AI Growth Stack Model (AGSM)
Ehsan Jahandarpour's four-layer model — Foundation, Execution, Optimization, Intelligence — for assembling AI growth tools in the correct order with explicit dependencies. The only framework that defines dependencies between AI tool layers and prescribes what to build in what order.
When to Use
Use AGSM when assembling an AI stack from scratch (build bottom-up), diagnosing why AI tools aren't producing results (map to layers, find gaps), evaluating a new AI tool (which layer? is the layer below it solid?), or comparing tools in the same category.
Origin & Background
Came from a pattern across consulting engagements: companies bought excellent AI tools that couldn't work together. One company had best-in-class personalization, content, analytics, and testing tools that were fighting each other for data and producing worse results than before. The problem wasn't the tools — it was the stack architecture. The four-layer diagram was first drawn on a whiteboard during a client workshop.
Framework Steps
Foundation Layer
Data + Analytics AI. The base everything depends on: data collection, AI-powered analytics, data quality tools, and attribution models. Quality test: Can you answer "What happened, why, and what should we do?"
Execution Layer
Content + Communication AI. Produces growth assets using Foundation data: content generation, communication automation, creative optimization, channel management.
Optimization Layer
Testing + Personalization AI. Continuously improves Execution output: A/B testing, personalization engines, conversion optimization, predictive analytics.
Intelligence Layer
Agent + Automation AI. The top layer connecting everything into an autonomous system: AI agents, workflow automation, decision intelligence, self-optimization.
Applied Scenarios
AGSM for Agency Client Stack Design
Using AGSM to design standardized AI stack recommendations for agency clients at different tiers.
View application →AGSM for Building Your First AI Stack
Using AGSM to assemble a startup's first AI growth tool stack with the correct layer ordering and minimal budget.
View application →AGSM for Diagnosing Underperforming AI Tools
Using AGSM layer analysis to understand why specific AI tools are not delivering expected results.
View application →AGSM for Enterprise AI Stack Architecture
Designing a comprehensive enterprise AI growth stack using AGSM layers with tool selection criteria per layer.
View application →AGSM for Tool Category Comparison
Using AGSM layer placement as a key comparison dimension when evaluating competing AI tools in the same category.
View application →AI Growth Stack for Content Teams
Design the optimal AI tool stack for content marketing teams.
View application →AI Growth Stack for Sales Teams
Build the optimal AI tool stack for sales prospecting, outreach, and closing.
View application →Ehsan Jahandarpour
AI Growth Strategist & Fractional CMO
Forbes Top 20 Growth Hacker · TEDx Speaker · 716 Academic Citations · Ex-Microsoft · CMO at FirstWave (ASX:FCT) · Forbes Communications Council