Leadership / Applied AI practice
How I work
with AI.
I bring experience leading data strategy and transformation to the practical use of AI.
My independent projects give me hands-on practice using AI for research, prototyping, and repeatable workflows. I define the problem, set requirements, and evaluate whether the result is useful.
I would bring that practice into my work by identifying useful applications, testing approaches, and helping teams decide what is ready to use and what still needs review.
Explore a project exampleSkills I bring to the work
Research and planning
I use AI to organize information, compare approaches, and identify questions worth investigating. I ask for supporting sources so I can distinguish a useful finding from a plausible answer that needs more work.
Before moving into implementation, I work through the requirements and trade-offs. That gives me something concrete to evaluate the result against.
Building and improving tools
I work with AI to develop prototypes and applications for problems I encounter in my own projects. I set the requirements and review how the result behaves, including whether the interface makes sense to the person using it.
The first version often reveals something the original brief missed. I use that feedback to refine the work, and I’m willing to revisit the approach when an implementation meets the instructions but misses the purpose.
Making recurring work repeatable
For recurring tasks, I define the inputs, expected outputs, and review points before developing a reusable workflow. This makes the process easier to evaluate and revise.
This also means deciding what should remain a human decision. A repeatable process is only useful if I can understand its results and recognize when something has gone wrong.
Independent project exampleA research-to-audio briefing
Collapse project
I wanted a way to listen to a discussion of AI developments rather than work through every source individually. I used AI to help develop a workflow that turns selected research into an audio briefing.
What I contributed
I defined the editorial requirements: what the briefing should cover, how the speakers should discuss it, and what would make it worth listening to.
I wanted a substantive conversation rather than a sequence of headlines or one speaker interviewing the other. I also set expectations for grounding the discussion in the supplied research and avoiding repeated material.
As the project developed, I requested changes to the format and the production process. Those decisions shaped both the listening experience and how the work could be checked.
What AI contributed
AI helped develop the workflow and prepare a discussion script from a research packet. Speech-generation tools turned the script into audio.
I also used AI assistance to investigate problems, revise instructions, and develop checks for the resulting content.
How I approached quality
A natural-sounding conversation can still misrepresent its sources. The workflow includes checks that compare the script with the research packet, alongside editorial checks for repetition and the requested discussion format.
These checks have limits. They help identify problems, but they don’t independently establish that every source is correct or that the briefing is complete.
What this demonstrates
This project involved more than generating audio. I translated a personal need into requirements, made editorial and technical decisions, and worked through revisions to the process.
The transferable skill is taking a loosely defined need and turning it into a workflow with clear inputs, quality checks, and review points. That is the approach I would bring to evaluating AI opportunities at work.
What I remain responsible for
I decide what problem is worth solving, what information is appropriate to use, and whether the output is ready to share.
I don’t treat a confident answer or a completed task as sufficient evidence of quality. I ask for sources and verification, question results that don’t fit the brief, and request revisions when needed.
My AI practice here comes from independent projects; my professional foundation is in data strategy and transformation. At work, I would apply that experience within the organization’s policies, approved tools, and confidentiality requirements.
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