Enterprise AI

Enterprise AI Blueprint: Pilot to Production

McKinsey & Company

Anton Nakaliuzhnyi

10 yrs QuantumBlack AI/ML @ McKinsey; MBA Harvard

Achuta Kadambi

Co-founder of two exited AI startups; PhD in AI, MIT

How to get enterprise stakeholders excited about your pilot idea.

How to Pitch your Enterprise Pilot

A technical and business framework of how to execute your pilot and report ongoing success metrics.

How to Execute your Pilot

We discuss root causes of why enterprise pilots die and how to avoid them.

How to Scale your Pilot

Automate and amplify institutional knowledge through AI agents that embody your cultural DNA

Research Director, Handshake

Lev Gorelov, MS

Co-founder and Principal Director of Handshake, a generative AI software studio

CEO, Handshake

Jeremy Kirshbaum

Founder of generative AI consultancy Handshake

Cohort 16

Cohort 17

The Problem: Your AI applications produce generic, surface-level outputs. You're solving complex, context-specific problems, but results don't reflect your organization's hard-won expertise.The Solution: We teach evergreen frameworks for building AI systems that learn from your unique data and workflows, with instructors available on-demand as your strategy and implementation consultants.What makes your organization valuable isn't raw information — it's the proprietary logic that mints insight from the data. We teach you to encode this logic into AI systems and scale your expertise authentically. You'll learn to orchestrate product development across domain experts, developers, and UX designers, transferring best practices into a product only your organization could build.We've spent half a decade embedding expertise into AI with knowledge-driven organizations like Visit California, Give.org, and Institute for the Future. We operated as strategic consultants, not just developers. For the price of a course, you're hiring us in the same capacity - consulting, scoping, iterating - to ensure you walk away with a prototype that solves real problems and a clear roadmap to production.

Transform Your Organization's Expertise Into AI-Powered Competitive Advantage

Encode Your Organization's Expertise Into AI Systems

Put Theory Into Practice With Support From Experienced Strategists and Builders

How To Build a Working Prototype You Can Actually Deploy

Lead Cross-Functional Teams

Cut Through the Hype and Identify Real Opportunities

Executive Leaders who need a framework to convert their company's domain expertise and workflows into a scalable, competitive AI asset

Project/Product Managers who need a framework to bridge domain knowledge with technical teams to scale expertise with AI

Consultants who want to guide clients in building differentiated, insight-driven and context-aware AI solutions

Build, evaluate, and ship successful AI products with real-world frameworks and hands-on experience to become an advanced AI product leader

AI PM Leader | ex Spotify, Apple, Cruise

Apple

Spotify

Arize AI

Cruise

Aman Khan

Product leader at Arize AI, Spotify, Cruise, and Apple

Cohort 3

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This is not a theoretical course about AI concepts. We'll of course teach you about the fundamentals of Agents, RAG and other AI concepts, but the purpose of this course is to get hands on. You’ll build real agents, evals and develop implementation plans!This course is a practical roadmap for PMs who want to go from "I understand what RAG is" to "I shipped an AI product that customers love." This is best suited for PMs who have at least played around with prototyping tools like Replit, Bolt but are looking to bring AI features from prototype to production inside their companies.- 4 week live sprint: 8 interactive live sessions and 3 office hours per week packed with vibe-coding labs, eval workshops, and peer feedback loops.- Built by and led by a current practitioner – I’m sharing the playbook I use as Head of Product at Arize AI (supports companies like Uber, Instacart, Discord, Reddit) and Spotify to help hundreds of engineers launch AI-powered products.- I'll also host a dedicated Slack community where you can communicate with me and other participants.- All sessions will be recorded and available to students asynchronously.

Design, prototype, and ship AI-powered products that delight customers

Build and deploy functional AI agents for your personal and team workflows

Evaluate AI product performance

Become proficient at using coding agents

Define Your H1/H2 AI Roadmap

Go from prototype to production

We'll help you get Cursor set up, but some familiarity with coding/debugging will go a long way here (i.e. being able to read python code)

Familiarity with code recommended (but not required)

Product Managers familiar with AI terms and concepts (LLMs, RAG, etc.) that wants to move beyond prompting to building actual AI products

PMs transitioning to AI-focused roles who see AI product leadership as their career differentiator in the next 1-2 years

Product Leaders and Executives responsible for AI strategy who need practical frameworks for evaluating and implementing AI initiatives

How to think through AI problems end-to-end: scoping, debugging, and knowing when to pivot.

ML Engineer with 10+ years of experience

Hugging Face

DataRobot

Contextual AI

Snowflake

Snorkel AI

Rajiv Shah

Enterprise AI veteran: Shipping AI to production, with clarity and humor

Pilot Cohort

AI Problem Framing is to AI teams what System Design is to software engineers and Product Sense is to PMs. Whether you're building, evaluating, or leading AI work, this is the foundational thinking skill that separates results from rework.--🚨 Pilot cohort: February 16 – March 20, 2026 - limited to 25 students. 🚨This is the first run of the course, that means:• Never before published material, you see it first!• Smaller cohort = more direct access to me• Pilot pricing won't last---What you get:🔄 The Loop, a 5-step framework you'll use to think through every AI project🧑‍🏫 Live sessions and office hours where you can bring your real problems📊 Access to 200+ case studies, the largest collection of AI reframing examples📋 Production-ready checklists for RAG, Forecasting, GenAI🎥 Lifetime access to all recordings---This course is for you if:• Your AI works in demos but fails in production• You've spent months on a model only to realize you solved the wrong problem• You lead AI work but came from engineering, product, or PMYou'll learn from 200+ AI failures in 5 weeks. You gain years of experience and recognize the scars.

Most AI fails from bad framing, not bad models. Learn to fix that.

Frame AI problems right the first time

Diagnose what's actually broken

Own the problem, not just the solution

Steer your team past expensive mistakes

We focus on framing and strategy, not explaining what models or training means. Basic vocabulary lets us go deeper faster.

You understand what AI/ML is at a conceptual level

Real experience gives you context. The frameworks click when you can map them to projects you've actually built or managed.

You've worked on or led AI projects in some capacity

This is about decision-making, not implementation. You'll learn what to build and why, not how to code it.

No coding during the course; frameworks for thinking, not tutorials

Engineers who can build AI but want to master outcome engineering: knowing what to build, not just how

Building RAG, agents, or ML models? Let's get your demos into production! Learn the thinking that makes the difference.

Managers who need to evaluate AI/ML proposals, set realistic milestones, and know when to escalate

Ship production features 2x faster by using coding agents across research, planning, implementation, testing, and review workflows

Creator of Stanford's AI Coding Course

Amazon

Stanford University

Y Combinator

Mihail Eric

Created Stanford's first AI coding class. Former YC founder and Amazon AI lead. 

Cohort 1

Today many developers use AI, but few are maximally productive with it. I created Stanford's first AI software development class, and after building a YC-backed coding company and leading AI at Amazon, I've seen how top engineers integrate AI into production workflows. My techniques have been used to train 200+ Stanford engineers and industry professionals.My goal is simple: make you dramatically more productive writing software with AI than without it.In this course, you'll learn practical, end-to-end workflows for using AI in real-world development, not toy examples.We'll cover:<ul><li>Building production features with AI agents using the research → plan → implement → test workflow</li><li>Configuring an optimal AI native dev environment for your specific tech stack (IDE, code review, tool integrations, and beyond)</li><li>Setting up review and CI processes that catch AI errors, hallucinations, and slop before production</li><li>Enabling multiple agents to work together on the same codebase without conflict, accelerating software delivery and throughput</li></ul>If you're ready to write better code, ship to production faster, and stay in control of your coding agents, let's get started.

Increase developer productivity with AI-first, production-ready workflows

Configure optimal AI-first developer environments to improve your coding output

Learn how to navigate the ecosystem of cutting-edge AI developer tools 

Exercise effective strategies for prompting and building with coding agents

Reduce hallucinations, software errors, and AI slop all while shipping faster

Become a manager of coding agents so you can keep many agents productive 

Develop a deep intuition for how AI coding platforms work under-the-hood

Engineers who want to increase their productivity with AI-generated code while making it actually production grade.

Engineering managers who want to ensure their teams aren't being left behind when it comes to the new way of developing software.

Those confused about how to develop with AI the right way (how to maintain context and manage multiple agents without compromising code)