Successes in AI
These summaries cover recent AI-assisted work, primarily focused on Adobe Experience Manager (AEM) implementations. Key areas include asset management, system integrations, troubleshooting, and migration planning, utilizing tools like Claude Code, Google Gemini, Grok, and ChatGPT to enhance efficiency across enterprise platforms such as
AEM Assets, Dynamic Media, Kafka, and Workfront.
1
Advancements in AI Tool Integration
I have significantly expanded my AI usage by installing Claude Code on my Mac terminal, which overcame earlier constraints like conversation length limits and data size restrictions. This allowed me to directly access large files, including multi-gigabyte logs and spreadsheets, and use Python scripts for complex analyses that were once impractical. Meanwhile, I continued relying on browser-based tools like Gemini for tasks such as estimation and planning.
2
Dynamic Media Troubleshooting
and Asset Analysis
I leveraged AI tools to resolve various Dynamic Media issues in AEM, including publishing delays where assets weren't available promptly on websites. By analyzing these multi-gigabyte log files, I pinpointed root causes like synchronization backlogs and connection timeouts post-Adobe releases, delivering executive reports with metrics and fixes. For specific asset sync failures and video playback glitches, AI enabled binary-level comparisons and re-encoding suggestions, addressing API errors and metadata mismatches to ensure seamless AEM-DM integration.
3
Ingestion Pipelines and Data Reconciliation
In testing Kafka-based asset ingestion, I generated Python scripts with AI help for metadata processing and upload validation, boosting success rates by fixing issues like metadata contamination. For large-scale reconciliations, I automated comparisons of oversized Excel files using scripts, detecting discrepancies without manual effort.
4
Migration to AEM Cloud Service
I generated the team migration runbook for a phased, zero-downtime migration of over 14 million assets from AEM 6.5 to AEM Cloud Service using AI, while referencing the existing architecture documentation as a guide. This runbook outlined operational procedures for each team phase, including roles, responsibilities, discovery checklists, and monitoring requirements, involving integrations with systems like Stibo PIM and Salesforce, with projected timelines of 84-135 days.
5
Integration Specifications
and Metadata Mapping
With AI, I created detailed specs for integrating with third-party image providers, including diagrams and effort estimates. I also mapped metadata fields between Workfront and AEM, and restructured folder organizations and permissions matrices for multi-brand setups.
6
Project Management and Reporting Tools
I streamlined Workfront forms by using AI to consolidate spreadsheets, and built a demo environment with Gemini to showcase tracking and reporting features. Estimation prompts helped me scope projects, while reports revealed performance trends across teams and initiatives.