Diba Kaya

How to Make Go/No-Go Decisions in AI Product Development

Google

Amazon

Twitch

Staff UXR, Insights & Behavioural Science

Learn why AI changes cognition and decision-making and why validating task success alone leads teams astray.

Why AI Breaks Traditional Product Validation

Use a decision framework to choose between decision support, human-in-the-loop, or full automation.

Decide How AI should Show Up In the Workflow

Anticipate downstream impacts on adoption, differentiation, and product longevity not just short-term metrics.

Identify Second-Order Risks Before AI Features Ship

Acquire and develop a critical skill for product managers who are leading and contributing to AI products.

February 2026 (please delete)

Feb 2026

GenAI Product Builder @ Google, ex-Meta

Marily Nika, Ph.D AI/ML

Product Manager AI&ML @ Google

Diego Granados

Senior Data, ML and GenAI Scientist

SHI International

National Association of REALTORS

George Zoto

Senior NLP/AI Scientist

December 2025

Cohort 4

“Does it work?”... “Is it good enough?”... “Can we ship it?”...How do you answer these questions for AI products? You’re responsible for “running evals” but what does that mean?How do you choose the right metrics, interpret fuzzy results, and make a confident decision?This course gives you a framework to do just that.<ul><li>Map user value to evaluation (eval) objectives so your metrics aren’t abstract. Define success then translate it into measurable criteria.</li><li>Choose metrics you can actually maintain: capability, safety, UX friction, latency, cost and “does this reduce support tickets or increase activation.”</li><li>Set ship/no-ship thresholds you can defend to leadership.</li><li>Build lightweight workflows that work in real teams: human review where it matters, automation where it lasts, documentation that drives decisions.</li><li>Consider domain constraints (e.g., healthcare safety) and know what to avoid: silent failures, misleading proxy metrics and tests that don’t reflect production.</li><li>Tie everything to ROI: impact vs unit cost, eval coverage vs reliability, and the minimum viable monitoring you need post-launch.</li></ul>Experience AI evals through a case-based approach with a real AI product that we evaluate together.

Eliminate uncertainty in shipping AI features

Make confident ship or hold decisions for AI features

Translate user value into clear evaluation goals

Choose the right metrics for capability, safety, UX, and cost

Set defensible thresholds leadership will trust

Build lightweight evaluation workflows teams can maintain

Navigate domain constraints and avoid common failures

PMs leading AI features, growth, or platform initiatives

PMs who partner with ML teams and want to set evaluation standards

PMs who need to make clear “ship or hold” calls without doing the engineering

A systems-first leadership OS for decision velocity, intelligent execution & AI-native transformation

AI Executive & Educator | Microsoft MVP 

Vidhi Chugh

AI Executive & Board Member | Microsoft MVP | AI Patentholder | 150k students

Cohort 2

Cohort 3

Most AI initiatives fail for reasons that have nothing to do with models, tools, or talent.They fail because of two recurring issues:1) Teams start with a solution instead of a problem2) Leaders miss a clear decision lens for when and how to pursue AI Experienced leaders know this pattern well:<ul><li>Promising ideas that lead to "Let’s just pilot and see"</li><li>Those pilots “work” technically but collapse when scaled</li><li>Trade-offs are being glossed over because problem is slowly reshaped to fit the solution</li><li>Costs, risk, and failure modes surface far too late</li></ul>AI amplifies this problem because its requirements are hard to specify upfront, and once commitments are made, decisions become expensive or irreversible.This course is anchored in decisions, not just knowledge. If these thoughts sound familiar, this course is for you:<ul><li>We’re betting too much on something we barely understand.</li><li>This could blow up with severe consequences.</li><li>Everyone wants agents; no one wants ownership.</li><li>If this fails, it’ll be on me.</li><li>Everything is moving too fast, but business impact still feels shallow.</li></ul>By the end of this course, you’ll own AI decisions, without relying entirely on vendors, technical teams, or external opinions.

A problem-first system for making high-stakes AI decisions

Opportunity Discovery Framework

Decision Velocity Toolkit

Experiential AI Systems Under Enterprise Constraints

AI Tradeoff Radar

Learn what “good” looks like, how to measure success. 

Enterprise leaders (heads of AI, data, or innovation) asked to sponsor or approve AI initiatives.

Entrepreneurs &amp; Founders who are either building an AI-first company or integrating AI agents into an existing business

Product &amp; Strategy Leaders looking for the tools to prioritize AI opportunities, assess feasibility, and measure ROI effectively

Use AI without outsourcing judgment — even when stakes are high.

AI Governance & Human Judgment

Microsoft

Accenture

University of Washington

Teresa Escrig, PhD in AI

Cohort 1

AI is powerful — and useful. But under pressure, it amplifies blind spots instead of correcting them.You move faster. But speed does not guarantee sound judgment.AI 202 is a structured program designed to help you:• Think clearly under pressure • Stop second-guessing • Reduce decision fatigue • Use AI as a precise collaborator — not a crutchThis is not about more tools. It’s about stronger judgment before decisions scale.In previous cohorts, participants completed the same self-assessment before and after five weeks.On a 1–5 clarity scale, average scores improved by more than one full point.Participants reported:• Less overthinking • Fewer reactive decisions • Greater steadiness under pressure • Increased confidence in executionSeveral began skeptical about AI. They left with a clear framework for what to delegate — and what not to.The goal is simple: Clearer decisions — before they scale across teams and outcomes.

AI amplifies your judgment. Strengthen it first.

By the end of the AI 202 course you'll be able to:

AI 101 for Conscious Creators (included at no additional cost).

Leaders with Decision Authority Founders, executives, and investors scaling responsibility without losing clarity

Leaders in Transition Redefining judgment, authority, and identity in the AI era

Leaders by Responsibility (Not Title) People whose choices affect others—and who want clarity before decisions scale

Lead AI initiatives with clear goals, defensible evaluation, explicit risk decisions, and exec updates that drive alignment and investment.

CPO, GM xBraintrust, Nextdoor, OpenTable

Braintrust 🧠

Nextdoor

LiquidSpace I Work from where it works.

OpenTable

BlackRock

Elena Luneva

f-CPO. Product executive (Braintrust, Nextdoor, OpenTable, LiquidSpace)

Ex-Alexa AI Principal PM

Elsevier

Schneider Electric

Polly Allen

Ex-Alexa AI PM turned founder at AI Career Boost

Magic

AI increases IC leverage, but it also breaks the management systems most teams rely on.Prototypes multiply. “Quality” becomes subjective. Risk can ship quietly. Leadership gets pulled into every review because no one is sure what good looks like, or who is accountable for evaluation, failure modes, and rollout decisions.If you’re a Director, Head of Product/Eng/Data, or VP with multiple AI initiatives in flight, you’re likely seeing the same pattern: lots of demos, uneven production outcomes, rising cost and reliability questions, and execs asking, “How do we know it’s good?” “What can go wrong?” “What’s the ROI?”This workshop gives you a practical operating system for leading AI-native work without slowing teams down.You’ll leave with:<ul><li>a portfolio and cadence that prevents orphan prototypes and experiments, </li><li>repeatable review process to assess risk and evaluation throughout projects, and</li><li>a new 'operating system' for AI-forward teams so that AI progress translates into ROI</li></ul>The result: faster alignment, fewer surprises, and AI work that ships to production with confidence.

Lead AI-heavy teams with new playbooks for prototypes, risk assessment, and eval

Install an AI-native operating cadence that keeps speed and alignment

Run AI risk reviews that drive decisions and confidence

Own and drive evaluation metrics and process, before and after launch

Reclaim 5 hours a week using new AI-first playbooks

Director/Head of Product: Leads AI initiatives; turns prototypes into production with clear quality, gates, and exec updates.

Eng leader (Sr Mgr/Dir/VP): Ships AI fast while managing reliability, security, and cost with consistent eval + risk reviews.

Data/ML leader (Dir/Head): Owns evals and performance; aligns product and execs with shared language and decision-ready tradeoffs.