Building an AI-Enabled Company Operating System
How I turned fragmented organizational knowledge into practical AI-assisted workflows across finance, product, governance, documentation, and operations, improving continuity and leverage without adding headcount.
At ESMC, important work and institutional knowledge were spread across too many places: meetings, email, SharePoint, Confluence, Jira, project documents, financial models, product decisions, policy work, and individual memory. The problem was not a lack of information. It was that the information was difficult to find, connect, reuse, and carry forward consistently.
In a resource-constrained environment, adding more people was not always the answer. The organization needed a better way to preserve context, turn scattered source material into usable work, reduce repetitive effort, improve documentation, and give staff stronger support across increasingly complex responsibilities.
I led the company-wide adoption of ChatGPT and built practical AI-assisted workflows around real operating needs rather than isolated experimentation. That included executive and product workflows, curated source materials, reusable documentation patterns, staff training, and connections to tools already supporting the organization’s work. The goal was simple: make good information easier to use and make strong work easier to repeat.
The Operating Problem
The organization had plenty of information, but not enough continuity. Decisions, source material, analysis, project history, and institutional knowledge lived across email, meetings, SharePoint, Confluence, Jira, financial models, product documentation, and individual memory.
That fragmentation created repeated work and made it harder to carry context from one decision to the next. At the same time, a small team was supporting increasingly complex finance, product, governance, data, and operating responsibilities without the option of simply adding headcount.
What Changed
AI became a practical operating layer rather than an isolated productivity experiment. Staff had better access to reusable context, curated source material, documented workflows, and repeatable ways to move from scattered information to analysis, decisions, documentation, and follow-through.
The result was stronger continuity across functions and less dependence on one person remembering where every answer lived. AI-supported workflows also became easier to extend into finance, executive decision support, product work, documentation, data analysis, and recurring operating processes.
What I Led
01 - COMPANY-WIDE CHATGPT ADOPTION
Led the rollout of ChatGPT across the organization and established practical use patterns tied to real work across finance, product, governance, documentation, and operations rather than generic experimentation.
02 - EXECUTIVE WORKSPACE & DECISION WORKFLOWS
Built an AI-enabled executive workspace that connected source material, financial and operating context, decisions, documentation, and recurring workflows so complex work could be carried forward with stronger continuity.
03 - KNOWLEDGE INFRASTRUCTURE & SOURCE CURATION
Created reusable source structures and documentation patterns across SharePoint, Confluence, Jira, and other operating materials so AI-assisted work could begin from better-grounded information rather than disconnected conversations or memory.
04 - PRODUCT, DATA & TECHNICAL WORKFLOW ENABLEMENT
Extended AI-assisted workflows into product, data, and modeling work, including repeatable patterns using ChatGPT, GitHub Copilot in VS Code, and Atlassian tools to support documentation, analysis, task tracking, technical context, and operating continuity.
05 - TRAINING, ADOPTION & OPERATING LEVERAGE
Developed training and practical workflow guidance so staff could use AI with stronger context, better source grounding, less rework, and more consistent output while increasing organizational capacity without adding headcount.
What This Demonstrates
This case demonstrates my ability to treat AI as operating infrastructure rather than a collection of disconnected tools. I can identify where information, decisions, documentation, and follow-through are breaking down, then design practical AI-assisted systems around the way people actually work.
The value did not come from simply giving employees access to ChatGPT. It came from connecting AI to curated source material, repeatable workflows, existing systems, training, and clear operating patterns. That created better continuity, reduced repetitive work, strengthened documentation and analysis, and gave a small organization greater operating leverage without simply adding headcount.
Capabilities: AI implementation · Executive workflow design · Knowledge architecture · Source-grounded AI · Process automation · ChatGPT adoption · GitHub Copilot · Atlassian/Rovo · Confluence & Jira · Staff enablement · Documentation systems · Data & analysis workflows · Change management · Operating leverage
OPERATING PRINCIPLE
AI creates leverage when it has context, trustworthy source material, and a real job to do. The goal is not more AI output. It is better work, better decisions, and less organizational knowledge lost between them.