My intelligence is not artificial.
Neither is yours.
Yet more leaders are quietly whispering about AI than openly talking about how they use it. They’re afraid that if they admit a model helped with the memo, the deck, or the strategy outline, they’ll look lazy, less intelligent, or less “real” as leaders.
At the same time, they know AI is not going away. It’s in their teams’ browsers, in their tools, and in their kids’ homework. The choice isn’t between AI or no AI. The real choice is whether we let AI dull our thinking – or design it to free up and amplify our human intelligence.
The Quiet Stigma Around Using AI
Surveys and on-the-ground stories show a clear pattern: AI shame.
- Nearly half of employees say they hide their AI use at work, worried they’ll be seen as lazy or incompetent (BCG AI-at-Work).
- Executives are some of the heaviest users and also some of the most likely to conceal that usage (BCG / HBR).
People aren’t ashamed of using Google or spellcheck. But say “AI” and suddenly they worry they’re “cheating.”
The cost of this shame is high:
- Teams underuse tools that could lift repetitive work off their plate.
- Leaders miss the chance to set smart norms for how AI should support, not replace, their people.
- Strategy conversations drift back to volume of output instead of quality of thinking.
If you lead a company, a brand, or a revenue engine, pretending you’re not using AI doesn’t protect your credibility. It just keeps you from shaping how AI and human intelligence actually work together.
AI Is the New Conveyor Belt, Not the New CEO
You’ve heard this before but will say it again, when Henry Ford introduced the moving assembly line, it didn’t make humans obsolete. It changed which problems humans spent their time on (Smithsonian).
The conveyor belt took over the repetitive motion – moving parts from station to station – so people could specialize, speed up, and focus on improving the system instead of pushing each piece by hand.
AI is that conveyor belt for knowledge work.
- It can sort, summarize, and generate drafts in seconds.
- It can process more data points than any team could reasonably hold in their heads.
- It can handle the grunt work of reading, formatting, and first-draft writing at a scale that changes what’s possible.
That is not an attack on human intelligence. It’s an invitation to move your intelligence up a level.
But only if you treat AI as the belt, not the boss.
What the Brain Science Really Says About Using AI
Leaders ask a fair question: “If I use AI too much, am I weakening my own brain, or my team’s?”
The short answer: It depends on how you use it.
We learn and remember best from humans
Brain research shows that more screens and digital tools have not automatically made us better thinkers or learners (Horvath).
Our brains still remember ideas best when we:
- Read closely, not just skim.
- Write things down. (Love my whiteboards)
- Talk them through with other people.
That’s how we build understanding and make ideas stick.
The forgetting curve applies to strategy too
Studies on the forgetting curve show that without reinforcement, we forget most new information within days or weeks (Simply Psychology).
A single workshop, deck, or clever all-hands doesn’t turn into real strategy unless teams:
- Revisit the ideas,
- Apply them in real decisions, and
- Talk about them in their own words.
If strategy only lives in slides or in an AI prompt history, it slides down the same curve.
MIT’s “Your Brain on ChatGPT”: off switch vs amplifier
MIT’s study on ChatGPT and brain activity gives us something more useful than fear (MIT Media Lab).
- When people let ChatGPT do the work from the start, their brain engagement drops, and they remember their own work less.
- When they think in their own words first and use ChatGPT after that to refine or extend, their brains light up more. They engage more deeply as they evaluate and edit.
AI didn’t damage anyone’s brain by itself. The sequence did.
- AI as starting point can be an off switch.
- AI as second step can be an amplifier.
That finding is a gift for leaders. It tells you exactly how to design AI use so it keeps your team’s intelligence active instead of putting it to sleep.
Why Revela Cares About How AI Is Used
At Revela, we’re storytellers, brand builders, and communicators. We sit in rooms, watch what’s really happening between leaders, teams, and clients, and then turn those observations into strategies and stories people can act on.
We also use AI openly. We use it to research faster, test ideas, and pressure-test messages, because the tools help us spend more time where our clients actually need us: in the room, listening, connecting dots, and crafting stories that land.
Recent research shows AI can raise knowledge workers’ productivity and quality when it is used on the right tasks. We see that in our own work. AI handles the heavy processing so our human intelligence can focus on nuance, context, and narrative.
That is why we care so much about how AI is used. The tools are powerful. The question is whether they are freeing up your intelligence – or quietly replacing it.
A Leadership Stance
For executives, this isn’t just a tech question. It’s a leadership identity question. If your job is to:
- See around corners,
- Make trade-offs, and
- Tell a story your team and your customers can act on,
then your intelligence cannot be outsourced.
It can be supported.
It can be extended. But it still has to lead.
Here’s a simple way to turn “My intelligence is not artificial” into a practical stance:
- I am not ashamed of using AI
I say when and how I used it, and I ask my team to do the same, because our goal is better thinking, not pretending we did it alone.
- I think in my own words first
For important memos, strategies, and narratives, I sketch what I believe, what I’m seeing, and what I’m unsure about before opening a tool.
- I use AI to react and accelerate, not to decide
I ask AI to pressure-test my thinking, offer alternatives, or make the language cleaner, but I never treat its output as the final answer.
- I own the final story
I decide what stays, what goes, and how I will explain it in a room full of clients or my own board.
Used this way, AI doesn’t replace your intelligence. It gives it more surface area.
From AI Shame to AI Ownership: Guardrails for Teams
If you want teams to move from hiding AI to owning it, they need simple guardrails they can remember and use.
1. Normalize: AI use is expected, not embarrassing
Make responsible AI use a visible norm, not a secret shortcut.
- Leaders model the behavior by saying in meetings and internal notes when and how they used AI: “I drafted this outline with AI, then refined it myself.”
- Celebrate smart AI wins in team updates: “We used AI to speed up research, which gave us more time to pressure-test the story.”
The message is clear: the issue is how you use it, not that you use it.
2. Sequence: Think first, then tool
For important work, AI doesn’t go first.
- On strategy, messaging, client stories, and key decisions, humans must sketch the problem and initial answer before opening a tool.
- AI is invited in to react, test, and expand – never to create the first version alone.
You can make this a simple rule: “If it’s important enough to put our name on it, we think first, then use AI.”
3. Ownership: Every AI-assisted asset has a human owner
AI can help create; a human is always accountable.
- Every AI-assisted document, deck, or campaign has a named human owner who reviews the output, edits for accuracy and tone, and is willing to defend it in a room of clients or the board.
- That owner signs off: “Reviewed and approved by [Name].”
AI is the tool. You still own the work.
4. Transparency: We log and label, we don’t hide
Make AI visible in the workflow.
- Use a simple convention in internal docs or project logs: “AI used for: research / draft / rewrite / translation.”
- Treat this as a neutral tag, not a scarlet letter. The goal is traceability and learning, not blame.
Over time, this shows you where AI genuinely adds value – and where it doesn’t.
5. Boundaries: What AI can never do for us
Draw a bright line around non-negotiables.
For example:
- AI does not decide strategy. Humans do.
- AI does not approve final client communications. Humans do.
- AI does not get direct access to sensitive client data or anything that would violate privacy, regulation, or internal policy.
Put these in plain language, with real examples of “no-go” use cases. That helps more than vague warnings.
When you put these guardrails in place, you give people permission to use AI well – and a clear standard for what “well” looks like.
Human in the Room, AI on the Line
In AI conversations, we often hear “human in the loop,” which usually means someone glances at AI output and approves or edits it. That’s oversight, not leadership.
What we’re really talking about here is AI on the line and human in the room.
- AI on the line is the conveyor belt handling processing, drafting, and pattern-finding.
- Human in the room is people listening, deciding, and telling the story.
In financial services, fintech, wealth, and other complex B2B spaces, this distinction matters even more.
- AI can help with workflow, processing, and assessing complex information at speed.
- Trust still comes from a human who can explain risk, trade-offs, and strategy in a way the client can handle.
Many companies treat human review as compliance.
Human in the room is different. It is where value is created, not just where risk is managed.
A Simple Way to Use AI That Respects Human Intelligence
You don’t need a complicated framework. You need a smarter sequence for your most important work.
Here’s one you can take straight into your next strategy or messaging session.
1. Start with a human working session
Get the right people in the room or on a live call. Ask:
- What are we seeing with clients?
- Where are they getting stuck?
- What is changing in the market?
Capture it in your own words. Debate. Prioritize. Make choices.
This is where you build strategy and real customer understanding.
We watched one team lean heavily on AI-driven presentation tools to build an all-facts deck. The numbers were right, the visuals were slick – and the field’s reaction was blunt: “This doesn’t follow the buyer or help them understand the value.” The presentation had data but no clear problem, no tension, and no obvious solve. Even numbers have a story. Without humans in the room to frame that story, the value went missing.
2. Ask AI to react, not lead
Feed your thinking into AI. Ask it to:
- Pressure-test the story,
- Show you alternate angles, or
- Draft versions for different buyer personas.
Use AI as a fast helper, not the owner of the idea. This matches how the MIT study suggests we keep our brains engaged: think first, then use the tool.
3. Close with human judgment and a human story
Before anything ships, someone in the room must be able to answer, in plain language:
- What problem are we solving?
- Why now?
- How will we explain this to a client without the deck?
You keep what fits, cut what doesn’t, and make sure it sounds like you. That final pass is what turns processed information into a story your team and your clients can remember and repeat.
Teams that work this way don’t feel like AI is replacing them. They feel like it is taking the heavy lifting off the line so they can focus on the thinking and the story.
Three Questions to Start an Honest Conversation About AI Use
If you want this to be more than another AI article, use it to start a real conversation with your team.
1. Where are we secretly using AI today or where can we use it that we aren’t? – and what would change if we did?
2. On our most important work, are we letting AI start the thinking, or are we using it after we’ve done the hard thinking ourselves?
3. When was the last time a client or internal stakeholder repeated our story back to us, unprompted – and did it sound like what we intended?
Our view at Revela is simple. AI belongs out in the open, on the table, helping us process faster, while human intelligence stays in the room, making the choices and telling the story.
To encourage teams open use of AI without outsourcing its intelligence, we can help you design the guardrails, workflows, and narratives to do that.
Start with a working session: we’ll sit in the room with your team, map how you’re using AI today, and co-create a simple set of story-first, human-in-the-room practices you can roll out across the business.
Contact us via form or connect with Alma on LinkedIn to explore what this could look like for your organization.
Author’s Note: How This Was Really Written
This piece took three days to craft. I spent that time digging into what I actually wanted to say, wrestling with structure, and rewriting until the story felt useful for leaders and practical for teams. 
I also used AI – very intentionally – to help find research that could validate and stress-test the ideas, and to speed up some of the drafting and refining.
In other words: the thinking and the story are human.
The processing had help.
Written by Alma (the human in the room)
with Pete, my Perplexity AI assistant on the line.
Frequently Asked Questions: AI Leadership in the Workplace
How should leaders use AI without losing credibility?
Leaders maintain credibility by thinking first and using AI second. MIT research shows that when people form their own ideas before engaging AI tools, brain engagement increases and output quality improves. The key: use AI to pressure-test and accelerate your thinking, never to replace it.
What is “AI shame” and why does it matter for teams?
AI shame is the tendency for employees and executives to hide their use of AI tools at work. BCG research shows nearly half of employees conceal AI usage, fearing they will appear lazy or incompetent. This limits productivity and prevents organizations from developing smart norms for responsible AI adoption.
What is the difference between “human in the loop” and “human in the room”?
“Human in the loop” typically means someone reviews AI output after the fact. “Human in the room” means people are leading the thinking, deciding the strategy, and owning the story before AI enters the process. Revela Advisors advocates for the latter because it keeps human intelligence active rather than passive.
How does the forgetting curve apply to AI-generated strategy?
Without reinforcement, teams forget 50% of new information within one hour and up to 90% within a month. Strategy that only lives in AI prompt histories or slide decks slides down the same curve. Teams must revisit ideas, apply them in real decisions, and articulate them in their own words.
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