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.
Chapter 1: The Journalist Is Still Responsible
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 help a journalist work faster.
It can organize information, summarize documents, compare sources, identify gaps in an argument, suggest questions, assist with editing and challenge assumptions the journalist may have overlooked.
But there is one thing it cannot do.
It cannot take responsibility for the journalism.
That responsibility remains human.
The journalist remains responsible for the work they produce.
Editors remain responsible for the decisions they make.
News organizations remain responsible for the systems they approve, the standards they set and the safeguards they require.
AI can become part of the process without becoming accountable for the result.
That distinction sounds simple.
In practice, it is not.
If an AI system produces a paragraph that eventually appears in an article and that paragraph contains an error, who is responsible?
If AI summarizes a document incorrectly and nobody catches it, who owns the mistake?
If a newsroom introduces an AI tool into its workflow without adequate safeguards, can responsibility simply be pushed down to the individual journalist?
These questions become more important as AI systems move deeper into journalism.
The answer cannot be that the technology is responsible.
Accountability still belongs to the people and organizations using it.
The Temptation of Speed
One of the most attractive things about artificial intelligence is speed.
Tasks that once required substantial manual work can sometimes be completed much faster with AI assistance.
Information can be organized quickly.
Long documents can be examined.
Ideas can be compared.
A first draft can take shape faster than it might have before.
For journalists working under deadlines, that can be incredibly useful.
But speed can create another danger.
The faster information appears, the easier it can become to accept it without examining it closely.
A polished answer can feel authoritative even when the underlying information is incomplete, misleading or wrong.
And an AI system does not have to invent a fact to cause problems.
It can omit crucial context.
It can emphasize the wrong detail.
It can flatten uncertainty.
It can steer attention toward one interpretation while neglecting another.
That is why human judgment becomes more important, not less.
AI does not eliminate the need for verification.
It increases the importance of knowing what needs to be verified and why.
Polished Does Not Mean Correct
One of the first lessons anyone working seriously with generative AI learns is that confident language does not guarantee accurate information.
An AI system can produce an answer that sounds professional, complete and perfectly reasonable.
It can also be wrong.
That creates a particular problem for journalism because credibility depends on the accuracy of what gets published.
A spelling mistake can be embarrassing.
A factual mistake can damage trust.
An unsupported accusation can create much more serious consequences.
The danger is not simply that AI makes mistakes.
Human beings make mistakes too.
The difference is that generative AI can produce those mistakes quickly and present them in language that already looks finished.
That means journalists cannot judge AI output by how polished it appears.
They have to judge it by the evidence behind it.
AI Output Is Not Evidence
This is one of the rules I have increasingly adopted in my own work: AI output is not evidence.
If an AI system tells me something happened, that does not make the statement true.
If it provides a date, statistic, historical claim or quotation, that information still has to be traced back to reliable evidence.
AI can help point me toward something worth investigating.
It can help organize what I find.
It can help compare multiple pieces of information.
But an AI-generated claim should never be treated as verified simply because the system produced it.
That distinction has changed the way I work.
Instead of asking only, “What is the answer?” I increasingly ask:
“What evidence supports the answer?”
Those are very different questions.
The first can produce information.
The second begins the verification process.
Human Oversight Has to Mean Something
There is a phrase that appears constantly in discussions about artificial intelligence:
Human in the loop.
It sounds reassuring.
But simply putting a human somewhere in the workflow does not automatically make the process responsible.
A journalist can still accept an AI-generated summary without checking it.
An editor can still approve a story too quickly.
A newsroom can still adopt an AI system without understanding its limitations.
Human oversight only works when the human is actually verifying, questioning and exercising judgment.
Otherwise, the human becomes little more than a rubber stamp.
The goal should not simply be to keep a person somewhere in the process.
The goal should be to make sure that person still has the time, authority and responsibility to challenge what the technology produces.
The Human Has to Push Back
Another lesson I have learned is that AI becomes more useful when I stop treating it as something that should agree with me.
If I present an argument and ask an AI system whether it is good, I may receive a helpful answer.
But I often learn more by asking it to attack the argument.
What am I missing?
What assumptions am I making?
What would a skeptical reader challenge?
What evidence would weaken my conclusion?
What alternative explanation should I consider?
That is where AI can become useful as an adversarial partner rather than simply a writing assistant.
But there is another layer to this.
The AI’s criticism also has to be challenged.
It can identify a weakness that does not actually exist.
It can suggest an alternative interpretation that is unsupported.
It can overcorrect.
So the journalist has to evaluate both the original argument and the AI’s response to it.
The human remains responsible for deciding what survives.
AI Is Not Just Another Spellcheck
Journalists have always used technology.
Search engines changed research.
Digital recorders changed interviews.
Transcription software changed note-taking.
Content-management systems changed publishing.
Spellcheck changed editing.
So why should generative AI be treated differently?
The difference is not that AI is the first technology journalists have ever used.
It is that generative AI can produce outputs that resemble reasoning, interpretation and writing.
It does not merely correct a misspelled word.
It can suggest an argument.
It can summarize a document.
It can rewrite a paragraph.
It can compare competing explanations.
It can recommend what information appears most important.
That gives it greater potential to influence editorial judgment.
And the closer technology moves toward those judgment-heavy parts of journalism, the more carefully journalists need to understand what the system is doing.
Judgment Cannot Be Automated Away
Journalism is filled with decisions that go beyond arranging facts.
Which facts matter most?
Which source is credible?
Which detail belongs near the top of the story?
Which claim requires additional verification?
When is there enough evidence to publish?
When should publication wait?
What context does the audience need?
Those decisions involve judgment.
AI may help inform that judgment, but it cannot remove human responsibility for the decision.
That becomes especially important when a story involves uncertainty.
Real reporting is rarely as clean as the completed article makes it appear.
Sources may disagree.
Documents may be incomplete.
Eyewitness accounts may conflict.
Statistics may require context.
People may have incentives to describe the same event differently.
A journalist has to work through those contradictions and decide what can responsibly be said.
AI can assist with that process.
It cannot own the decision.
Responsibility Is Bigger Than a Byline
I often think about this in very simple terms.
If my name appears on an article, I am responsible for what is in it.
But responsibility in journalism extends beyond the byline.
Editors, producers, researchers and others may shape journalism without their names appearing prominently on the final product.
Whether my name appears on the work or I contribute behind the scenes, I am still responsible for the decisions I make in producing it.
And organizations have responsibilities too.
A newsroom cannot introduce AI tools, encourage their use and then pretend every consequence belongs only to the reporter.
Management decisions matter.
Training matters.
Editorial standards matter.
The design of the workflow matters.
Responsibility is shared, but it does not disappear.
What That Means for This Book
Artificial intelligence will play a significant role in the creation of this book.
It will help me organize research.
It will challenge arguments.
It will help examine drafts.
It may suggest language that ultimately survives into the finished manuscript.
But it will not receive editorial authority over the work.
I will decide what stays.
I will decide what goes.
I will decide whether the argument reflects the evidence.
And I will be responsible for what is published.
If an AI system produces something inaccurate, misleading, unsupported or simply not good enough, I cannot defend the final result by saying:
“The AI told me.”
That is not an editorial defense.
A Different Kind of Partnership
None of this makes AI unimportant.
Quite the opposite.
Used carefully, it can become one of the most useful tools available to a modern journalist.
But the most productive relationship may not be one in which the journalist asks AI to do the thinking.
It may be one in which AI helps the journalist think harder.
Challenge this.
Compare these sources.
Find the weakness.
Show me another interpretation.
What am I overlooking?
Where does this argument fail?
Those kinds of questions turn AI from a shortcut into something closer to an intellectual sparring partner.
And that may ultimately be one of its most valuable roles in journalism.
Responsibility Remains Human
The tools journalists use have always changed.
The responsibilities underneath the profession have changed much less.
Verify the information.
Understand the evidence.
Provide context.
Be fair to the people involved.
Correct mistakes.
Do not publish something simply because it sounds convincing.
Artificial intelligence does not erase those responsibilities.
It makes them more visible.
The journalist may have a new assistant.
The newsroom may have faster tools.
The organization may be able to accomplish more in less time.
But when journalism reaches the public and someone asks, “Can I trust this?” the answer cannot be:
“The AI said it was right.”
The answer has to come from the work behind the story.
And responsibility for that work remains human.
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.
Sam Houston secures CUSA tournament berth with consecutive sweeps of Jacksonville State
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.
Sam Houston began the volleyball season picked to finish last in Conference USA.
On Saturday, the Bearkats secured a place in the conference tournament, with their goal of a top-three finish still within reach.
The Bearkats completed consecutive home sweeps of Jacksonville State at Johnson Coliseum, winning Friday’s match 25–17, 25–16, 25–23 before taking Saturday’s match 25–23, 25–12, 25–16.
Saturday’s victory improved Sam Houston to 16–9 overall and 7–5 in conference play.
The ESPN+ broadcast reported that the Bearkats had clinched their tournament berth, with New Mexico State’s victory over Delaware also contributing to qualification.
The weekend also brought a milestone for the program.
Friday’s victory marked the first time Sam Houston had reached 15 wins since finishing 18–10 in 2021, and its first 15-win season since joining CUSA in 2023.
Coach Brenda Gray made clear that the Bearkats’ ambitions extend beyond qualifying.
“Our goal is to get to the tournament, but our goal this year is to be top three,” Gray said in her ESPN+ postmatch interview Saturday.
Sam Houston, Middle Tennessee and Liberty each stood at 7–5 in conference play after Saturday’s matches.
The Bearkats travel to Middle Tennessee on November 5–6, giving them an opportunity to gain ground against a team sharing their conference record.
Serving and shared production
Sam Houston’s two victories came with contributions throughout the lineup.
No Bearkat reached double figures in kills in either match, but the team produced 33 kills Friday and 34 Saturday.
Chloe Monson led Sam Houston with nine kills in each match and added 10 digs Saturday.
Bella Wood contributed five kills in both victories while collecting seven service aces across the series.
The Bearkats finished with 18 aces over the two matches, compared with Jacksonville State’s eight.
Brenlin Garlitz supplied 16 assists and four aces Friday.
Lauren Pyle added nine assists, while libero Shaelin White led the defense with 11 digs.
On Saturday, Garlitz and Pyle recorded 12 and 11 assists, respectively, and White finished with a team-high 14 digs.
Chelsea Butler contributed four kills on a .500 hitting percentage and five blocks.
Annika Brooks added five block assists.
Monson said having teammates contribute eased the pressure to carry the offense herself.
“It definitely takes the pressure off me trying to have a perfect game every single time,” she said in her ESPN+ interview.
Gray also praised the execution from the service line.
“I think our serving was on point today, wherever I was asking them to go, they were delivering the ball,” she said.
Jacksonville State tests Friday’s finish
Sam Houston won Friday’s opening two sets by eight and nine points, respectively.
The third required the Bearkats to respond to a closer finish and a late reversal.
A successful Jacksonville State challenge changed an apparent 22–19 Sam Houston lead to 21–20.
Monson and Wood answered with kills to extend the advantage.
The Gamecocks stayed within reach, however, saving two match points before Butler delivered the deciding kill for a 25–23 finish.
A final challenge did not change the outcome.
Sam Houston finished the match hitting .238, compared with Jacksonville State’s .140.
In her postmatch interview with Sam Houston SportsTalk, Gray emphasized keeping the team’s attention on its own play and approaching the remaining schedule one match at a time.
With conference positioning still unsettled, the Bearkats returned Saturday needing another victory.
An opening-set escape
Saturday’s first set became the pivotal stretch of the second match.
Sam Houston started with five consecutive points and later led 17–9.
The Bearkats still held a six-point advantage at 22–16 before Jacksonville State scored seven straight points to move ahead 23–22.
Sam Houston answered by winning the final three rallies.
Monson’s kill, assisted by Wood, tied the score.
A bad set by Jacksonville State’s Delaney Moon put the Bearkats ahead, and a Claire Evans attack error completed the 25–23 win.
The Bearkats took the set despite recording eight attack errors against 10 kills, finishing with a .051 hitting percentage.
Their offense became considerably more efficient in the second set.
Jacksonville State scored the opening two points, but Sam Houston responded with a seven-point run.
The Bearkats produced 14 kills against two errors, hitting .444 in a 25–12 victory while holding the Gamecocks to five kills.
In her postmatch interview, Gray discussed communication and adjustments during the opening set, along with serving targets intended to disrupt Jacksonville State’s offense.
Monson also pointed to passing and blocking discipline as areas of emphasis.
Five straight points seal the sweep
Jacksonville State opened the third set with a 5–1 lead, but Sam Houston recovered to move ahead 15–9.
The Gamecocks later narrowed an 18–12 deficit to 18–15.
Kills by Monson and Wood restored a five-point advantage before Jacksonville State scored again to make it 20–16.
Sam Houston then won the final five points.
Rachelle Reed began the closing run with a kill assisted by White.
Faith Roulston followed with a kill from Pyle, and Butler added another.
Attack errors by Evans and D’Yanna Spicer supplied the final two points.
Audrey Terry served the last four rallies as the Bearkats completed the 25–16 finish.
Sam Houston hit .241 for the match and held Jacksonville State to .032.
Both teams finished with nine team blocks.
Evans led the Gamecocks with 12 kills Saturday, while Azavia Thomas added seven.
Jacksonville State finished the series 3–21 overall and 0–12 in conference play.
A higher finish remains within reach
The preseason coaches poll placed Sam Houston 10th with 17 points.
Middle Tennessee was tied for eighth with 18.
Both have moved beyond those predictions and enter their upcoming series with seven conference wins.
For Sam Houston, the trip to Murfreesboro offers a direct test against another team pursuing position near the top of the standings.
“We got to be better today than we were yesterday. And I thought we were,” Gray said after Saturday’s victory.
The Bearkats have secured their tournament place.
With Middle Tennessee next and WKU following at home, their remaining matches will determine how close they come to the top-three finish Gray set as a goal.
Human Judgment and Editorial Responsibility: Why the Publisher Still Makes the Final Call
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.
By this stage of the FIFA reporting project, artificial intelligence had touched many parts of the workflow.
It helped organize research.
It compared sources.
It surfaced questions.
It helped structure timelines.
It challenged emerging arguments.
It assisted with outlining, drafting and revision.
It helped identify claims that deserved additional verification.
But there was one responsibility that never moved.
The final editorial decision belonged to the journalist and publisher.
That distinction is easy to state.
In practice, it is one of the most important boundaries in AI-assisted journalism.
AI can generate options.
It can recommend changes.
It can identify potential problems.
It can even produce an answer that appears stronger than the one the journalist originally had.
But eventually someone has to decide: Is this accurate enough to publish?
Is the evidence strong enough?
Is the wording fair?
Does the story provide enough context?
Are we overstating what we know?
Is there something important missing?
Those are not merely technical questions.
They are editorial judgments.
And accountability for those judgments cannot be transferred to the software.
The Final Decision Is More Than Fact-Checking
Accuracy is essential, but editorial judgment involves more than verifying individual facts.
A story can contain accurate facts and still be misleading.
The facts may be presented in the wrong proportion.
Important context may be missing.
A headline may imply more than the article proves.
A technically correct sentence may still create an unfair impression.
An allegation may be properly attributed but given more prominence than the available evidence justifies.
That means the final review cannot simply ask: Is every sentence factually correct?
It also has to ask: Does the finished story fairly represent what the evidence actually shows?
AI can help identify places where those questions should be asked.
It cannot assume responsibility for answering them.
Editorial Judgment Begins With Knowing the Subject
Human oversight works best when the person reviewing AI output understands the subject being discussed.
That matters because not every AI error is obvious.
A fabricated date may be relatively easy to catch.
A subtle distortion of context can be much harder.
A journalist familiar with the subject is more likely to notice when:
– a source is being given too much weight,
– a conclusion does not fit the chronology,
– an organization’s role is being oversimplified,
– an important distinction has disappeared,
– or a sentence technically works but does not accurately reflect the story.
This is one reason professional expertise becomes more important rather than less important when AI enters the workflow.
The faster a system can produce polished material, the more important it becomes to have someone capable of evaluating what that material actually means.
AI can accelerate production.
Expertise determines whether that acceleration leads somewhere useful.
Knowing What Not to Publish Is Part of the Job
One of the most important forms of editorial judgment is restraint.
Journalism often rewards discovery.
Find the new detail.
Find the stronger angle.
Find the development that moves the story forward.
But not every interesting possibility belongs in the article.
A claim may be plausible but insufficiently sourced.
A connection may be compelling but unproven.
An AI-generated interpretation may make sense without being strong enough to publish.
The journalist has to be willing to leave those things out.
That can be difficult because generative AI makes it very easy to continue developing an idea.
A speculative connection can become a paragraph.
That paragraph can become a section.
That section can begin to feel like part of the story simply because it has been written well.
But polished language does not create evidence.
Sometimes the strongest editorial decision is deletion.
Sometimes it is qualification.
Sometimes it is another round of reporting.
And sometimes it is simply deciding: We do not know enough to publish this.
Fairness Requires More Than Balance
Editorial responsibility also includes fairness.
Fairness does not necessarily mean giving every side identical space.
Nor does it mean treating every claim as equally credible.
It means representing the evidence honestly and giving relevant parties a fair account of what is known, what is disputed and what remains uncertain.
AI can complicate this because a generated summary may create artificial symmetry.
It may present two competing claims as though they have equal evidentiary support.
Or it may do the opposite and give one interpretation disproportionate weight because that interpretation appears more frequently in the material it processed.
The journalist has to evaluate the relative strength of the evidence.
If one claim is supported by documents and multiple independent reports while another is merely asserted, those positions should not necessarily be presented as equivalent.
At the same time, disagreement should not be erased simply because one interpretation appears stronger.
The goal is not artificial balance.
The goal is an accurate representation of the evidence.
That requires judgment.
Headlines Are Editorial Decisions Too
The headline may be only a few words, but it can carry enormous editorial weight.
An article can be carefully qualified while the headline overstates the conclusion.
That can happen with or without artificial intelligence.
AI simply makes it easier to generate dozens of headline options quickly.
Some will be accurate.
Some will be clever.
Some will be dramatic.
And some may promise more than the reporting can support.
That means headline selection belongs inside the same verification process as the article itself.
Does the headline accurately describe the story?
Does it imply causation the article does not establish?
Does it turn analysis into fact?
Does it emphasize controversy at the expense of context?
Would the headline still be defensible if someone read only those words?
The best headline is not necessarily the most clickable one.
It is the strongest headline that remains faithful to the reporting.
AI Cannot Be Held Professionally Accountable
There is another reason responsibility remains human.
AI cannot be held professionally accountable for a published story.
The journalist and publisher can.
The AI system does not answer a reader who challenges a factual error.
It does not receive a correction request.
It does not explain a sourcing decision to an editor or audience.
It does not maintain a professional relationship with a source.
It does not carry the publication’s reputation.
The journalist and publisher do.
That makes accountability more than a philosophical principle.
It is practical.
When Vallano Media publishes an article, Vallano Media is responsible for that article.
A disclosure that AI assisted with research or drafting does not transfer that responsibility elsewhere.
If something is wrong, “the AI produced it” is not an adequate defense.
The publication still chose to publish it.
Transparency Matters Because Responsibility Matters
That is also why disclosure became part of Vallano Media’s AI-assisted reporting process.
Transparency is not about attaching a warning label to the journalism.
It is about accurately describing how the work was produced.
If AI assisted with research organization, outlining or drafting, readers should not be left with the impression that those tools played no role.
At the same time, disclosure should not exaggerate AI’s role either.
There is a difference between: AI assisted with the process
and: AI independently produced the journalism.
The first can describe a professional workflow.
The second would imply a very different level of automation and editorial control.
For Vallano Media, useful disclosure is intended to help readers understand that distinction.
The goal is not to make AI the center of the story.
The goal is to be honest about where it participated.
Human Review Is Not a Ceremonial Final Step
There is a danger in describing human oversight as though it happens only after AI has done everything else.
That model would look like: AI completes the work → human approves it.
That is not the workflow Vallano Media developed.
Human judgment appeared throughout the process.
The journalist determined which questions mattered.
The journalist selected and evaluated sources.
The journalist decided which AI suggestions deserved further investigation.
The journalist chose which arguments survived.
The journalist determined whether another round of reporting was necessary.
The journalist edited the draft.
The journalist verified the claims.
And the journalist made the publication decision.
Human oversight was not a final checkbox.
It was embedded throughout the workflow.
That distinction matters because meaningful oversight requires the ability to intervene early, not merely approve or reject a finished product at the end.
Judgment Includes Knowing When the Story Is Finished
Editorial judgment also determines when to stop.
That can be particularly difficult with AI.
A system can always generate another question.
Another angle.
Another rewrite.
Another possible connection.
Another suggested source.
Another headline.
The availability of more possibilities does not mean the reporting needs to continue indefinitely.
Eventually the journalist has to determine whether the story has reached the standard required for publication.
Is the central question sufficiently answered?
Have the important claims been verified?
Have relevant perspectives been represented?
Are the remaining gaps clearly acknowledged?
Would additional research materially change the story?
If the answer is no, the reporting may be ready.
AI can help identify unfinished work.
It cannot decide when professional judgment says the work is finished.
Editorial Responsibility Also Means Correcting the Record
Human accountability continues after publication.
If new information emerges, the journalist has to evaluate it.
If an error is discovered, the publication has to decide whether a correction or clarification is necessary.
If circumstances change, a follow-up may be warranted.
The responsibility does not end when the publish button is pressed.
AI can assist with organizing or comparing new developments against earlier reporting.
But the obligation to maintain the integrity of the published record still belongs to the publication.
That is part of trust.
Readers are not merely trusting that every article will be perfect.
They are trusting that the publication will take responsibility when something needs to be corrected.
Human Judgment Is Not Infallible
Keeping final responsibility with a human does not mean human judgment is automatically correct.
Journalists make mistakes.
Editors miss things.
Sources can be misunderstood.
Bias can influence decisions.
The value of human judgment is not perfection.
Its value is that a responsible professional can examine the evidence, explain the decision, reconsider it, correct it and be held accountable for the result.
That accountability creates a standard against which the work can be evaluated.
AI can assist the decision-making process.
It cannot replace the person or organization responsible for the consequences of that decision.
AI Can Make Judgment More Visible
One unexpected result of using AI in the workflow was that it made some editorial decisions easier to see.
Many traditional journalism decisions happen almost automatically for experienced professionals.
Is this source strong enough?
Does this wording overstate the evidence?
Is this attribution necessary?
Should this claim be included?
When AI produces an alternative, those decisions become explicit.
The journalist has to explain why one version works and another does not.
Why one source is acceptable and another is weak.
Why one interpretation is supported and another is speculative.
Why a more dramatic sentence should be rejected.
That can make the editorial process more deliberate.
AI does not create the judgment.
But interacting with it can force the journalist to articulate the judgment that was already necessary.
The Publisher Remains the Last Line of Defense
Every stage of the workflow can contain safeguards.
Strong sources.
Verification.
Timelines.
Red-team review.
Fact-checking.
Editing.
Disclosure.
But eventually there has to be a final line of responsibility.
For Vallano Media, that line remains the publisher.
The AI system does not get the final vote.
The journalist does.
That does not make AI unimportant.
It defines the relationship.
AI can advise.
The publisher decides.
That principle is not a limitation on AI-assisted journalism.
It is what allows AI assistance to exist inside a trustworthy editorial process.
The Goal Is Not Human Versus AI
The lesson from the FIFA reporting project was not that human judgment and artificial intelligence are competing for control of journalism.
That framing is too simple.
The more useful question is how the strengths of each can be used without confusing their roles.
AI can process information quickly.
It can help organize complexity.
It can surface patterns and questions.
It can generate options.
It can challenge assumptions.
Human professionals bring context, expertise, skepticism, ethical judgment and accountability.
The best workflow does not require pretending those capabilities are identical.
It depends on understanding that they are different.
The technology can expand what the journalist is able to do.
The journalist still determines what the journalism becomes.
What Comes Next
The FIFA project began as a reporting assignment.
It became a practical test of research, verification, AI assistance and human editorial judgment.
But the lessons do not end with journalism.
The same basic problem appears anywhere AI is used to support professional work: How do you gain the benefits of the technology without surrendering the judgment that gives the work its value?
That question matters in public relations.
It matters in research.
It matters in business.
It matters in communications.
It matters anywhere people are responsible for decisions made with AI-assisted information.
Next week, chapter 6 will examine what the workflow can teach professionals beyond journalism—and which principles remain useful even when the work changes.
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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