The widely cited failure rate on enterprise AI initiatives is 95%. This course argues the root cause sits upstream of execution, in how a business decides what to build in the first place. Discovery is the only real point of leverage.
Once goals reach a roadmap and get communicated to a board or to investors, they set in stone. Discovery is the only process where redirection reads as we found a better opportunity rather than as an admission of incompetence.
The course is deliberately opinionated on two points. First, opportunity discovery is a growth function rather than a cost-reduction function, so the default reflex of using AI to raise productivity and cut headcount is treated as the wrong answer throughout. Second, finding opportunities is inseparable from change management, because the frameworks are useless if the business will not participate.
It is narrow and deep by design. Discovery, influence, and feasibility together make up 53% of its entire framework register. It is built for the person who has to run the room on Tuesday.
Data and AI strategists, product managers, technology and product leaders, consultants, and the vendor-side roles whose whole job is structured value discovery.
Business value consultants and AI value engineers. The published job description is close to a restatement of lessons 6, 8, and 9.
Senior individual contributor strategists who are not in the room where it gets decided.
AI product managers who need the upstream half before the roadmap half.
Consultants running use case discovery workshops. Lesson 13 is a full toolkit for when the room goes wrong.
Forward deployed engineers. Roughly half the job is discovery, and it is the half with almost no training available.
Analytics and data science managers asked to find AI opportunities with no method for doing it.
The strongest matches are the business value consultant or AI value engineer, the senior individual contributor strategist, the AI product manager, and the consultant running discovery workshops, each at 14 out of 15.
Check the role match →This course teaches how to find and defend opportunities. If your problem is earning a C-level mandate and building the strategy document, take the Data & AI Strategist Certification. If your problem is turning an approved opportunity into a roadmap and a price, take AI Product Management. Nothing technical is taught here.
Business Value Consultant and similar titles.
AI Strategist and similar titles.
AI Product Manager and similar titles.
Manager / Senior Manager, AI Advisory and similar titles.
Forward Deployed Engineer and similar titles.
Analytics Manager and similar titles.
Every problem below is drawn from a situation named in the course material. The second column is the reason most people enroll.
Without upfront estimation there is no basis for prioritization, budget, or buy-in, so the portfolio drifts toward whatever is loudest or newest.
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.
Replacing an existing feature with a more expensive AI version, with no re-engineered workflow and no new monetization, is a net negative that also forecloses better uses of the same resources.
Too many unproven assumptions get baked into a product before it reaches market. When it fails there are too many candidate root causes to diagnose and too much money spent to reverse course.
Data-generating processes treated as exhaust rather than as an asset, including arrangements where a partner captures the value and the originator gets nothing.
The reflex response when costs rise and pricing power falls, and the course's central worked counter-example. Discovery is treated as a growth function throughout.
Opportunity discovery is where the year of work gets chosen. If you are not in it, you inherit the results and have no recourse, and reopening the decision later costs credibility rather than winning the argument.
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.
The silent room. Figure out why, restate the mission, explain the framework, review the business goals, and prompt with their pain points. Have ideas in your back pocket and do not take over.
Executives arrive enthusiastic, non-technical, and having just seen a demo. The four questions filter hype without dampening enthusiasm: is the technology ready, is the business model ready, is it feasible for us, are we too late.
Problem space definition translates an opportunity into something buildable without prescribing how it gets built, and defines success in business KPIs rather than model accuracy.
Participants leave the session wondering why they were there. The recurring move is to redirect rather than take over, so the room keeps ownership of the opportunity it named.
Watch in order. Later lessons assume the vocabulary of earlier ones, particularly the first-principles definition of AI value in lesson 1 and the workflow as the unit of analysis in lesson 2, both of which recur nearly everywhere after.
Articulate the technology model alongside business and operating models, and define discovery as moving parts of the first two into the third.
Complexity and uncertainty as the test, defaulting to the cheapest technology that delivers the outcome.
Top-down with executives using four hype-resistant questions, and bottom-up governance that vets frontline ideas without flooding the data team.
Problem, data, and solution, without prescribing solutions to your technical teams.
With a floor strong enough to carry the initiative on its own.
Silence, hype-chasing, blanket objections, and prioritization by squeaky wheel or job title.
Exercises are ungraded and are the core of the course: a no-risk environment to hit the barriers you would otherwise hit live, in front of your executives.
14 sections and 70 lessons, roughly 7.5 hours of video, watched in order because later lessons assume earlier vocabulary.
The intended destination for everything the exercises surface. This is where the course gets extended against your specific situation.
Course updates for the life of the course, plus drop-in office hours and email support.
Most lessons end with an exercise applied to your own business rather than the worked case.
Qualified, sized, and defensible, built on your own business.
You are asked to substitute your own business into every exercise. You are never asked to share specifics.
“A lot of topics piqued my attention, especially the initial assessment and opportunity discovery. Getting buy-in from C-leaders was invaluable.”
“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.”
“The initial assessment and opportunity discovery changed how I work.”
“His hands-on approach and real-world examples made complex concepts accessible. I left with a framework I could use the following week.”
Vin Vashishta is the author of From Data To Profit (Wiley) and a LinkedIn Top Voice since 2017. Every framework in this course has been used in the field, with clients including Airbus, Siemens, Walmart, and JPMorgan Chase.
Every framework is taught twice: how it should be in a perfect setup, and how it actually is. In 13 years of practice the instructor reports never having seen a perfect setup, so the frameworks are built to flex.
For most people, yes. Opportunity discovery is the upstream skill the other three courses depend on, and it is the lowest-cost entry point into the curriculum.
It is also the tighter fit if your job is running the room rather than owning the mandate or the roadmap. The course matcher scores 40 job titles against all four courses.
A real employer and real opportunities. Every exercise asks you to substitute your own business, your own rivals, and your own strategic goals for the worked examples.
Confidentiality is respected. You are never asked to share specifics.
None formally required. Prior exposure to the AI Strategy or AI Product Management courses will make the feasibility assessment in lesson 8 familiar, but it is taught from scratch here.
No technical background is needed, and none is taught.
Yes. Drop-in office hours run twice weekly and are the intended destination for everything the exercises surface. Bring what you do not understand, what you doubt will work in your business, and the barriers you foresee.
This is treated as a living course. Office hours are where its content gets extended against your specific situation.
About 7.5 hours of video across 70 lessons, plus the exercises, which are the actual work. Most people spread it across a few weeks.
Lessons are meant to be watched in order, because later ones assume the vocabulary of earlier ones.
No, and that is the point. Roughly half of the forward deployed role is discovery: walking into a customer, finding the workflow worth changing, and proving it created value.
That half has almost no training available. Lessons 2, 8, 9, and 13 map to it directly.
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.
Ask your manager what approval path applies before you spend anything. A short written request works best when it names the cost of one misdirected initiative and what you will produce within 90 days of finishing. Whether it is approved is between you and your employer.
Enroll and begin the first lesson today. Tuition is $295, with lifetime course updates and drop-in office hours.
Enroll and start today →