# AI Product Management: From 0 to ROI > AI Product Management: From 0 to ROI is an eight-week live certification taught by Vin Vashishta on monetizing AI: opportunity discovery, feasibility, platform roadmaps, pricing, and go-to-market for AI and agentic products. URL: https://www.datascience.vin/course-ai-product-management.html Format: Live cohort Price: $1,600 Enroll: https://app.acuityscheduling.com/schedule.php?owner=22867384&appointmentType=90283608 Updated: January 2027 - **Format:** 8 weeks, live - **Schedule:** Saturdays · 8:00–9:30am PT - **Q&A:** About an hour after each session - 1-hour 1:1 session with Vin - A year of drop-in office hours, twice a week - Recordings and slides after every session - 1-year access to the self-paced companion ## Why this course The business sells what can't be built. The technical side builds what can't be sold. No one owns the space between. This course builds the Disruptor's Mindset for that missing middle: the products, platforms, and pricing that turn AI investment into revenue. ## The shift | Old pattern | Disruptor's Mindset | |---|---| | Ask the business for AI ideas and get silence or magic | Reverse the flow so the business brings you opportunities in its own language | | Check feasibility after the executive says yes | Explore the problem, data, and solution spaces before anything reaches a roadmap | | Judge each initiative on its own | Sequence initiatives so each one generates the data the next one needs | | Price AI like SaaS, ads, or tokens | Price what the investment improves, and bridge toward outcome-based pricing | | Scale first, fix the economics later | Break even, optimize, then scale | | Present the technology | Present the workflow: old, new, how it grows the pie, how it's monetized | **If you're technical: Strategy is shoulders up.** Expect the first two weeks to feel uncomfortable. The tools that made you successful get set aside, and the course is designed to teach you through that discomfort. **If you're on the business side: Understand the platform you're selling.** Agentic platform architecture, maturity, and feasibility, deep enough to align monetization with what can actually be built. No MBA or ML background required. ## Problems this course solves ### Problems you're facing - **“Find 20% efficiency with Copilot.” No use case, no context.** A mandate handed down with no link to how it monetizes or transforms anything, while you still have your day job. Weeks one to three give you the framing to turn a mandate into an opportunity with a value case attached. - **There is no definition of your role.** Week one defines it concretely: conceive new ways to monetize data and AI, identify and develop new markets, and coordinate the business to execute. - **An executive brings you a directionally wrong idea and you can't just say no.** Saying no damages the relationship. Saying yes wastes a year. Blame the framework: let the framework reveal the problem instead of you. - **“Just give me a number.”** You're asked to size an investment before anyone knows what's being built. Opportunity Estimation in ranges, backed by rapid discovery, gives you a number you can defend. - **You're seen as a cost center.** Weeks one and two reframe everything you do in top-line and bottom-line terms, so your work reads as core to how the company makes money. - **You understand the concepts and can't execute them yet.** An expected state around weeks three and four. Weeks four to eight are implementation, and every framework is revisited at a deeper layer to close the gap. - **You're asked to do AI strategy and AI product management at once.** Both are full roles. Don't put on the red cape, plus how responsibilities get split across leadership when you can't hire the second role. - **You can't tell how the pieces fit together.** The roadmap weeks are the assembly instructions, and Parallel Maturity in week seven shows how all of it interlocks. - **Non-technical CEOs with unrealistic expectations and enormous urgency.** They want magic, now. The four-step workflow presentation and week three's discovery approach redirect this without confrontation. - **Executives feel they have to get technical, and slow everything down.** The missing middle exists so they can stay in business language. You learn to occupy it. - **Your clients aren't ready to hear where they actually are.** How to deliver an honest maturity assessment without losing the client, covered in week eight and in confidential one-on-ones. - **You came from a technical background and you keep meddling.** Problem, Data, Solution Space Exploration moves the build conversation to the technical team instead of letting you answer it yourself. - **Strategy means giving up the tools that made you successful.** “Strategy has nothing to do with your hands” is the most uncomfortable sentence in the course. Weeks one and two are deliberately jarring, and the course teaches you through it. - **Your technical team is buried in unqualified requests.** The PM as shield: only ideas that survive the problem and data spaces reach the solution space. - **Clients don't know what they want or what would help them.** Bottom-up discovery reframed as teaching two triggers, complexity and uncertainty, so clients can recognize their own opportunities. - **Small-business CEOs demand execution detail immediately.** Weeks four to six give you the granularity to answer at the workflow level: what, how, how much, and what has to change. - **You don't know how to scope or price an engagement.** Typical engagement length, kickoff, and where opportunity discovery sits inside an assessment, covered in session six and office hours. - **You need funding before you can do the work that justifies the funding.** Week three rapid discovery plus week eight estimation give you the “now what” to hold in reserve when they say yes. - **You can't tell whether a role has a path to success.** The frameworks double as a diagnostic for whether an organization is set up to let you succeed. - **You're stuck between staying technical and going advisory.** A recurring one-on-one topic. The course is explicit that delegation is the constraint, and capability usually isn't. ### Business problems - **“Our AI strategy is a platform architecture diagram.”** A stack, a vendor list, and Copilot licenses explain none of why technology creates value or how it gets monetized. Three Core Pillars of Strategy and architecture as an alignment target. - **No one owns monetization.** The business sells what can't be built and the technical side builds what can't be sold. The Missing Middle is the role that owns the space between. - **Asking for AI opportunities returns bad ideas or silence.** Top-down and bottom-up opportunity discovery reverse the flow so the business articulates opportunities in its own language. - **Feasibility gets checked after approval.** Named pitfall #2 and the single largest source of saved spend. Problem, Data, Solution Space Exploration runs before anyone says yes. - **AI is being monetized the way digital and cloud were.** Ads monetize customer information and tokens monetize compute. AI improves intelligence, autonomy, expertise, and outcomes. The AI Monetization Pyramid and Bridge Pricing Model. - **Users show up and don't pay.** Single-digit paid conversion on major AI products. Monetization aligned to platform architecture, and pricing strategy in week eight. - **Technology creates value and no one can say why.** “Why do you use technology?” “Because we have technology.” Business, Operating, and Technology Models make it intentional. - **Executives can't reach for the technology lever.** Most C-suites have never been able to say “here are the KPIs I have to hit, what have we got?” and receive an answer. Top-down discovery and workflow re-orchestration fix that. - **No opportunity pipeline, so the company goes all-in on one bet.** The Opportunity Pipeline keeps continuously re-evaluating whether you're working on the highest-value initiative available. - **Frontline teams hold the opportunities and have no route to surface them.** Bottom-up discovery gated by the complexity-and-uncertainty heuristic, so the data team isn't flooded. - **Working in silos.** Named pitfall #1: operations improves a workflow without asking what new opportunities it could create. Cross-model opportunity discovery. - **Expensive initiatives fail on adoption while the technology works fine.** The $80 billion Metaverse question. The Adoption Journey and the four feasibility questions. - **Initiatives are evaluated in isolation.** Named pitfall #3: the flywheel never starts. Parallel Maturity and the Roadmap Layer Cake. - **The business commits to something the technical team has never built.** Problem, Data, Solution Space Exploration plus Reliability, Utility, Profitability. - **No one is assessing data, and none of it is monetized.** Data as an Asset and the AI Monetization Pyramid give data a revenue path. - **Most enterprise data can't be used for models or even analytics.** It lacks the contextual components that make it useful. Data space exploration and the Data Generation Maturity Model. - **The company has far more data than it knows, and no way to capture it.** Value sits in workflows, spreadsheets, and the person in the back room who knows where every order goes. Engineering Access. - **Agents hallucinate because there's no information structure underneath them.** Without ontologies and knowledge graphs, a model invents what to do. The information layer and the Agentic Operating System. - **Freemium quietly destroys the business.** Inference costs scale with usage in a way SaaS never did. Unit economics, tokenomics, and the Drug Dealer Model. - **The legacy business model pays the bills and can't simply be broken.** SaaS is funding the transformation. The Bridge Pricing Model, sequenced against the maturity model. - **Costs scale with success.** Frontier-model investment plus per-use inference makes inherited pricing unsustainable. Tokenomics and local, domain-specific model strategy. - **Transformation is treated as a project with an end date.** Continuous improvement became continuous transformation and is now continuous disruption. There is no finish line. - **A business model over-fitted to one technology wave can't adopt the next.** The Technology Wave Maturity Journey. - **Decisions are made against a marketplace that no longer exists.** Competitors change the landscape faster than information reaches decision makers. Decision Dominance. - **Over-optimizing internal efficiency degrades the customer.** Airlines pushed customers into apps that couldn't serve them and lost share. The Internal/External Optimization Balancing Act. - **Scaling before break-even locks in an unprofitable model.** The Optimize-Before-Scale GTM sequence: break even, optimize, then scale. - **Information moats are being annihilated.** Where enough public information exists, a decades-old knowledge advantage can disappear in a product cycle. Moat Assessment and the Arc of Disruption. ## Course outline Weeks one and two establish the strategic constructs and will feel unfamiliar. Weeks three and four make discovery repeatable and confront it with reality. Weeks five to eight are execution: platforms, roadmaps, design, pricing, and go-to-market. ### Week 1: The missing middle and what we're actually building *Why AI monetization fails, and what the role really is* - The Missing Middle - The Six Concurrent Revolutions - Technology Wave Maturity Journey - Agentic AI platform architecture: interface, agent, information, and simulation layers - Information Product Maturity Model (L0 to L5) - Workflow re-orchestration - Case: SAP's twelve-year climb from ERP to Joule - Exercise: Present one real workflow in four steps: old workflow, new workflow, how it grows the pie, how it's monetized. No technology in the presentation. ### Week 2: Opportunity discovery and the three pillars *Making the business legible, and treating data as an asset* - Business, operating, and technology models - Data as an Asset - The AI Monetization Pyramid - The Drug Dealer Model and the Barbell - The Opportunity Pipeline - Ecosystem business models - Cases: Reddit, Lyft, and hyperscaler unit economics - Exercise: Map your three models and identify three candidate transfers into the technology model. ### Week 3: Pragmatic futurism and turning discovery around *Being three to five years early without being wrong* - Pragmatic Futurism and the Arc of Disruption - Top-down discovery: the four feasibility questions - Bottom-up discovery: the complexity-and-uncertainty heuristic - The Adoption Journey - Moat Assessment and tokenomics - Cases: Nvidia, Verizon, Peloton, and the Metaverse - Exercise: Run the four-question screen on one technology leadership is excited about. Is there an adoption journey? ### Week 4: When opportunity discovery meets reality *The cautionary tale, and the framework that would have prevented it* - Problem, Data, Solution Space Exploration - Rapid productizing instead of rapid prototyping - Strategic debt - Blame the Framework - The Orchestration Imperative - Case: Vin's own resume-matching product, and the strategy mistake behind it - Exercise: Where is Amazon Prime's opportunity for its own ChatGPT moment? Bring a position. ### Week 5: Platforms, surfaces, and getting from opportunity to initiative *What you're actually building, and how a small idea becomes a platform* - Four surfaces, four platforms: product, operations, decision, foundational model - The Intelligent Core - Feature, product, platform - Long-chain workflows - Cases: Amazon Prime, JPMC, Apple's supply chain, Walmart - Exercise: Take one modest initiative and trace it up to the platform it implies. ### Week 6: Roadmaps that survive moving ground *A multi-year roadmap when the ground keeps changing* - The Roadmap Layer Cake - Parallel Maturity - The Agentic Operating System - The Internal/External Optimization Balancing Act - Decision Dominance - The Data Generation Maturity Model - Cases: car insurance, United and Delta, Disney - Exercise: Build a maturity-sequenced roadmap for one workflow using the cheapest technology that works today. ### Week 7: Parallel maturity, design, and measuring what you created *Everything advances at once, and you orchestrate it* - Human-Machine Maturity Model - Adoption and reliability maturity models - WIT cycles and DIKW - Local vs. global success metrics - Engineering Access - Case: the recruiting workflow decomposed end to end - Exercise: Formalize how you monetize data and information into a checklist, then find what it's missing. ### Week 8: Pricing, estimation, and go-to-market *All of the frameworks, pointed at the market* - The Bridge Pricing Model - Multi-dimensional tiering - Opportunity Estimation: underperform, expected, outperform - TAM, SAM, SOM - The Optimize-Before-Scale GTM sequence - Cases: Agentforce, Disney+ and Netflix, Cursor, Anthropic - Exercise: The final session is partly a working exam: bring cases, apply the frameworks aloud, and get them stress-tested. ## By the end, you'll be able to - Reverse the flow of ideas so the business brings you opportunities - Kill bad initiatives early and cheaply with a feasibility framework - Translate an opportunity into a roadmap that aligns technology, workflow, adoption, and GTM - Estimate and defend value in ranges C-level leaders will fund - Price AI correctly and bridge from today's pricing to outcome-based pricing - Go to market in a sequence that survives competitors ## Questions **What is AI Product Management: From 0 to ROI?** AI Product Management: From 0 to ROI is an eight-week live certification taught by Vin Vashishta on monetizing AI: opportunity discovery, feasibility, platform roadmaps, pricing, and go-to-market for AI and agentic products. **How much does it cost, and what's included?** $1,600 for the full certification. It includes 1-hour 1:1 session with Vin, a year of drop-in office hours, twice a week, recordings and slides after every session, 1-year access to the self-paced companion. **How long does it take?** Eight weeks, with one live session every Saturday from 8:00 to 9:30am PT and about an hour of Q&A after each one. The next cohort starts Saturday, January 9. Office hours continue for a year after the course ends. **Do I need a technical background?** No. There's no MBA or machine learning prerequisite. If you come from a technical background, expect the first two weeks to be uncomfortable, because strategy is shoulders up and the tools that made you successful get set aside. **Can I expense it?** Some students do, and approval depends entirely on your employer's policy. A reimbursement guide written to forward to your manager is linked on this page. **Will it teach me the machine learning?** No. None of the four core courses teach model evaluation, MLOps, training or serving operations, SRE, or security implementation. They teach the other half of the job: deciding what is worth building, proving it will create value, pricing it, and getting an organization to act on it. **Which course should I take?** Take AI Product Management if you have the mandate and need a roadmap that makes money. If you'd rather start with a lower-cost entry point, AI Opportunity Discovery is the upstream half of this job and the two are designed to run in sequence. The self-paced counterpart is AI Product Strategy. The course matcher scores 40 job titles against all four core courses. ## Instructor Vin Vashishta is the author of From Data to Profit (Wiley) and has applied every framework in this course with clients including Airbus, Walmart, Siemens, and JPMorgan Chase. More than 9,000 professionals in 47 countries have taken his courses.