AI Product Management: An 8-Week Course for AI Product Managers

What this course is

AI Product Management is an 8-week Applied AI foundation course for working product managers transitioning into AI product roles or expanding existing PM practice with AI features. The curriculum sequences eight weekly modules across the AI product lifecycle: AI product foundations (what is genuinely different about AI products), scoping AI features under capability and latency-cost-quality constraints, evaluation methodology (eval set design, eval cadence, what good looks like), working with AI engineering teams (PRD patterns for AI features), AI product strategy (competitive moats, build versus buy, model selection), AI ethics in product decisions (bias, fairness, transparency aligned with NIST AI Risk Management Framework), pricing AI products (token economics, value capture), and a capstone in which the learner authors a complete AI product spec. Every module pairs reading with a hands-on artifact. Content cites NIST AI Risk Management Framework, NIST AI 600-1 Generative AI Profile, the official product and pricing pages of frontier AI labs (Anthropic, OpenAI, Google DeepMind), and peer-reviewed product research. The cybersecurity convergence appears throughout because AI products that ship in 2026 have to address prompt injection, data leakage, and the cybersecurity-AI seam at the product layer. Authored by Julian Calvo, Ed.D. in Learning Sciences with the M.S. Applied AI specializing in Cybersecurity in progress at Northeastern University.

The course follows the AI product lifecycle from scoping through pricing rather than the chapter order of a general product book. Week 1 grounds the learner in what is genuinely different about AI products (probabilistic outputs, capability discovery, evaluation as the new design surface). Weeks 2 through 7 walk the lifecycle in dependency order: scoping, evaluation, engineering partnership, strategy, ethics, pricing. Week 8 integrates the work into a complete AI product spec. Pedagogically the design draws on Kolb's experiential learning cycle (1984) and on product research methods from Cagan and Marty. Evidence quality is opinionated. AI product claims are anchored to AI lab official documentation, NIST AI RMF, or peer-reviewed product research. Generic AI hype without primary-source backing is excluded.

What you will learn

• Name the three structural differences between AI products and traditional software products
• Map your product to one of the four named AI product archetypes (assistant, agent, generation, classification)
• Apply the latency-cost-quality envelope to scoping decisions and identify the binding constraint
• Design evaluation sets sized for the feature scope (30 to 200 cases) with named subgroup analysis
• Set evaluation cadence across pre-merge, pre-release, and ongoing operation
• Author an AI PRD with the six AI-specific sections (model selection, eval spec, latency budget, cost budget, prompt and tool design, security)
• Run the three pre-engineering conversations (capability calibration, evaluation alignment, failure-mode triage) before engineering starts
• Pick the competitive moat the product is building and the model strategy that supports it
• Apply NIST AI Risk Management Framework characteristics to ship trustworthy AI products in cybersecurity-aware markets
• Walk token economics from API to gross margin and pick the right pricing pattern for the product

8-week curriculum

1. Week 01 · 6h · 4 topics

   AI Product Foundations

   What is genuinely different about AI products versus traditional software products: probabilistic outputs, capability discovery, evaluation as the new design surface, and the latency-cost-quality envelope every AI feature operates inside. The four named AI product archetypes and how the rest of the course maps to them.

   Learning objectives and topics

   Learning objectives.

   • Name three structural differences between AI products and traditional software products
   • Map four named AI product archetypes (assistant, agent, generation, classification) to your product portfolio
   • Identify the latency-cost-quality envelope for an AI feature in your product
   • Author a one-page AI product foundations document the course returns to in every later week

   Topics.

   • Three structural differences between AI products and traditional software
   • Four named AI product archetypes
   • The latency-cost-quality envelope
   • Authoring the AI product foundations document

   Assessment: 8 questions · 360 minutes total

2. Week 02 · 6h · 4 topics

   Scoping AI Features

   Capability mapping for the model the team has chosen, the latency-cost-quality trade-offs that drive scope, the named scope-cutting techniques that ship AI features in eight weeks instead of eight months, and the scoping document the learner produces at the end of week 2.

   Learning objectives and topics

   Learning objectives.

   • Run a capability mapping session against the model the team has chosen and produce a capability inventory
   • Apply the latency-cost-quality envelope from week 1 to a candidate AI feature and identify the binding constraint
   • Use four named scope-cutting techniques to bring an AI feature into a shippable size
   • Author an AI feature scoping document with named in-scope and out-of-scope items

   Topics.

   • Capability mapping for the chosen model
   • Latency-cost-quality trade-offs that drive scope
   • Four named scope-cutting techniques
   • Authoring the scoping document

   Assessment: 8 questions · 360 minutes total

3. Week 03 · 6h · 4 topics

   AI Evaluation Methodology

   Evaluation set design (sample size, distribution coverage, edge case selection), evaluation cadence (pre-merge, pre-release, ongoing), what good looks like for the four AI product archetypes, and the evaluation specification the learner authors at the end of week 3.

   Learning objectives and topics

   Learning objectives.

   • Design an evaluation set sized for the feature scope (30 to 200 cases) with explicit distribution coverage
   • Set evaluation cadence across pre-merge, pre-release, and ongoing operation
   • Define what good looks like with named metrics for each of the four archetypes
   • Author an evaluation specification document the engineering team can implement directly

   Topics.

   • Evaluation set design
   • Evaluation cadence: pre-merge, pre-release, ongoing
   • What good looks like for each archetype
   • Authoring the evaluation specification

   Assessment: 9 questions · 360 minutes total

4. Week 04 · 7h · 4 topics

   Working with AI Engineering Teams

   PRD patterns for AI features (the named sections every AI PRD includes), the working partnership between PM and AI engineering teams, the three named conversations every AI feature requires before engineering starts, and the AI PRD the learner authors at the end of week 4.

   Learning objectives and topics

   Learning objectives.

   • Write a PRD that includes the AI-specific sections engineering needs (model selection, eval spec, latency budget, cost budget)
   • Run the three named pre-engineering conversations (capability calibration, evaluation alignment, failure-mode triage)
   • Establish the working rhythm with AI engineering teams (eval reviews, release reviews, model upgrade reviews)
   • Author a complete AI PRD against a real candidate feature

   Topics.

   • AI-specific PRD sections
   • Three named pre-engineering conversations
   • Working rhythm with AI engineering teams
   • Authoring the AI PRD

   Assessment: 8 questions · 420 minutes total

5. Week 05 · 6h · 4 topics

   AI Product Strategy

   Competitive moats specific to AI products (data, distribution, evaluation rigor), build versus buy decisions across model layer and tooling layer, model selection across frontier and open-weights, and the strategy memo the learner authors at the end of week 5.

   Learning objectives and topics

   Learning objectives.

   • Identify the four competitive moats specific to AI products and assess your product against each
   • Run a structured build-versus-buy decision across model layer, tooling layer, and evaluation layer
   • Choose between frontier closed-weights, frontier open-weights, and fine-tuned model paths with named criteria
   • Author a strategy memo that names the moat the product is building and the model strategy that supports it

   Topics.

   • Four competitive moats specific to AI products
   • Build versus buy across three layers
   • Model selection across frontier and open-weights
   • Authoring the strategy memo

   Assessment: 8 questions · 360 minutes total

6. Week 06 · 6h · 4 topics

   AI Ethics in Product Decisions

   Bias and fairness in AI product decisions, transparency requirements (NIST AI RMF, EU AI Act, US state laws), the named ethics framework the PM applies before shipping, and the ethics review document the learner authors at the end of week 6.

   Learning objectives and topics

   Learning objectives.

   • Apply the NIST AI Risk Management Framework characteristics (validity, safety, security, accountability, transparency, fairness, privacy) to a real AI product decision
   • Identify the named bias and fairness failure modes for the four AI product archetypes
   • Map the regulatory transparency requirements (NIST AI RMF, EU AI Act, US state pay transparency, consumer disclosure) to your product's surface
   • Author an ethics review document that ships alongside the PRD

   Topics.

   • NIST AI RMF as the working ethics framework
   • Bias and fairness failure modes by archetype
   • Regulatory transparency requirements
   • Authoring the ethics review document

   Assessment: 8 questions · 360 minutes total

7. Week 07 · 6h · 4 topics

   Pricing AI Products

   Token economics from the model API up to the product surface; value capture across consumer, prosumer, and enterprise tiers; the named pricing patterns AI products use in 2026; and the pricing model the learner authors at the end of week 7.

   Learning objectives and topics

   Learning objectives.

   • Walk the token economics from API cost to gross margin at the feature level
   • Pick the right pricing pattern (per-seat, usage-based, tiered, hybrid) for the product
   • Calculate the unit economics that a free tier and a paid tier require to sustain
   • Author a pricing model document with named tiers, named limits, and named gross margin targets

   Topics.

   • Token economics from API to gross margin
   • Named pricing patterns for AI products in 2026
   • Free tier unit economics
   • Authoring the pricing model document

   Assessment: 8 questions · 360 minutes total

8. Week 08 · 6h · 4 topics

   Capstone: Complete AI Product Spec

   Integrating the foundations document, scoping document, evaluation specification, AI PRD, strategy memo, ethics review, and pricing model into a single AI product spec that a real AI engineering team could implement against. The capstone deliverable for the course.

   Learning objectives and topics

   Learning objectives.

   • Integrate the seven prior week deliverables into a single AI product spec
   • Author a 20 to 30 page spec that an AI engineering team could implement against without follow-up questions
   • Defend the spec against three named failure modes (capability mismatch, ethics gap, unit economics gap)
   • Earn the DecipherU AI Product Management certificate of completion

   Topics.

   • Integrating the seven prior deliverables
   • What an implementable spec looks like
   • Three named failure modes the rubric tests
   • Earning the certificate of completion

   Assessment: 8 questions · 360 minutes total

Capstone

Author a complete AI product spec that an engineering team could implement against without follow-up questions

The capstone integrates the seven prior weekly artifacts (foundations document, scoping document, evaluation specification, AI PRD, strategy memo, ethics review, pricing model) into a single 20 to 30 page AI product spec. The spec has nine required sections (executive summary, foundations, scope, quality specification, engineering specification, trust and safety specification, commercial specification, strategy and competitive positioning, risks and rollback). The capstone is graded against three named failure modes: capability mismatch, ethics gap, unit economics gap. A passing capstone earns the DecipherU AI Product Management certificate of completion.

Who it is for

• Working product managers transitioning into AI product roles at AI-native enterprises
• Product managers at traditional companies who need to ship AI features in their existing product
• Senior product managers expanding their practice with AI evaluation and AI strategy
• Product leads at startups building AI-native products from scratch
• Technical product managers partnering with AI engineering and ML engineering teams
• Cybersecurity product managers shipping AI security features at security vendors

Who it is not for

• Product managers without prior production product experience. Build foundational PM skills first.
• Engineers wanting to learn AI engineering. The course is for AI product roles. Engineers should look at AI Career Transition or AI Engineering Mastery.
• Anyone seeking proprietary exam prep. The course is portfolio-driven and does not credential against any vendor exam.
• Product managers unwilling to author seven artifacts across the eight weeks. The course requires shipping defendable documentation.

Prerequisites

• At least 2 years of product management experience shipping production features
• Working familiarity with PRD authoring, user research, and engineering partnership
• Basic LLM literacy as a user (prompting, API exposure, structured output)
• Comfort reading API documentation and a basic understanding of model capabilities
• Willingness to commit 50 to 60 hours of focused study and artifact production across 8 weeks

What you get

• 55 hours of original Applied AI product curriculum across 8 weekly modules with cybersecurity convergence content woven in
• Seven portfolio-grade artifacts produced across the 8 weeks (foundations document, scoping document, evaluation specification, AI PRD, strategy memo, ethics review, pricing model)
• Certificate of completion issued for learners who finish all 8 weekly assessments and submit a capstone that scores at least 4 of 5 across the three named failure modes. The certificate is a digital credential with a verifiable URL listing the curriculum and the assessment outcomes.
• Lifetime access to course updates as NIST AI RMF, EU AI Act, and the Applied AI product landscape evolve
• DecipherU community access (Defender tier and above) for peer review of the capstone AI product spec and post-course Q&A

Author

Frequently asked questions

Who is this AI product management cybersecurity-aware course for?

Working product managers (2-plus years experience) transitioning into AI product roles or expanding existing PM practice with AI features. The course covers consumer, prosumer, and enterprise AI products, and includes cybersecurity-aware coverage for product managers shipping AI security features at security vendors. Basic LLM literacy as a user is required; AI engineering depth is not.

What primary sources does the course cite?

NIST AI Risk Management Framework (NIST AI 100-1), NIST Generative AI Profile (NIST AI 600-1), EU AI Act (Regulation 2024/1689), Colorado AI Act, frontier AI lab official product and pricing pages (Anthropic, OpenAI, Google), and peer-reviewed product research (Cagan, Porter). Generic AI hype without primary-source backing is excluded.

How long does the AI product management course take to complete?

Roughly 50 to 60 hours of focused study and artifact production across 8 weekly modules. Most learners complete it in 8 to 12 weeks at 5 to 7 hours per week. Self-paced. The capstone is a complete AI product spec the learner can show to a hiring panel or use as a working document for an actual AI feature ship.

Will the course prepare me for a specific AI product certification?

It is not a proctored exam prep course. The curriculum maps to the work products expected in AI product manager, AI senior product manager, and AI principal product manager roles, and to the Northeastern M.S. Applied AI specializing in Cybersecurity coursework. The capstone (a complete AI product spec) is the portfolio artifact most AI product hiring panels weight heavily in 2026.

How does the course cover AI ethics and the cybersecurity convergence?

Module 6 is dedicated to AI ethics in product decisions and applies the NIST AI Risk Management Framework seven characteristics (validity, safety, security, accountability, explainability, privacy, fairness) as a working checklist. The cybersecurity convergence appears throughout because AI products in 2026 must address prompt injection, data leakage, and audit trail design at the product layer. Cybersecurity product managers shipping AI security features will find the convergence content directly applicable.

How is this course different from AI Career Transition?

AI Career Transition ($397, 8 weeks) covers the engineering transition arc: resume, portfolio project, network, interview, compensation. AI Product Management (this course, $397, 8 weeks) covers product practice: scoping AI features, evaluation methodology, AI PRDs, strategy, ethics, pricing. Both are Applied AI foundation courses at the same price and live in the same /ai/courses catalog. Pick the one that matches the role you are moving into.

Companion course

Engineer transitioning into AI? AI Career Transition is the companion 8-week course.

AI Product Management teaches the AI product practice arc. AI Career Transition teaches the engineering transition arc: resume, portfolio, network, interview, compensation. Both are $397 one-time and live in the Applied AI foundation catalog.

Related cybersecurity and Applied AI content

• Applied AI vertical overview
• AI Product Manager career guide
• AI Security Operations Mastery (cybersecurity convergence course)
• Cybersecurity for AI courses (secure AI systems themselves)
• Cybersecurity foundations courses