Do You Sound Like a Human?
This newsletter was written by one of our staff members at The Nelson Pages!
“Once a new technology rolls over you, if you’re not part of the steamroller, you’re part of the road.”
Stewart Brand, 1987
I recently sat down to update my resume for a new job. Like most applicants, I wanted my materials to stand out and highlight my expertise. The job description seemed intentionally broad, asking for leadership, innovation, collaboration, strategic thinking, and communication skills. As I began revising, I did what many others are doing: I opened an AI tool.
Within seconds, it transformed my straightforward descriptions. My experience leading curriculum implementation became “driving transformative instructional initiatives.” Facilitating professional learning became “maximizing organization capacity through strategic stakeholder engagement.” It sounded impressive, professional, and sophisticated.
It also didn’t sound like me. The words were accurate, technically, but they lacked individuality and personality. More importantly, they probably wouldn’t distinguish me from any other applicant using the same tools. I am an educated, experienced professional. Why did I think AI could represent me better than I could represent myself?
And what about the other side? The reader?
How many resumes have you read from applicants who maximize efficiency and scalability, drive value-added results, and enhance stakeholder engagement but give few concrete examples? How many emails have you been copied on with impeccable grammar but vague language that left you unsure of the actual message? How many polished presentations have you sat through that looked professional but lacked evidence or data, with presenters who struggled with follow-up questions and explanations?
Artificial Intelligence.
So what are leaders actually doing right now around AI? Why are we acting this way? What should we do instead?
You know AI when you see it.
You might be frustrated by it or roll your eyes. Maybe you chuckle a little because surely they don’t think nobody will notice. (Don’t they know everyone is using it, probably even you?) Perhaps you feel a little insulted, wondering why someone wasted their time being less than genuine, and now, they’ve wasted yours too. Or maybe you’ve accepted that this is simply the new reality. AI is here. People are using it. The game has changed.
Maybe you’ve accepted you’re part of the road, accepting the steamroller as it rolls over you.
But what if you haven’t? What do you do? Can you trust any applicant? Do your employees lack skills or lack ethics? Is the issue competence, character, or culture? Why is this happening? Is it you?
The easiest answer is AI.
It’s easy to point the finger at the newest tool that has inundated our personal and professional lives. It’s also easy to blame people. They’re lazy. They want short cuts. They aren’t creative enough. They don’t want to put in the work.
But the easiest answer is rarely the whole answer.
What these explanations reveal is not necessarily a problem with AI or with people. They reveal something fundamental about human nature. Our brains are constantly searching for efficiency. We look for patterns. We conserve energy. We automate routines. We choose the path of least resistance whenever possible.
The impulse isn’t new; AI simply amplifies it.
And it’s not only that our brains are trying to save energy, we might also consider that technology has evolved faster than we have: human lag (Griffin 2022). The intelligence we created has outpaced our capacity to understand it, regulate it, and use it wisely.
Throughout history, technological advances have arrived faster than our social systems could adapt. We created cars before having traffic laws. We created social media before we understood its psychological effects. We created smartphones before we understood the impact of constant connectivity.
Now, we have created systems capable of producing writing, presentations, code, and even artwork in seconds, yet our expectations, policies, hiring practices, and leadership approaches remain rooted in a world where those outputs require more time and effort.
The challenge isn’t that AI exists; the challenge is that we are still behaving as if it doesn’t.
And our response is predictable because it’s the easiest.
We make a rule, a policy, and consequences. We post our new AI policy to our websites. We include warnings in application materials. We send out memos and make announcements. We assume that awareness will eliminate behavior. The logic seems clear-cut: identify the problem, prohibit the behavior, and expect compliance.
But leadership isn’t that simple.
Authoritarian responses often produce resistance over responsibility. People tend to focus on hiding behavior rather than understanding. They comply when someone is watching and avoid when someone is not.
Once again, we choose the easiest road (or our brains do). We react rather than adapt. We choose control over leadership. And despite our efforts, we remain part of the road.
Fortunately, we have another choice, one that requires reflection and vulnerability. It is more challenging but likely to be far more impactful and satisfying.
Choose authenticity. AI isn’t just new to your employees and applicants; it’s new to you too. Be real about that. Be transparent about how you use it and don’t use it. Share where you believe human judgment remains essential. Talk openly about your uncertainties. Admit what you don’t know. Model curiosity instead of certainty.
Authenticity creates permission for others to engage honestly rather than perform compliance.
Choose adaptation. Radical adaptation. Ask yourself whether your systems are still measuring the things that matter. Have your meetings become more about outputs, deliverables, and metrics? What would happen if more time were spent on discussing process, decision-making, and learning? What if collaboration became more important than presentation?
Consider your hiring practices. Are you still asking applicants about their roles and responsibilities? What if you asked them to describe projects they're proud of or to explain skills they’ve developed and how. Maybe forgo those resumes (Too extreme? Good. Innovation often feels extreme before it feels obvious.) Even before AI, how different or personal were resumes, really? Over time, they have become increasingly standardized, following similar templates and containing similar buzzwords.
If your AI policy claims to value human work, then create opportunities for people to demonstrate their humanity. Ask for stories and reflection. Ask questions that require thinking rather than formatting.
Choose accountability. Not consequences. Not control. Not surveillance. Accountability is not about catching people. It is about creating environments where responsibility matters. It is about establishing expectations while also modeling them. It is about influence rather than authority. It’s about holding people and holding spaces where accountability can flourish.
Strong leaders do not merely tell people what not to do. They help people understand why choices matter. They create cultures where integrity is valued because it contributes to collective success, not because someone might get caught violating a policy.
Technology will continue advancing whether we are ready or not. More powerful tools will emerge. More tasks will become automated. More opportunities for shortcuts will appear.
The question is not whether AI will change the way we work. It already has.
The question is whether we will change the way we lead.
You don’t have to become part of the road - cemented, immobile, waiting for the next steamroll of technology.
You can choose to adapt.
You can choose to lead.
You can choose to roll on.
*Footnotes:
Griffin, J. (2022, August 19). Human Lag: What is it and how does it contribute to the Capacity Gap? Medium. https://joanne-griffin.medium.com/human-lag-what-is-it-and-how-does-it-contribute-to-the-capacity-gap-6dc5d45c32a3
As always, this section is about unlocking/disrupting your thinking. As you read, think about the ways in which your current models of leadership are serving (or not serving) you well!
The Songbox and the Forest Council
For generations, the birds of Greenwood Forest earned their place in the annual Spring Gathering by singing.
Each year, young birds would stand before the Forest Council and perform a song of their own creation. The council listened carefully—not for perfection, but for originality, courage, and understanding. Some birds sang beautifully. Others stumbled through forgotten notes. But every song revealed something about the bird who sang it.
Then one spring, a merchant arrived carrying a curious invention. It was a small silver box with a brass crank on the side.
"What does it do?" asked the birds.
The merchant smiled. "It makes songs."
The birds laughed. "Impossible."
The merchant turned the crank. Out poured a melody so beautiful that the entire forest fell silent. The notes danced through the trees like sunlight through leaves. Every phrase seemed perfectly composed.
The birds were astonished.
Soon, every bird wanted a Songbox.
The nightingales used it to create harmonies. The robins used it to improve their performances. The wrens used it to experiment with new styles. The boxes became so common that within a few seasons hardly any bird wrote songs without one.
At first, the Forest Council was impressed. The performances had never sounded better. Every song was polished. Every melody was elegant. Every bird seemed talented.
But after several years, something strange began to happen. The council noticed that many songs sounded remarkably similar. More concerning, when council members asked birds about their music, they often struggled to explain it.
A finch performed a magnificent ballad but could not describe why it had chosen certain notes.
A thrush sang an intricate melody but could not explain what inspired it.
One young sparrow delivered a breathtaking performance yet froze when asked how she might change the song if a section wasn't working.
The council grew worried.
"The Songboxes are ruining music," declared Owl, the eldest member.
"They make birds lazy," said Hawk.
"They should be banned," agreed Raven.
The council quickly drafted new rules.
No Songboxes during auditions.
No Songboxes during rehearsals.
No Songboxes within sight of the gathering grounds.
The rules were nailed to trees throughout the forest. For a few weeks, the council felt satisfied. Surely the problem was solved. Yet the next spring, little had changed.
Some birds secretly used Songboxes before auditions. Others borrowed melodies from friends who used Songboxes. Many simply became better at hiding them. The council became frustrated.
One evening, as the members debated harsher penalties, an old tortoise slowly approached. The tortoise had never served on the council, but he was known throughout the forest for asking inconvenient questions.
"I have listened to your concerns," he said. "May I ask something?"
The council reluctantly agreed.
"Before the Songboxes arrived, what were you trying to learn from the songs?"
The members looked at one another. Finally, Owl answered. "We wanted to discover which birds understood music."
The tortoise nodded. "And are songs still the best way to discover that?"
The council fell silent. The question lingered in the air.
The following year, the Forest Council changed its approach. Birds still sang. But afterward, they were asked different questions.
They were asked to explain how they developed their songs. They were asked to describe a mistake they had made while learning. They were asked to teach a young bird a simple melody. They were asked to create a short tune on the spot from sounds they heard in the forest around them.
Some birds still used Songboxes. Some did not.
But the council began learning far more about each bird than a polished performance alone could reveal. To everyone's surprise, the Songboxes themselves became less important. The birds who truly understood music could explain it, adapt it, and build upon it. The birds who relied entirely on the boxes could not.
Years later, a young robin asked the old tortoise whether the council had defeated the Songboxes.
The tortoise chuckled. "No."
"Then what did they do?"
"They stopped trying to control the tool and started improving the test."
The robin thought about this for a moment. "And that solved the problem?"
The tortoise smiled. "It solved the council's problem. The Songboxes were never the problem."
Apply the Learning - A Case Study: The Hiring Dilemma
Maya Hernandez spent six years building her organization's learning and development department. As Director of Talent Development for a growing education technology company, she was responsible for hiring a new Learning Solutions Manager—a role that would lead professional development, support clients, and collaborate across multiple departments.
The position attracted more than 200 applicants.
At first, Maya was encouraged by the quality of the applications. Nearly every resume highlighted leadership, innovation, collaboration, and strategic thinking. Cover letters described candidates who were passionate about improving outcomes, driving impact, and creating meaningful change.
But as she continued reading, something felt off.
Many applications used remarkably similar phrasing. Candidates described themselves as "results-oriented professionals," "transformational leaders," and "strategic problem-solvers." Nearly everyone claimed to have improved efficiency, increased engagement, and delivered measurable results. Few provided specific examples.
During the first round of interviews, Maya's concerns grew.
Several candidates spoke confidently about projects listed on their resumes but struggled to explain their decision-making processes. One applicant referenced a district-wide implementation effort yet could not clearly articulate the challenges involved or how success had been measured. Another candidate described herself as an expert facilitator but had difficulty providing examples of how she adjusted training based on participant feedback.
After a particularly frustrating day of interviews, Maya met with her hiring team.
"We need a policy," one manager said. "Let's require applicants to certify that they didn't use AI in their materials."
Another proposed adding an AI detection tool to the screening process.
A third suggested eliminating cover letters altogether.
The discussion quickly shifted from identifying talent to preventing misuse of technology.
Yet Maya wasn't convinced those solutions addressed the real problem. After all, was AI the issue?
The organization itself had encouraged employees to explore AI tools. Internal teams used AI to draft communications, summarize meetings, and brainstorm ideas. Senior leaders frequently discussed the importance of innovation and adaptation.
Would it be fair to prohibit applicants from using a tool that employees were encouraged to use?
On the other hand, Maya worried that heavy AI use might mask important gaps in communication, critical thinking, and expertise. The role required employees to facilitate workshops, respond to client questions, and make complex decisions in real time. No AI tool would be available to answer every question during a live training session.
Case Study Questions
What assumptions are team members making about AI, applicants, and authenticity?
How might the organization's own use of AI influence expectations for candidates?
If you were Maya, what changes would you make to the hiring process, and why?
Where do you see examples of authenticity, adaptation, and accountability competing with one another?
What leadership actions might help the organization move beyond simply creating another policy?
