by Mark HuYoung
It’s time to acknowledge something many executives are feeling, even if they don’t always say it out loud.
AI is here. It’s moving fast. And for a lot of capable leaders, it raises a mix of curiosity, pressure, uncertainty, and quiet concern.
Some executives fear it. Some ignore it. Some know it matters but aren’t sure how to address it well without sounding either alarmist or behind. That’s understandable. Most leaders already have plenty on their plate: customers, people, margin, capital, culture, board expectations, talent issues, market uncertainty, and the daily joy of an inbox that seems to reproduce overnight.
For a while, many leaders could treat artificial intelligence as something for the technology team to sort out.
Now, AI has moved into the middle of the business.
It’s showing up in board discussions, operating reviews, investment committee meetings, recruiting conversations, customer service, supply chains, finance, marketing, and private conversations employees are having about their own futures.
This doesn’t mean every leader needs to become a technologist. I don’t need every CEO candidate I meet to explain how large language models work over coffee. Most of us are still trying to understand why every software company suddenly has an AI button.
But leaders do need working fluency with AI.
Not just vocabulary. Not just enthusiasm. Not just the ability to mention AI in a meeting and look appropriately serious.
Fluency.
To me, AI fluency means a leader can:
- Ask better questions
- Make better decisions
- Guide the team with confidence
- Protect the business from risk
- See where real value might be created
- Know where human judgment still matters most
In the same way financial literacy became part of the price of admission for senior leadership, AI fluency is becoming part of the credential.
The market is already telling us this. CEOs are increasingly identifying themselves as the primary AI decision maker. Boards are beginning to ask whether their own members understand AI well enough to oversee the risks and opportunities. Sponsors and investors are asking how AI changes the value creation plan.
This isn’t just a technology trend. It’s a leadership standard changing in real time.
In a prior piece, I wrote about AI as a copilot. I still like that analogy.
A copilot doesn’t take away the pilot’s responsibility. It helps the pilot see more, process more, reduce workload, and make better decisions under pressure.
That’s still useful. But the conversation is moving beyond whether AI can help us draft emails or summarize documents. Those are helpful applications, and I use them, but they’re only the beginning.
The better question is this: how does AI change the way we create value?
That means asking:
- How does AI improve judgment?
- How does it change speed?
- How does it affect talent?
- How does it alter cost structure?
- How does it shape customer experience, risk, and enterprise value?
That’s why AI fluency is becoming a credential.
In leadership, a credential isn’t just a degree or a title. It’s evidence that someone can be trusted with serious responsibility. A CEO who understands capital allocation earns one kind of trust. A CHRO who understands how culture connects to performance earns another. And a business leader who can translate AI into better decisions, better workflows, and better outcomes will increasingly earn a new kind of trust.
A leader who pushes AI entirely to IT may miss an important leadership moment. Not because that leader is incapable, but because AI is no longer just a tool issue. It’s becoming a business model, talent, risk, and value creation issue.
I see this in our search work all the time. The best leaders are rarely the ones who sound the most certain the fastest. They are the ones who know how to learn in public, ask good questions, listen carefully, and bring the room along.
People don’t follow leaders because they pretend to know everything. They follow leaders who can tell the truth, keep learning, and still create confidence.
AI fluency has four parts.
A fluent leader understands the basic differences between generative AI, predictive AI, automation, copilots, agents, and analytics.
The leader doesn’t need to build the model. But the leader should know enough to ask:
- What data is being used?
- What decision are we trying to improve?
- Where is human judgment required?
- What could go wrong?
- How will this create measurable value?
Those questions sound simple, but they’re not. They cut through a lot of noise.
I have sat through plenty of meetings where everyone nodded along because the deck looked polished and the language sounded current. But a good question can clear the fog quickly. What are we actually improving? Who owns the decision? What happens if the tool is wrong?
That’s where leadership starts.
A fluent leader uses AI in real work. Not only through an assistant. Not only when the innovation team brings a demo. Personally.
The best executives I see are using AI to:
- Challenge assumptions
- Summarize complex material
- Prepare scenario plans
- Find blind spots
- Pressure test board narratives
- Improve communication
They aren’t outsourcing their thinking. They’re sharpening it.
There is humility in that. It takes a secure leader to say, “Let me see where my thinking may be thin.” AI can help with that. So can a board member, a trusted colleague, or a spouse who has no interest in preserving your ego.
The point isn’t that AI has all the answers. It doesn’t. The point is that leaders who use it well often get to better questions faster.
AI fluency isn’t just knowing what is possible. It’s knowing what to do next.
Many companies have pilots. Many have task forces. Many have a steering committee, which can be helpful, although sometimes a steering committee is where good ideas go to sit quietly until everyone forgets why they were excited.
The harder work is translation:
- How does a use case become a changed workflow?
- How does that workflow improve the customer experience, forecast, close, candidate process, or margin?
- Who owns the change?
- How will we know it worked?
Tools don’t implement themselves. Workflows don’t redesign themselves. And people don’t automatically trust a new way of working because someone announced it at town hall.
Someone must connect the idea to the work.
AI adoption isn’t only a systems project. It’s also a human event.
People worry about status, competence, identity, job security, and whether the thing they have spent 20 years mastering is becoming less valuable. That concern deserves respect.
In my experience, resistance often isn’t stubbornness. Sometimes it’s confusion. Sometimes it’s fatigue. Sometimes its fear wearing a navy blazer and using very professional language.
The leader’s job isn’t to mock that reaction or bulldoze through it. The leader’s job is to name reality clearly and help people move.
That takes emotional intelligence. It takes patience and communication. It also takes firmness. People need empathy, but they also need direction.
The best leaders can hold both.
What AI Fluency Looks Like in Practice
AI fluency is less about technical brilliance and more about disciplined curiosity.
The fluent leader asks better questions:
- Where are we confusing activity with value?
- Which workflows exist only because our systems were built around human bottlenecks?
- Which decisions require judgment, and which mostly require pattern recognition?
- Where are people spending time on work that teaches them nothing, delights no customer, and creates no advantage?
The fluent leader also changes the meeting.
Instead of asking for a broad AI strategy, she asks for three workflows where AI could improve speed, cost, quality, or customer experience within 90 days.
Instead of asking whether the company “has an AI policy,” he asks whether employees know:
- Which tools are approved
- Which data can be used
- How outputs are validated
- When human judgment is required
The fluent leader also models learning in public.
People watch how leaders respond when they don’t know something. If the leader gets defensive, the organization learns to hide. But if the leader gets curious, the organization learns to learn.
I have seen strong executives lose a room because they wanted to sound smart before they built trust. I have also seen quieter leaders create real momentum by saying, “I am learning this too, and here is why it matters.”
People can follow that.
Boards and Sponsors Will Test for More Than Buzzwords
Boards and private equity sponsors are practical people. They may listen politely to broad AI language, but over time they’ll look for substance.
The AI questions they ask will become sharper because the stakes are tied to capital allocation, risk oversight, management credibility, talent strategy, and exit value.
In public companies, boards are pushing leaders to move faster while also trying to separate useful AI application from hype. That tension is understandable. Boards are right to push urgency. CEOs are right to push realism.
The executive worth backing is the one who can hold both truths at once.
In private equity, the test will be even more direct. Sponsors will ask how AI changes the value creation plan:
- Can this management team expand EBITDA through workflow redesign rather than blunt cost cutting?
- Can it improve pricing, sales productivity, inventory turns, working capital discipline, customer retention, or back-office throughput?
- Can it use AI to compress diligence, accelerate integration, and improve operating cadence without creating hidden risk?
The map isn’t the territory.
A slide that says “AI enabled” isn’t the same as a changed business. A dashboard isn’t transformation. A pilot isn’t scale. A vendor demo isn’t operating leverage.
At NorthWind, when we assess executives for sponsors, we are increasingly interested in how leaders learn.
The strongest candidates don’t need to pretend they have all the answers. They can explain:
- Where AI is relevant in their function
- Where it carries risk
- Where their company’s data isn’t ready
- Where workflow redesign is required
- How they would bring people along
Less convincing answers tend to stay at the level of generalities:
- “We are all over AI.”
- “Our CIO has that covered.”
- “We are experimenting with several tools.”
Those answers may be true. They’re also a starting point, not the destination.
In executive assessment, especially for high stakes roles, the market will want more than exposure. It will want evidence of judgment, application, learning velocity, and the ability to lead people through change.
The Global Marketplace Is Raising the Bar
The current global corporate marketplace is demanding.
Leaders are dealing with geopolitical fragmentation, industrial policy, supply chain reconfiguration, energy constraints, capital discipline, uneven labor markets, workforce anxiety, and a technology cycle moving faster than most planning processes.
AI sits inside all of that.
It affects productivity, but also cyber risk. It affects customer experience, but also brand trust. It affects hiring, but also retention. It affects cost structure, but also the meaning people attach to their work.
That should get every executive’s attention, not as a threat, but as a signal.
AI fluency isn’t simply about replacing work. It’s about increasing the value of people who know how to work differently.
In my world, that matters because leadership markets are talent markets:
- The people who learn faster become more valuable.
- The teams that learn faster become harder to compete against.
- The companies that learn faster create more options when the market shifts.
- And markets always shift. Usually right after someone confidently says they won’t.
The Neuroscience of Learning Still Applies
One reason many executives hesitate with AI is that it makes them feel like beginners.
That feeling can be uncomfortable, especially for people who’ve built careers by being competent, prepared, and in control.
But being a beginner isn’t a character flaw. It’s the entry fee for learning something that matters.
The encouraging news is that the adult brain is more adaptable than many people assume. We can learn new patterns. We can build new habits. We can get better through repetition, feedback, and practice.
That doesn’t mean change is effortless. It means change is possible.
This is why AI fluency rarely comes from a one-hour lunch and learn. Fluency comes from repeated use in real work:
- Asking AI to prepare for a customer meeting, then judging what it missed
- Using AI to analyze a board deck, then comparing its critique to your own
- Building a prompt library, improving it, sharing it, and learning from colleagues
The analogy to golf is useful.
A golfer doesn’t change a swing by reading about shallowing the club. The body has to feel the new move, repeat it, receive feedback, and learn under pressure. At first, the new move feels awkward because the old pattern is familiar.
AI fluency works the same way. Leaders become fluent by practicing until better questions and better workflows become natural.
No one becomes a better golfer by buying the newest driver and leaving it in the trunk. AI is similar. The tool matters. The reps matter more.
What Successful Leaders Are Showing Us
The leaders and companies moving well aren’t treating AI as a side project. They’re connecting it to mission, workflows, people, structure, and speed.
Several patterns stand out:
- Satya Nadella’s Microsoft offers one pattern: combine strategic conviction with a learning culture. His shift from a “know it all” culture to a “learn it all” culture matters because AI strategy without learning culture becomes a purchasing program.
- Jamie Dimon’s JPMorgan Chase offers another pattern: treat AI as enterprise infrastructure. The point is not to sprinkle AI language across the company. The point is to connect technology, structure, decision rights, and speed.
- Morgan Stanley offers a practical example of AI embedded into professional judgment. Its AI tools help financial advisors summarize meetings, surface action items, and draft client follow up, while the advisor retains discretion over the relationship and advice. That is the copilot idea implemented well: less administrative drag, more human attention available for the client.
- Walmart offers a scale example. Its leadership has been candid that every job will change in some way, while also investing in tools and training to help employees build new capabilities. That is more than technology adoption. It is change-management at enterprise scale.
The common thread isn’t that these leaders found a magic tool. The common thread is that they connected AI to the work, the culture, and the operating system of the business.
That’s what good leaders do. They take a big abstract thing and turn it into decisions, habits, roles, routines, and results.
How Leaders Can Build AI Fluency
The path is practical. It doesn’t require mystery. It requires discipline.
Rather than beginning with novelty, begin with your calendar, board materials, customer data, operating reviews, recruiting scorecards, investor updates, market maps, and decisions.
Ask: where do I need more context, sharper synthesis, faster scenario planning, or better communication?
Try a few role specific uses:
- If you are a CEO, ask AI to play the board member most likely to challenge your plan.
- If you are a CFO, ask it to find the weak assumptions in your forecast narrative.
- If you are a CHRO, ask it to pressure test the leadership behaviors your culture is actually rewarding.
- If you are in my seat, ask it to help compare candidate patterns without letting it replace judgment.
The last part matters.
Pick a recurring process that matters: a sales forecast, monthly close, candidate slate review, procurement negotiation, customer onboarding sequence, or pricing decision.
Then map every step. Identify where AI can assist, augment, automate, or create a better decision path.
Most companies don’t need a dramatic speech about transformation. They need three workflows that are slow, expensive, inconsistent, or frustrating, and a leader willing to improve them.
This distinction matters. AI rarely replaces a whole executive role cleanly. It changes bundles of tasks.
Leaders who understand task architecture will make better talent decisions than leaders who think only in job titles.
That distinction is especially important in talent work. A job title is often a container. The real value is inside the work itself: decisions, relationships, judgment, expertise, influence, pattern recognition, and execution.
AI changes some of those pieces faster than others.
AI governance shouldn’t live only in a binder on a shelf. It should answer practical questions:
- Which data is approved?
- Which tools are approved?
- Which outputs require human review?
- Who owns model risk?
- Who approves external use?
- What gets logged?
- What happens when the tool is wrong?
Governance doesn’t have to be theatrical. It just has to be clear. If people need a law degree and three passwords to understand the AI policy, they’re probably going to find a workaround.
People need permission to learn. The enterprise still needs performance.
The right message isn’t, “Mistakes don’t matter.” The better message is, “We’ll experiment responsibly, learn quickly, and hold ourselves accountable for outcomes.”
That’s the emotional balance leaders must strike. Too much fear, and people hide. Too little accountability, and experimentation becomes theater.
Different roles require different forms of fluency:
- A CFO needs AI fluency around forecasting, controls, fraud, working capital, investor communication, and productivity.
- A CHRO needs AI fluency around workforce planning, learning, recruiting, performance management, and culture.
- A COO needs AI fluency around throughput, quality, maintenance, scheduling, procurement, and customer delivery.
- A CEO needs enough fluency across all of it to set priorities, allocate capital, and keep the organization honest.
Generic AI training usually has a short shelf life. Leaders don’t need abstract inspiration for long. They need role specific application.
From the perspective of a retained search firm serving boards, CEOs, and sponsors, I believe AI fluency will increasingly show up in executive assessment in five ways.
Executives will be asked for actual use cases
Not just use cases they watched or funded from a distance. Use cases they helped lead.
The interviewer will listen for the business problem, workflow, data, governance, adoption, economics, and lessons learned.
In search, specificity is often where the truth lives. Anyone can speak in themes. The details tell you whether someone has truly been an operator.
Leaders will be tested for learning agility
A strong executive will be able to say, “Here is what I believed six months ago, here is what I believe now, and here is what changed my mind.”
That answer reveals humility, pattern recognition, speed of adaptation, and confidence. The best leaders aren’t embarrassed that they learned something. They’re more worried about becoming the last person in the room to notice that the facts changed.
People leadership will matter more
The questions will be practical:
- Can this person reduce fear without sugarcoating reality?
- Can they reskill rather than simply replace?
- Can they communicate why AI matters without making employees feel obsolete?
These may sound like soft questions. In practice, they are operating questions.
Culture isn’t the poster in the hallway. Culture is what people do when the work gets hard, the facts change, and the future becomes less certain than the plan.
AI must connect to value creation
In a PE backed company, AI fluency becomes most relevant when it improves the value creation plan.
The question is where it changes revenue quality, margin structure, working capital, customer retention, speed of execution, or the exit narrative.
That’s where sponsors will focus. They aren’t asking about AI because it’s fashionable. They’re asking because speed, insight, margin, and execution all matter when time is compressed.
Judgment remains the premium skill
AI can generate options. It can’t own accountability.
AI can detect patterns. It can’t carry moral responsibility.
AI can draft the message. It can’t build trust on behalf of a leader. That still has to be earned.
Capital is a commodity; leadership is the alpha. In the AI era, that leadership will be measured partly by how well an executive combines human judgment with machine capability.
This is the part I keep coming back to. The better the tools become, the more judgment matters. AI may make average work faster. It won’t make weak judgment better.
Encouragement for the Executive Who Feels Behind
If you are still getting your bearings with AI, you are in good company. Many capable leaders are.
The important thing is to avoid letting anxiety become avoidance.
Start small but start daily. Use AI for 20 minutes a day on real work:
- Ask it to critique your thinking.
- Ask it to find blind spots.
- Ask it to role play a skeptical board member, frustrated customer, high potential employee, or investment committee member.
Then evaluate the answer. Keep your judgment in the loop.
Learn with others. Create a small AI learning circle with peers from different functions. Every two weeks, each person brings one use case, one failure, one prompt, and one question.
That simple rhythm builds confidence. It reduces the awkwardness of learning something new. It spreads practical knowledge faster than formal training alone.
Teach what you learn. The fastest way to deepen fluency is to explain it to someone else.
That applies across the leadership team:
- A CEO who teaches the board what AI can and can’t do becomes more fluent.
- A CFO who teaches the finance team how to validate AI generated analysis becomes more fluent.
- A CHRO who teaches managers how to talk about AI and fear becomes more fluent.
Stay human. The point of AI fluency isn’t to make leadership less human.
It’s to become more available for the human work only leaders can do: discernment, courage, empathy, trust, accountability, imagination, and conviction.
Also, give yourself a little grace. None of us were handed an AI manual. Most leadership learning happens through reps, mistakes, feedback, and the occasional moment when you realize the thing you resisted might end up making you better.
The leaders who thrive won’t simply be the ones who chase every tool.
They will be the ones who:
- Learn faster than the market changes
- Communicate more clearly than the noise around them
- Act with urgency and wisdom at the same time
AI fluency isn’t a fad. It’s part of the new operating literacy of leadership.
Boards will test it. Sponsors will test it. Employees will feel whether it is real. Customers will benefit when it is applied well. Investors will reward it when it creates measurable value.
But the deeper reason to build AI fluency isn’t fear. It’s stewardship.
Leaders have been given responsibility for people, capital, institutions, and futures that extend beyond themselves. When a tool emerges that can change how knowledge work is done, how decisions are made, how customers are served, and how people learn, the leader’s opportunity is to respond with curiosity, discipline, and discernment.
The work is becoming fluent enough to steward it wisely.
The future won’t belong to leaders who know every technical answer. It’ll belong to leaders who can keep learning, keep listening, keep discerning, and keep bringing people with them.
That’s a credential you can’t fake.