ProductDecode
Module 2 • Lesson 14• 50 mins

Estimate Costs and Non-Linear Effort for Production AI Features

Identify non-linear effort cycles, decompose 3 project allocations (Linear Code, Data Prep, Eval Loops), and forecast recurring token OpEx models at scale.

Identify non-linear development components in AI product lifecycles
Decompose 3 effort buckets: Linear Code, Data Prep, and Eval Loops
Transform fixed timeline commitments into Maximum Iteration Budget caps

Estimate Costs and Non-Linear Effort for Production AI Features

Following implementation tier selection in Lesson 9, PMs must model realistic engineering effort and financial expenditures. The fundamental distinction between traditional software development and AI initiatives is: Traditional software effort scales linearly with functional scope, whereas AI development contains significant non-linear, probabilistic effort cycles.

Running Example: FinTrack Expense developing an automated receipt processing feature: "AI parses mobile photos of handwritten receipts to categorize line items into corporate tax ledgers."

TIẾP CẬN CŨ / LỖI THỜI
Legacy Software Estimation (Linear)
More screens = More story points; deterministic delivery dates
CHUẨN AI PM / HIỆN ĐẠI
AI PM Non-Linear Estimation
API integration in 2 days + Eval cycles for 4 weeks; capped by Max Iteration Budget
MODULE 2 AUDIT SCORECARDComprehensive 5-Axis Feasibility Audit

Module 2 Capstone: Comprehensive AI Opportunity Feasibility Audit for FinTrack Logistics

1. Invoice OCR TaggingExtraction & Categorization
SHIP TO PRODUCTION
DATA READINESS95% (2M records)
FAILURE STAKESLow (Tolerant)
GROSS MARGIN88%
OVERALL READINESS94/100
PM EXECUTION DIRECTIVE:

Deploy 100% to production; optimize speed with Fast SLM.

Module 2 Synthesis: Feasibility auditing is the core duty of an AI PM. Ship only when passing all 5 gates (Capability Fit, Data Readiness, Tech Gates, Failure Stakes, Unit Economics).