I enrolled in AI Mastery to build practical judgment and a stronger process lens for applying AI responsibly within my organization – a non-profit working to improve transparency and integrity in food systems.
I was already working in systems- and process-heavy environments and experimenting with AI in limited ways.
My interest wasn’t in novelty or automation for its own sake.
I wanted to understand how AI could support real workflows without adding noise or risk.
How I’ve been using AI
AI has become a practical thinking partner in my work.
I’ve used it to support technical understanding, workflow design, and documentation—especially when unfamiliar systems would otherwise slow progress.
With AI as an assistant (and a very patient JSON expert), I was able to work through a significant error in a database-integrated tool. It had been built by a more experienced colleague who was unavailable for a week, and redundancy hadn’t yet been developed.
Using iterative prompting—clearer instructions, constraints, context, screenshots, and follow-up questions. I learned how to interpret JSON structures and data flows (including APIs and MCPs) well enough to keep most work moving and avoid bottlenecks.
That same approach also supported personal work on a newly conceived iPhone app, allowing me to create something I never would have imagined possible with my level of knowledge.
AI as a tool for knowledge transfer & continuity - an example
I created a custom GPT to support knowledge transfer for a major project I had led while in a different department and that was needed by my successor.
It was trained with documents providing historical context, decision rationale, and domain-specific information.
The GPT gave my successor the ability to ask questions like 'Why was this approach chosen over alternatives?' or 'What was the rationale behind this timing requirement?' without interrupting active work, helping preserve institutional knowledge while reducing reliance on ad hoc explanations.
What changed for me: The biggest shift has been in how I approach problems.
I didn't just learn how specific tools work—I learned the underlying concepts that apply across platforms. That means I can evaluate new tools, adapt to changing stacks, and choose the right fit for the problem at hand.
I now see AI literacy as a combination of technical understanding and systems thinking:
knowing what problem I’m solving,
how work flows,
why context matters,
and being specific about the output I’m looking for.
I learned to treat AI outputs as drafts to interrogate, not answers to accept without discernment.
This has improved the quality of results, my own thinking, and how I communicate with colleagues for better outcomes.
Creative exploration: Beyond professional use, I had fun experimenting creatively.
I used AI to bring poems I had written years ago to life as playful, fully formed songs.
I also transformed existing artwork into new designs and short videos.
This reinforced that AI doesn’t have to be purely utilitarian—used well, it can enhance curiosity and creativity.
Key takeaways: Clear problem framing matters more than tool choice.
Iteration improves both outputs and thinking.
For example, while working through a spreadsheet-driven data cleanup, iterating with AI pushed me to clarify system constraints, test assumptions, and refine the logic step by step—improving not just the outcome, but how I reasoned about the data itself.
AI is most effective when it supports—not replaces—existing expertise.
I gained transferable AI literacy—not just proficiency in one tool, but understanding of how AI tools think, so I can be discerning about which one serves a given workflow, team, or mission goal.
Choosing the right AI stack for different kinds of work feels like the next stage of the journey.
*Written with the assistance of AI. I provided the substance, judgment, and boundaries; AI offered scaffolding, reflection, and iteration.
I was already working in systems- and process-heavy environments and experimenting with AI in limited ways.
My interest wasn’t in novelty or automation for its own sake.
I wanted to understand how AI could support real workflows without adding noise or risk.
How I’ve been using AI
AI has become a practical thinking partner in my work.
I’ve used it to support technical understanding, workflow design, and documentation—especially when unfamiliar systems would otherwise slow progress.
With AI as an assistant (and a very patient JSON expert), I was able to work through a significant error in a database-integrated tool. It had been built by a more experienced colleague who was unavailable for a week, and redundancy hadn’t yet been developed.
Using iterative prompting—clearer instructions, constraints, context, screenshots, and follow-up questions. I learned how to interpret JSON structures and data flows (including APIs and MCPs) well enough to keep most work moving and avoid bottlenecks.
That same approach also supported personal work on a newly conceived iPhone app, allowing me to create something I never would have imagined possible with my level of knowledge.
AI as a tool for knowledge transfer & continuity - an example
I created a custom GPT to support knowledge transfer for a major project I had led while in a different department and that was needed by my successor.
It was trained with documents providing historical context, decision rationale, and domain-specific information.
The GPT gave my successor the ability to ask questions like 'Why was this approach chosen over alternatives?' or 'What was the rationale behind this timing requirement?' without interrupting active work, helping preserve institutional knowledge while reducing reliance on ad hoc explanations.
What changed for me: The biggest shift has been in how I approach problems.
I didn't just learn how specific tools work—I learned the underlying concepts that apply across platforms. That means I can evaluate new tools, adapt to changing stacks, and choose the right fit for the problem at hand.
I now see AI literacy as a combination of technical understanding and systems thinking:
knowing what problem I’m solving,
how work flows,
why context matters,
and being specific about the output I’m looking for.
I learned to treat AI outputs as drafts to interrogate, not answers to accept without discernment.
This has improved the quality of results, my own thinking, and how I communicate with colleagues for better outcomes.
Creative exploration: Beyond professional use, I had fun experimenting creatively.
I used AI to bring poems I had written years ago to life as playful, fully formed songs.
I also transformed existing artwork into new designs and short videos.
This reinforced that AI doesn’t have to be purely utilitarian—used well, it can enhance curiosity and creativity.
Key takeaways: Clear problem framing matters more than tool choice.
Iteration improves both outputs and thinking.
For example, while working through a spreadsheet-driven data cleanup, iterating with AI pushed me to clarify system constraints, test assumptions, and refine the logic step by step—improving not just the outcome, but how I reasoned about the data itself.
AI is most effective when it supports—not replaces—existing expertise.
I gained transferable AI literacy—not just proficiency in one tool, but understanding of how AI tools think, so I can be discerning about which one serves a given workflow, team, or mission goal.
Choosing the right AI stack for different kinds of work feels like the next stage of the journey.
*Written with the assistance of AI. I provided the substance, judgment, and boundaries; AI offered scaffolding, reflection, and iteration.
