The same field-tested frameworks as the live cohorts, available on demand. No waiting for a start date and no fixed calendar. Move at your own speed, revisit anything, and keep access for a full year.
Drop-in office hours run twice weekly and continue for a full year. The instructor treats office hours as the primary venue for rigorous evaluation of your work, and several exercises are explicitly flagged for it.
Start immediately. No cohort wait and no application. Enroll and begin the first module today.
Your own pace. Move fast or slow, pause when work gets busy, and rewatch any lesson as often as you need.
A full year of access. Twelve months with every course you enroll in, plus office hours and email support.
Graded work, not just video. The larger courses carry assignments, per-section exercises, and a capstone built on your own business.
Lower entry point. Start from a fraction of the cohort price, with optional 1:1 support when you want it.
A fit for a senior calendar. For a VP, a year of drop-in office hours often works better than eight fixed Saturday mornings.
Each course is built around a specific failure mode rather than a topic. Open any problem below to see which course addresses it and where, then start there rather than at the beginning.
AI as a checkbox driven by board, analyst, and shareholder pressure, which produces no commitment to workflow re-engineering and therefore no return. There is a documented four-step response.
AI is among the most expensive options available and gets used by default rather than by justification. Complexity and uncertainty are the two-category test for when it is genuinely the right tool.
Tokens, predictions, conversations, or seats get chosen because they are measurable, not because more of them means more value delivered. Not every token is created equal.
Scattered projects across the enterprise, every team convinced it has the agent to rule all agents. Nothing consolidates and nothing compounds.
Data-generating processes treated as exhaust rather than as an asset, including arrangements where a partner captures the value and the originator gets nothing.
Dirty data, BI-era reporting, legacy systems that cannot go offline, and a target state involving knowledge graphs. No visible path between them, so nothing starts.
You have absorbed a great deal of strategy content and none of it told you what to do on Monday morning. That thread runs through the entire monetization course as a recurring segment.
The data is not clean, the platform is not ready, the governance is not written. This is the reason the work never starts and no momentum ever gets built.
Without an ROI estimate you cannot defend a priority, justify a budget, or explain what is lost by switching to the next shiny object. You are reduced to arguing from opinion against people arguing from opinion.
Executives arrive enthusiastic, non-technical, and having just seen a demo. You need questions that filter hype without dampening enthusiasm.
Model releases every three to six months and new paradigms constantly. You want durable structure rather than another tool list, which is why every course teaches the framework layer.
The objection that stops the work. The case selection answers it directly: a regulated bank, a manufacturer, a retailer, and a physical-product company rather than technology-first darlings.
Begin with a core discipline, then go deeper by the role you are growing into. Every course is self-paced with a year of access.
A complete sequenced system for sourcing, qualifying, sizing, and defending AI opportunities. Discovery, influence, and feasibility make up 53% of its framework register.
Platform architecture and monetization treated as the same problem from two sides. Pricing at 18 frameworks and architecture at 17, and the only governance content in the portfolio.
The strategist curriculum, self-paced. From the initial assessment to a strategy that ships and earns C-level buy-in.
AI products from zero to ROI, self-paced. Opportunity discovery, product strategy, and the bridge between technical and business teams.
Build rapport and trust with executives, align your narrative with how deciders actually decide, and learn to ask and answer questions like a CEO.
Build and lead a data organization that meets business needs: strategy implementation, innovation, and execution.
Translate business problems into solutions and own the full data science lifecycle with a value-first lens.
The business, strategy, and mindset shifts required to start something of your own.
Vin Vashishta is the author of From Data To Profit (Wiley) and a LinkedIn Top Voice since 2017. The self-paced courses use the same frameworks applied with clients including Airbus, Siemens, Walmart, and JPMorgan Chase.
Designed for technical professionals with no business background, and for leaders who need AI to earn its budget.
Your outcomes are the priority, so office hours and email support are there whenever you need them, for a full year after you enroll.
“It was a great course and so easy to follow. When my schedule got crazy, I was still able to listen to the sessions afterwards.”
“A lot of topics piqued my attention, especially the initial assessment and opportunity discovery. Getting buy-in from C-leaders was invaluable.”
“Where was this five years ago? I sent all my reports to take it so they would not stumble in the dark.”
If you have to decide what gets built, start with AI Opportunity Discovery. It is the upstream skill everything else depends on and the lowest-cost entry point into the curriculum.
If you own a platform P&L, its pricing, or its governance, start with AI & Agentic Platform Monetization. The course matcher scores 40 job titles against all four courses.
The frameworks are the same. What changes is the format: you start immediately, move at your own speed, and revisit anything, with a full year of access including drop-in office hours.
What you give up is the live pressure-testing, the cohort, and the confidential 1:1. If finishing is your problem rather than access, take the cohort instead.
A full year with every course you enroll in, including office hours and email support. Office hours are cross-cohort, so you hear the questions coming from live classes as well.
Most people come back around month three. The first three months tend to be a honeymoon because everything is new, and the first real barrier usually shows up right after that.
Yes. Drop-in office hours run twice weekly and are treated as the primary venue for rigorous evaluation of your work. Several exercises in the monetization course are explicitly flagged for office hours.
For a VP-level calendar, that combination often works better than eight fixed Saturday mornings.
The larger courses do. Platform Monetization is graded across an opening assignment, six per-section exercises, a second assignment, and a final Parallel Maturity roadmap for your own business.
Opportunity Discovery includes applied exercises throughout and a capstone.
Some students do, and approval depends entirely on your employer's policy. Many companies have a training or learning budget, and some do not extend it to external certifications.
Reimbursement assistance guides are published on the Data & AI Strategist and AI Product Management certification pages, written to be forwarded to a manager. Whether it is approved is between you and your employer.
Forty job titles mapped to four certifications, each with a fit score and the problems it was written to solve.
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