AI work notes
Before changing direction, organize the evidence you can show
How I start with target roles, find evidence in my work, and decide whether to learn, build a project, or pursue a certificate.
Start with roles you actually want
“Familiar with AI tools” tells a hiring manager little about the problems I can solve. When preparing my next step, I choose public roles I actually want and mark required, preferred, and unmentioned qualifications before grouping recurring requirements.
I read job descriptions to decide where to put my preparation. Rather than guessing my direction from market headlines, I ask whether I have an experience that explains each requirement. If not, am I missing knowledge, responsibility at that scope, or a deliverable someone can inspect?
Find the problems you repeatedly solve
Positioning can easily turn into abstract labels. For “cross-functional communication,” I ask which disagreement I clarified, which trade-off I made, and who could move forward afterward. Actions and outcomes help me see the problems I repeatedly solve.
I bring together my résumé, public experience, and self-written retrospective summaries from different periods. I ask AI to attach sources to each proposed label, then look for contrary evidence and another explanation. A label with only one supporting story stays provisional. I turn internal material into a shareable summary.
Explain AI ability through a workflow
For interview practice, I use five parts: the original problem, AI’s task, the judgment I retained, validation, and the actual outcome. I can mention the tool, but I want the listener to understand how I redesigned the work and handled errors.
An illustrative answer is: “AI extracted fixed fields from public sources; I checked each field against the original and corrected discrepancies with a note.” When writing my own version, I use work I actually did. Without a measurement, I describe observations rather than asking AI for a saving percentage.
Put certification in the gap it can address
After earning the Google Cloud Generative AI Leader certificate, I wanted to compare it with my target roles: which knowledge did it help me develop, and which abilities still needed work evidence? It gave my learning a route; I still needed to build evidence of delivery separately.
Before choosing a certificate now, I ask whether the role mentions it, whether the syllabus addresses a confirmed gap, and whether I am hoping it can replace a project or experience. I retain the wording of the public syllabus and job requirements. I plan learning for a knowledge gap and a separate small project when delivery evidence is missing.
Keep a small, traceable achievement record
The achievement record I want is simple: the problem, my decision, the deliverable, and the outcome with its limits. I add a safe link or summary and distinguish my contribution from the team’s result. AI can organize the fields; I choose what deserves keeping.
I start with one gap at a time: learn a topic, rewrite a true story, or build a bounded project. Then I check which requirement it addresses. Preparation grounded in traceable work helps me see whether I am moving toward the role I want.