These AI workflows are designed to support executive operations in high-stakes, fast-moving environments.
Each workflow addresses a specific operational failure point such as information overload, fragmented tracking, and missed execution. AI is used to synthesize inputs, detect patterns, and surface risks, while human oversight remains central to decision-making.
Current workflows include:
Executive Weekly Digest: consolidates operational data to highlight progress, blockers, and decisions requiring attention.
Operator’s Bookshelf Importer: structures insight from reading into a searchable, reusable knowledge system.
These systems are built to reduce noise, improve execution, and support more informed leadership decisions without adding friction.
Tools
ChatGPT, Google Apps Script
Time to build
90 mins
Time saved
2 hours/week
Use cases
Turning reading into structured, searchable knowledge
Capturing insights from books, articles, and research
This workflow automates the ingestion of book data into a structured Notion database using a hybrid extraction and validation approach.
A user submits a book URL, which is programmatically fetched and parsed to extract deterministic metadata, including Open Graph tags and schema.org Book data when available. Publisher and source-level structured data are prioritized to ensure accuracy and consistency. When required fields are missing or incomplete, an AI model is used selectively to infer a concise summary, normalize category values, and resolve remaining gaps.
All extracted data is surfaced in a review layer, allowing the user to verify and approve the record prior to persistence. Upon approval, the system creates a new Notion database entry, maps values to the correct property types, initializes reading progress fields, and applies the book’s cover image as the Notion page cover.
This approach ensures high-quality, consistent records while eliminating manual data entry, copy-paste workflows, and asset management, resulting in a reliable and scalable personal knowledge catalog.
Tools
ChatGPT, Google AI Studio
Time to build
3 hours
Time saved
3 hours/week
Use cases
Weekly leadership briefings
Tracking execution across teams and projects
Flagging stalled decisions and follow-through gaps
Reducing meeting prep and status update noise
Supporting faster, more informed executive decisions
This workflow automates the ingestion of book data into a structured Notion database using a hybrid extraction and validation approach.
A user submits a book URL, which is programmatically fetched and parsed to extract deterministic metadata, including Open Graph tags and schema.org Book data when available. Publisher and source-level structured data are prioritized to ensure accuracy and consistency. When required fields are missing or incomplete, an AI model is used selectively to infer a concise summary, normalize category values, and resolve remaining gaps.
All extracted data is surfaced in a review layer, allowing the user to verify and approve the record prior to persistence. Upon approval, the system creates a new Notion database entry, maps values to the correct property types, initializes reading progress fields, and applies the book’s cover image as the Notion page cover.
This approach ensures high-quality, consistent records while eliminating manual data entry, copy-paste workflows, and asset management, resulting in a reliable and scalable personal knowledge catalog.
