Selected work · Capability overview
AI Enablement
I develop AI capability inside the team.
The pilot builds local capability so the department can keep improving its own workflows.
Proof story 02 · Sandbox VR · Marketing
AI enablement: capability that stays in the department
AI could clearly speed up a marketing workflow. The open question was whether the people closest to the work could identify, build, validate, operate, and improve those workflows themselves.
- Problem
- Manual marketing workflows consumed capacity, and every automation request routed back through me.
- Intervention
- A two-week applied cohort where two Marketing Super Users mapped, built, tested, calibrated, and shipped their own AI-enabled workflows.
- Result
- Roughly 16 hours a month returned without openings and roughly 32 with one to two, plus two local builders who can build the next one.
- Designed the cohort sequence and the work-based assessment
- Facilitated live instruction on workflow decomposition, context design, and Claude Projects
- Coached the builds between sessions and ran calibration
- Handed ownership, maintenance, and the executive presentation to the Super Users
Results
What actually moved
16 hrs/mo
Capacity returned in months without new-store openings
32 hrs/mo
Capacity returned in months with one to two openings
2
Local builders able to maintain and extend the workflows
2 weeks
Pre-work through handoff
Eight stages, two hard gates
The build sequence participants actually ran
The pilot produced two capability owners who kept finding and building workflow improvements after it ended.
01
Map
Record the existing workflow, document each step, document effort, name failure points, capture baseline measures.
Gate: No baseline, no build.
02
Diagnose
Decide whether this workflow should be built, redesigned first, or left alone.
03
Build
Workflow decomposition, context design and project setup, then independent building between sessions.
04
Test
Hunt for failure cases and separate plausible output from usable output.
05
Calibrate
Work the inconsistent outputs: strengthen source material, tighten context, add safeguards.
06
Apply
Run it on real work, tracking time, manual steps, review effort, errors, and how often help was needed.
Gate: Nothing irreversible runs unattended.
07
Operationalize
Document ownership, quality checks, failure points and maintenance so the workflow outlives the cohort.
08
Hand off
Ownership transfers, support ends, and the next workflow starts inside the department.
It works. I know why it works. I know when it does not work. I can maintain it. Someone else can use it.
Capacity returned
Two operating conditions, reported separately
16 hrs/mo
Returned in months with no new store openings
32 hrs/mo
Returned in months with one to two openings
Participant-calculated capacity. Participants timed the existing process under each operating condition, timed the redesigned workflow, and applied the difference to normal monthly volume. Each condition is reported separately.
Third-party evidence
What people who worked with me observed
LinkedIn recommendations from people who saw this work up close.
She has a genuine passion for systems and finding better ways of working, and she quickly became our team’s go-to leader for all things AI. Danielle not only built systems and workflows using AI automation, but generously brought the rest of us along with her, teaching us new tools and helping us incorporate AI into our own workflows.
Jay Reynolds Jr.
People team peer
She can jump into a brand new subject, teach herself everything she needs to know, and before long she is the person everyone goes to with questions. Most impressively, that is how she became our AI expert and built Apollo, our company's own internal chatbot.
Chandler Curione
People team peer