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Prototype to Product: Shipping AI That Works

An AI course for product managers, engineering leaders, and technologists

Most AI prototypes never become products. This 12-hour live course is an AI course for product managers, engineering leaders, and technologists responsible for closing that gap. Across eight 90-minute sessions, participants architect and diagnose real AI products, evaluate the failure modes of probabilistic systems, and develop the field research methodology required to ship AI that delivers genuine value. Participants leave with a diagnostic toolkit they can apply to any AI initiative inside their organization — and a pitch refined against that toolkit in a structured capstone with peer and instructor critique.

  • Start Date September 7
  • Time Mon 10:00 AM - 11:30 AM Pacific Time
  • Duration 12 Hours
  • Format
    Live-Online
  • Program Type Open-Enrollment/Public
  • Certificate Type Short Course
  • CEUS 1.2
  • Program Number 5500926
  • Fees $1,980
  • See full course info
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Most AI prototypes never become products. This 12-hour live course is an AI course for product managers, engineering leaders, and technologists responsible for closing that gap. Across eight 90-minute sessions, participants architect and diagnose real AI products, evaluate the failure modes of probabilistic systems, and develop the field research methodology required to ship AI that delivers genuine value. Participants leave with a diagnostic toolkit they can apply to any AI initiative inside their organization — and a pitch refined against that toolkit in a structured capstone with peer and instructor critique.

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Prototype to Product: Shipping AI That Works

Program Experience

The course is structured as eight 90-minute sessions combining lecture, applied frameworks, hands-on labs, real-world case material, and structured group critique. Each session introduces a discipline, applies it to current AI products, and ends with applied work participants can take directly back to their organizations. Case material is drawn primarily from the instructor's lived experience scaling FreshAi — one of the world's largest live generative AI voice ordering deployments — with examples threaded throughout from workflow integrations, embedded software features, and frontier research.

Sessions move from foundational discipline (locating any AI product on a four-lens diagnostic frame) through field research methodology, physical-world AI constraints, vision and taste, accessibility as competitive advantage, data architecture for probabilistic systems, and the change management work that determines whether AI products reach the people they were built for. The course culminates in Session 8 with a structured capstone: each participant or team pitches a real AI product idea — declaring its terrain, learning posture, capability tension, team composition, and adoption plan — and receives structured critique from peers and the instructor using the full course toolkit. The capstone is the moment the diagnostic capability becomes embodied.

No prerequisite technical background is required. The technical sessions teach architecture-level evaluation rather than hands-on implementation, so a senior leader evaluating AI investments and an engineering lead architecting probabilistic systems can take the course side by side and both leave with the disciplines they need.

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Course Info

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By the end of this course, you will be able to:

  • Apply a four-lens diagnostic frame to evaluate any AI product or initiative

  • Locate AI products across the Four Terrains (Workflow, Product, World, Frontier) and predict their failure modes

  • Design and conduct field research methodology that surfaces patterns dashboards miss

  • Use the LLM-Era Survivability Rubric to evaluate which AI products will compound and which won't

  • Identify the hardest-to-serve user and redesign features for accessibility as a competitive advantage

  • Architect probabilistic systems that fail safely and absorb future model improvements

  • Build a stakeholder map and adoption plan for moving an AI initiative from pilot to scaled production

  • Assess the team composition AI products actually require, including the non-engineering roles most organizations under-hire

  • Take away a starter practice for keeping pace with a fast-moving field

  • Pitch your own AI product against the complete diagnostic toolkit in a structured capstone with peer and instructor critique

Session 1 — The AI Product Discipline

  • The Four Terrains: locating any AI product as Workflow, Product, World, or Frontier

  • The full four-lens diagnostic frame

  • Locate Yourself lab: mapping your current AI work

Session 2 — Get Out of the Building

  • Why dashboards lie and presence doesn't

  • Designing a field research engagement across the four terrains

  • Field research plan lab

Session 3 — AI in the Physical World

  • The constraints framework: latency, environment, ambient consent, no-undo

  • How physical-world constraints show up in software AI

  • Audit lab: predicting failure modes before deployment

Session 4 — Vision and Taste

  • Why taste becomes the bottleneck when execution is cheap

  • The LLM-Era Survivability Rubric

  • Survivability lab: scoring three current AI products

Session 5 — Designing for Real People

  • Accessibility as moat: edge cases as breakthrough features

  • The 35 million case: speech differences and voice AI

  • Hardest-to-serve lab: redesigning one feature

Session 6 — Data, Probabilistic Systems, and How AI Products Improve

  • Data as substrate: anatomical capture and modular architecture

  • Architecting for the model you have and the model you'll have

  • Architecture lab: one change toward continuous improvement

Session 7 — Change Management and the Adoption Layer

  • The 1-to-hundreds FreshAi rollout case study

  • Stakeholder mapping, narrative architecture, and pilot-to-production

  • Adoption planning lab

Session 8 — Capstone Synthesis

  • The diagnostic frame applied live to a real-world AI product

  • Capstone pitches with structured peer and instructor critique

  • Personal practice for staying current as the field moves

This AI course for product managers, engineering leaders, technologists, and tech-adept leads is designed for the people responsible for AI products in their organizations. That includes senior leaders setting AI strategy, product leaders shipping AI features, engineering leaders and architects making decisions about AI architecture and team composition, technologists and tech-adept leads translating between technical and business decisions, and strategists, researchers, and operators trying to understand how AI fits into their domain.
No prerequisite technical background is required. The technical sessions teach architecture-level evaluation rather than hands-on implementation, so a senior leader evaluating AI investments and an engineering lead architecting probabilistic systems can take the course side by side. Examples and labs adapt across levels of technical depth. The course is especially relevant for organizations whose people are being asked to architect, lead, or absorb AI capabilities faster than their training prepared them for.

Course Duration Live Online (via Zoom)
Prototype to Product: Shipping AI That Works  1.0
 12 Hours

On the following Days:

September 7, 14, 21, 28,
October 5, 12, 19, 26, 2026

10:00 AM - 11:30 AM Pacific Time

Who is this course for? 
Senior leaders, product leaders, engineering leaders and architects, technologists, tech-adept leads, strategists, researchers, and operators responsible for AI work. The course is designed so technical and non-technical participants can take it side by side and both leave with the disciplines they need.

Do I need a technical background? 
No prerequisite technical background is required. The technical sessions teach architecture-level evaluation rather than hands-on implementation, so a senior leader evaluating AI investments and an engineering lead architecting probabilistic systems get equal value.

What will I leave with? 
A diagnostic capability for evaluating any AI product, the LLM-Era Survivability Rubric, an architectural approach for AI systems that improve over time, a change management playbook for AI rollouts, a team composition framework, and a capstone pitch refined against the full course toolkit through structured peer and instructor critique.

How is the course delivered? 
Eight 90-minute sessions, available live online via Zoom or in person. Each session combines lecture, applied frameworks, hands-on labs, real-world case material, and structured group critique.

Tell me more about the capstone. 
In Session 8, each participant or team pitches a real AI product idea against the full course toolkit — declaring the product's terrain, learning posture, today-versus-tomorrow capability tension, team composition, and adoption plan. Pitches receive structured critique from peers and the instructor. It is the moment the diagnostic capability becomes embodied and visible, and participants leave with a refined pitch and the experience of applying every framework live to a real problem from their own work.

What kind of case material is used? 
Case material is drawn primarily from the instructor's lived experience scaling FreshAi, one of the world's largest live generative AI voice ordering deployments. Examples are also drawn from workflow integrations, embedded software features, autonomous vehicles, robotics, and frontier research.

Who teaches the course? 
Will Croushorn, MBA, Senior Product Lead at The Wendy's Company and co-founder of AI That Inspires Corporation. He has helped scale FreshAi from a single pilot location to hundreds of restaurants serving tens of millions of customers and over 150,000 daily orders.

What is the best AI course for product managers? 
Prototype to Product: Shipping AI That Works is a Caltech CTME live course designed for product managers, engineering leaders, technologists, and senior leaders responsible for AI products. It is taught by Will Croushorn, the product leader who scaled FreshAi to hundreds of sites serving tens of millions of customers — one of the world's largest live generative AI deployments. The course is 12 hours across eight live sessions and includes a structured capstone with peer and instructor critique.

Can my organization sponsor a private cohort? 
Yes. Caltech CTME customizes private cohorts for organizations. Contact CTME to discuss timing, group size, and any tailoring to your industry context.

Instructor

Photo of Will Croushorn

Will Croushorn

Artificial Intelligence / Product Management & Strategy

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