GenAI Workflow Design
From Meeting Notes to Structured CRM Actions
A practical example of using AI to remove administrative work without pretending every step should be fully autonomous.
- reduction in manual administrative work
- ~90%
The problem
Customer and franchise consulting conversations generated valuable information, but turning notes into usable CRM data created repetitive administrative work.
The workflow included tasks such as reviewing raw meeting notes, identifying important business context, summarizing the conversation, extracting follow-up actions, organizing next steps, and preparing information for CRM entry and reporting.
The work was necessary, but much of it did not require a person to manually rewrite the same information into multiple formats.
My role
I designed and prototyped a workflow that used generative AI and automation to transform unstructured notes into structured outputs.
How I approached it
Why human validation stayed in the loop
A major implementation constraint was the CRM/API environment. Rather than forcing brittle end-to-end automation, I designed the workflow around the actual system constraints.
The AI handled the high-volume transformation work. The human stayed responsible for validating important context, checking sensitive or ambiguous information, and confirming the final CRM update.
This reduced the administrative burden dramatically without sacrificing trust.
The system / workflow
Outcome
The workflow eliminated roughly 90% of the manual administrative work involved in turning raw notes into structured CRM-ready documentation and follow-up actions.
What I learned
This is a small workflow compared with an enterprise transformation, but it demonstrates the way I approach AI implementation: understand the actual work, identify where AI has leverage, design around real system constraints, keep humans where judgment matters, and optimize for adoption and reliability rather than novelty.