This article was adapted from a conversation on 87:60, Rebellion Group’s podcast about the ideas, shifts, and uncomfortable truths shaping work, creativity, leadership, and culture. In this episode, James Dowd and BJ Kito discussed AI adoption, critical thinking, ethical experimentation, and why the future belongs to people who learn how to think with the tools instead of waiting for permission to use them.
Most AI conversations start in the wrong place.
They start with fear.
Will AI replace jobs?
Will it steal creative work?
Will it make people lazy?
Will it expose private data?
Will it create more risk than value?
Will it make me less necessary?
Those are fair questions. But they’re not the whole conversation. They’re not even the most useful part of the conversation.
Because AI isn’t waiting for us to feel ready.
It’s already here. It’s already embedded in the tools we use, the platforms we rely on, the search engines we trust, the CRMs we update, the analytics we review, the content systems we publish through, and the workflows we’re trying to make less painful.
So the question isn’t really, “Should we use AI?”
The question is: Are we going to learn how to use it well, or are we going to stand around waiting for someone else to figure it out first?
That’s the real issue. Not AI itself. The mindset around AI.
“AI is omnipresent at this moment. It’s more about what tools you are using that leverage it.”
— BJ Kito
That’s where businesses need to get clearer.
AI isn’t some separate futuristic thing that gets wheeled into a conference room once leadership approves an innovation initiative. It’s not one platform. It’s not one chatbot. It’s not one policy document. It’s not one big transformation project.
AI is becoming a layer inside work.
Which means AI adoption is not just a technology challenge.
It’s a thinking challenge.
The AI Gap Is Becoming a Thinking Gap
There are companies and teams that are already using AI to move faster, think wider, synthesize better, prototype quicker, automate repetitive work, and expand what’s possible.
Then there are teams still asking if they’re allowed to open the tool.
That gap is going to matter. Not because AI magically makes people brilliant. It doesn’t. A weak thinker with AI is still a weak thinker. They just produce weak work faster.
But a sharp thinker with AI? That’s different.
That person can explore more ideas. Challenge their own assumptions. Pressure-test language. Build frameworks. Compare audiences. Translate complexity. Find patterns. Create drafts. Improve workflows. Build momentum.
AI doesn’t eliminate the need for human intelligence. It raises the standard for it.
“This is now a tool that enhances everything that we can do, making it faster. It shouldn’t replace you if you actually learn how to use it.”
— James Dowd
That last part matters most: if you actually learn how to use it.
Because the people most at risk are not simply the people whose jobs can be touched by AI. That’s almost everyone.
The people most at risk are the ones who refuse to build fluency. They wait. They resist. They dabble once, get a bad answer, and decide the tool doesn’t work. They need step-by-step instructions before they’ll experiment. They mistake caution for strategy. They confuse being uncomfortable with being correct.
That’s the danger. Not AI replacing human judgment, human judgment refusing to evolve.
“Using AI” Is Too Vague to Mean Anything
One reason organizations struggle with AI is because the phrase “use AI” is almost meaningless on its own.
Use AI for what? Strategy, research, media planning, operations, workflow automation… The list goes on, those are not at all on the same playing field.
They don’t require the same tools, the same guardrails, training, or level of risk tolerance.
That’s why “we need to implement AI” is usually a bad starting point. It sounds decisive, but it’s vague.
AI becomes useful when it’s connected to a real business need. Where are we slow, repetitive, guessing, underusing data, and a host of other challenges?
Start there. Not with the tool. With the work.
“There isn’t a need to come in and just replace systems with AI. It’s about how you can use AI in product development, infrastructure, operations, systems, workflows, or outreach.”
— BJ Kito
AI is not one thing. It’s a capability that shows up in many places. The strategic move is knowing where it belongs, where it doesn’t, and what it needs to improve.
That’s the practical conversation.
The Best AI Use Starts With Curiosity
A lot of people are waiting for permission. They want someone to say exactly which tool to use, what prompt to type, what output to trust, and what the official use case is.
That’s understandable. AI can feel big, chaotic, fast, and intimidating.
But waiting for perfect clarity is also how people fall behind.
You don’t build AI fluency by reading the policy. You build it by using the tools, testing the outputs, learning the limitations, and figuring out what’s actually valuable.
At Rebellion, we’ve been thinking about AI adoption in three simple moves: Explore. Test. Adopt.
“Explore AI and just go figure it out. Ask it things. Test it. Learn how it works here versus over there. Adopt it and ask how you can use it in your normal day?”
— James Dowd
That’s the process.
- Explore means you give yourself room to play before you demand an immediate business case. You try tools. You ask questions. You push them into strange corners. You see what they can do.
- Test means you compare. Which tool is better for strategy? Which one handles technical writing better? Which one produces generic mush? Which one can analyze a document? Which one can help shape a framework? Which one breaks when the task gets more complex?
- Adopt means the useful stuff becomes part of how you work. Not as a novelty. Not as a stunt. As behavior.
That’s where the real value starts.
AI doesn’t become powerful because someone gave a lunch-and-learn. It becomes powerful when people change how they think, work, draft, question, build, review, and decide.
AI Doesn’t Remove Critical Thinking. It Demands More of It.
There’s a lazy version of AI use.
You type a vague request, get a generic answer, copy it… and move on.
That’s not intelligence. That’s intellectual outsourcing.
It’s also why so much AI-generated work feels flat. The machine didn’t fail; the human gave it nothing to work with.
The better the thinking, the better the output.
That means you need to know the audience, the context, the constraints, the goal, the tone, and the strategy. You need to know the thing that would make the answer not just correct, but useful.
AI can accelerate that production, but it can’t replace discernment.
“What do you need to actually be able to explore and experiment and try out different platforms? Critical thinking.”
— James Dowd
This is the part too many organizations miss. They think AI training is about teaching people prompts.
Prompting matters. But prompting is really just structured thinking. It’s the ability to explain what you want, why you want it, what matters, what doesn’t, and how success should be judged.
That’s not a technical skill. That’s a human skill, a strategic skill, a communication skill, and a leadership skill.
Prompting Is Just Direction
There’s an old exercise where someone has to write instructions for making a peanut butter sandwich.
It sounds simple until you realize how much context you assume.
Where’s the bread? What kind of bread? How much peanut butter? Is there jelly? Is anyone allergic? Is this for a child, a lunchbox, a photo shoot, or a restaurant menu?
AI works the same way.
If you say, “Build me a landing page,” don’t be shocked when the result is useless.
AI still needs direction. It needs to know the audience, the offer, the visual direction, the emotional tone, the conversion goal, and more.
And that direction still needs to come from a person who understands the work.
“They needed a creative doc. They needed visual direction. But then after they input all that, in 30 minutes, they had a pretty phenomenal landing page. We still need humans with AI. We’re just truly the directors now, no longer the machine.”
— James Dowd
That’s the whole thing.
AI can build faster when humans direct better. It doesn’t replace the brief. It punishes the absence of one.
The Human Role Doesn’t Disappear. It Moves Upstream.
The fear is that AI will make people unnecessary. The reality is more interesting.
AI makes certain tasks easier. It makes some production faster. It reduces friction. It creates first drafts. It organizes raw material. It can generate options that used to take hours.
But that doesn’t make humans less important. It changes where human value sits.
Less time typing from scratch. More time deciding what’s worth saying.
Less time formatting information. More time interpreting what it means.
Less time producing one option. More time evaluating many.
Less time doing repetitive work. More time shaping direction.
The human role moves upstream into judgment, taste, strategy, ethics, and decision-making.
That’s good news for people who want to think. It’s bad news for people who only want to execute instructions.
AI Ethics Need Guardrails, Not Paralysis
AI creates real ethical questions around data privacy, client confidentiality, copyright, and security matters, and more.
Especially in industries like healthcare, education, financial services, nonprofits, public-sector work, and client services, organizations need to be careful about what gets uploaded, where data goes, how outputs are reviewed, and who’s accountable for final decisions.
But ethics can’t become an excuse for avoidance.
There’s a difference between responsibility and paralysis.
A responsible organization doesn’t say, “Use anything however you want.” But it also doesn’t say, “Nobody touch anything until a committee spends two years writing a document no one understands.”
The better answer is practical: Protect sensitive information, use approved tools for sensitive work, teach people what not to upload, review AI-assisted work before it goes live, create internal standards, make humans accountable for final output, and let people experiment safely.
Governance should create confidence. Not fear.
Education and Experimentation Have to Happen at the Same Time
One of the big AI debates is sequence.
What comes first? Education? Governance? Leadership? Policy?
The answer is that these things can’t happen one at a time. Not anymore. The tools are changing, he market is moving, customer behavior is shifting too quickly, search is evolving, and workflows are transforming. All too quickly.
If you wait for the perfect AI policy before anyone experiments, the policy will be outdated before the behavior changes.
That doesn’t mean moving recklessly. It means learning while doing.
“AI education and doing are hand in hand. Simultaneous. You learn AI by using AI.”
— BJ Kito
That’s the right operating principle.
Teach while people test, create guardrails while people explore, update policy based on real behavior, share what works, fix what breaks, and build confidence through practice.
That’s how AI adoption actually happens.
Not as a memo. As a muscle.
The Bigger Risk Is Refusing to Change
Every major shift creates defenders of the old world.
People say it’s overhyped. They say they’ve seen this before and not much will change. That the fundamentals are the fundamentals, and the new thing is just a tool.
And sometimes they’re partly right. But they usually miss the ecosystem effect.
The elevator didn’t just change how people moved through buildings. It changed buildings themselves. Which changed cities. Which changed where people lived, how they commuted, where they worked, and how commerce evolved.
Technology doesn’t just alter the task. It alters the world around the task.
AI will do the same.
It won’t simply transform the work we do. AI will redefine what people expect from it. Faster execution. More personalized communication. Quicker access to insight. Broader strategic thinking. Instant an
That’s the bigger shift.
The people waiting for AI to “settle down” are misunderstanding the moment.
This isn’t about one tool becoming stable. It’s about a new layer of work becoming normal.
AI Search Makes This Even More Urgent
For marketers, AI adoption is not only an internal productivity issue.
It’s also an external visibility issue.
Search is changing. People are no longer only typing keywords into Google and clicking through a list of blue links. People are asking conversational questions, relying on AI assistants, searching through generative engines, and expecting direct, synthesized answers instead of lists of links.
That changes how brands need to think about content.
Traditional SEO still matters. But it now overlaps with AEO, GEO, AIO, and AI search visibility.
The brands that win in this environment will create content that’s clear, useful, answerable, and genuinely human, not keyword-stuffed pages, AI-generated filler, or thought leadership that says very little.
AI search rewards content that helps machines understand what humans should trust. That means sharper points of view, cleaner structure, better answers, stronger topical authority, and more original thinking.
This is where AI and human intelligence need each other.
AI can help identify questions. Humans need to answer them with taste and truth.
AI can help structure content. Humans need to make it worth reading.
AI can help create velocity. Humans need to create meaning.
Because the future of search is not just about being found. It’s about being chosen as a credible answer.
The Best AI Users Won’t Be the Most Technical People
There’s a temptation to think AI belongs to technical teams. It doesn’t.
The best AI users aren’t defined by how well they use the technology. They’re defined by how well they think. They understand the customer. They frame problems clearly. They recognize when an idea feels flat, when language lacks life, when a workflow is broken, when a client is worried about something they haven’t said aloud, and when the data tells only part of the story. AI amplifies those instincts—it doesn’t replace them.
AI doesn’t only reward technical knowledge.
It rewards clarity. And clarity comes from knowing what matters.
AI fluency is moving out of the realm of specialists and into everyday work. It belongs alongside the skills every professional is expected to have: writing, presenting, researching, solving problems, and navigating the digital world.
You don’t need everyone to become an AI expert; you need everyone to become AI-capable.
What Businesses Should Do Now
AI adoption doesn’t need to start with a massive transformation plan.
It can start much smaller. But it does need to start.
1. Make experimentation normal
Give people permission to use AI. Not secretly. Not shamefully. Not only when they’re desperate. Make experimentation part of the culture.
2. Teach critical thinking, not just tools
Don’t train people only on which buttons to press. Train them to define problems, give context, critique outputs, improve prompts, question assumptions, and apply judgment.
3. Connect AI to real business problems
Don’t use AI for theater. Use it where the work is slow, repetitive, unclear, expensive, or underdeveloped.
4. Build practical guardrails
People need to know what they can use, what they can’t upload, which tools are approved, and when human review is required.
5. Share use cases internally
The fastest way to build adoption is for people to see how their peers are using AI in real work.
6. Reward learning
People won’t experiment if every imperfect attempt is treated like failure. Reward curiosity. Reward useful testing. Reward smarter questions.
7. Move
The biggest mistake is waiting too long.
AI is already here. The market is already adapting. Customers are already changing. Search is already shifting. Work is already moving.
Waiting is a decision and usually not a good one.
Final Thought: Go Use It
AI adoption isn’t about becoming less human. It’s about becoming more capable more curious, adaptive, informed, creative, and more strategic. It’s about being willing to test what’s possible.
The people who use AI best won’t be the ones who blindly trust it. They’ll be the ones who know how to challenge it. They’ll ask better questions, give better direction, reject generic output, protect sensitive information, and bring taste, context, ethics, and judgment to every interaction. That’s the human advantage; not doing what AI can do, but doing what AI can’t.
And that’s the starting line. Not mastery. Just orientation. Open the tool, ask the question, and test the answer. Think critically. Then, try again.
AI isn’t arriving someday. It’s already here, reshaping how work gets done. The people who learn to work with it thoughtfully and intentionally will be the ones who shape what comes next.
FAQ
Is AI going to replace jobs?
AI will change jobs, eliminate some tasks, and create new expectations for how work gets done. The bigger risk for many professionals is not AI itself. It’s failing to learn how to use AI to think, work, create, communicate, and make decisions better.
Why do companies struggle with AI adoption?
Companies struggle with AI adoption because they treat it as a technology rollout instead of a behavior change. People need permission, practice, guardrails, examples, confidence, and a reason to use AI in their actual work.
What skills matter most in an AI-enabled workplace?
The most important skills are critical thinking, communication, judgment, curiosity, adaptability, taste, ethical decision-making, and problem definition. AI can generate outputs. Humans still need to decide what’s useful, true, relevant, and worth doing.
How should businesses start using AI?
Businesses should start by identifying where work is slow, repetitive, unclear, or under-informed. Then they should test AI against those specific problems with clear guidelines, human review, and a willingness to learn from real use.
What does responsible AI use mean?
Responsible AI use means protecting confidential data, understanding tool limitations, reviewing outputs, checking for bias or inaccuracies, using approved platforms for sensitive work, and keeping humans accountable for final decisions.
Is prompt engineering still important?
Prompting is important, but the deeper skill is clear thinking. A good prompt defines the goal, context, audience, constraints, examples, tone, and standard for success. Prompt engineering is really the ability to communicate direction clearly.
How does AI affect search marketing?
AI is changing how people search for information. Brands now need content that is clear, structured, useful, answerable, and credible enough to appear in AI-generated responses. SEO, AEO, GEO, and AIO all depend on creating content that both humans and machines can understand and trust.
How can marketing teams use AI?
Marketing teams can use AI for audience research, content planning, campaign development, creative exploration, SEO and AI-search optimization, reporting, workflow automation, customer insight, message testing, and faster synthesis of complex information. The best results still require strong strategy and human taste.