Rebuilding the agency operating model with AI and automation.
MIT AI in Business · Real-world agency case study
Media agencies spend a disproportionate amount of time on execution, verification, and reporting. This case explores how AI can restructure those workflows while keeping human judgment at the center.
The Problem
Fragmented tools, manual checks, and repetitive reporting slow down execution.
Proof of delivery is hard to demonstrate.
Creative production and operational control do not scale efficiently.
The System
Machine Learning
Forecasting, budget allocation, segmentation
Generative AI
Creative support, iteration, reporting drafts
Automation
Execution checks, reconciliation, pacing alerts
Agentic Layer
Supervised AI agents reporting to humans
Governance & Control
AI systems execute and monitor operational tasks.
Supervisory agents consolidate outputs.
Humans remain responsible for validation, strategy, and client decisions.
Outcomes
- Reduced manual workload
- Faster feedback loops
- Increased transparency and trust
- Scalability without additional headcount
Context
Programmatic specialist with several years of experience across the digital media ecosystem.
Focused on building consistent, data-driven media strategies that reach the right audiences in relevant contexts.
Increasingly focused on AI and automation to rethink how media operations scale — including through MIT's AI in Business program.
Acknowledgment
This project was shaped through ongoing sparring, exploration, and real operational constraints.
Thanks to Majan for the trust, support, and space to use a real agency as a living case study. The work is still evolving, but it already changed how I think about what agencies can become.
Developed as part of the MIT "AI in Business" program.
Read the full paper (PDF)