Case Study
Enterprise AI Enablement for Marketing Operations
A practical AI operating model for accelerating internal tooling, augmenting governed workflows, and scaling team adoption.
The Problem
AI adoption in enterprise marketing operations cannot be treated as a shortcut around governance. Campaign work still depends on accurate hierarchy, attribution, routing, tokens, UTMs, launch steps, approvals, documentation, and reporting logic.
The opportunity was to use AI where it created real leverage: helping me build internal tools faster, reducing manual campaign-operations translation work, and making complex operating knowledge easier for marketers and MOPs teammates to use.
The Build
First, I use AI as an engineering accelerator. LLM-assisted development workflows help me move faster on Python/Flask architecture, SQL validation, UI iteration, API integration, QA, and documentation while I still own the requirements, data model, governance rules, and final implementation.
Second, I use AI and automation to augment campaign operations workflows. Structured campaign inputs can become governed outputs: Marketo program setup, token population, UTM generation, Snowflake-backed hierarchy updates, list validation, forecasting, Asana handoffs, and launch context.
Third, I use AI for the documentation and enablement layer. I translate legacy business language, process notes, and operating rules into clean Markdown artifacts that are readable for people and accessible to AI systems, so the knowledge layer becomes easier to maintain, search, reuse, and embed in future workflows.
What Changed
- Developed a practical AI maturity model: chat-based reasoning, AI-assisted SQL/Python development, context-managed dev workflows, and AI-supported audit and enablement use cases.
- Leveraged Glean to build documentation and process-support agents that help marketers navigate campaign build workflows, operating rules, and launch guidance.
- Used AI-assisted development to support Asana parsing, Marketo workflow automation, forecasting, list validation, and campaign documentation around the Python/Flask campaign operations app.
- Converted legacy documentation and process knowledge into Markdown-based enablement assets designed for both human adoption and AI-era retrieval, summarization, and workflow support.
- Piloting Snowflake Cortex lifecycle attribution audits that help summarize SQL outputs and identify MOPs follow-up actions for campaign attribution cleanup.