Measuring the Impact
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.
One of the easiest claims to make about artificial intelligence is that it saves time.
That may be true.
But I wanted something better than a feeling.
I wanted evidence.
Not perfect evidence.
Not a controlled experiment.
Not a scientific study.
Just enough documentation to answer a practical question:
“What actually changed after I integrated AI into my work?”
That meant looking at time.
Output.
Workflow.
And the way I was spending my attention.
The answer turned out to be more complicated than simply saying:
“AI made me faster.”
It did make some parts faster.
But the bigger change was that I started working differently.
Before AI, Time Was Concentrated in Production
Before I built an AI-assisted workflow, producing an article meant I handled nearly every stage manually.
Research.
Organization.
Drafting.
Editing.
Rewriting.
Headline development.
Formatting.
Publishing.
For an article of more than 800 words, I could easily spend three to four hours moving from the idea to publication.
That number is not a controlled benchmark.
I was not timing every stage in those years.
Different articles required different amounts of research.
Some were easier than others.
Some involved interviews.
Some involved more complicated subjects.
So I cannot take a four-hour article from the past, compare it to a forty-minute article today and declare that AI made me six times more productive.
That would be cleaner than the evidence allows.
But the comparison is still useful.
My process used to require significantly more manual effort across nearly every stage.
Today, much of that effort has been redistributed.
I Started Measuring the Workflow
As I became more serious about AI integration, I started paying attention to production time.
Not because speed was the goal.
Because I wanted to know what was changing.
One of the earliest useful benchmarks came from this book.
The first day’s chapter took about “43 minutes” from the first draft through the publication-ready stage, excluding research that had already been done.
Later in the challenge, another chapter moved through the editorial process, locked manuscript, main graphic, tags and teaser in roughly “10 minutes.”
Those numbers are interesting.
They are also easy to misuse.
The chapters were different.
The research requirements were different.
I was becoming more familiar with the workflow.
The AI systems were operating within a structure I had already built.
And some of the improvement may have come from repetition and experience, not AI alone.
That matters.
I was not running a controlled experiment where every variable remained the same.
So I do not look at those numbers as proof of a specific productivity percentage.
I look at them as evidence of something more practical:
“The workflow was becoming easier to execute.”
The Indiana Article Gave Me Another Benchmark
A separate article gave me another useful data point.
The drafting-to-final stage took about “36 minutes and 54 seconds.”
Scheduling and production added roughly another “31 minutes.”
That matters because it shows why “writing time” alone can be misleading.
A finished article is not just a draft.
There is still production.
Formatting.
Graphics.
Scheduling.
Final checks.
Publishing infrastructure.
If I only measured the drafting stage, I could tell myself a much more impressive story.
But it would not represent the full work.
That became one of the lessons of tracking time:
“Measure the process you actually perform, not the piece that makes the best statistic.”
Faster Drafting Was Only Part of the Change
AI definitely reduced friction in drafting.
I no longer had to stare at a blank screen for as long.
I could start with research notes, identify the structure, build an outline and move into the draft faster.
But that was not the most important change.
The most important change was what I could do with the time that opened up.
I could challenge the argument.
I could check the claims.
I could go back to the sources.
I could run a truth pass.
I could red-team the story.
I could inspect the graphic.
I could compare versions.
I could spend more time deciding whether something should survive rather than spending all my time trying to produce the first version.
That is a different kind of productivity.
Output Increased Because Friction Decreased
Another change was output.
Once the workflow became repeatable, I could produce more without feeling as if every article had to begin from zero.
Research had a process.
Drafting had a process.
Verification had a process.
Editing had a process.
Graphics had a process.
Publishing had a process.
The result was not simply that AI could generate text faster.
The result was that I was building a publishing system.
That distinction matters.
A faster writer can produce more words.
A better system can produce more finished work.
Those are not the same thing.
More Output Does Not Automatically Mean Better Work
There is a danger here.
Once production becomes easier, it is tempting to produce simply because you can.
Another article.
Another post.
Another graphic.
Another idea.
AI can make quantity feel almost effortless.
But volume is not the same as value.
If the additional output is poorly researched, repetitive or unnecessary, then more productivity may just mean more noise.
And more capacity is only useful if I choose what deserves that capacity.
That is why I do not want to measure success by article count alone.
The better question is whether the additional capacity allows me to produce work that is:
more consistent,
better verified,
more useful,
more complete,
or more sustainable.
If the answer is no, then the increased output is not much of an achievement.
AI Changed My Role
There was another change that was harder to measure with a stopwatch.
My role changed.
Earlier, I was doing nearly every task myself.
Now I increasingly direct the tasks.
I decide what needs research.
I decide what needs verification.
I decide when AI should challenge the work.
I decide what version survives.
I decide when the manuscript is locked.
I decide when the graphic is ready.
I decide what gets published.
That does not mean I stopped working.
It means the work moved upward.
More of my effort went into coordination, judgment and approval.
I became less focused on executing every individual task manually and more focused on managing the system that produced the work.
That is one of the biggest changes AI created for me.
And it is difficult to express with a simple productivity number.
The Work Became More Repeatable
Before the workflow existed, every article depended more heavily on memory.
Did I check that number?
Did I verify that quote?
Did I review the headline?
Did I challenge the argument?
The more tasks I handled manually, the easier it was for the process to vary from article to article.
Now the workflow gives me checkpoints.
That does not guarantee quality.
But it creates consistency.
I know what happens after the draft.
I know what happens after verification.
I know when the red-team pass begins.
I know when the manuscript becomes locked.
That repeatability matters because publishing regularly requires more than creativity.
It requires a system that can work again tomorrow.
Some Work Actually Increased
AI did not make every part of journalism faster.
In some areas, it created additional work.
AI output has to be checked.
Sources have to be verified.
Rewritten sentences have to be compared against the evidence.
Graphics have to be inspected.
Generated suggestions have to be evaluated.
A polished AI answer can deserve extra scrutiny because fluency can make a weak or false claim look settled.
So there are moments when AI saves time in one part of the process and adds work somewhere else.
Sometimes AI does not remove time from the process.
It moves the time somewhere else.
That is not necessarily a problem.
The important question is whether those additional minutes improve the final work.
If they do, then the effort may be valuable.
The Fast Workflow Was Not Built Overnight
There is another part of the productivity story that is easy to miss.
The workflow I use today did not appear instantly.
It sits on top of months of learning.
Experimentation.
Failed prompts.
Revisions.
New workflows.
Abandoned ideas.
Understanding what AI does well.
And understanding where I do not trust it.
The current efficiency exists partly because of that earlier investment.
Someone starting from zero should not expect to reproduce my current workflow speed immediately.
The faster process came after building the system.
That setup time counts too.
Failed Attempts Count
There is another temptation when measuring productivity:
Remember the successes.
Forget the failures.
A graphic that works on the first generation is easy to count.
The five bad versions before the final one are easy to forget.
A clean draft looks efficient.
The abandoned structure that came before it can disappear from the measurement.
If I want an honest record, I have to include rework.
Failed experiments.
Bad outputs.
Wrong directions.
The things that did not survive.
A fair measurement system cannot count only the examples that went smoothly.
The failures are part of the cost of the workflow too.
Efficiency Is Not the Same as Cutting Corners
This distinction has become increasingly important to me.
Efficiency means removing unnecessary friction.
Cutting corners means removing necessary safeguards.
Those two things can look similar from the outside.
Both may reduce time.
But they produce very different results.
If AI helps me organize notes faster, that is efficiency.
If I stop reading the source because AI summarized it, that is cutting a corner.
If AI helps identify weak claims before publication, that is efficiency.
If I accept its factual corrections without checking them, that is cutting a corner.
The goal is not to make every stage shorter.
The goal is to make the whole process better.
Time Saved Needs Somewhere to Go
This may be the most important measurement question of all.
What happens to the time AI saves?
If I save thirty minutes and immediately fill those thirty minutes with more low-value work, I have increased activity.
Not necessarily productivity.
If I use the time to verify a source, improve the argument, learn a new skill, work on another important project or simply reduce unnecessary workload, then the savings become meaningful.
AI does not decide where the time goes.
I do.
That makes time savings a management question as much as a technology question.
The Numbers Need Context
I have started collecting timing benchmarks because I want a record.
But the numbers need context.
A ten-minute workflow does not mean every chapter should take ten minutes.
A forty-minute article is not automatically better than a three-hour article.
A fast result may reflect:
better preparation,
a familiar subject,
stronger systems,
less research,
greater experience,
or a simpler assignment.
A slower result may reflect deeper reporting or more complicated evidence.
And the examples I track are not automatically representative of every kind of journalism I produce.
An interview-heavy story may require a completely different amount of time.
So might an unfamiliar subject.
A breaking story.
Or a piece that requires deeper original reporting.
That is why I do not want to turn speed into the standard.
The numbers are signals.
They help me see how the system is changing.
They do not tell the whole story.
Measuring Speed Can Change the Work
There is another complication.
The moment I start measuring speed, I also have to watch whether the measurement itself is changing how I work.
If I know the clock is running, do I rush?
Do I become reluctant to go back to research because it will hurt the number?
Do I treat a ten-minute workflow as something I need to beat?
That would defeat the purpose.
The measurement is supposed to help me understand the workflow.
The workflow should not start serving the measurement.
If the story needs more time, it gets more time.
Quality Is Harder to Measure
Time is easy to measure.
Quality is not.
I can tell you that a process took forty minutes.
It is much harder to reduce the quality of the finished journalism to a single number.
Did I use stronger sources?
Did I catch an error before publication?
Did important context survive the editing process?
Did the headline accurately represent the reporting?
Did the workflow make my work more consistent?
Did I reduce avoidable corrections?
Those questions may ultimately matter more than the stopwatch.
That means any serious measurement of AI-assisted journalism needs more than productivity statistics.
It needs evidence about the quality of the work too.
The Better Measurement May Be Capacity
The more I have worked this way, the more I think the real benefit may be capacity.
How much high-quality work can I responsibly manage?
How many different projects can I keep moving?
How quickly can I move from research to publication without losing oversight?
How much attention remains available for the decisions that actually require me?
That is different from measuring words per hour.
Capacity includes time.
But it also includes cognitive load.
Consistency.
Repeatability.
Quality control.
And the ability to keep operating without every project becoming exhausting.
Capacity may be the most important benefit I see right now.
Future measurements may show that consistency, quality control or reduced cognitive load matter even more.
That is why I want to keep measuring.
Measuring the Wrong Thing Can Create the Wrong Incentive
Metrics influence behavior.
If I only measure production time, I will naturally try to make everything faster.
If I only measure output, I will try to publish more.
If I only measure traffic, I may start choosing stories based on clicks.
That is why measurement needs to reflect the kind of journalism I actually want to produce.
Some useful questions might be:
Did I verify the central claims?
Did the story survive the red-team pass?
Did I catch problems before publication?
Did the workflow reduce unnecessary repetition?
Did the finished work meet the standard?
Did the process become easier to repeat?
Those questions are less exciting than saying AI made me five times faster.
They are also more useful.
Efficiency Has a Cost Side
AI-assisted work is not free simply because it saves time.
There are tools.
Subscriptions.
Training.
Experimentation.
Failed generations.
And the time required to build and manage the system.
Those costs matter too.
A productivity measurement that counts only minutes saved and ignores what it took to create those savings is incomplete.
The goal is not to prove that the technology has no cost.
The goal is to understand whether the total system gives me enough value to justify it.
I Am Still Building the Dataset
This is not the final measurement.
I am still early in the process.
The timing records from this writing challenge are part of a larger dataset I am building around my own work.
More articles will give me better comparisons.
More mistakes will show which safeguards need improvement.
More repetition will show whether the gains are durable or just the result of novelty.
And a better record will help me distinguish what came from AI from what came from experience, repetition and improved systems.
That is how I want to approach AI productivity.
Not with one impressive example.
With a record.
What Changed?
So what changed after integrating AI?
I became faster in parts of the workflow.
I increased the amount of work I could manage.
The process became more structured.
The work became more repeatable.
More of my attention shifted from producing the first version to reviewing, challenging and directing the work.
Some tasks became easier.
Some verification work increased.
Some time disappeared.
Some time simply moved.
And my role changed from doing every task manually to managing a system of tasks.
That is more difficult to fit into a marketing slogan.
But it is closer to what actually happened.
AI did not simply make me faster.
It changed how I work.
And I am still measuring exactly what that change means.
Because the real question is not:
“How much faster can I publish?”
It is:
“What can I do better with the time, attention and capacity AI gives back?”
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
Sam Houston Holds Off Pepperdine 85–77 as Free Throws Prove Decisive
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 had built a 15-point second-half lead against Pepperdine, but with just over three minutes remaining Wednesday night, the Bearkats found themselves facing a much more difficult finish than the scoreboard had suggested earlier.
Pepperdine had reduced the deficit to 67–63 with 3:30 remaining.
Moments later, a layup brought the Waves within two points.
What followed was not an offensive explosion from the field.
Instead, Sam Houston relied on free-throw shooting to preserve an 85–77 victory at Bernard G. Johnson Coliseum.
The Bearkats finished 34 of 38 from the free-throw line, an 89.5% performance that helped overcome inconsistent shooting from the field.
Jacobe Coleman and Aidan McDowell led Sam Houston with 20 points apiece.
Yoda Oke added 14 points and eight rebounds, while Justin Begg contributed 13 points and five rebounds.
The victory demonstrated both Sam Houston’s ability to respond under pressure and the areas that will require improvement as the season progresses.
A First-Half Rally Changes the Momentum
Pepperdine appeared to be gaining control late in the opening half, forcing Sam Houston to respond before halftime.
The Bearkats answered with a decisive offensive stretch.
McDowell connected from three-point range, while Oke contributed inside as Sam Houston worked its way back into the contest.
The Bearkats continued building momentum during the closing minutes.
McDowell delivered the final basket, hitting a three-pointer at the buzzer to send Sam Houston into halftime with a 41–35 lead.
The sequence transformed the complexion of the game.
Instead of entering the locker room trailing, Sam Houston had established a six-point advantage and carried that momentum into the second half.
A Comfortable Lead Becomes a Battle
Sam Houston extended its advantage after halftime, eventually moving ahead 51–36.
The 15-point margin suggested the Bearkats might be approaching a comfortable victory.
Pepperdine had other ideas.
The Waves responded with a 7–0 run, forcing Sam Houston to regain its offensive rhythm.
Coleman helped stop the momentum with a turnaround jumper, while Zion Pipkin added free throws to restore a double-digit advantage.
One of the game’s more memorable sequences came when Begg recorded a steal that eventually led to an alley-oop dunk by Enel St. Bernard, assisted by Pipkin.
The play provided an energetic moment for the Bearkats, but it did not end Pepperdine’s resistance.
The Waves continued attacking, gradually reducing the deficit.
With 3:58 remaining, Pepperdine connected on a three-pointer to cut Sam Houston’s lead to 65–63.
Pipkin answered with a basket to make it 67–63 with 3:30 remaining.
But Pepperdine responded again.
Alex Constanza converted a layup with 3:14 left, reducing the deficit to 67–65.
A game that had appeared under control had become a one-possession contest, leaving Sam Houston little room for mistakes.
The Bearkats’ response would determine the outcome.
Free Throws Become the Difference
Sam Houston did not need a series of three-pointers or spectacular field goals to regain control.
It needed to make its free throws.
Oke converted three consecutive attempts to extend the Bearkats’ advantage to 70–65.
Coleman later made three free throws after being fouled on a three-point attempt, pushing the lead to 73–65 with 2:06 remaining.
Additional free throws from St. Bernard and McDowell helped restore a double-digit margin.
After Pepperdine pulled within two, Sam Houston scored its next eight points from the free-throw line, rebuilding a 75–65 advantage.
The Waves continued scoring during the final minute, but the Bearkats consistently answered at the line.
Coleman, McDowell and Begg converted additional free throws in the closing seconds to secure the eight-point victory.
The final statistics revealed just how important those opportunities had been.
Sam Houston made 23 field goals and connected on only five of 20 three-point attempts, a 25% performance from beyond the arc.
Its second-half shooting was particularly revealing.
The Bearkats made just eight field goals after halftime and missed all five of their three-point attempts.
Yet Sam Houston still scored 44 second-half points.
Of those 44 points, 28 came from the free-throw line.
The team finished 28 of 32 on second-half free throws, converting 87.5% of its attempts.
That production became the defining statistical difference.
Pepperdine made 10 three-pointers compared with Sam Houston’s five, but the Waves converted only 15 of 27 free throws.
Sam Houston’s ability to capitalize on its opportunities at the line helped offset Pepperdine’s advantage from beyond the arc.
Contributions Beyond the Leading Scorers
Although Coleman and McDowell led the Bearkats offensively, Sam Houston received contributions from several players.
Oke’s 14 points and eight rebounds provided an important presence, particularly during the second half.
Begg added 13 points, five rebounds and two assists, contributing to the team’s offensive production despite struggling with his field-goal shooting.
St. Bernard finished with seven points and nine rebounds, while Pipkin contributed seven points.
Riccardo Greppi added four points and six rebounds.
Those contributions were significant during a game in which Sam Houston needed scoring opportunities beyond its perimeter shooting.
The Bearkats’ ability to draw fouls and convert their opportunities became particularly valuable as Pepperdine reduced the lead.
The performance also illustrated the importance of finding different ways to score when an offense struggles to establish a consistent rhythm.
A Victory With Lessons Before Illinois
Sam Houston’s victory over Pepperdine demonstrated that the Bearkats could respond when a substantial lead began slipping away.
The first-half rally showed their ability to change momentum quickly.
Their ability to draw fouls and convert free throws allowed them to withstand Pepperdine’s late challenge.
But the victory also exposed weaknesses.
The Bearkats struggled from three-point range, failed to make a second-half three-pointer and allowed a 15-point advantage to shrink to two.
Those are areas that could become more consequential against stronger opposition.
Sam Houston’s next challenge comes Friday against No. 3 Illinois at State Farm Center in Champaign.
The Pepperdine game provided an early example of how the Bearkats can find ways to win without consistently shooting well.
Against Illinois, maintaining offensive rhythm and limiting extended scoring droughts could be considerably more important.
For now, Sam Houston can take satisfaction in an 85–77 victory that required composure in the closing minutes.
From Draft to Publish — The Full 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.
A draft can look finished long before it is ready to publish.
That is one of the biggest lessons I have learned from working with AI.
The writing may be clean.
The structure may work.
The grammar may be correct.
The headline may sound strong.
The article may even feel complete.
But publication requires more than a good draft.
It requires a process.
A draft has to survive verification.
Challenge.
Editing.
Quality control.
And one final decision:
Is this actually ready to go out into the world?
That is what the full workflow is designed to help me answer.
But there is an important distinction.
“Completing the workflow is not the same as completing it well.”
A checklist cannot guarantee good journalism.
The value comes from the judgment exercised inside every stage.
The Draft Is a Starting Point
By the time I reach the first complete draft, a lot of work has already happened.
The research has been gathered.
The evidence has been organized.
The story has taken shape.
The first version exists.
That feels like progress.
It is.
But the draft is not the finish line.
It is the point where the next stage begins.
Now the question changes from:
“Can I write this?”
to:
“Can I defend this?”
That is where the editorial workflow becomes essential.
Step One: Check the Claims
Before I think about style, I look at the substance.
What factual claims am I making?
Which ones are central?
Which ones are current?
Which ones are sensitive?
Which ones depend on a source I need to revisit?
Which ones sound more certain in the draft than they did in the research?
The goal is not to verify only the obvious numbers and dates.
It is to inspect the statements the reader is being asked to believe.
That includes interpretation.
Causation.
Context.
Attribution.
Comparison.
The stronger the claim, the stronger the evidence needs to be.
Step Two: Go Back to the Sources
The next step is source verification.
I open the documents again.
I check the original quote.
I confirm the number.
I reread the section of the report.
I look at the official record.
I make sure the source actually supports the sentence I wrote.
This can feel repetitive.
That is part of the point.
The draft is a transformation of the research.
Something can change during that transformation.
A qualification can disappear.
A number can lose context.
A sentence can become stronger.
A quote can be shortened in a way that changes its meaning.
Going back to the source reconnects the prose to the evidence.
Step Three: Run the Truth Pass
The truth pass asks a broader question:
What in this draft might be wrong, misleading, incomplete or overstated?
I look at the article differently during this stage.
Not as the writer.
As the skeptic.
What would I challenge if someone else wrote this?
Which sentence makes me hesitate?
Which claim depends on an assumption?
Which section needs another source?
Which statement might be technically accurate but missing important context?
Sometimes the answer is:
Nothing significant.
Sometimes the truth pass sends me back into research.
That is not a failure.
That is the workflow working.
Step Four: Red-Team the Story
Once the factual foundation is solid, I try to break the article.
This is where the red-team stage comes in.
I ask:
What is the strongest challenge to the central argument?
What evidence could weaken the conclusion?
What might a knowledgeable critic question?
What context is missing?
What is the weakest part of the story?
Does the headline promise more than the article proves?
AI can help generate those challenges.
But I do not accept them automatically.
Some critiques are useful.
Some are irrelevant.
Some are simply wrong.
The goal is not to obey the red team.
The goal is to expose the story to resistance.
Step Five: Decide What Survives
The red-team stage creates another decision point.
What actually needs to change?
A weak claim may need another source.
A sentence may need softer wording.
A section may need more context.
A dramatic headline may need to come down.
A paragraph may need to disappear completely.
And sometimes the story survives the challenge exactly as written.
That matters too.
The goal is not endless revision.
It is identifying material weaknesses.
Then fixing the ones that matter.
Step Six: Edit for Voice
Once the reporting survives the challenge, I focus on the writing.
Does it sound like me?
Does the rhythm work?
Is the article direct?
Is it repetitive?
Did AI make the language too formal?
Too generic?
Too cautious?
Too dramatic?
Are the strongest lines still there?
Has anything become polished at the expense of specificity?
This is where I try to make the article sound finished without making it sound manufactured.
The writing should become clearer.
Sharper.
More readable.
But still mine.
Step Seven: Read for Meaning, Not Just Grammar
Grammar matters.
So does punctuation.
But one of the most dangerous editing mistakes is concentrating so much on the sentence that I stop checking what the sentence actually means.
A small wording change can alter:
causation,
certainty,
timing,
attribution,
or scale.
“Led” is not the same as “won.”
“Reported” is not the same as “confirmed.”
“After” is not the same as “because of.”
A cleaner sentence is not automatically a more accurate sentence.
So after the voice pass, I read again for meaning.
Step Eight: Editorial Quality Control
Then comes the final QC pass.
This is where I stop developing the story and inspect the complete product.
Names.
Dates.
Numbers.
Quotes.
Attribution.
Headline.
Graphics.
Disclosure language.
Formatting.
Anything that might create a problem after publication.
This is also where production begins to overlap with editorial work.
The article itself can be correct while something around it is wrong.
A graphic may contain an error.
A caption may overstate the story.
A headline may be stronger than the evidence.
A link may go to the wrong source.
The entire publication package can affect how the journalism is understood.
Step Nine: Inspect the Graphic
AI-assisted graphics deserve their own check.
The visual may look excellent.
That does not mean it is finished.
I look for:
incorrect text,
strange anatomy,
misleading symbols,
factual mistakes,
unlicensed or inappropriate third-party visual elements,
and anything that changes the meaning of the story.
But visual quality control is bigger than catching an extra hand or a misspelled word.
A visual can be technically clean and still imply something the reporting does not support.
The same principle applies to the graphic as the article:
Do not approve it just because it looks polished.
Look at what it actually communicates.
Step Ten: Lock the Manuscript
Eventually, the editing has to end.
That is when I lock the manuscript.
Locked means the article has completed the required editorial stages.
The reporting has been checked.
The claims have been tested.
The writing has been edited.
The QC pass is complete.
It is ready for production.
Locked does not mean perfect.
It does not mean the article can never change.
New evidence may emerge.
A late source response may change the story.
An error may become visible only after publication.
If that happens, I should correct or update the work.
A strong workflow reduces risk.
It does not eliminate the possibility of correction.
Locked simply means I have reached the standard required to publish responsibly with the information available to me.
Step Eleven: Build the Publication Package
The article is only one part of what the audience sees.
The full publication package may include:
the headline,
main graphic,
tags,
disclosure,
social copy,
preview text,
links,
and sometimes a teaser for what comes next.
Each element needs review.
If the article is careful but the social post exaggerates it, the publication can still mislead.
If the headline is accurate but the graphic creates a false impression, there is still a problem.
And every derivative asset should be checked against the verified story itself.
Not against another derivative asset.
A caption based on a social post based on a summary of the article creates opportunities for meaning to drift farther from the original reporting.
The evidence remains the reference point.
Version Control Is Editorial Control
AI-assisted workflows can produce a lot of versions.
Several drafts.
Several headlines.
Several graphics.
Several social posts.
Several rewritten paragraphs.
That creates another risk.
Publishing the wrong one.
The strongest version is useless if an outdated draft reaches the website.
The corrected graphic does not help if the earlier version is uploaded.
The accurate headline does not matter if an abandoned option gets scheduled.
“Version control is part of editorial control.”
I need to know which manuscript is locked.
Which graphic is final.
Which headline is approved.
Which copy is actually being published.
That may sound administrative.
It is also editorial.
Step Twelve: Final Human Approval
At the end of the workflow, one decision still belongs to me.
Publish?
That sounds simple.
But it carries everything that came before it.
Do I trust the sourcing?
Do I understand the evidence?
Did I challenge the story?
Did I review the AI assistance?
Does the writing sound right?
Does the headline fit the reporting?
Does the graphic work?
Is there anything I would hesitate to defend after publication?
Final approval matters only if I am genuinely willing to stop the process.
If “Publish” is inevitable no matter what I find, then the final review is ceremonial.
Meaningful human oversight requires the authority to say:
Not yet.
If something is wrong, I go back.
If the work is ready, I publish.
That final approval matters because of the decisions that led to it.
The Workflow Can Move Backward
The full process is not perfectly linear.
A red-team pass can send me back to research.
Editing can reveal a factual question.
QC can uncover a problem in the headline.
A graphic can expose that the concept itself is unclear.
New evidence or a late source response can change the story near the end of the process.
The arrows sometimes move backward.
That is normal.
A workflow should not trap the work in a rigid sequence.
It should give the work checkpoints.
Places where I know what question I am supposed to ask next.
Deadlines Change the Pace, Not the Responsibility
Real journalism does not always happen under ideal conditions.
Breaking news moves quickly.
Live sports move quickly.
Deadlines compress the available time.
Staffing may be thin.
The workflow has to function in that environment too.
That may mean some stages happen quickly.
Several stages may overlap.
The level of review may depend on the risk of the story.
But deadlines should not erase the steps that protect accuracy.
Speed can change how I execute the workflow.
It should not change what I am responsible for.
The Workflow Is Not the Journalism
This is important.
The workflow is not the thing that makes the journalism good.
The journalism still depends on:
good reporting,
strong evidence,
sound judgment,
fairness,
context,
clarity,
and accountability.
A checklist cannot replace those things.
A workflow simply creates a structure around them.
It makes it harder to accidentally skip an important step.
That is useful.
But process is not proof.
A completed checklist does not guarantee a correct article.
Completing the workflow is not the same as completing it well.
The thinking still matters.
The Workflow Becomes Faster With Practice
There is another benefit.
The more often I use the workflow, the faster it becomes.
I know what to look for during the truth pass.
I know what kinds of questions work during red-teaming.
I know where graphics often fail.
I know which parts deserve more scrutiny.
That familiarity reduces friction.
But it should not reduce skepticism.
The process can become faster.
The judgment should not become automatic.
The More Automation, the More Important the Checkpoints
As AI-assisted workflows become more capable, some steps may become more automated.
That can be useful.
But automation creates another question:
Where does human judgment still have real authority?
If the system automatically summarizes the research, drafts the article, proposes the headline, generates the graphic and prepares the social copy, the workflow may look efficient.
It also creates more places where an early error can travel downstream.
The more automated the workflow becomes, the more important it is to know where a human can inspect, challenge and stop it.
Automation should not make responsibility harder to locate.
Speed Is Not the Victory
One of the easiest things to measure is time.
How fast did the draft happen?
How quickly did the article get published?
How many minutes did the workflow take?
Those numbers can be useful.
But speed is not the victory.
The real question is:
Did the workflow help produce something I can stand behind?
Sometimes that takes ten minutes.
Sometimes forty minutes.
Sometimes several hours.
Sometimes the correct answer is:
Not ready yet.
That is still a successful outcome.
AI Changes Where the Work Can Happen
AI does not remove the work.
It changes where some of the work can happen.
Less time may go into producing the first draft.
AI can assist with the more repeatable and support-oriented parts of the process.
That creates the possibility of reallocating more attention toward:
verification,
challenge,
comparison,
editing,
and judgment.
But that redistribution is not automatic.
Saving twenty minutes does not guarantee I will spend those twenty minutes verifying a source.
I still have to make that choice.
AI creates the opportunity.
The journalist decides how to use it.
The Full Workflow Is Really a Series of Decisions
Research.
Draft.
Truth pass.
Verification.
Red-team.
Voice edit.
Quality control.
Production.
Publish.
Those stages look like tasks.
They are really decisions.
What is true?
What matters?
What needs evidence?
What deserves doubt?
What should stay?
What should go?
What sounds right?
What is ready?
AI can help at almost every stage.
But it cannot take ownership of those decisions.
That remains human.
Before I Hit Publish
The final button is the easiest part.
Everything before it is the journalism.
The research.
The verification.
The challenge.
The editing.
The judgment.
The refusal to publish something just because it looks complete.
That is what the workflow protects.
Not perfection.
Responsibility.
So my final question is not simply:
“Does this look finished?”
It is:
“Have I done enough to stand behind it?”
And one more question follows:
“If this is challenged after publication, can I explain how I know what I published?”
When the answer to both is yes, then I hit publish.
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
Sam Houston hosts conference-unbeaten WKU in regular-season finale
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.
Brenda Gray and Travis Hudson have each reached 800 career wins at the programs they still lead.
Their teams meet in Huntsville this week with different stakes.
Western Kentucky can finish unbeaten in Conference USA play, while Sam Houston has two home matches left to improve its tournament seed.
The Bearkats host the Hilltoppers at Bernard G. Johnson Coliseum in the final week of Conference USA regular-season play.
First serve is set for 6 p.m. Thursday, Nov. 12, and 1 p.m. Friday, Nov. 13, both Central time.
The conference tournament follows Nov. 20–22 in Bowling Green, Kentucky.
Hudson earned his 800th win when WKU swept Middle Tennessee in November 2025.
Gray reached the milestone on Oct. 16 with Sam Houston’s five-set win over Delaware.
Both coaches are alumni of the universities they lead.
Gray, a 1982 Sam Houston graduate, has coached the Bearkats since 1984.
Hudson graduated from Western Kentucky in 1994 and took over the Hilltoppers in 1995 at age 24.
WKU (20–6, 14–0 CUSA) has won 15 straight matches.
Its Nov. 7 sweep of Liberty secured an eighth consecutive CUSA regular-season title and the No. 1 tournament seed.
The win also gave the Hilltoppers their 27th straight season with at least 20 wins, the longest active streak of its kind in NCAA Division I women’s volleyball, according to WKU Athletics.
Sam Houston (17–10, 8–6 CUSA) returns to Huntsville, where it’s 8–2 this season, after splitting two matches at Middle Tennessee.
The Bearkats won the opener by taking the final five points of the deciding set, then lost the rematch in four sets.
One matchup to watch is Chloe Monson against WKU’s block.
Monson led Sam Houston with 17 kills in the win over Middle Tennessee, while Izzy Van De Wiele recorded seven blocks in WKU’s sweep of Liberty.
If the Bearkats can give Monson clean swings against that front line, they’ll have a better chance of handing the Hilltoppers their first conference loss.
Own Your Niche: Why AI Makes Expertise More Valuable, Not Less
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 now write an article, build a website, analyze a spreadsheet, summarize an interview, create an image, research a market and generate a business strategy in minutes.
That creates an obvious temptation.
If AI can perform so many professional tasks, perhaps the winning strategy is to become an AI expert.
The examples emerging across communications, marketing, consulting, journalism and other professional fields point toward something more complicated.
For many experienced professionals, AI may make existing expertise more valuable rather than less.
AI can accelerate execution.
It can organize information, surface patterns, generate possibilities and remove hours of repetitive work.
But it does not automatically know which problem matters.
It does not inherently understand what an experienced professional notices after years of doing the work.
And it can produce a beautifully written answer that is incomplete, inappropriate or simply wrong.
That changes the opportunity.
The professionals best positioned for the AI era may not be those who abandon their expertise to become generic AI specialists.
They may be the ones who “own their niche and add AI to it.”
From Experimentation to Integration
The 2026 AI Marketing Industry Report from Social Media Examiner offers one glimpse of how quickly that transition is happening.
In a July survey of 681 people recruited primarily from the Social Media Examiner community, 73% said they used AI for work every day, compared with 37% in 2024.
Eighty-four percent said their use had increased during the previous year, while 78% expected it to increase again during the next 12 months.
But the more interesting story is what happens after adoption.
Eighty-six percent agreed that AI saves time, and the same percentage said it increases productivity.
Yet only 30% reported significant, measurable results.
Thirty-nine percent said they were seeing results that were difficult to measure.
At the same time, 78% expressed concern about the accuracy and reliability of AI-generated content, while 58% worried that dependence on AI could reduce human skills.
Only 11% said autonomous AI agents were already part of their regular workflow, although another 24% were experimenting with them and 44% planned to adopt them.
The survey has important limitations.
Sixty percent of respondents worked in organizations with 10 or fewer people, 31% worked in marketing, public relations or advertising, and 63% were based in the United States.
Social Media Examiner also notes that some year-to-year comparisons are directional because question wording and samples changed.
The findings should not be treated as representative of every profession or business.
But within that audience, the direction is difficult to miss.
The conversation is shifting from:
“Should I use AI?”
toward:
“How do I integrate AI into real work without losing the judgment that makes the work valuable?”
Start With the Profession, Not the AI
Marketing strategist Jim MacLeod illustrated the distinction in a LinkedIn post about building a website with Claude and Claude Code.
He had previously described how easy AI made the process.
Then he added two important words:
“For me.”
MacLeod explained that he had built more than 60 websites over roughly 25 years.
AI made building another one easier because he already understood what a functioning website required.
A beginner using the same technology might be able to create something that looks finished without knowing what had been forgotten.
That distinction matters well beyond web development.
An experienced journalist understands that a polished paragraph is not necessarily an accurate paragraph.
A public relations professional knows that sending more pitches does not necessarily create stronger media relationships.
A translator recognizes linguistic nuance that may not be obvious from grammatically correct output.
A marketer knows that a strategy must connect to actual customers, positioning and business objectives.
A sports writer working against a late-night deadline understands that a game story is not simply a collection of statistics.
Someone still has to recognize what happened, what mattered and what deserves to lead the article.
AI can accelerate pieces of each workflow.
It does not automatically supply the accumulated judgment behind them.
That leads to one of the simplest principles for professionals considering AI:
“Start with the profession, not the AI.”
Understand the work first.
Then determine which parts technology can make faster, easier or more effective.
But expertise does not have to come before every use of AI.
The technology can help beginners learn unfamiliar subjects, prototype ideas and enter fields that previously presented substantial technical barriers.
Reduced barriers, however, do not eliminate unknown unknowns.
Someone working outside their established expertise may need more testing, outside review and willingness to consult people who understand the field deeply.
Expertise Can Become a System
AI also gives experienced professionals another opportunity: turning knowledge accumulated over years into repeatable systems.
Houston communications strategist Kami Watson Huyse has increasingly framed her AI work around that idea.
Through Zoetica and her professional training, Huyse has described using AI to capture her thinking and build practical workflows and tools while maintaining guardrails around disclosure and professional judgment.
That is significantly different from asking AI to manufacture expertise.
The professional already possesses the expertise.
AI helps organize parts of that thinking, apply it consistently and make it easier to repeat.
Huyse’s approach also reflects a philosophy she has emphasized throughout her work with AI: technology should support human-centered communication rather than erase the human element from it.
Houston communications firm Elmore has expressed a similar position.
Its public materials have described AI as useful for productivity and tactical work while continuing to emphasize human expertise, nuance, context, relationships and strategic thinking.
The pattern is important.
The more mature form of adoption is not:
“expertise out, AI in.”
It is:
“expertise first, AI layered on top.”
Good AI Adoption Offloads Friction — Not Understanding
Podcast entrepreneur Hala Taha provided another useful warning in a LinkedIn post about preparing for an interview.
Taha had been podcasting for eight years when the AI tools she routinely used for interview research stopped working.
She found herself panicking over how she would prepare.
Then she remembered something obvious: she had conducted interviews for years before generative AI existed.
Taha wrote that AI had made her research faster but that she was retaining less of the material.
Her response was not to abandon AI.
She redesigned the workflow.
AI could still help with research, but she would prepare the final brief herself.
She would personally consume the most important material, take notes and make sure she actually absorbed the research rather than merely skimming an AI-generated summary.
That leads to an important distinction:
There is nothing inherently valuable about spending three hours on work that technology can help complete in 30 minutes.
But not every time-consuming part of professional work is useless friction.
Some of it creates expertise.
Reading source material builds context.
Writing questions forces someone to decide what they actually need to know.
Discussing a problem with colleagues exposes assumptions.
Making mistakes creates pattern recognition.
Working through difficult decisions builds judgment.
Professionals therefore face a more complicated challenge than simply becoming faster.
They have to determine which parts of the old process were inefficient — and which parts were quietly teaching them how to do the job.
The Jagged Frontier
Research conducted with Boston Consulting Group consultants illustrates why that distinction matters.
In a pre-registered experiment involving 758 knowledge workers, researchers examined performance across realistic management-consulting tasks using GPT-4.
For a set of tasks judged to be within the model’s capabilities, participants using AI completed more tasks, worked faster and produced higher-quality solutions.
But on a complex managerial task selected to fall outside the model’s capability frontier, participants using AI were 19% less likely to produce a correct solution than participants without AI.
The researchers described this uneven boundary as a “jagged technological frontier.”
AI can be extraordinarily capable in one area and unexpectedly weak in another — even when two tasks appear similarly difficult to the human doing the work.
The problem is that the boundary is not always obvious beforehand.
The study does not establish that AI will reduce performance whenever professionals move beyond their own expertise or whenever a model encounters a difficult problem.
What it does demonstrate is how difficult it can be for users to recognize where an AI system’s capabilities end.
That means one of the most valuable professional skills in the AI era may be recognizing when technology has crossed from useful assistance into territory where its confidence exceeds its competence.
That is not primarily a prompting skill.
It is judgment.
When AI Is Allowed to Overrule Expertise
The danger becomes especially clear when organizations reverse the relationship between expertise and technology.
In one LinkedIn post, an independent translator with roughly 25 years of experience described receiving one of her translations back from a client agency after the work had been “corrected” by AI.
According to her account, only a handful of changes had been suggested.
They were wrong.
This is one practitioner’s experience, not evidence of an industry-wide pattern.
But the workflow problem it illustrates is worth considering.
The process effectively became:
“expert produces work → AI reviews expert → AI suggestions are treated as corrections → expert must defend the original”
A more responsible process would look like this:
“expert produces work → AI flags possible issues → professional evaluates the flags → only verified corrections survive”
AI can provide a second opinion.
It should not automatically become the higher authority simply because its response sounds confident.
But professional experience should not be treated as infallible either.
Experts can carry outdated assumptions, habits and blind spots.
AI can sometimes surface an alternative the experienced practitioner overlooked.
The strongest relationship is therefore not one in which the human automatically overrides AI — or one in which AI automatically overrides the professional.
It is a process in which “both can be questioned and evidence decides whenever possible.”
Master the Craft, Not Just the Tool
By late 2026, this idea had moved beyond individual practitioners and into professional development.
The agenda for PRSA Houston’s October 28 PR Day listed 15 breakout sessions across three tracks.
Its Emerging Trends and Innovation track included sessions on AI-powered communications, AI search, generative-engine optimization, machine-mediated discoverability and human influence in an AI-saturated workplace.
One session title summarized the larger issue particularly well:
“Master the Craft, Not Just the Tool.”
Other tracks continued to emphasize strategic leadership, stakeholder engagement, ethical decision-making and core communications skills.
The agenda itself is the relevant evidence.
It shows that an established professional organization was incorporating AI into mainstream professional development while continuing to emphasize the underlying craft.
It was not telling communicators to abandon public relations and become technologists.
It was treating AI as another layer of modern public relations practice.
That is what mature professional adoption may increasingly look like.
AI becomes part of the craft.
It does not become a replacement for having one.
The examples in this article come disproportionately from communications, marketing and adjacent professional communities.
They should not be treated as proof that every profession is adopting AI in exactly the same way.
But they provide a useful window into a broader question facing many fields:
“How can professionals use increasingly capable technology without giving up the knowledge and judgment that made their work valuable in the first place?”
The Answer Is Becoming Cheap. Judgment Is Not.
Generative AI has created an unusual problem.
Weak thinking can now arrive beautifully written.
A poorly framed question can produce a polished strategy.
An unsupported assumption can become a persuasive presentation.
Incorrect information can be expressed with the same grammar, formatting and confidence as accurate information.
That changes where professional value sits.
The advantage increasingly belongs to the person who knows:
Experienced professionals often possess knowledge they themselves may struggle to articulate.
Years of work create instinct.
A journalist senses that a claim needs another source.
A PR professional knows that the CEO is not always the person a reporter actually wants to interview.
A consultant notices that the client is asking for a solution before fully understanding the problem.
A translator recognizes that technically correct wording still feels wrong.
A sports writer knows that the statistically biggest play may not actually explain why a game changed.
These reactions can look like intuition.
But professional intuition is often “compressed experience.”
AI can help surface possibilities around that experience.
It should not automatically override it.
The Question Is No Longer What AI Can Do
As AI becomes more capable, the central professional question is changing.
It is no longer simply:
“What can AI do?”
The answer to that question expands constantly.
A better question is:
“What should AI do?”
Some tasks may eventually be delegated almost entirely.
Others can be accelerated but still require human steering.
And some responsibilities should remain primarily human because they involve accountability, ethics, relationships, consequences or judgment that should not simply be transferred to a machine.
That is why the goal should not be maximum automation.
It should be “appropriate automation” — using AI where it strengthens the work while preserving the human expertise, judgment and trust that give the work value in the first place.
Own Your Niche
The early years of generative AI rewarded curiosity.
The next stage will reward integration.
Professionals do not need to memorize every model, chase every application or reinvent themselves every time a new platform launches.
They need to understand their work.
They need to recognize which parts can be made more efficient.
They need to preserve the skills that created their judgment.
They need to verify what machines produce.
They need to recognize where their own knowledge ends.
And they need to know when technology should assist rather than decide.
AI can help beginners cross barriers that once kept them from experimenting.
It can help experienced professionals scale knowledge they spent years developing.
It can challenge assumptions on both sides.
It can make execution cheaper.
It can make answers abundant.
It can shorten the distance between idea and output.
But easier execution does not eliminate the need to understand what good work looks like.
“AI expands what people can do. Expertise, testing and verification help determine whether what they did is actually good.”
Own your niche.
Add AI to the expertise.
“Don’t replace the expertise with AI.”
Sources:
Harvard Business School / BCG — Jagged Technological Frontier
Zoetica / human-centered AI
Elmore / Six reasons you still need a communications firm in the age of AI
PRSA Houston / PR Day 2026
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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