Let’s talk about the little voice in your head that shows up right after AI helps you finish something.
You’ve been staring at a blank page for 45 minutes.
The board update needs to go out. There’s a grant report waiting. Someone needs a first draft of the new policy. You have meeting notes that somehow need to become actual next steps.
So you open an AI tool.
Ten minutes later, you have a starting point.
And then, somewhere between relief and hitting “save,” a thought creeps in:
Wait… is this cheating?
If you’ve had that thought, I don’t think you’re alone.
And honestly? I think it’s an important question.
Not necessarily because using AI is cheating, but because the discomfort behind the question tells us something about how quickly our definition of “doing the work” is changing.
We’ve had this argument before
Think about how many tools we use every day that have quietly become part of doing our jobs.
Spellcheck catches mistakes.
Excel performs calculations we certainly could do ourselves.
Templates keep us from recreating the same document every month.
CRM automations send reminders.
Calendars figure out when eight people are simultaneously available, which may actually qualify as magic.
None of those tools make the work less legitimate.
They reduce the effort required to complete a task so we can spend more time on the parts requiring human judgment.
AI can do the same thing.
But there is an important difference.
AI doesn’t always feel like a tool.
Sometimes it feels like another person did part of the work.
And that’s where things get uncomfortable.
Maybe the better question isn’t “Did AI help?”
Maybe the question is:
Who did the thinking?
There is a meaningful difference between using AI to help you express an idea and asking AI to decide what you believe.
If I know what I want to communicate but I’m struggling with structure, AI can help me organize it.
If I have meeting notes and need to pull out potential action items, AI can help me identify them.
If I’ve written something painfully long, AI can help me make it shorter.
If I’m preparing for a difficult conversation, AI can help me think through questions I haven’t considered.
But if I ask AI:
“Tell me what our organization should do.”
…and then copy the answer into a recommendation without applying my own experience, context, skepticism, or judgment?
That’s different.
AI can accelerate thinking. It shouldn’t replace responsibility.
The nonprofit version of this question matters even more
For nonprofits, this isn’t just about personal productivity.
Our work may involve donor information, client information, financial data, health-related information, employee information, board materials, grant requirements, and details about people and communities who have trusted organizations with their stories.
That means “Can AI do this?” isn’t enough.
We also need to ask:
- Should this information be entered into this tool?
- Do we understand what happens to the data?
- Are we authorized to use it this way?
- Does our organization have an AI policy?
- Does a human need to verify the output?
- Would we be comfortable explaining how AI was used?
Efficiency is valuable.
Trust is more valuable.
My very unscientific AI test
When I’m thinking about whether AI belongs in a task, I like four simple questions.
1. Is AI assisting my thinking or replacing it?
Using AI to brainstorm ten questions for a software vendor?
Helpful.
Asking AI which vendor your nonprofit should purchase based solely on marketing materials?
Probably not the decision-making process I’d recommend.
The human needs to own the conclusion.
2. Do I understand what I’m giving the AI?
This is the one that deserves a giant pause button.
Before pasting information into any AI tool, ask what is actually in it.
Names?
Donor information?
Client records?
Financial information?
Employee details?
Passwords or credentials?
Confidential strategy?
A surprisingly useful habit is to stop before pasting and ask:
Would I put this information into a tool if I didn’t know exactly where it was going?
If the answer is no, stop.
3. Can I verify the result?
AI can produce an answer that sounds remarkably confident and is completely wrong.
That combination matters.
If you’re unable to evaluate whether the output is accurate, you probably shouldn’t be the final reviewer.
This becomes especially important when AI is being used around legal requirements, cybersecurity, finance, healthcare, compliance, or other areas where “That sounded right” isn’t an acceptable quality-control process.
4. Would I be comfortable explaining how I used AI?
This might be my favorite test.
Imagine someone asked:
“Did AI help you with this?”
Would you calmly say:
“Yes. I used it to organize the initial draft, then I reviewed it, changed the recommendations, verified the information, and wrote the final version.”
Great.
But if your immediate reaction is:
“Well… technically…”
That discomfort might be worth exploring before you hit send.
AI should probably do more of the boring stuff
There are parts of nonprofit work where human attention is incredibly valuable.
Meeting with a donor.
Listening to a client.
Coaching an employee.
Thinking through a strategic decision.
Understanding why a process isn’t working.
Building trust with a community.
Leading people through change.
And then there are tasks like:
“Please turn these 17 bullet points into three categories.”
I am perfectly comfortable letting a computer take a first pass at that.
The goal shouldn’t be to prove we can do everything manually.
The goal should be to preserve human capacity for the work where being human actually matters.
That’s an important distinction.
But “AI made it” isn’t a quality standard
There is another side to this.
AI has made it incredibly easy to produce more.
More emails.
More presentations.
More policies.
More social posts.
More reports.
More words.
That doesn’t automatically mean more value.
In fact, organizations could easily use AI to dramatically increase the volume of content everyone else has to read.
Please don’t.
Perhaps the question we should ask isn’t:
“How much more can we produce with AI?”
Maybe it’s:
“What can we do better because AI reduced the effort somewhere else?”
That’s the productivity conversation I want nonprofits to have.
So… is using AI cheating?
I don’t think using AI is inherently cheating.
I think outsourcing your responsibility is the problem.
Use AI to create a starting point.
Use it to challenge an assumption.
Use it to summarize information you are permitted to share with the tool.
Use it to explore alternatives.
Use it to improve clarity.
Use it to turn the intimidating blank page into something you can actually react to.
Then bring the human part back in.
Your experience.
Your context.
Your voice.
Your skepticism.
Your knowledge of the organization.
Your understanding of the people affected.
And most importantly:
Your accountability for the final result.
Because perhaps the real line isn’t between work created with AI and work created without AI.
It’s between work we thoughtfully own…
and work we simply pass along.
A five-question check before you hit send
Before using AI-generated work, ask:
1. Did I give the AI information I was actually authorized to share?
2. Have I checked the important facts rather than trusting the output?
3. Did I add the organizational context the AI couldn’t possibly know?
4. Does the final result actually reflect my judgment and voice?
5. Am I willing to take responsibility for what happens next?
If you can’t confidently answer those questions, don’t hit send yet.
If you can?
Maybe you didn’t cheat.
Maybe you just used a tool well.
A Stack for Good thought
Technology should give nonprofit teams more capacity for mission, not create another reason to feel guilty about how the work gets done.
The challenge with AI isn’t proving that we can do everything without it.
It’s learning when to use it, when not to use it, and how to keep human judgment firmly in the driver’s seat.
And honestly?
That’s not cheating.
That’s governance.
Suggested closing CTA
Wondering whether your organization is actually ready for AI?
Before adding another tool to the stack, take a step back and look at policy, data, ownership, staff readiness, and risk.
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