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.
AI can accelerate the learning curve.
It should not be mistaken for completing it.
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:
Good AI adoption offloads friction.
Bad AI adoption can offload understanding.
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:
What question is actually worth asking?
What information is missing?
What assumption should be challenged?
What evidence matters?
When is the answer too convenient?
And when should the AI be ignored completely?
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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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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Tags: AI Adoption, AI Agents, AI and Journalism, AI and Marketing, AI and Public Relations, AI Integration, AI Productivity, AI Research, AI Skills, AI Strategy, AI Verification, AI Workflows, Artificial Intelligence, Boston Consulting Group, Elmore, Future of Work, generative AI, Hala Taha, Human Judgment, Human-Centered AI, Jagged Technological Frontier, Kami Watson Huyse, Professional Development, Professional Expertise, PRSA Houston, Responsible AI, Social Media Examiner, Zoetica Media
About Cheval John
Cheval serves as Managing Director of Vallano Media, LLC, where he oversees company's editorial strategy, business development and digital media operations. Operating at the intersection of social media management and sports, Vallano Media is building a dual-pillar brand that does two things: deliver real growth for businesses and translate the sports strategies into practical business lessons. Cheval hosted "What's The Word?" a podcast about finding out what inspires people to choose their respective careers and how social media impacted their lives and business from 2013 to 2017. He has spoken at Social Media Week Lima in Ohio and at Social Media Day Houston 2017. He was named a Houston Top 25 Social Media Power Influencer (2016 and 2017) and Twitter (Now X)Top 50 Influencer by Onalytica in 2018.