Samsung

NOAA

UCLA

NASA

Direct Supply

Vyoma Gajjar

AI Architect | Lecturer | Advisor | Scout

Map AI Usage to Deliverables

MIT Sloan Management Review

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

Map recurring revenue tasks, design AI workflows, and deploy live agents — built around your role in three hands-on sessions.

Cohort 5

Revenue Exec and AI Coach

Diligent

Apple

Klue

Rogers

Jody Geiger

Revenue Exec with real experience building and leading AI-first Revenue Orgs.

Cohort 4

Cohort 6

54% of sellers consider themselves AI-proficient. Only 10% actually are.Most sellers use AI personally. Nothing transfers to the job. Meanwhile, 70% of the workweek goes to non-selling tasks: prep, research, admin, follow-up.There are three levels of AI in revenue work: Chat: you ask, it answers. Copilot: AI works inside your actual workflows. Agents: AI runs repeatable tasks on your behalf.Most sellers never get past level one. Staying there has a cost. The quota gap between sellers with AI systems and those without is measurable and widening.Revenue Drivers moves you through all three.This isn't a module to watch, it's a live build workshop. You leave with a working Revenue AI Vault, deployed workflows built around your actual role and accounts and a live Agent.Sellers who make this shift are 3.7x more likely to hit quota and 2x more likely to exceed targets. AI-enabled teams see 30%+ higher win rates.Revenue Drivers participants have cut prep time from 10 hours to 10 minutes and outbound effort by 90%. AI expertise doesn't happen organically. It has to be engineered.(Sources: Section AI Proficiency Report 2025 | Salesforce State of Sales 2024 | Gartner | Bain 2025)

Most sellers are stuck at chat. This moves you to copilot, then to agents.

Map your revenue work as a system.

Build your revenue AI Vault.

Design workflows that run without you.

Delegate clearly, not vaguely. 

Deploy a live agent in your tools. 

Leave with a system.

Quota Carrier (sellers, partner mgrs, biz dev): Want to use AI for more than writing emails, but don't know how to get started.

System Orchestrator: (Enablement, PMM, Rev Ops) Wondering where AI can help with messaging, coaching, and reporting.

Manager (sales leaders, team leads): Wanting to use AI to coach reps faster, inspect deals with clarity, and improve forecast accuracy.

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

Workshop Lead: Netflix, Google, LinkedIn

Google

Netflix

Microsoft

LinkedIn

Paul Bryan

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

Cohort 1

<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

Bring your team from AI curious to AI native to AI future-proofed.

Founder, Free Agency + AI Muscle

OpenAI

NVIDIA

YouTube

Ramp

Canva

Sherveen Mashayekhi

Founder @ Free Agency + AI Muscle. AI product leader, angel investor, and coach.

Look, I'm going to be real with you: people on your team are probably intimidated by AI, worried about how they're being measured, and using it at a fraction of its potential.That's because you're mostly asking them to play a game of catch up -- to adopt new tools "ASAP" and to keep up with the rest of the industry, without the proper training and resources to do it well.And you're probably worried that you aren't getting them up to speed as fast as they deserve/need.When you try to fix this, you're probably over-relying on one vendor tool or trying to get your most advanced team member to upskill everybody else. We know how that goes.I've coached dozens of founders + leaders on organizational AI adoption, as well as hundreds of individual contributors on top 1% AI use.Here's what actually works: building a collaborative team rhythm of (1) higher-order judgement and learning through (2) hands-on experimentation and systemic AI use.In this 1-day workshop, we'll bypass anything tool-specific or that'll be outdated by the next model update -- and evolve the operating rhythm for you and your team to get and stay ahead in this new era of work. No more AI theater. Just real capability.

C-suite, VPs, and Directors looking to uplevel their team's adoption and future-forward approach to AI.

Across functional areas -- strategies that work whether you're in engineering, product, design, marketing, or ops.

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

Principal Data Scientist @ Ontra 

Stripe

Nextdoor

PwC India

Ontra

AppFolio

Shane Butler

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

Data Science Leader |Ex-Microsoft

eBay

Grammarly

Superhuman

Sravya Madipalli

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

Head of Data @ Ontra AI | Ex-LinkedIn

Pinterest

Hai Guan

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

The future belongs to full-stack builders, not narrow specialists. As AI reduces the value of deep domain silos, the people who thrive will be the ones who can work across disciplines and ship.You can already prototype designs, write code, and generate content with AI. But 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 a complete analytical case study - 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.