ML vs AI Projects: Two Workflows, Ideation to Production

Your AI project is not an ML project. One creates a capability, the other constrains one. Why the risk curves are opposite, what each workflow looks like from ideation to production, and the code at the points where they diverge.

A classifier takes six weeks to get working and two weeks to ship. An LLM feature takes an afternoon to get working and six months to ship.

Both of those are true. The two kinds of project fail at opposite ends of the lifecycle, and most of the planning damage I’ve watched teams do comes from running one playbook on the other kind of work.

Two words everyone uses differently

An ML project builds a model out of your data. You gather examples, define a target, train something, and the artifact belongs to you. Fraud scoring, demand forecasting, defect detection, ranking. The capability doesn’t exist until you make it.

Read the full story with code on my substack: https://brianjamescurry.substack.com/

About the Author

Brian Curry is a Kansas City–based Machine Learning & AI Strategist, Marketing Economist, and Applied Researcher. He drives Vector1, a personal open-source research initiative advancing marketing economics, causal inference, cognitive AI architectures, and the economics of intelligent automation.

He has led advanced data science and analytics initiatives across major enterprises including Koch Industries, Tractor Supply, Hallmark, Garmin, and AT&T. His work applies economic thinking and causal inference to connect marketing investment to outcomes, operational decisions to economic value, and AI adoption to competitive advantage.

Key Research & Open-Source Projects (GitHub: @Bodhi8):

• PyCausalSim — A Python framework for causal discovery and inference through simulation. Enables testing of counterfactual scenarios to identify true structural drivers.
• Papilon — A complex system and causal discovery library for Media Mix Modeling (MMM) and adaptive systems.
• Regimecast — Applied research and tooling for time-series regime detection and forecasting.
• CarryNet — Open-source research and modeling focused on commodity carry trade analysis.
• Foodmesh-ml — Machine learning, optimization, and simulation framework for modeling distributed food systems.
• Memory-Node Encapsulation (MNE) — A cognitive AI architecture and novel data structure for artificial episodic memory.
• The Autonomy Economy — A macroeconomic framework projecting the dual impacts of massive productivity growth and extreme price deflation in an AI-automated world.
• Meaning-Flow — Research focused on semantic content modeling and automated coverage gap analysis.

Contact: brian at vector1.ai | Vector1 | GitHub | LinkedIn

This article may be shared with attribution under Creative Commons Attribution 4.0 International (CC BY 4.0).