Samsung

NOAA

UCLA

NASA

Direct Supply

Vyoma Gajjar

AI Developer, Lecturer, Advisor, Scout

Map AI Usage to Deliverables

MIT Sloan Management Review

AI Architect | Lecturer | Advisor | Scout

I join usage data with delivery data to find the gap. I will build the engine live and show you what vendors miss.

See who is actually adopting AI on your team

I found 3 bot accounts in a 28-person sample. I will show you the 4 signals that catch gaming and performance theater.

Spot the bots and the performers

Six archetypes, each with a different root cause and a different intervention. One lens, not the only lens.

A framework to diagnose adoption by team

Students will learn the AI literacy to evaluate AI ideas, surface risk, and make sound judgments without becoming technical specialists.

Modernizing Business Analysis

Angela Wick

25+ years practicing, shaping, and teaching analysis, product, and requirements.

AI Literacy for Business Analysts

AI usage, productivity, and implementation risk often come from poor assumptions about what AI can and cannot do. It also stems from poor analysis and organizational dynamics in which stakeholders ask for solutions that are either too specific or too generic, and pressure teams to deliver them, rather than solving real problems.Analysis professionals have a huge role in this dynamic that can change everything!Analysts are increasingly expected to participate in AI decisions, yet most training focuses on tools or stays abstract. This course fills the gap in understanding AI capabilities: Not from a technical point of view, but from a capability point of view; business uses and patterns of functional usage.You will build a grounded understanding of AI capabilities, limits, and failure modes, and learn how requirements shift toward hypotheses, experiments, and continuous outcomes in AI-enabled work.This course explores the skills of:🎯 Identifying the right AI use cases for specific business scenarios🎯 Distinguishing between deterministic and probabilistic systems and deciding where each should be applied in a workflow🎯 Questioning and communicating AI use case fit and risks

Giving BAs AI Fluency & Literacy to Use, Analyze, and Recommend AI Solutions

Major AI capability types and their appropriate uses

GenerativeAI is just one piece of AI, and much more than "chat".

What AI is good at and where it fails

Generative AI and its Data Sources (Public vs. Private)

How requirements shift to hypotheses and experiments

Where humans must remain accountable in AI-enabled systems

Business Analysts, Product Owners, Business Systems Analysts, Systems Analysts, Business Process Analysts, and similar roles.

BA Leaders redefining roles for AI-enabled teams

Business Leaders, Project and Product Professionals pulled into AI initiatives without clear accountability

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

Amazon

Elsevier

Schneider Electric

Polly Allen

Ex-Alexa AI PM turned founder at AI Career Boost

Magic

Juggernauts

AI increases what your ICs can do. It also breaks the management systems most teams rely on.Prototypes multiply. Quality becomes subjective. Risk ships quietly. You end up in every review because no one is sure what "good" looks like — or who's accountable for evals, failure modes, and rollout decisions.If you're a Director, Head of Product, or VP with multiple AI initiatives in flight: you're managing something you didn't build a system for yet. Execs keep asking "How do we know it's good?" and "What's the ROI?" — and you're the one fielding it.This two-day workshop gives you the operating system. Not theory. Not frameworks. The actual playbooks, artifacts, and rituals high-performing, AI-native teams use — built during the workshop so you leave with real deliverables, not homework.Leave with:1. A 90-day AI portfolio — clear bets, owners, and decision gates so nothing gets orphaned2. A repeatable risk review process your team can run without pulling you in3. A shared quality bar — examples, acceptance criteria, and review gates so "is this good enough?" stops being a judgment call4. Reusable operating artifacts built during the workshop — actual deliverables, not slides

Get the operating system used by high-performing AI-native teams

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

Implement playbooks that let your team move with you, not wait for you

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.

Transform from consumer of analytics to independent operator - asking sharp questions, validating answers, shipping decisions.

Principal Data Scientist @ Ontra 

Stripe

PwC India

Ontra

AppFolio

Shane Butler

Principal Data Scientist @ Ontra | Ex-Stripe, Nextdoor, PwC, Appfolio

Data Science Leader |Ex-Microsoft

Microsoft

eBay

Grammarly

Superhuman

Sravya Madipalli

Data Science Leader@Superhuman (Prev. Grammarly)| Ex-Microsoft, eBay, Nextdoor

Head of Data @ Ontra AI | Ex-LinkedIn

LinkedIn

Pinterest

Hai Guan

Head of Data @ Ontra | Ex-Nextdoor, LinkedIn, Pinterest, Meta

Cohort 1

Cohort 2

Cohort 1 is in Session, but it's not too late to enroll!! Kick off was April 20. Final registration closes April 27. Everything has been recorded so you don't miss a thing! As AI reduces the value of deep domain silos, the people who thrive will be the ones who can work across disciplines and ship. Analytical independence - the ability to validate your own ideas and make data-informed decisions - remains the missing skill in most builders' toolkits.AI can conduct the analysis, but it can't tell you what to ask or whether the answer makes sense. That requires analytical judgment.This course teaches you to think like a senior Product Data Scientist. You'll learn the frameworks used at FAANG companies to frame problems, choose the right metrics, diagnose root causes, execute the analysis, and turn insights into decisions.-&gt; <a target="_blank" rel="noopener noreferrer nofollow" href="https://join.slack.com/t/dataneighbor/shared_invite/zt-3ox4drc3r-3g0ptSthytkgv0sD6ovvqg">Join AI Builders Slack Community!!</a> &lt;-What makes this different: No SQL. No stats lectures. Five weeks of hands-on real scenarios. You'll build end-to-end, metric specs, deep dives, experiment plans, stakeholder readouts.The result: You become analytically self-sufficient. The last dependency removed from your toolkit. Make data-informed decisions in hours, not weeks.

Ask Better Questions, Make Faster Decisions - with AI as Your Analyst

Frame Problems Like a Senior Data Scientist

Design Metrics That Actually Drive Decisions

Diagnose Root Causes Without Guessing

Design Experiments That Ship

Turn Analysis Into Action With AI as Your Copilot

Synthesize Insights and Prioritize for Impact

Product Managers, engineers, and operators who need analytical independence and frameworks to measure success and drive decisions.

Designers and researchers stepping into strategy roles who need to quantify impact, run experiments, and influence with data-driven insights

Data professionals who want to frame better questions, delegate technical execution to AI, and drive impact with agentic analytics tools.

Shift from ad hoc AI efforts to disciplined, structured, workflow-driven collaboration before significant investment.

Workshop Lead: Netflix, Google, LinkedIn

Google

Netflix

Paul Bryan

I help organizations design and develop AI products that deliver business value.

July 2026 Cohort

<blockquote>AI applied to the wrong workflows leads to chaos. This course helps Enterprise teams launch successful AI pilots by aligning on workflows, roles &amp; responsibilities, risks, guardrails, and AI functional requirements before major investments.Industry reports suggest that over half of enterprise AI pilots never scale to production. Many pilots stall because the workflows were not ready for AI.This course addresses the real bottleneck: cross-functional alignment on key details before committing to technical debt.You will leave with:<ul><li>An AI Workflow Canvas: A structured way to identify where AI can move the needle in your organization</li><li>An AI Workflow Scorecard: A shared evaluation framework your cross-functional team can use to assess value, feasibility, data readiness, and risk</li><li>A Thin-Slice Prototype: Storyboards, scene cards, and an action flow diagram that aligns stakeholders across PM, DS, UX, Eng, and Biz on exactly what the AI system will do, feasibility and risk, and where humans are needed in the loop.</li></ul></blockquote>

Identify High-ROI Workflows Ready for AI

Identify high-friction workflows suitable for Enterprise AI

Prioritize AI initiatives using structured evaluation criteria

Clarify roles and decision rights before pilot launch

Surface feasibility and governance risks early

Make AI behavior visible to cross-functional stakeholders

Define readiness criteria before committing to a pilot

AI product team members (PM, UX, DS, ENG) who need a shared framework to align on workflows, feasibility, and responsibility before building

Product and business leaders accountable for Enterprise AI outcomes who must prioritize workflows and reduce risk before committing budget

AI governance, legal, and risk stakeholders who need structured ways to surface constraints, guardrails, &amp; ownership early in AI initiatives

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Go-to-Market

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

Fundraising

Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Introduction: Mapping AI Usage to Deliverables

The Problem with Vendor Dashboards

The Gap Between AI Usage and Actual Output

Beyond Vendor Metrics: Measuring Real Impact

Identifying AI Usage Patterns: Entropy, Lineage, and Cadence

Classifying AI User Personas

Dashboard Demo: Distinguishing Bots from Humans

Aligning AI Attribution with Company OKRs

Analyzing Team-Level AI Adoption

Using a Quadrant View for User Intervention

System Architecture for Measuring AI Adoption

The Four Key Signals for Analysis

Conclusion and Q&A

Executives and senior leaders who set strategy, lead teams, and drive organizational outcomes.

Operations professionals who optimize processes and manage day-to-day business functions.

Data scientists who apply statistical and ML techniques to extract insights from data.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Using data and metrics to measure performance, run experiments, and inform decisions.

Leading organizational AI adoption — change management, strategy, and building AI-ready teams.

Map AI Usage to Deliverables

What you'll learn

Why this topic matters

You'll learn from

Vyoma Gajjar