Live cohorts and self-paced certifications for the people who have to decide what gets built, prove it will pay, and get an organization to act on it. Start by finding the course built for the job you actually hold.
Forty roles were scored against the four certifications on three things: how much of the published job description each course covers, how closely its problems match what you are living through, and whether you can realistically buy it.
Search your title, open the row, and see the problems that course was written to solve.
Every framework in the curriculum is tagged by course and by the job it does, so each course has a measurable fingerprint rather than a marketing description.
Strategy that easily navigates the politics. Its largest block is influence, change, and organization at 26 frameworks, more than any family in any course. Built for the person whose recommendations keep getting ignored.
An execution course. Discovery, architecture, maturity, roadmap, pricing, go-to-market. Zero frameworks in influence and change, which tells you it assumes you already have the mandate and cannot convert it into a plan.
A practitioner course, narrow and deep. Discovery, influence, and feasibility make up 53% of its entire framework register. Built for the person who has to run the room on Tuesday, and the lowest-cost way into the curriculum.
A business model course. Pricing at 18 frameworks and platform architecture at 17 dominate, and it is the only course in the portfolio with governance and trust content. Built for someone who owns a platform P&L.
Four more self-paced courses cover executive presence, strategic leadership, the value-centric data professional, and the move from employee to founder. Browse the full catalog.
Two kinds show up in every cohort. The first belongs to the business: expensive, broken, or stalled. The second belongs to you, and it is usually the real reason someone enrolls. Most training solves the first and leaves you to work out the second alone. Open any problem to see how it is addressed and where.
The workflow never changed, so no value could be created. AI attached to an unchanged process is bolt-on AI, and there has never been bolt-on AI with positive ROI. You learn the diagnostic test and the method for redesigning the workflow underneath it.
An opportunity pipeline narrows to five or ten by selecting for the profile of outperformance rather than by whoever lobbied hardest, and turns prioritization into a recurring quarterly act instead of a once-every-three-years scramble.
ROI cannot be promised for someday, and it is not credible at the token level. The calculation belongs at the workflow level, in three defensible bands, produced before the money is spent.
Usage looks strong in the dashboards while single-digit percentages of users pay. Roughly 3% of Copilot users pay rather than using free tiers. Freemium then compounds it, because inference cost scales with usage in a way SaaS never did.
A technology stack, a vendor list, or a plan to buy licenses. None of it explains why technology creates value or how any of it gets monetized, and no C-level leader can pull a lever that answers in architecture.
POC purgatory: gate one to gate two and back again until someone ships a demo. Five gates with explicit abort criteria fix it, plus the warning sign that if an AI build is 80% done, 80% of the cost and timeline is still ahead of you.
The most common constraint in every cohort. Bottom-up discovery is built for exactly this: start with frontline teams, stack two or three wins, build a coalition, and earn the meeting. Coalition Building maps the roughly six-month path from unknown in the building to a C-level mandate.
Opportunity discovery is where the year of work gets chosen. If you are not in it, you inherit the results and have no recourse. Going back later to reopen the decision costs credibility rather than winning the argument.
Framework Certainty: hear the challenge, name the framework, position it as the bridge, position yourself as the implementer. The live cohorts run pushback drills, where the instructor plays your CEO and resists.
If it is not broken, why fix it. Nothing you deliver is understood as core to how the company makes money. The reframe puts everything you do in top-line and bottom-line terms.
Very few roles in this field are well defined. You may have been handed AI ownership with no description of what the job is, what good looks like, or what you are accountable for.
Sharpest for anyone in a middle layer whose value proposition is being automated. Own opportunity discovery and you are tied to the P&L, which is ground truth, and the ability to apply and adapt frameworks is the part AI is not replacing.
Pick the format that fits how you actually work. If finishing is the problem rather than access, take the cohort.
Start today, move at your own speed, and revisit anything. Built for practitioners who want depth without a fixed calendar.
Six to eight weeks of live instruction with Vin, weekly Q&A, and a group working through it alongside you. Structure and accountability that get you finished.
Vin Vashishta is the author of From Data To Profit (Wiley), a LinkedIn Top Voice since 2017, and an advisor to enterprise teams building AI strategy from the ground up. Clients have included Airbus, Siemens, Walmart, and JPMorgan Chase.
The curriculum is the same field-tested frameworks, deliberately lightweight. Heavy frameworks get used once because they look impressive, then abandoned because there is never time to run them twice.
Students leave able to run an initial assessment, discover high-value opportunities, win C-level buy-in, and build strategy that ships. Support does not stop when class ends.
“Getting buy-in for your projects from C-leaders was invaluable for me. The initial assessment and opportunity discovery changed how I work.”
“One of the most valuable learning experiences at this stage of my career, as I transitioned into a leadership role. Incredible, digestible content for technical folks.”
“His hands-on approach and real-world examples made complex concepts accessible. I left with a framework I could use the following week.”
Career and executive brand coaching for people navigating a role that has no definition yet, a promotion case that has to be built, or a transition into strategy from somewhere technical.
Decide whether a role has a path to success before you take it, and build the case for the one you want next.
Become the person leadership calls when the AI question comes up, rather than the person who hears about it afterward.
Work through an assessment, a pipeline, or a board presentation you are holding right now.
Weekly writing on AI strategy, monetization, and the economics underneath both. Over 700 published articles and frameworks, and the source most of this curriculum was drafted in.
Match it to what you own. If you own enterprise strategy, meaning the assessment, the opportunity pipeline, ROI, the strategy document, and the C-level mandate, start with the Data & AI Strategist Certification. If you own a product or platform roadmap and need it to make money, start with AI Product Management. The material is explicit that owning both at once means doing both badly.
If you are not sure what to build in the first place, start with AI Opportunity Discovery. It is the upstream skill everything else depends on, and it is the lowest-cost entry point. The course matcher maps 40 job titles to the right course with a fit score for each.
Self-paced starts immediately, moves at your speed, and includes a year of access with office hours. It suits self-directed practitioners who want depth without a fixed calendar. Individual course prices are published on the catalog, ranging from $175 to $595.
Live cohorts run six to eight weeks with live instruction, weekly office hours, cohort accountability, a one-hour 1:1 session with Vin, and a year of access to the self-paced companion. AI Product Management is $1,600 and the Data & AI Strategist Certification is $2,400. If finishing is the problem, take the cohort.
Little. The curriculum contains 331 named frameworks tagged by course. Of those, 230 appear in exactly one course and only 4 appear in all four.
The framework count per course also shows what each one really is. Strategy is dominated by influence, change, and organization at 26 frameworks. Product management has zero in that family and is built around execution. Monetization is dominated by pricing at 18 and platform architecture at 17, and it is the only course with governance content.
No. There are no prerequisites on any certification, no MBA and no machine learning background. Everything is taught in business language so it can be used with executives who have neither.
If you come from engineering, expect the strategy material to feel unfamiliar at first. Every framework is taught twice: how it looks in a perfect setup, and how it looks against the constraints you actually have.
No, and it is worth knowing that before you enroll. None of the four courses teach model evaluation, MLOps, training or serving operations, SRE, or security implementation.
What is taught is 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.
Drop-in office hours continue for a full year after the course ends, plus email support. Office hours are cross-cohort, so you also hear the questions coming from other classes and previous cohorts.
Most people return 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.
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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