AI Productivity

1: Inbox & Documents

Reducing hours lost daily processing emails and staring at blank pages when drafting proposals or reports. This is achieved by mastering native AI capabilities within Gmail and Google Docs. Participants learn to use Gemini or ChatGPT side-panels to summarize long threads, extract key requests, and generate draft replies. In Google Docs, the focus is on using AI to expand a single concept into a structured outline and executive summary.

Outcome: A streamlined inbox workflow and a library of templates for rapid document generation.

2: Digital Cloning

Ensuring AI writing does not feel robotic or lack a unique personal voice and brand. Participants train the AI on specific vocabulary and sentence structures to create a reusable architecture that mimics their personal style. As well, participants build tailored assistants using Custom GPTs and Gems designed to ingest messy transcripts and automatically output structured minutes, action plans, and delegation instructions.

Outcome: A personal assistant that transforms raw transcripts into deadline-driven execution plans in your style.

3: Output Control

Eliminating generic or poorly formatted AI outputs that typically require significant manual editing. By replacing trial-and-error with rigid frameworks, participants practice the Context Sandwich (layering requests between background context and desired format) and the Constraint Box (setting strict boundaries on word count and resources to eliminate AI decision paralysis). forcing step-by-step reasoning and recommendations, evaluating scenarios from different perspectives.

Outcome: A personal library of plug-and-play prompt templates guaranteed to produce high-quality formatting.

4: AI “Second Brain”

Consolidating vital data buried in reports and PDFs that is difficult to retrieve when needed. Utilizing Google NotebookLM to upload proprietary documents and meeting notes, participants learn to chat directly with a closed ecosystem of data, extract precise citations, and map relationships between documents.

Outcome: A queryable knowledge base that retrieves insights exclusively from uploaded proprietary data.

5: Wikis & Instructions

Rescuing institutional knowledge that is siloed and reducing the time spent creating training materials manually. Using AI to transform unstructured brain-dumps or voice notes into standardized documentation, participants generate text-based Wikis for team knowledge sharing and draft comprehensive Standard Operating Procedures.

Outcome: A functional internal Wiki and three finalized SOPs for critical workflows.

6: Deep Research

Moving past traditional search engines that prioritize ads and chatbots that may hallucinate facts. By mastering Perplexity to conduct deep-dive research, the focus is on using conversational follow-ups to analyze market trends and competitor data while ensuring factual accuracy through cited sources.

Outcome: A comprehensive, heavily cited research brief on a complex industry topic.

7: Spreadsheet Automation

Simplifying the analysis of raw data and the creation of complex formulas, which are usually tedious manual tasks. Using AI to act as a data analyst, participants learn to paste raw data or messy text into the AI to clean information, highlight anomalies, and generate specific formulas like VLOOKUP or INDEX/MATCH.

Outcome: A formatted data report and a set of AI-generated formulas ready for immediate use.

8: Content Repurposing

Ending the manual conversion of reports into slide decks or various communication formats, which is a duplication of effort. Using ChatGPT or Gemini, participants convert raw information into presentation outlines and speaker notes while repurposing a single asset into different formats, such as emails or brief updates.

Outcome: A slide-ready text outline and a week’s worth of repurposed communication assets.

9: No-Code AI Agents

Removing the need for manual data entry and moving information between apps, which kills productivity. Using Zapier to connect tools like Gmail and ChatGPT, participants build an autonomous background agent—such as a workflow where an incoming email triggers an AI-generated summary and logs the data in a spreadsheet.

Outcome: A live, functional automated workflow running in the background.