
Notes scattered across documents.
Then more tabs.
Artificial intelligence changed that part of my work faster than almost anything else.
Not because AI replaced research.
It did not.
It changed how I move through research.
It can help me find possibilities.
Organize information.
Compare sources.
Spot contradictions.
Generate follow-up questions.
Identify what I still do not know.
But the most important lesson I learned is also the simplest:
“AI can help me find information. It cannot decide what counts as evidence.”
That distinction changed the way I research.
Research Is Not the Same as Searching
For years, online research often began with a search engine.
Type the question.
Open the results.
Read.
Compare.
Take notes.
Search again.
That process still matters.
But AI adds another layer.
Instead of asking only:
“Where can I find information about this?”
I can ask:
“What claims do I need to verify?”
“What sources would be most useful?”
“What parts of this issue are disputed?”
“What questions am I not asking?”
That changes research from a simple hunt for information into a more structured investigation.
The goal is no longer to collect as much information as possible.
The goal is to identify the information that matters.
AI Is Useful at the Beginning
One of the most useful places for AI is at the beginning of a research project.
When I know very little about a subject, AI can help me map the terrain.
Who are the major organizations involved?
What terminology should I understand?
What historical events might matter?
Which documents should I look for?
What questions would a reporter normally ask?
That can save time.
Instead of wandering through the subject without direction, I can start with a rough map.
But a map is not the territory.
AI may omit something important.
It may misunderstand the subject.
It may treat a disputed claim as settled.
It may suggest a weak or nonexistent source.
So that map is a starting hypothesis.
It is not a verified research plan.
It gives me somewhere to begin.
Not somewhere to stop.
Turn the Topic Into Claims
One of the biggest improvements in my research process came when I stopped treating an article as one large question.
Instead, I break it into claims.
What am I actually asking the reader to believe?
Maybe the article contains a claim about a date.
A statistic.
A historical event.
A policy.
A quote.
A trend.
A cause.
A comparison.
Each claim creates its own research problem.
That matters because different claims require different evidence.
A score can be verified through official statistics.
A historical claim may require archives.
A quotation requires the original interview, transcript or recording.
A policy claim may require an official document.
A trend may require data across time.
AI can help me identify those claims.
But once the claims are visible, I still need to find the evidence that supports them.
Ask AI What Would Prove It
A useful research question is not:
“Is this true?”
A better question is:
“What evidence would prove or disprove this?”
That changes the task.
If I ask whether something is true, AI may simply give me an answer.
If I ask what evidence would establish the claim, I am forced to think about the source.
What document should exist?
Who would know?
What data would show it?
What record could confirm it?
What evidence would contradict it?
That question turns AI into a research assistant rather than an authority.
Research the Claim Against Yourself
There is another part of that process.
Do not only ask what supports the claim.
Ask what could weaken it.
Complicate it.
Or overturn it.
Once I form a theory about a story, it becomes easy to see the evidence through that theory.
If I think a trend exists, I should ask what evidence would show that it does not.
If I think one event caused another, I should look for alternative explanations.
If I think an institution changed policy at a particular moment, I should ask what evidence might show that the change happened earlier or for another reason.
AI can help generate those challenges.
But it cannot decide whether the challenge is valid.
It can tell me where to look.
I still have to investigate.
Source Hierarchy Still Matters
Not all sources are equal.
A social media post is not the same as an official record.
A press release is not the same as independent reporting.
A news article summarizing a study is not the same as the study itself.
AI can help me locate different types of sources.
But I still have to evaluate them.
Who produced this?
Why?
When?
What evidence does it rely on?
Is the source firsthand?
Is it independent?
Is it current?
Does the source have something to gain from the claim?
Those questions existed long before generative AI.
They matter even more now because AI can flatten very different kinds of sources into one smooth answer.
“AI can flatten source quality. The journalist has to restore the hierarchy.”
Smooth Summaries Can Hide Uneven Evidence
One of AI’s strengths is synthesis.
It can take several sources and produce a clean summary.
That is useful.
It can also be dangerous.
A summary may make five sources sound equally credible even when they are not.
One might be an official record.
Another might be commentary.
Another may simply repeat the first source.
Another may be outdated.
Another may contain an error.
The AI summary can erase those differences.
That means I have to separate the summary from the evidence underneath it.
If the summary contains an important claim, I want to know:
Which source supports it?
Is that the original source?
Are the other sources actually independent?
Do they confirm the same thing?
The cleaner the synthesis looks, the more important it may be to inspect what produced it.
Fast Synthesis Can End Research Too Early
There is another danger.
AI can make the research feel finished before it is finished.
The answer appears quickly.
The sources look organized.
The explanation sounds coherent.
And suddenly it feels as if the subject has been solved.
That feeling can be misleading.
Fast synthesis can create premature research closure.
The journalist stops looking because the answer already looks complete.
That is another version of the same problem that appears in AI-assisted drafting.
Polish creates confidence.
Confidence creates stopping.
But the evidence may still be incomplete.
The research is finished when the reporting supports what I intend to publish.
Not when the AI response looks convincing.
Finding Contradictions
One of my favorite research uses for AI is comparison.
If I give it multiple documents, I can ask:
Where do these accounts disagree?
Which dates conflict?
Which statistics differ?
What changed between two versions of the same document?
That can help surface problems quickly.
But AI cannot automatically tell me which version is correct.
A contradiction is a signal.
Not a conclusion.
If two sources disagree, the next question is:
Why?
Maybe one is wrong.
Maybe one is outdated.
Maybe they use different definitions.
Maybe they cover different time periods.
Maybe the denominator changed.
Maybe they are not actually describing the same thing.
That last question matters.
“Are these sources really contradicting each other, or are they measuring different things?”
Research begins where the contradiction appears.
Use AI to Generate Follow-Up Questions
Good research creates more questions.
That is another place AI can help.
After reviewing the evidence, I can ask:
What is still unresolved?
What would a skeptical reader want to know?
What question would an editor ask?
What does this source not explain?
What additional document could clarify this?
What assumption am I making?
Those questions can lead me toward stronger reporting.
Sometimes the most useful AI output is not an answer.
It is the next question.
A Missing Record Does Not Automatically Disprove Something
Research has another trap.
You search for evidence.
You cannot find it.
So the temptation is to conclude that it does not exist.
That can be dangerous.
Records may not be digitized.
Archives may be incomplete.
A page may have disappeared.
The relevant document may use different terminology.
The event may be documented somewhere you have not searched yet.
So failure to find evidence is not automatically evidence that something never occurred.
Sometimes the correct conclusion is simply:
“I have not found evidence for this claim.”
That is different from:
“This claim is false.”
The distinction matters.
AI-Suggested Sources Are Leads
AI may recommend reports, studies, archives, websites, experts or documents.
That can be extremely useful.
But the recommendation itself proves nothing.
A suggested source may be outdated.
It may be irrelevant.
It may be mischaracterized.
It may not exist in the form the AI described.
So I treat an AI-suggested source as a lead.
Not a credential.
I open it.
Inspect it.
Confirm what it is.
Then decide whether it belongs in the research.
Do Not Let AI Replace Reading
There is a temptation once AI becomes good at summarizing.
Why read a 70-page report when AI can summarize it in two minutes?
Because the details matter.
A summary may miss a footnote.
A limitation.
A definition.
A qualification.
A methodology problem.
A sentence that changes the meaning of the conclusion.
If a document is important enough to support the article, it is important enough for me to inspect directly.
AI can help me navigate long documents.
It can tell me where to look.
It can identify relevant sections.
It can help explain technical language.
But I do not want AI deciding what the document means for me.
Use AI to navigate the document.
Do not outsource the interpretation of the evidence.
Search Results Are Leads, Not Proof
AI-assisted search can make research feel incredibly fast.
Ask a question.
Receive sources.
Open links.
Move on.
But every source surfaced by AI should be treated as a lead until I inspect it.
Does the page exist?
Does it say what the AI claimed?
Is it the correct date?
Is the quotation complete?
Is the source current?
Does the source itself cite someone else?
That last question matters.
Sometimes what looks like three sources is actually one source repeated three times.
Ten sources are not necessarily stronger than three if all ten trace back to the same underlying claim.
Research is not counting links.
It is tracing information back to where it originated.
The Original Source Matters
Whenever possible, I want to get closer to the original evidence.
If a news article says a university made an announcement, I look for the university announcement.
If a story cites a government report, I look for the report.
If a statistic appears in several articles, I look for the original dataset.
If someone quotes an interview, I look for the interview.
That does not mean the original source is automatically truthful.
An institution can frame information in its own interest.
A company can highlight favorable data.
A government agency can make mistakes.
A court filing can contain allegations rather than established facts.
Closer to the source does not always mean closer to the whole truth.
Original evidence gets me closer to what was actually recorded or stated.
It does not remove the need to evaluate credibility, motive, completeness or context.
AI Can Help Organize the Evidence Trail
Research becomes difficult when the evidence pile grows.
Which source supports which claim?
Where did the statistic come from?
Which article was secondary?
Which document was original?
Which fact still needs confirmation?
AI can help organize that.
I can build a claim list.
A source table.
A chronology.
A list of unresolved questions.
A distinction between confirmed facts, interpretations and unknowns.
That organizational role may be one of AI’s most practical research benefits.
It reduces the mental load of managing information.
But organization is not verification.
A beautifully organized false claim is still false.
Know When the Research Is Not Finished
One of the hardest decisions in journalism is knowing when you have enough.
There is always another source.
Another search.
Another document.
Another question.
AI makes it easier to keep searching forever because it can constantly generate new angles.
That can become its own problem.
The goal is not complete knowledge.
That is impossible.
The goal is enough reliable evidence to support what you intend to publish.
Sometimes the evidence is strong.
Sometimes it is incomplete.
Sometimes the honest conclusion is:
We do not know yet.
That answer can be frustrating.
It can also be the most accurate result of the research.
Research Should Leave a Trail
One thing I increasingly value is the ability to explain how I know something.
What source supports the claim?
Where is the original document?
What evidence did I reject?
What remains uncertain?
Why did I choose one source over another?
That creates a verification trail.
Not necessarily something every reader will see.
But something I should be able to reconstruct.
That matters in AI-assisted journalism because the final prose may look effortless.
The evidence behind it should not be.
The Research Room Has Changed
AI has changed what happens before I write.
I can move through large amounts of information faster.
I can organize sources more efficiently.
I can compare documents quickly.
I can generate questions I might have overlooked.
I can test the strength of an argument before I commit to it.
But none of those capabilities remove the basic responsibility of research.
Find the source.
Read it.
Test the claim.
Look for contradictions.
Understand the context.
Verify what matters.
And know when the evidence is not strong enough.
AI can help me move through the research room.
It cannot replace the research room.
Because journalism does not begin with the answer.
It begins with the evidence.
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
The Twilight Zone of Indiana Football: When Winning Is No Longer Enough
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.
The clock showed 19 seconds remaining at Memorial Stadium in Bloomington.
Minnesota had just taken a 16–13 lead on Luke Drzewiecki’s 60-yard field goal.
Indiana, ranked fourth nationally, needed an unlikely response to avoid its second loss of the season.
Grant Wilson, who had replaced starting quarterback Josh Hoover in the second half, connected with Charlie Becker for 33 yards, moving the Hoosiers to Minnesota’s 42-yard line.
After an incomplete pass, Indiana faced a decision with two seconds remaining.
Paddy McAteer lined up for a 60-yard field goal of his own.
The kick was good.
Two 60-yard field goals in the final 19 seconds of regulation had sent the game into overtime.
Video Courtesy of Bleacher Report YouTube Channel From CBS Sports Commentary
According to Indiana’s official postgame notes, it was the first game in Football Bowl Subdivision history in which opposing teams each made a field goal of at least 60 yards.
Wilson then found Becker for a 25-yard touchdown on the first play of the extra period.
Indiana’s defense stopped Minnesota on its ensuing possession, securing a 23–16 victory.
It was a remarkable escape for a team that had struggled offensively, committed costly mistakes and endured an unusually poor performance on special teams.
Yet the most revealing aspect of the afternoon was not simply how Indiana won.
It was what such a victory now meant.
Only a few years earlier, Indiana football was associated with decades of frustration and the challenge of establishing consistent competitiveness in the Big Ten.
By October 2026, the Hoosiers were defending national champions, ranked among the country’s elite, and facing a different kind of scrutiny.
Winning was no longer the entire story.
How Indiana won had become part of the conversation.
Welcome to the Twilight Zone of Indiana football.
From Historical Frustration to Championship Expectations
Indiana’s transformation under head coach Curt Cignetti is among the more extraordinary developments in modern college football.
The program’s history offered little reason to anticipate such a dramatic reversal.
Indiana had spent decades attempting to establish the kind of sustained football success associated with the Big Ten’s traditional powers.
Then came Cignetti’s arrival in November 2023.
What followed changed the program’s competitive identity, culminating in Indiana’s first national football championship following the 2025 season.
By October 31, 2026, Indiana stood at 8–1 overall and 5–1 in Big Ten play.
Its latest victory had come one week after another overtime escape, a 13–6 win at Michigan.
For much of its history, Indiana football had sought relevance.
Now the discussion centered on whether a fourth-ranked team was performing convincingly enough to remain among the national championship contenders.
That reversal raises a larger question: How did Indiana build an organization capable of changing expectations so quickly?
The answer extends beyond the head coach.
The Decision That Changed the Program
When Scott Dolson announced Cignetti’s hiring on November 30, 2023, Indiana was coming off a 3–9 season.
The Hoosiers had endured three consecutive losing seasons, and their struggles were not limited to one disappointing year.
The hiring represented a significant decision for an athletic department attempting to establish a more competitive football program.
Cignetti arrived from James Madison, where he had compiled a 52–9 record across five seasons.
He had also guided the Dukes through their transition to the Football Bowl Subdivision, demonstrating that he could win while navigating organizational change.
Dolson led a rapid coaching search that concluded with Cignetti’s appointment.
But the hiring was not simply a matter of identifying a successful coach.
Indiana needed to create an environment in which that coach could succeed.
University President Pamela Whitten and Board of Trustees Chair Quinn Buckner were part of the institutional leadership supporting the appointment.
In Indiana’s original hiring announcement, Dolson acknowledged their support and that of the university’s trustees.
That distinction matters.
Dolson made the athletic department’s hiring decision.
Cignetti assumed responsibility for the football program.
University leadership helped establish the institutional backing necessary for a major commitment.
Indiana’s subsequent success cannot be attributed to one individual acting alone.
Investing in Success — and Then Investing to Keep It
The financial decisions following Cignetti’s arrival offer another perspective on Indiana’s transformation.
In November 2024, during his first season, Indiana agreed to an eight-year extension that included an average annual compensation figure of $8 million, plus a $1 million annual retention bonus.
By October 2025, the university had announced another eight-year agreement, this time averaging approximately $11.6 million annually.
These commitments came before Indiana completed its national championship season.
That timing is important.
The university was not merely rewarding a championship after the fact.
It was responding to evidence that the football program had become nationally competitive and taking steps to retain the leadership responsible.
Following Indiana’s 27–21 victory over Miami in January 2026, which completed a 16–0 championship season, Cignetti’s contract underwent another review.
According to the Indiana Daily Student’s examination of the revised agreement, the eight-year contract was valued at $105.6 million, averaging $13.2 million annually before performance incentives.
The progression illustrates two different organizational challenges.
The first is building a successful program: identifying leadership, improving operations, recruiting personnel and creating a competitive culture.
The second is maintaining that success after the organization becomes valuable enough that competitors may seek its people and methods.
Indiana’s investment also extended beyond its head coach.
Contract provisions addressed competitive staffing resources, reflecting the importance of retaining assistants and supporting the broader football operation.
However, higher compensation does not guarantee future championships.
It represents a commitment of resources, not proof of continued success.
That is the financial risk Indiana now faces: sustaining an elite program requires ongoing expenditure, even as the competitive results remain uncertain.
The Championship That Changed Everything
On January 19, 2026, Indiana defeated Miami 27–21 in the College Football Playoff National Championship Game.
The Hoosiers finished 16–0.
It was the first national football championship in Indiana history, an achievement that transformed the program’s standing almost overnight.
The victory also created a new problem.
Expectations do not remain fixed after an organization reaches its highest objective.
Supporters, administrators, athletes and observers begin measuring performance against what has already been achieved.
Indiana was no longer being compared primarily with its own difficult history.
It was being measured against the standard established by its championship season.
That helps explain why the 2026 campaign has produced such an unusual conversation.
Indiana’s victories have not become less valuable.
The standards by which its performances are judged have changed.
That does not mean every close victory signals a declining program, or that all Indiana supporters are dissatisfied.
Rather, the Hoosiers’ championship has created a level of scrutiny that would have seemed extraordinary only a few years earlier.
A program once seeking consistent bowl eligibility now faces questions about whether its victories meet the expectations associated with a national championship contender.
A Victory That Exposed Both Strengths and Weaknesses
The Minnesota game offered a particularly revealing example.
According to the official game statistics, Indiana accumulated 419 yards of offense but converted only two of 13 third-down opportunities and scored on two of four red-zone possessions.
Those numbers illustrated the difference between moving the football and consistently finishing drives.
The defense helped keep the Hoosiers within striking distance despite difficulties elsewhere.
Special teams also experienced execution problems.
Cignetti acknowledged those issues afterward.
He described the defense as consistently effective, the offense as unsettled at times and the special teams performance as unusually poor.
Yet McAteer’s program-record 60-yard field goal forced overtime.
Wilson’s touchdown pass to Becker supplied the winning points, and the defense completed the job.
The outcome should not erase the mistakes.
Nor should the mistakes erase the victory.
Both are necessary to understand where Indiana stands.
A team capable of overcoming substantial errors possesses qualities worth recognizing.
At the same time, repeatedly relying on dramatic recoveries is not a substitute for consistent execution.
Cignetti’s task is no longer simply to convince Indiana that winning is possible.
He must now manage a program expected to win, while confronting opponents determined to defeat the defending national champions.
The Next Chapter Is More Difficult to Measure
Indiana’s rise offers a useful case study in leadership, organizational investment and changing expectations.
Dolson’s hiring decision established an important foundation.
Cignetti and his staff transformed the program’s competitive performance, while university leadership supported the resources necessary to pursue and retain that success.
Those decisions contributed to Indiana’s rise, but financial investment alone cannot explain a national championship.
Recruiting, player development, coaching, preparation and execution all played roles.
Whether the organization can sustain that success over multiple seasons remains an open question.
The distinction is important because an extraordinary turnaround and a sustainable organization are not necessarily the same thing.
A single championship can change a university’s reputation.
Sustaining that reputation requires continued decisions about personnel, resources, culture and performance.
Indiana is now confronting those decisions from a position few would have imagined before Cignetti arrived.
And perhaps that is the strangest part of this story.
On October 31, the Hoosiers needed a 60-yard field goal as time expired, an overtime touchdown and one final defensive stop to escape Minnesota.
They won.
They improved to 8–1.
And the questions continued.
For a football program once defined by its inability to win consistently, that may be the clearest evidence of how completely its world has changed.
Indiana football has entered a different reality — one in which winning is expected, and winning alone may no longer be enough.
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
From Doing Everything Myself to Directing the Workflow
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.
For most of my career, writing meant doing nearly everything myself.
Research the story.
Find the sources.
Take the notes.
Organize the information.
Write the draft.
Rewrite the draft.
Check the facts.
Edit the copy.
Come up with the headline.
Prepare the article for publication.
Then start all over again with the next story.
That process was familiar because it was the way I had always worked.
It was also exhausting.
Journalists often wear several hats, especially when they work independently or in smaller organizations.
A reporter may also become the researcher, editor, publisher, social-media producer and, depending on the project, even the person creating graphics.
For a long time, I simply accepted that as part of the job.
Then artificial intelligence changed the way I thought about the work.
Not because AI could suddenly do everything for me.
It could not.
What changed was that I began to realize I no longer had to perform every part of the process in exactly the same way.
I could direct the workflow.
That was a much bigger change than simply learning how to use a chatbot.
The First Stage: Asking Questions
My early use of artificial intelligence was not sophisticated.
Like many people, I started by asking questions.
I treated AI almost like a conversational search tool.
Ask something.
Get an answer.
Ask another question.
Move on.
There was nothing particularly strategic about it.
At that point, I was using AI.
I was not yet working with AI.
That distinction would become important.
The real change began when I stopped thinking only about individual questions and started thinking about the entire process.
What happens before I write?
What happens after I write?
Where do mistakes usually occur?
Which parts take the most time?
Which parts require judgment?
Which parts are repetitive?
Which parts could benefit from another set of eyes?
Once I began thinking that way, AI stopped being only a question-and-answer tool and started becoming part of a workflow.
The Work Did Not Disappear
It is easy to describe AI as a labor-saving technology.
Sometimes it is.
But that description can be misleading.
The work did not disappear.
The work changed.
Instead of spending all of my time manually performing every task, I increasingly spent time deciding what needed to happen, what AI could assist with and what still required my direct attention.
That sounds subtle.
It is not.
Consider research.
A traditional workflow might involve opening a series of tabs, reading articles, taking notes and trying to identify connections manually.
An AI-assisted workflow might help organize documents, compare information or identify questions more quickly.
But somebody still has to decide which sources are trustworthy.
Somebody still has to notice when important context is missing.
Somebody still has to recognize when two sources appear to contradict each other.
And somebody still has to decide what belongs in the final story.
That person is still me.
AI can reduce some forms of manual work while creating another responsibility: supervision.
From Doing Everything to Directing the Workflow
This is the part I did not understand when I first started using AI.
I thought the main benefit would be speed.
Write faster.
Research faster.
Edit faster.
Those benefits are real.
But the deeper change has been learning how to direct the work.
The shift was not from working to supervising.
It was from performing every task manually to deciding which tasks I should still perform, which AI could assist with and where human approval had to remain.
One workflow may be focused on research.
Another may challenge the argument.
Another may examine factual claims.
Another may help transform a completed article into a different format.
Another may help evaluate whether the final product still sounds like me.
Some of those workflows are simple and manually triggered.
Others are more structured.
Not every useful AI workflow needs to be autonomous.
The point is not to remove the human from the process.
The point is to build a process where the human knows when to step in.
I Did Not Start With a Master Plan
The strange part is that this happened gradually.
I did not sit down one morning and announce that I was going to build a collection of AI-assisted workflows.
Much of it came from trial and error.
I encountered a problem.
I tried something.
Sometimes it worked.
Sometimes it did not.
I needed a better way to verify information.
A workflow developed.
I needed a way to challenge weak arguments.
Another workflow developed.
I needed to repurpose an article without losing its meaning.
Another process emerged.
Some workflows worked immediately.
Others had to be changed.
Some became more useful after repeated use.
Some probably were not worth keeping.
Over time, those individual solutions began to form a system.
That is when I started realizing that I was no longer simply using AI.
I was designing ways to work with it.
The Journalist Becomes the Editor of the Output
Traditional journalism has always involved editing.
The difference now is that the journalist may also have to edit what the machine produces.
That means more than correcting grammar.
It means questioning assumptions.
Rejecting weak suggestions.
Checking sources.
Restructuring an answer.
Recognizing when the system has misunderstood the assignment.
Sometimes the most important thing I do is tell the AI:
No.
That is wrong.
That is unsupported.
That does not sound like me.
That is not the question I asked.
Try again.
Those moments matter because they reveal something that gets lost when AI is described as automatic.
The process is often interactive.
The final result may emerge from several rounds of challenge, correction and refinement.
That makes the human role different from traditional writing, but not less important.
In some situations, it can become more demanding because the journalist must supervise both the journalism and the AI-assisted process.
Direction Requires Knowing What Good Looks Like
There is another problem with telling people to simply “use AI.”
You have to know what you are trying to produce.
AI cannot compensate for having no standards.
If I do not know what a strong news story looks like, I cannot reliably judge whether AI has helped create one.
If I do not understand verification, I may not recognize an unsupported claim.
If I do not understand my own voice, I may allow AI to replace it with generic language.
Delegation only works if the journalist still understands the task well enough to judge the result.
That matters because over-delegation carries its own risk.
If journalists stop practicing the underlying skills of research, verification, writing and editing, they may become less capable of recognizing when AI has produced weak work.
AI can increase capability.
It does not automatically provide judgment.
The better I understand journalism, the better I can direct AI inside a journalistic workflow.
A Different Kind of Productivity
Before I began using AI seriously, productivity often meant working harder or working longer.
There were only so many hours available.
If an article took three hours, producing another article often meant finding another three hours.
AI changes that equation, but not as simply as the hype sometimes suggests.
The goal is not to produce as much content as possible.
The goal is to use saved time intelligently.
The most useful question is not:
“How much faster can AI make me?”
It is:
“What should I do with the time AI gives back?”
If AI helps reduce the mechanical parts of a task, I can spend more time checking the work.
I can challenge the argument.
I can examine another source.
I can rethink the headline.
I can decide that a story is not ready.
That is a much healthier definition of productivity than simply publishing more.
Speed only matters if the quality survives.
When the Workflow Fails
Directing AI-assisted work also means accepting that the workflow will sometimes fail.
AI may misunderstand the task.
It may give me information that looks useful but is not.
It may repeat itself.
It may introduce language that sounds polished but weakens the point.
It may send research in the wrong direction.
Those moments are not exceptions to the process.
They are part of the process.
The important questions are simple:
Did I catch the problem?
Do I understand why it happened?
Do I need to change the workflow?
That is how the system improves.
Not because AI becomes perfect.
Because the human process around it becomes better.
Building the System While Using It
One of the unusual things about my experience is that I built many of these workflows before I knew what to call them.
I was solving problems.
I was creating repeatable steps.
I was assigning specific roles to AI.
I was deciding where human approval had to remain.
Only later did I realize that what I had been building resembled structured AI workflows and, in some cases, agent-like systems.
Most of those systems were not fully autonomous.
They were usually manually triggered and specialized.
That distinction matters.
Not every useful AI workflow needs to operate independently.
Sometimes the value comes from building a repeatable process that helps the human work more consistently.
I had not learned the terminology first and then applied it.
I developed the practice first and learned the terminology afterward.
That may be one reason the systems felt natural.
They were built around real problems rather than abstract concepts.
The Managerial Shift
This change also affected how I thought about my role inside Vallano Media.
I was no longer simply writing articles.
I was increasingly managing a publishing system.
Research needed direction.
Verification needed standards.
Drafting needed oversight.
Graphics needed review.
Publishing needed coordination.
AI could assist with many of those functions, but I remained the person deciding how the pieces fit together.
That is where the shift from writer to director became clear.
The word “director” does not mean standing above the work.
It means coordinating it.
The work still has to be understood.
The standards still have to be maintained.
The final decision still has to be made.
But I no longer have to perform every task manually in order to remain in control.
What Other Journalists Can Learn From This
The lesson is not that every journalist needs dozens of workflows.
The lesson is simpler.
Start by examining the work you already do.
Where do you spend the most time?
Where do mistakes occur?
Which tasks are repetitive?
Which tasks require judgment?
Which tasks could be assisted without handing away editorial control?
Then test one small process.
Do not automate the entire newsroom.
Do not start with the highest-risk task.
Pick something manageable.
Maybe it is organizing notes.
Maybe it is generating questions to consider.
Maybe it is comparing two versions of a draft.
Maybe it is asking AI to challenge an argument before publication.
Then examine what happened.
Did it help?
Did it save time?
Did it introduce problems?
What still required human judgment?
That is how a workflow begins.
The Work Is Still Mine
AI has changed how I work.
It has not changed who is responsible for the work.
That was the lesson of Chapter 1.
Chapter 2 adds another layer.
Responsibility does not require doing everything manually.
Control does not mean refusing assistance.
The journalist can delegate parts of the process without delegating accountability.
That is the shift I am still learning to make.
From doing every task myself to directing a workflow.
From asking AI isolated questions to designing repeatable processes.
From thinking only like a writer to thinking more like an editor, manager and publisher.
The technology did not remove me from the process.
It changed where I add the most value.
And increasingly, that value comes from knowing what to direct, what to question, what to verify and when to say no.
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
I’m Taking the Nonfiction Writing Challenge Again-This Time With AI
By Cheval John | Vallano Media November 1st, 2026
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.
Twelve years ago, I participated in a nonfiction writing challenge.
Generative AI was not part of my writing process.
There was no ChatGPT sitting beside me, no AI assistant helping me organize research and no system I could ask to challenge an argument before I published it.
The process was more traditional: research, notes, writing, rewriting and editing.
Now it is 2026, and I am doing the challenge again.
This time, the blank page has company.
For the next 30 days, I will be working on a new book, Journalist and AI — Volume 1, while documenting what happens when artificial intelligence becomes part of the nonfiction writing process.
This is not an experiment in having AI write a book for me.
It is an experiment in what happens when a writer uses AI as a tool while remaining responsible for the research, reporting, verification, judgment, writing and final product.
That distinction matters.
Twelve Years Later
A lot has changed since 2014.
Writers already had search engines, digital publishing tools, social media and numerous technologies that had transformed how information was gathered and distributed.
But mainstream writers did not have the kind of easily accessible generative AI systems available today.
Now I can use AI to help organize research, examine competing ideas, identify weaknesses in an argument, compare information and assist with editing.
But that does not automatically make the writing better.
It does not automatically make the research accurate.
AI can accelerate good work.
It can also accelerate bad work.
It can help organize information more quickly, but it can also produce convincing errors, flatten a writer’s voice or reinforce a weak assumption if nobody challenges it.
That is part of what I want to explore over the next 30 days.
Why Do This Again?
I have already written books.
So this challenge is not about proving that I can finish one.
I am returning to it because the writing environment has changed so much that I want to experience the difference for myself.
Where does AI actually save time?
Where does it improve the work, and where does it create new problems?
Most importantly, can AI help a writer become more productive without sacrificing accuracy, judgment, transparency or individual voice?
Those questions are part of the reason Journalist and AI exists.
Instead of discussing them only in theory, I am going to put them to the test while writing the book itself.
This Is Not a Perfect Comparison
There is one thing I want to make clear from the beginning.
This is not a controlled experiment between 2014 and 2026.
AI is not the only thing that has changed since 2014.
I have changed too.
I have another 12 years of experience as a writer, publisher and researcher.
Publishing technology has changed.
The projects are different.
The way I organize information has changed.
So if I work faster in 2026 than I did in 2014, I cannot simply say AI caused the entire difference.
AI is an important variable, but it is not the only one.
That limitation is part of the experiment, and I do not intend to hide it.
What I can document is how AI affects the process I use today.
How AI Will Be Used
Artificial intelligence will be part of this project, but it will have defined roles.
I will use it to help organize ideas, analyze information, identify questions, compare sources, examine arguments and assist with editing.
I will also use it as an adversarial partner.
One of the most useful habits I have developed while working with AI is asking a different kind of question.
Instead of only asking, “Is this good?” I often ask:
“What is wrong with this?”
I use AI to red-team ideas and drafts — to search for weak arguments, missing context, unsupported assumptions and alternative interpretations.
But the AI’s criticism is not automatically correct either.
I still have to evaluate it.
The same principle applies to research.
AI can help me find patterns, organize information and work through material faster than I could manually.
But an AI-generated statement is not evidence.
If something matters to the book, it still has to be verified against reliable sources.
What AI Will Not Do
AI will not receive final editorial authority over this project.
I will
I decide what belongs in the book.
I decide whether an argument accurately reflects the evidence.
I decide whether the writing sounds like me.
And if an AI system produces something inaccurate, misleading, unsupported or simply not good enough, I am responsible for catching it.
A system capable of producing polished language quickly can also produce polished mistakes quickly.
Speed cannot become a substitute for judgment.
What Readers Are Going to See
For the next month, I want to make parts of this process visible.
There will be successes.
There will probably be mistakes.
There will be AI suggestions that improve the work and others that I reject.
There may be research that changes the direction of a chapter.
There may be sections that get rewritten completely because the first approach does not work.
And if AI wastes time, gives me weak information, sends me in the wrong direction or produces writing that I throw away, that belongs in the record too.
The goal is not to create the illusion that writing with AI is effortless.
The goal is to document what actually happens.
I will not be publishing every chapter of the book as I write it.
Readers will instead see selected parts of the process, observations from the experiment and what I learn along the way.
At the same time, Vallano Media will continue its regular Tuesday and Thursday publishing schedule, with additional articles published when news, reporting or other projects warrant it.
The book challenge will happen alongside that work rather than replace it.
My Rule for the Next 30 Days
The Write Nonfiction in November challenge does not require me to work on the project every single day.
That is a rule I am adding for myself.
From November 1 through November 30, I will work on Journalist and AI — Volume 1 every day.
Some days may produce a large amount of writing.
Other days may be spent revising, verifying information, reorganizing a chapter or questioning an argument that is not working.
The point is not to chase an arbitrary daily word count.
The point is to keep moving the manuscript forward.
Thirty Days
By November 30, I want more than a word count.
I want a complete first manuscript of Journalist and AI — Volume 1.
But I also want a record of how we got there.
How much did AI accelerate the process?
Where did human judgment remain indispensable?
Did greater speed allow more time for verification and deeper thinking, or did it simply encourage more production?
Did AI help preserve my voice, or did I sometimes have to push back against its tendency to make everything sound polished and generic?
And after 30 days, will I view the relationship between journalists and artificial intelligence differently than I do today?
I do not know all of those answers yet.
That is exactly why I am doing this.
In 2014, I participated in a nonfiction writing challenge using the tools available to me at the time.
Twelve years later, I am returning in a completely different technological environment.
The technology has changed.
The responsibility of the writer has not.
For the next 30 days, you are going to see the process.
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
Lessons Beyond Journalism: What AI-Assisted Workflows Can Teach Other Professionals
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.
The FIFA reporting project began as a journalism assignment.
But by the time Vallano Media had worked through the full process—research, source evaluation, verification, AI-assisted analysis, drafting, red-team review and publication—a broader lesson had emerged.
The workflow was not useful only because it helped produce journalism.
It was useful because it provided a way to think about professional work with AI.
The specific tasks may change from one field to another.
A public relations professional is not doing the same work as a journalist.
A business owner is not following the same process as a researcher.
A communications strategist is not evaluating information in exactly the same way as an editor.
But the underlying problem is often similar: How do you use AI to increase your capacity without allowing the technology to replace the judgment that gives the work its value?
That question extends far beyond a newsroom.
The examples that follow are applications of the underlying principles, not claims that every profession should adopt the Vallano Media newsroom workflow unchanged.
Different fields have their own standards, risks and responsibilities.
The Principles Travel Better Than the Workflow
The exact reporting workflow should not be copied mechanically into every profession.
Different fields have different standards.
Different risks.
Different forms of accountability.
Different definitions of evidence.
But several principles from the journalism process travel well.
Start with reliable information.
Understand where the information came from.
Use AI to assist analysis, not manufacture certainty.
Challenge important conclusions.
Keep a human responsible for consequential decisions.
Be willing to reject the output.
Those principles are not uniquely journalistic.
They are professional.
And that distinction matters.
The long-term value of AI may depend less on whether people learn one perfect prompt and more on whether they learn how to integrate the technology into systems that already contain checks, expertise and accountability.
Public Relations Has a Similar Problem
Public relations may be one of the clearest examples.
AI can help a communications professional organize research, analyze coverage, compare messages, identify audience questions, brainstorm campaign ideas, draft releases, generate social copy, prepare talking points and summarize stakeholder feedback.
Those capabilities can save time.
But the final communication still requires professional judgment.
A press release can be grammatically perfect and strategically wrong.
A social post can sound polished while creating an unnecessary reputational risk.
A generated response to a crisis can technically answer a question while completely misreading the tone of the moment.
The technology can generate words.
The communications professional still has to understand the audience, the organization, the context and the consequences.
That is similar to what happened in the FIFA project.
AI could help organize and analyze the material.
It could not decide what Vallano Media should ultimately say.
Business Decisions Have the Same Risk
The same principle appears in business.
AI can analyze large amounts of information quickly.
It can summarize customer feedback.
It can compare competitors.
It can help explore pricing strategies.
It can organize financial information.
It can generate business-plan ideas and identify possible opportunities.
Those outputs can be valuable.
They can also be dangerously persuasive when the underlying assumptions are weak.
A detailed forecast is still only as reliable as the information and assumptions behind it.
A confident market analysis can still overlook an important competitor.
A recommendation can still be inappropriate for the actual financial position or strategy of the company.
The lesson from journalism applies here too: A polished answer is not automatically a sound decision.
The business owner still has to know enough about the business to challenge what AI produces.
Research Requires the Same Discipline
Research is another clear example.
AI can help organize large collections of documents.
It can assist with literature searches.
It can compare studies.
It can identify recurring themes.
It can suggest questions for further investigation.
But research also depends on distinctions that AI can blur if used carelessly.
What is the original source?
Was a claim actually supported by the study being cited?
Does the evidence establish correlation or causation?
Are two studies measuring the same thing?
Is an apparent consensus genuine or simply repeated citation?
Those questions closely resemble the source-evaluation issues discussed earlier in this series.
AI can process the material.
The researcher still has to understand what the material can legitimately support.
Communications Work Still Requires Context
Professional communication often depends on information that is technically correct but highly sensitive to context.
An internal memo.
A customer response.
A statement after a controversy.
An executive message.
A presentation.
A negotiation email.
AI can help draft all of them.
But tone, timing and audience matter.
The same sentence can work in one context and fail badly in another.
That means a professional cannot evaluate the output only by asking: Is this well written?
The better questions may be: Is this appropriate for this audience?
Does this message reflect the facts?
Does it create an unintended implication?
Is this the right moment to say it?
Those are judgment questions.
AI can help generate alternatives.
It cannot automatically resolve every social, organizational or reputational consequence of choosing one.
Expertise Becomes More Important, Not Less
One of the recurring themes throughout the FIFA project was that AI worked best when paired with subject knowledge.
That principle becomes even more important outside journalism.
The more capable AI becomes at producing professional-looking work, the easier it becomes to mistake presentation for expertise.
A person can receive a polished legal-sounding explanation without understanding law.
A detailed financial analysis without understanding finance.
A sophisticated communications strategy without understanding public relations.
A research summary without understanding methodology.
That creates a dangerous illusion.
AI can sometimes make novice work look expert.
But the ability to generate professional-looking output is not the same as possessing professional judgment.
The person using the tool still needs enough expertise to recognize when the output is strong, weak, incomplete or inappropriate.
AI Is Often Most Useful Before the Final Decision
Across different professions, AI may be most valuable in the stages leading up to a decision.
It can help expand the range of possibilities.
It can surface questions.
It can compare options.
It can organize evidence.
It can challenge assumptions.
It can produce drafts.
It can show different ways to frame a problem.
Those activities can significantly increase a professional’s capacity.
But there is an important boundary.
As a task becomes more consequential, the need for clearly defined human oversight, expertise and accountability generally increases.
That decision may involve whether to publish, send a statement, make an investment, change strategy, act on research or approve a recommendation.
The professional does not have to perform every intermediate step manually.
But someone still has to own the final decision.
The Best AI Workflow May Include Deliberate Friction
Efficiency is often presented as one of AI’s greatest advantages.
And it is.
But professional work does not always benefit from eliminating every delay.
Sometimes friction is useful.
Verification is friction.
Source checking is friction.
Red-team review is friction.
Asking for another perspective is friction.
Re-reading the underlying evidence is friction.
Those steps slow the process.
They also make the process safer.
The lesson from the FIFA workflow was not that AI should remove friction from everything.
It was that AI should remove unnecessary friction while preserving the friction that protects quality.
That distinction may be one of the most transferable ideas in the entire project.
Professionals Need Systems, Not Just Prompts
Much of the public conversation about generative AI focuses on prompting.
Write a better prompt.
Use a more detailed prompt.
Build a prompt library.
Those skills can be useful.
But prompting alone is not a professional workflow.
The more important questions are: What happens before the prompt?
What sources are being used?
What assumptions are already built into the task?
How will the output be checked?
Who decides whether the result is good enough?
What happens if the answer conflicts with professional knowledge?
What is the correction process if something goes wrong?
Those questions create a system.
A good prompt can improve an answer.
A good workflow improves the entire process around the answer.
AI Should Not Become the Excuse
There is another principle that transfers easily beyond journalism.
Professionals cannot use AI as an excuse for poor decisions.
If a business sends an inappropriate message, saying AI wrote it does not remove responsibility.
If a researcher cites unsupported information, AI does not become responsible for the citation.
If a public relations professional publishes an inaccurate statement, the tool does not absorb the reputational consequences.
The organization still made the decision to use the output.
That means AI governance does not begin with the technology.
It begins with responsibility.
Who is accountable for the final result?
Who has authority to reject the output?
Who has enough expertise to evaluate it?
Who corrects the mistake?
If those questions do not have clear answers, the workflow is incomplete.
The Human Role Is Changing, Not Disappearing
AI does change professional work.
Some tasks become faster.
Some repetitive work becomes easier.
Some forms of analysis become more accessible.
People may spend less time organizing information manually and more time evaluating what it means.
That can be a meaningful change.
But it does not necessarily mean the professional becomes less important.
Depending on the task, AI can shift more time toward evaluation, interpretation and judgment.
Less time may be spent producing the first draft.
More time may be spent deciding whether the first draft is actually good.
Less time may be spent organizing documents.
More time may be spent evaluating contradictions.
Less time may be spent generating options.
More time may be spent determining which option is responsible.
That shift may ultimately be more important than the question of whether AI can perform a particular task.
The Measure Should Be Better Work
The temptation with new technology is to measure success by adoption.
How many tasks are automated?
How many prompts are used?
How much time is saved?
How much content is generated?
Those numbers can be interesting.
But they do not necessarily tell us whether the work improved.
A better measurement is outcome-oriented.
Did the research become stronger?
Did the analysis become clearer?
Did the professional notice something they otherwise might have missed?
Did the workflow reduce avoidable errors?
Did it create more time for higher-value judgment?
Did the final work become more useful to the audience?
If the answer is yes, AI may be improving the process.
If the answer is simply that more work was produced faster, the benefit is much less clear.
The Workflow Changes. The Principles Remain.
The specific workflow documented in this series will evolve.
AI tools will change.
Professional standards will change.
New capabilities will appear.
Some current limitations will disappear.
New risks will emerge.
But several principles are likely to remain useful: Evidence before conclusions.
Verification before confidence.
AI assistance without surrendered judgment.
Transparency about how the work was produced.
Human responsibility for consequential decisions.
Those principles matter in journalism.
They also matter anywhere professionals use AI to make work faster, deeper or more complex.
The workflow changes.
The principles remain.
From Case Study to Working Model
The FIFA project did not begin with the intention of creating a framework for AI-assisted professional work.
It began with a story that needed to be reported.
The workflow developed because practical problems had to be solved.
How should sources be organized?
How could AI help without becoming the source?
How should generated conclusions be verified?
How could the system be used to challenge assumptions?
Where should human judgment intervene?
Who remained accountable?
Answering those questions gradually produced something larger than the original reporting project.
It produced a working model.
Not a perfect model.
Not a universal one.
But one that can be tested, adjusted and adapted.
That may ultimately be the most useful lesson from the entire project.
The future of professional AI use may not depend on finding one system capable of doing everything.
It may depend on professionals learning how to build workflows in which technology can contribute significantly without confusing assistance with authority.
For Vallano Media, that distinction became the foundation of the process.
And it remains the principle that connects every chapter of this series.
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
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