AI Terms Leaders Should Actually Know - Especially if You Work in a Mission-Driven Organization
AI is everywhere right now,and if you’re a leader in a nonprofit or mission-driven organization, you’re probably feeling two things at once:
- Pressure to “do something” with AI
- A sense that much of the conversation is happening around you, not with your reality in mind
We work with organizations whose missions center people and real impact, not hype. And here’s the truth we keep coming back to:
You don’t need to understand everything about AI.
But you do need a shared language to talk about it responsibly—across leadership, staff, and boards.
Below are a few AI terms leaders should actually know, explained in plain English, without the jargon.
1. AI vs. Generative AI
These are not the same thing.
AI is a broad umbrella. It includes systems that detect fraud, recommend content, or predict outcomes.
Generative AI is a specific type of AI that creates things - text, images, summaries, code. Tools like ChatGPT live here.
Why it matters: Most of the buzz (and anxiety) right now is about generative AI - not AI as a whole. Conflating the two makes decision-making harder than it needs to be.
2. Prompting
A prompt is simply how you ask the AI for something.
Despite how it sounds, this isn’t about technical skill—it’s about leadership clarity.
- Who is the audience?
- What’s the goal?
- What context matters?
In a prior piece, I described ChatGPT as an intern. This still holds. The quality of the output depends heavily on how well you explain the task.
Why it matters: Leaders who can model good prompting help their teams use AI more effectively - and more thoughtfully.
3. Hallucinations
Yes, that’s the real term.
A hallucination is when an AI tool confidently gives you information that is inaccurate or completely made up.
It’s not lying. It’s predicting text that sounds right.
Why it matters: In mission-driven work where accuracy, trust, and credibility are everything, AI output should never be treated as final. Review isn’t optional.
4. Bias
AI systems reflect the data they’re trained on, and that data comes from the real world. Bias isn’t a hypothetical risk—it’s already present in the systems many organizations use every day.
Which means AI can reinforce:
- Cultural blind spots
- Harmful assumptions
Why it matters: For organizations committed to positive impact, AI must be used with eyes wide open.
5. Context Window
This is a simple concept with big implications.
A context window is how much information an AI tool can “hold” at one time. When you exceed it, older information drops off.
Why it matters: If an AI tool seems to forget earlier instructions, this is usually why. It’s also why breaking work into stages often produces better results.
6. Human-in-the-Loop
This is one of the most important ideas for leaders.
Human-in-the-loop means AI supports the work, but people remain accountable for decisions, judgment, and outcomes.
Why it matters: This aligns directly with how most nonprofits already operate. AI should reduce friction, not remove responsibility.
7. Automation vs. Augmentation
Automation replaces tasks.
Augmentation supports people doing the work.
At FLEX, we see the most responsible (and effective) uses of AI as augmenting:
- Drafting first passes
- Summarizing information
- Supporting analysis
Why it matters: Leaders who frame AI as augmentation create less fear and better adoption.
8. Data Privacy
If you work with sensitive communities, donors, or partners, this one is non-negotiable.
Not all AI tools handle data the same way. What you put in may be stored, reviewed, or used to improve systems.
Why it matters: Responsible AI use starts with understanding what should never be shared with a tool.
The Bottom Line
AI literacy is quickly becoming a leadership skill. Not because leaders need to be technical, but because they need to be thoughtful, accountable, and values-driven.
The goal isn’t to chase every new tool.
It’s to make grounded, values-aligned decisions that serve your mission.
That’s where real impact lives.
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