A self-paced AI product management course for people who already do product work. You follow one product manager, Sol, through 200 days, from an AI feature promised on a stage to a careful launch, and you make the same decisions in a Scenario Sim in every module, then on a feature of your own. Imagine walking into your next roadmap review able to say what “good” means for your AI feature, what one use of it costs and what you will watch after launch. No coding. About 11 hours over 8 weeks.
Bodhih AI Product Management is a self-paced online course for product managers, product owners, founders and business analysts who must decide whether and how to ship a feature with AI in it. Eight 75-minute modules cover problem framing and the non-AI baseline, discovery, data and model choices, evaluation, design for errors, unit economics, safety and privacy requirements, and launch and monitoring. Each module has an animated video, a browser simulation and a task on your own product. It needs no coding and leads to a Bodhih credential.
Facts on this page last checked by Siddharthan VGJ, Director, Bodhih Training.
Start without it. The animated concept video from Module 1 (Is AI the right solution? Framing the problem), narrated by a synthetic voice, with captions.
A leader has announced an AI feature and you are the one who has to make it real. Nobody wrote down which problem it solves, whether a simpler answer would do, or how anyone would know that it worked. You need a first question that is not “which model?”.
A demo is a handful of chosen examples. It cannot tell you how often the feature is right on real inputs, how good it needs to be, what one use of it costs, or whether the price of the plan covers that. Those are product questions, and they have answers.
Every AI feature is wrong some of the time. If the screen was designed only for the right answer, and security, privacy and legal arrive as one review in the last week, the launch is decided by surprises. Both can be planned as requirements from the start.
For people who own the outcome of a product or feature and have been handed “add AI”. You finish each module with a document about your own feature: a problem statement, an evaluation plan, a cost sheet, a launch plan.
For people who turn a need into requirements, acceptance criteria and tests. Modules 4 and 7 are about exactly that: what “good” means, and what the feature must never do, each written so that a test can pass or fail.
For people deciding where a small company spends its next three months. Module 1 asks whether AI is the right answer at all, and Module 6 asks what one task costs against what a customer pays.
For the people who work beside product. Module 5 is about the moment the feature is wrong on screen, and the whole course gives engineering what it asks for: decisions it can test, cost and maintain.
This course is not for you if you want a first course in product management, live teaching with a cohort, a project marked by a person, or hands-on training in coding, model building or one supplier’s tool: it is self-paced, tool-neutral and built on simulations.
Four statements, each from a primary source that we opened on 14 November 2026 and each stated with its limits. Naming a source describes where a fact comes from; none of these organisations has endorsed this course.
The EU AI Act sorts AI systems into four levels of risk. According to the European Commission’s page, its transparency rules took effect in August 2026 and the rules for high-risk uses apply from 2 December 2027. Dates have moved before, and which rules apply to a product is a question for a lawyer.
European Commission, “AI Act” policy page, Read on 14 November 2026The generative AI profile from the US standards body NIST names twelve risks, among them confabulation, data privacy and information security. The profile is voluntary, and the core framework it builds on is being revised.
NIST, “Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile” (NIST AI 600-1), July 2024; read on 14 November 2026The OWASP 2025 list of the top ten risks for language model applications puts prompt injection first, and also includes excessive agency and unbounded consumption. It is a community consensus list, not a measure of how often incidents happen.
OWASP, “Top 10 for LLM Applications 2025”, 2025; read on 14 November 2026In one 2023 study, the accuracy of a widely used AI service on a simple maths task fell from 84% to 51% between two versions three months apart, while it improved on other tasks. It is a preprint, and that was one task among seven: the finding is change in both directions, not decline.
Chen, Zaharia and Zou, “How Is ChatGPT’s Behavior Changing over Time?” (preprint, arXiv:2307.09009), 2023; read on 14 November 2026Each module ends with a task on your own product, so you finish with working documents, not only notes. Notice how the questions change as you go, from “can it do this?” to “how often, for whom, at what cost, and what happens when it fails?”. Nobody can promise a job, a promotion or a successful feature, and this course does not; it gives you methods, practice and sources you can open.
Write a problem statement that names no technology, measure a non-AI baseline, and give a verdict: AI, not AI, or AI for part of it.
Name the assumptions behind an AI feature, test each one cheaply on real inputs, and write a stop rule before the pilot starts.
Choose between prompting, retrieval, fine-tuning and buying, starting with the lightest route, and ask the right questions about data and suppliers.
Write criteria with the people who do the task, build a test set from real inputs, and set a launch bar and a critical-error limit before you see results.
Design how the feature shows doubt, fails and hands control back, and estimate the cost and the wait of one task against what the customer pays.
Turn safety and privacy concerns into requirements with tests and owners, and plan a staged launch with signals, alert levels and a rollback you have tried.
The pathway runs in order: a baseline assessment, then eight modules (is AI the right solution; discovery and prototyping; data and models; evaluation; designing for uncertainty; unit economics; safety, privacy and compliance; launch, monitoring, drift and the roadmap) and a final assessment. Every module has the same shape: a chapter of the story, a video, three skills, a worked example, a Scenario Sim and a task on your own product.
Measured before you start and again on the same instrument at the end, so the change is recorded rather than claimed.
The baseline assessment, Form A: 20 scenario questions on all eight competencies, about 25 minutes. It is not pass/fail; it sets your baseline, and you take the parallel form at the end.
Write the problem as an outcome for a user, measure a non-AI baseline first and run the one-page AI fit test: AI, not AI, or AI for part of it. Video: Start without it. Scenario Sim: Does this need AI?
Test three risks separately: is it wanted, is it good enough on real inputs, and what happens when it is wrong. Cheap prototypes, a learning plan and pilot safety rules. Video: The demo and the hundred. Scenario Sim: Twelve businesses.
What a product manager must know about a model, the four routes and their trade-offs, five questions for any dataset, reading a model card, and a supplier exit plan. Video: Four routes up the same hill. Scenario Sim: Four routes.
Define good with the people who do the task, build a test set that mirrors real use, choose who grades what, and set the launch bar before the run. Video: One number, two mistakes. Scenario Sim: Define good.
Design for the moment the feature is wrong: expectations, doubt shown in words, sources, an honest "I cannot answer", useful friction, feedback and user control. Video: The right amount of trust. Scenario Sim: One tap to send.
Count the cost and the wait of one task, compare them with plan revenue and user needs, pull levers in order with the test set after each, and choose a pricing shape. Video: The meter on every reply. Scenario Sim: Thirty-four per cent.
Turn safety, misuse, privacy and regulatory concerns into requirements that each have a test, an owner and one person who can waive them. A risk map, a red-team exercise and when to call counsel. Video: Requirements, not a gate. Scenario Sim: Seven of fifty.
Launch as an experiment: stages, four signals with alert levels and owners, a rollback you have tried, drift, and maintenance on the roadmap. Video: The test set never retires. Scenario Sim: Five, twenty-five, all.
The final assessment, loaded from Form B: 20 parallel scenario questions on all eight competencies, about 25 minutes. Score 70% or more (3 attempts) for the credential.
Finishing this pathway issues a credential backed by the evidence you produced along the way. It is verifiable by link and is also issued as an Open Badge 2.0, so an employer can check it without taking your word for it.
Module 3 compares four routes (prompt, retrieve, fine-tune, buy) by cost, time and control, and has you choose one in the sim Four routes.
Module 4 covers criteria, test sets, human graders and model judges, and a launch bar written before the run, practised in Define good.
Module 6 takes one task apart into its cost drivers, sets a ceiling as a share of revenue and a wait target, and tests each lever, in Thirty-four per cent.
Module 5 covers over-reliance, doubt shown in words, sources, an honest “I cannot answer” and useful friction, practised in One tap to send.
Module 7 turns misuse, privacy and regulation into requirements with tests, owners and waivers, at principle level, in Seven of fifty. It is not legal advice.
Module 8 covers staged rollout, four signals, alert levels, rollback and maintenance on the roadmap, practised in Five, twenty-five, all.
What comes with the course, and what the practice is like.
Does this need AI?, Twelve businesses, Four routes, Define good, One tap to send, Thirty-four per cent, Seven of fifty and Five, twenty-five, all. In each you take the product manager’s chair, make a series of decisions and see what follows. They are browser simulations with fixed outcomes, not real AI tools.
The at-work tasks build a set of one-page documents about a feature of your own: a problem statement and baseline, a learning plan, a data and model decision sheet, an evaluation plan, a failure walk-through, a cost-per-task sheet, safety requirements and a launch plan.
Each module credits its sources with their limits and ends with checked Go deeper links to universities, standards bodies, research papers and AI builders’ own documentation. They are links to those sources’ own material, not partnerships or endorsements.
The Product Manager’s Operating System, The Go-to-Market Launch Plan Kit and The AI Agent Deployment Playbook.
See the product manager kitCompany seats with reporting on each learner’s progress and before-and-after assessment scores, so a product team can share one vocabulary for baselines, test sets, cost per task and launch signals.
Ask about team seats| What is compared | A live cohort or university AI product course | AI Product Management at Bodhih |
|---|---|---|
| Format and time | Fixed dates and a time zone for a live cohort, or a multi-course subscription series of about 40 to about 125 hours. | Self-paced, about 11 hours, planned over 8 weeks. Start and stop when you like. |
| Price | Live cohort certifications were listed at roughly US$2,500 to US$3,000, and one university evening class at about US$435, when we read their pages on 15 November 2026. Subscription platforms charge by the month or year. | $124, once. A launch price applies until 10 January 2027. |
| Where it starts | Often from machine learning theory, or from product management basics. | From the decisions a working product person owns in a generative AI feature. It assumes you already know product work. |
| Cost, safety and privacy | In the syllabuses we read, a session or a section at most, and sometimes absent. | A full module on unit economics and a full module on safety, privacy and compliance as requirements. |
| Practice | A graded project, often reviewed by an instructor or by peers. | Eight browser simulations and eight tasks on your own product. No real AI tool is called and nobody marks your documents. |
| Honest limit | A live instructor, a cohort, a marked project, and in some cases a university or large-brand certificate. | None of those. A Bodhih credential that is not accredited by an outside body, videos with a synthetic voice, and no human review of your work. |
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Straight answers on time, price, tools and the credential. If yours is not here, ask us and you will have a reply the same working day.
People who already work on products: product managers, product owners, founders, business analysts, and the designers and engineering leads who work with them. It is for the moment you have to decide whether, what and how to ship with AI in it. It is not a first course in product management.
No. There is no coding and no mathematics beyond percentages. Module 3 explains what a product manager needs to know about data and models in plain language, and the course does not teach how to train a model.
No. You earn a Bodhih credential with an Open Badge 2.0 that you can share and that anyone can verify. It is Bodhih’s own credential. It is not accredited by a university, a government or any outside body, and the organisations whose research we cite have not endorsed it.
The labs are simulations. Each module has a Scenario Sim that runs in your browser: you make decisions as a product manager and see the consequences, which are written in advance. No real AI tool is called, and you do not need an account with any AI supplier. The videos have no presenter: each module has one short animated concept video, narrated by a synthetic voice.
None by name. The course is tool-neutral: it teaches decisions that hold whichever supplier you use, and it quotes no model names, versions or prices. Go deeper links point to builders’ own documentation if you want that detail.
About 11 hours: eight modules of 75 minutes, a baseline assessment and a final assessment. It is self-paced and planned for about 8 weeks, and you can go faster.
Complete all eight modules and score at least 70% on the 20-question final assessment. You have three attempts. There is no project marked by a person.
The course is $124 for an individual and $99 per seat for companies. Until 10 January 2027 the price shown on this page is a launch price, about half the regular price. After that date the regular price applies.
Those offer live teaching, a cohort and sometimes a marked project or a university certificate; this course offers none of them. It is shorter, self-paced and lower in price, it assumes you already know product work, and it gives full modules to unit economics and to safety, privacy and compliance.
No. It covers what a product manager must decide and whom to involve. For depth, see Bodhih Agentic AI for Business, Bodhih AI Security and Bodhih Responsible AI and Privacy. Regulation is taught at the level of principles, with the date each source was read, and nothing in the course is legal advice.
We cannot promise that, and nobody honestly can. What the course gives you is practice at the decisions and a set of working documents on your own product: a problem statement, an evaluation plan, a cost sheet, a requirements list and a launch plan.
The course is $124 for an individual, or $99 per seat for companies, with three Pro Kits included. The launch price ends on 10 January 2027. You can start today with the baseline assessment, then write the problem statement for one AI idea on your own roadmap in Module 1. For a team, write to solutions@bodhih.com or call +91 99000 11601.
Starting records where you stand today. That baseline is what the re-measurement is compared against, and it is never rewritten afterwards.
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