SB
Spandana Bala
AI Product Manager
Hyderabad · India
Now at · Infosys
AI Product Leadership Academy · Product & market ownership · Enrolling now
AI Product Manager
From AI Possibility to Market Impact
AI capability alone does not create a successful product. Learn to select a consequential problem, define a defensible AI product strategy, prove quality and trust, establish viable economics — and lead launch, adoption and growth with an investment-ready case.
100K+
alumni community
1,000+
hiring partners
4.8/5
avg class rating
12
partner centres
7
Digital Edify centres
Where our AI product alumni work
Microsoft
Amazon
Salesforce
ServiceNow
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
Direct answer
What is an AI Product Manager?
An AI Product Manager decides which customer problem and AI product an organisation should pursue, why that product can create durable value, and how it should be validated, launched and improved. The role combines traditional product discovery and strategy with working fluency in data, models, RAG, agents, evaluation, human oversight, security, economics and lifecycle operations. Unlike an AI Product Owner, the Product Manager is centred on market selection, business viability, launch and growth.
The market-to-production chain
From market thesis to investment-ready launch — one owner.
01DISCOVER
- •Market structure, segments & ICP
- •JTBD, workflows & alternatives
- •Buyer, user & affected stakeholders
- •Opportunity thesis & entry wedge
02DESIGN
- •Product strategy, positioning & non-goals
- •Human-centred AI experience & prototype
- •Product-system & build–buy–partner trade-offs
- •Agent workflows, autonomy & controls
03LAUNCH
- •Representative evaluation & release evidence
- •Unit economics, pricing & packaging
- •GTM, design partners & adoption
- •Roadmap, growth & product operations
What the AI Product Manager owns
- •Target segment, customer problem and product opportunity
- •Product vision, strategy, positioning and explicit non-goals
- •Business case, unit economics, pricing and packaging hypotheses
- •Go-to-market, launch, adoption and growth strategy
And answering for whether the product should exist at all.
How this course differs · 2026
Not a renamed Product Owner course — and not a prompt-engineering course.
Beyond the Product Owner remitMarket & commercial depth
Where the Product Owner runs the team backlog and release, this programme goes deeper into the market and the business.
- •Market segmentation & beachhead selection
- •Category, positioning & differentiation
- •AI business models & unit economics
- •Pricing, packaging, procurement & enterprise GTM
Product judgement, not prompt libraries★ The durable outcome
Enough prompting, RAG and agent fluency to make product decisions — assessed on judgement, evidence, trade-offs and leadership.
- •Choose a valuable problem — or reject the AI idea
- •Design a trustworthy, controllable product experience
- •Evaluate the complete system, not the demo
- •Create viable economics & lead market impact
The decision ladder — defend the simplest sufficient solution
NO CHANGE / REDESIGN
Process first
Fix the workflow before naming a technology.
RULES / SEARCH
Deterministic wins
Rules, search or automation when behaviour must be exact.
PREDICTIVE ML
Learned patterns
Score, classify and forecast where data supports it.
GENAI / RAG
Grounded generation
Generate and answer — grounded in permissioned sources.
BOUNDED AGENT
Governed autonomy
Tool-using agents with approvals, limits and rollback.
Who should join
Built for people moving into AI product leadership.
Product Managers & Product Owners
Founders & innovation leaders
Sr BAs & transformation consultants
UX researchers & product designers
Growth, pre-sales & customer-success pros
Enterprise-domain leaders (AI products)
Prior experience: three or more years recommended in product, business, consulting, technology, design, analytics or customer work. No production coding is required; an AI Product Foundations route covers product, Agile, AI, RAG, agents, metrics and responsible-AI vocabulary where needed.
What you will be able to do
Choose the product — and prove it deserves investment.
Select the opportunityChoose a segment and consequential problem using market, user, workflow and competitive evidence.
Judge where AI winsDecide where AI creates meaningful advantage — and where a simpler non-AI approach is better.
Define the strategyVision, strategic choices, value proposition, positioning, product principles and non-goals.
Design trustworthy experiencesCommunicate uncertainty, support human control, design graceful failure and recovery.
Make system trade-offsDiscuss data, models, RAG, agents, tools, latency, reliability and cost with technical teams.
Prove quality with evaluationTask-specific evaluations with datasets, rubrics, traces, analytics and human review.
Model the businessCost per successful task, gross margin, pricing, packaging and go-to-market hypotheses.
Lead launch & growthEvidence-gated roadmaps, staged rollout, adoption diagnosis and product operations.
Course curriculum
Twelve modules. From market thesis to investment-ready launch.
01
AI Product Management in the AI Era
Foundation
Product management as continuous ownership of customer and business outcomes — and the uncertainty introduced by data, model behaviour, variable cost and trust.
+
01
AI Product Management in the AI Era
FoundationProduct management as continuous ownership of customer and business outcomes — and the uncertainty introduced by data, model behaviour, variable cost and trust.
Topics
•PM vs PO vs project manager vs Platform Owner vs tech lead
•AI-native, AI-enabled, internal & platform product models
•Uncertainty — data, behaviour, cost & trust
•Product operating models for AI
•Workshop: deconstruct three AI products
Portfolio artifactAI Product Manager Role CharterProduct Teardown
02
AI, ML, GenAI and Agentic AI Foundations
Foundation
Working fluency in predictive ML, foundation models, GenAI, multimodality, RAG, tools and agents — plus hallucination, bias, drift, over-automation, latency and cost.
+
02
AI, ML, GenAI and Agentic AI Foundations
FoundationWorking fluency in predictive ML, foundation models, GenAI, multimodality, RAG, tools and agents — plus hallucination, bias, drift, over-automation, latency and cost.
Topics
•Predictive ML, foundation models & GenAI
•Tokens, embeddings, prompting & RAG
•Tools, agents & model adaptation
•Failure modes — hallucination, drift, over-automation
•Lab: classify 20 opportunities; defend the simplest solution
Portfolio artifactAI Suitability & Solution Decision Canvas
03
Market Intelligence, Customer and Workflow Discovery
Discover
Market structure, segmentation, ICP, buyers and affected stakeholders — JTBD, switching forces, workflow observation, competitive intelligence and willingness-to-pay research.
+
03
Market Intelligence, Customer and Workflow Discovery
DiscoverMarket structure, segmentation, ICP, buyers and affected stakeholders — JTBD, switching forces, workflow observation, competitive intelligence and willingness-to-pay research.
Topics
•Segmentation, ICP, user vs buyer vs economic buyer
•Jobs-to-be-Done & switching forces
•Workflow observation & service blueprints
•Alternatives analysis & willingness-to-pay
•Lab: five interviews, workflow map, research limitations
Portfolio artifactResearch RepositoryWorkflow MapCustomer Insight Brief
04
Opportunity, Wedge and Product Strategy
Strategy
Compare opportunities on urgency, value, data advantage, competition, feasibility and trust — select a focused entry wedge and define the strategy narrative.
+
04
Opportunity, Wedge and Product Strategy
StrategyCompare opportunities on urgency, value, data advantage, competition, feasibility and trust — select a focused entry wedge and define the strategy narrative.
Topics
•Opportunity scoring & wedge selection
•Vision, diagnosis, guiding policy & coherent actions
•Positioning, principles, non-goals & differentiation
•Defensibility & data advantage
•Workshop: investment-committee simulation — three wedges
Portfolio artifactOpportunity ThesisAI Product Strategy NarrativeProduct Metric Tree
05
Human-Centred AI Experience and Prototyping
Design
Assistant, copilot, recommender, generator, workflow and agentic experiences — expectations, confidence, sources, controls and graceful failure.
+
05
Human-Centred AI Experience and Prototyping
DesignAssistant, copilot, recommender, generator, workflow and agentic experiences — expectations, confidence, sources, controls and graceful failure.
Topics
•Experience patterns — assistant to agentic
•Communicating confidence, sources & limitations
•Edit, approve, reject, undo & escalate controls
•Graceful failure, accessibility & multilingual support
•Lab: prototype + trust and recovery testing
Portfolio artifactAI Experience BlueprintPrototype & Test Findings
06
Data, Architecture, Platforms and Interoperability
Design
Data readiness, vendor-neutral architecture, cloud vs private vs hybrid, model routing, vendor dependency — with product-level MCP and A2A awareness.
+
06
Data, Architecture, Platforms and Interoperability
DesignData readiness, vendor-neutral architecture, cloud vs private vs hybrid, model routing, vendor dependency — with product-level MCP and A2A awareness.
Topics
•Data ownership, quality, freshness & lineage
•Reading vendor-neutral AI architecture
•Cloud / private / hybrid & model routing
•MCP & A2A awareness; identity & delegated consent
•Lab: three architectures, two vendor proposals
Portfolio artifactProduct-System ArchitectureBuild–Buy–Partner Decision Memo
07
Agentic Products and Human-Agent Operating Models
Design
Deterministic workflows vs LLM workflows vs agents — objectives, tools, memory, stop conditions, permissions, budgets, recovery and orchestration patterns.
+
07
Agentic Products and Human-Agent Operating Models
DesignDeterministic workflows vs LLM workflows vs agents — objectives, tools, memory, stop conditions, permissions, budgets, recovery and orchestration patterns.
Topics
•Workflow vs agent decision
•Objective, instructions, context, memory & tools
•Routing, parallel, evaluator–optimizer & handoff patterns
•Autonomy levels, approval queues & segregation of duties
•Lab: bounded enterprise agent with approvals & escalation
Portfolio artifactAgent WorkflowTool ContractAutonomy MatrixException Playbook
08
AI Evaluation, Experimentation and Product Analytics
Evaluate
Quality across model, retrieval, generation, tool use, safety, experience and business outcomes — offline evaluation connected to live product analytics.
+
08
AI Evaluation, Experimentation and Product Analytics
EvaluateQuality across model, retrieval, generation, tool use, safety, experience and business outcomes — offline evaluation connected to live product analytics.
Topics
•Normal, edge, adversarial, multilingual & high-impact cases
•Deterministic, rubric, model-based & human graders
•Trace review & error taxonomy
•Shadow tests, staged rollouts, A/B & rollback thresholds
•Lab: 30–50-case suite; compare two variants; recommend
Portfolio artifactEvaluation DatasetTrace ReviewError TaxonomyQuality–Cost–Latency Scorecard
09
Responsible AI, Security, Privacy and Regulation
Govern
Benefits and risks for users, affected people, business and society — oversight, transparency, redress, prompt injection, excessive agency and safe decommissioning.
+
09
Responsible AI, Security, Privacy and Regulation
GovernBenefits and risks for users, affected people, business and society — oversight, transparency, redress, prompt injection, excessive agency and safe decommissioning.
Topics
•Impact mapping & human oversight
•Privacy, minimisation, permissions & auditability
•Prompt injection, jailbreaks & insecure tool use
•Model / vendor risk, safe change & decommissioning
•Lab: product risk committee & red-team review
Portfolio artifactAI Impact AssessmentRisk RegisterControl MapGovernance RACIIncident Plan
10
AI Economics, Pricing, Packaging and Go-to-Market
Commercial
The AI cost stack, cost per successful task, gross margin and business case — seat, usage, outcome and hybrid pricing, design partners and enterprise buying.
+
10
AI Economics, Pricing, Packaging and Go-to-Market
CommercialThe AI cost stack, cost per successful task, gross margin and business case — seat, usage, outcome and hybrid pricing, design partners and enterprise buying.
Topics
•Cost per request, workflow & successful task
•Gross margin & quality–cost trade-offs
•Seat / usage / outcome / tiered / hybrid pricing
•Design partners, paid pilots, procurement & enablement
•Lab: base, expected & stress scenarios; defend pricing
Portfolio artifactAI Business CaseUnit-Economics ModelPricing HypothesisGo-to-Market Brief
11
Roadmaps, Delivery, Launch, Growth and Product Operations
Operate
Outcome- and evidence-gated roadmaps, staged rollout, enablement and change — diagnosing activation, retention, churn, quality, cost, drift and incidents.
+
11
Roadmaps, Delivery, Launch, Growth and Product Operations
OperateOutcome- and evidence-gated roadmaps, staged rollout, enablement and change — diagnosing activation, retention, churn, quality, cost, drift and incidents.
Topics
•Outcome roadmaps with evidence gates
•Staged rollout, flags & support readiness
•Activation, retention, expansion & churn diagnosis
•Model, vendor, prompt & policy change management
•Workshop: recover a stalled AI product
Portfolio artifactTwelve-Month Outcome RoadmapRelease & Adoption PlanProduct Operations Dashboard
12
Capstone, Portfolio, Interview and Board Defence
Capstone
Integrate customer, market, product, system, evaluation, governance and commercial evidence into one investment decision — defended before a live board.
+
12
Capstone, Portfolio, Interview and Board Defence
CapstoneIntegrate customer, market, product, system, evaluation, governance and commercial evidence into one investment decision — defended before a live board.
Topics
•Executive memo, demo & limitations disclosure
•Product sense, metrics & strategy interview cases
•System trade-off, evaluation & pricing cases
•Live Product Investment Board & individual defence
•30/60/90-day AI product plan
Portfolio artifactComplete Capstone PortfolioExecutive Investment Memo30/60/90-Day Plan
Honest scope — each module builds analyst, consultant and platform-owner fluency with a working implementation slice and portfolio evidence; development and scripting depth belongs to the Forward Deployed AI Engineer bridge.
Tools you'll master
The analyst & consultant toolkit, one real engagement.
Real-time projects
You don't watch videos. You run engagements.
Three full-production projects, each threaded through the entire curriculum. By the project, you've built the whole stack around them.
Hero project
AI Career & Learner Success Product — the capstone
Act as the AI Product Manager leading one focused product wedge — course discovery, a grounded programme assistant, an at-risk learner copilot, a counsellor-assist workflow — from market thesis to an investment-ready launch plan defended before a live board.
- 01Opportunity thesis, product strategy, positioning and metric tree
- 02Experience blueprint, tested prototype and product-system decision memo
- 03Evaluation scorecard, governance evidence, unit economics and pricing
- 04Ten evidence gates — Problem Gate to Investment Gate — defended live
Opportunity thesisPrototypeUnit economics10 gates
Enterprise
Alternative enterprise capstones
ServiceNow incident resolution, Salesforce lead qualification, Workday employee support, an HRMS onboarding agent, a cross-platform employee-service product — or your own approved domain product.
ServiceNowSalesforceWorkdayYour domain
Evaluate & release
Evaluation, economics & board defence
Run a 30–50-case evaluation suite comparing two product-system variants, model base / expected / stress economics, defend pricing and pilot terms, and present the executive investment memo with a limitations disclosure.
Eval suiteCost per taskPricingBoard memo
Project
Your implementation engagement in a controlled project environment.
Pick a real partner workflow. Run the engagement end to end — workshops, to-be design, backlog, UAT and a governed AI rollout plan — and defend it before a review panel.
Download the real world project
Full scope, sample deployment contexts, project milestones, and grading rubric — PDF, 14 pages.
Production-style capstoneCareer support included
Your instructor
Taught by engineers who shipped agentic AI to production.
MK
Manikanta Kona
Founder, Digital Edify · Enterprise AI Architect
Enterprise AI · Agentic solutions · Requirements & evaluation · Governance
"An agent in production is where analysis earns its keep — the spec, the guardrails and the evaluation evidence are what separate a demo from a deployment. That judgment is what we teach."
Enterprise
AI PLATFORMS
100K+
ALUMNI COMMUNITY
4.8 /5
AVG. CLASS RATING
Manikanta is the founder of Digital Edify and brings 15 years of enterprise platform architecture from AT&T, Salesforce, Cox Communications, and Broadcom — where he led enterprise platform and AI rollouts for Fortune-500 banks, telcos, and insurers. Most recently he architected production agentic-AI deployments that replaced traditional triage tiers with autonomous case-handling.
His classes get you two things other programs don't give you: a founding architect who's shipped enterprise AI from inside the Fortune 500, and a curriculum updated monthly — so when hiring managers ask about agent specs, evaluation sets or HITL design, you've already built it. M.S. in Engineering, Purdue University.
RK
Ravi Krishna
Chief Technologist, Digital Edify · Implementation & Delivery Lead
Discovery · Strategy · Evaluation · Economics · Go-to-market
"Implementations don't fail in configuration — they fail in discovery. Workshops that
surface the real process, requirements developers build without rework, and UAT that proves it: that's
what I teach."
10 yrs
IMPLEMENTATION & DELIVERY
1,000+
HIRING PARTNERS
4.8 /5
RATING
Ravi is Chief Technologist at Digital Edify, where he leads the implementation and delivery practice. After years running enterprise transformation programs, he now teaches the product craft — discovery run gate by gate, strategy that gets to real decisions, and investment evidence that stands up in front of a steering committee.
His delivery modules are built from real engagement post-mortems, not slide decks. Expect to leave with working workshop kits, requirement and UAT templates, and a delivery-governance playbook you can run on day one.
HIRING PARTNERS · INDUSTRY VOICES
What employers say about Digital Edify grads.
Real feedback from talent leaders at the enterprise partners hiring our AI-native BA graduates.
Digital Edify graduates bring practical portfolio evidence and a structured approach to AI-enabled delivery.
Aakash Mehta, Partner Programme Lead
We've worked with Digital Edify alumni who bring useful project context and evidence-led delivery practices.
Anita Sharma, Senior Manager, Deloitte
The PM programme is comprehensive — strategy, economics, plus AI evaluation gates. Grads come pre-trained for enterprise.
Rahul Bhatt, Solutions Lead, Mphasis
Their PM track produces managers who build investment-grade product cases on day one. Genuinely rare.
Deepak Pillai, Senior Architect, TCS
What sets Digital Edify apart is the investment-case layer baked into the PM track. Our enterprise clients ask for exactly this profile.
Suresh Menon, Practice Lead, Accenture
Their product fundamentals are rigorous, and the capstone with real workshop artifacts is what closes interviews for us.
Vikram Iyer, Director, Infosys
Digital Edify's PM grads get products to validated launch twice as fast in the first 90 days. Our internal metrics back this up clearly.
Lakshmi Nair, VP Engineering, Wipro
Best AI product pipeline we've sourced from in India. Their projects are production work, not toy code.
Karthik Subramanian, Engineering Director, Cognizant
Strong strategy and evidence foundation. Their graduates bring useful project context to enterprise engagements.
Arun Joshi, Practice Director, Capgemini
We've worked with Digital Edify alumni across analytics and AI delivery teams, where practical fundamentals and clear evidence matter.
Sanjay Verma, Talent Director, IBM
ITOM + Predictive Intelligence is exactly the talent gap we've been struggling to close. Digital Edify is filling it for us reliably.
Anjali Desai, Practice Head, LTIMindtree
Their PM track delivers managers who navigate strategy, evaluation and GTM on customer engagements unsupervised.
Ramesh Iyer, Senior Manager, Tech Mahindra
Digital Edify graduates have joined digital delivery teams with practical analysis, process and agent skills.
Geetha Pillai, Talent Acquisition Lead, Cyient
Digital Edify grads who blend agent specification with evaluation evidence land production-ready on day one. Rare combination, well-trained.
Priya Reddy, Talent Lead, Microsoft
Program certifications
An Agent‑Ready credential, not a participation trophy.
Digital Edify · Institute Certificate
Applied Certificate — AI Product Manager
Presented to
Spandana Bala
For demonstrating the ability to select a customer and market opportunity, define an AI product strategy, make informed product-system trade-offs, establish evaluation and governance evidence, model commercial viability, and present an investment-ready launch and growth plan.
Manikanta Kona
CEO · Digital Edify
AGENT
READY
2026
READY
2026
01
Skills-focused institute credential
Awarded on a live Product Investment Board defence — minimum 70% overall, with evaluation and responsible-AI minimums path —
names that hiring managers already scan for on resumes.
02
Project artifact included
Every certificate carries your project name, the partner org, and a link to the
deployed agent-specification artifact — proof, not a promise.
03
Enhanced skill validation
Graded on the evidence chain: market, strategy, system, evaluation, economics
deployment, safety and monitoring. No pass/fail — a level 1‑5 band.
04
Verifiable on a public URL
Each credential has a public verification page recruiters can check in 10 seconds — no PDF
back‑and‑forth.
Job roles
Roles this program prepares you for.
AI Product Manager
Own market selection, product strategy, economics, launch and growth for AI products.
GenAI Product Manager
Lead GenAI assistants, copilots and RAG products from thesis to market.
Agentic AI Product Manager
Own bounded agent products — autonomy, controls, economics and adoption.
Technical Product Manager — AI/ML
Bridge product strategy and system trade-offs with engineering teams.
Product Manager — AI Applications
Ship AI-enabled application products with evaluation and governance evidence.
AI Product Strategy Consultant
Advise organisations on AI product selection, viability and roadmaps.
Product Lead — AI-enabled domain
Own an enterprise domain product line built on AI capability.
Associate PM / AI Product Analyst (entry)
Honest early-career targets while building product evidence.
AI Product Owner (adjacent)
The delivery-ownership counterpart — backlog, increments and releases.
AI Platform Owner (path)
The senior enterprise specialisation — shared services for many teams.
What employers should see in your portfolio: that you can take an engagement from discovery to value — map the process, write requirements developers build without rework, run the workshop, accept against criteria, and govern AI use cases with guardrails and metrics.
Job placement support
Your first AI PM offer isn't a lottery ticket. It's a built process.
GitHub, LinkedIn, resume — and most importantly, warm intros into our enterprise hiring partners. Our
placement team works your search like an account, not a helpdesk.
01 / PORTFOLIO
A portfolio, not a graveyard.
Guidance on assembling a consulting portfolio — process maps, workshop artifacts, backlog and UAT evidence, and your AI rollout plan — reviewed 1:1, not via template.
02 / RESUME PREP
Rewrite, don't proofread.
A one-page resume rebuilt around the artifact chain you shipped, the agent you deployed, and the business outcome. Reviewed against role-relevant portfolio and interview criteria.
03 / LINKEDIN + INTROS
Where most opportunities actually live.
Profile tuning plus direct warm introductions into our hiring-partner network — Infosys, TCS, Deloitte, Accenture, Cognizant, NTT Data, Capgemini. You leave with recruiter contacts, not a generic "good luck."
Product alumni
Hundreds of product careers launched — here are eight.
NV
Naveen Vedala
AI Product Analyst
Hyderabad · India
Now at · TCS
TA
Tejashwini Addla
HRSD Specialist
Hyderabad · India
Now at · Deloitte
TD
Tharunesh Dillikar
GenAI Product Manager
Seattle · United States
Now at · Accenture
MM
Mujahed Mohammed
Technical PM — AI/ML
Hyderabad · India
Now at · Accenture
BK
Bhargav Kumar Murala
Product Operations Analyst
Hyderabad · India
Now at · Capgemini
SL
Sai Manasa Leburi
ITOM Engineer
New York · United States
Now at · NTT Data
RD
Rahul Dhamma
AI Product Analyst
Hyderabad · India
Now at · Cognizant
Our locations
Come chat with us — over coffee, or over Zoom.
One flagship campus in Hyderabad, plus online classes running on Indian and US timezones.
Flagship campus
Hyderabad
2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave ·
Hyderabad, Telangana
Call
+91 8142998866
US desk
+1 256 388 7766
Hours
Mon–Sun · 7 AM–9 PM
Online class
Global
Weekend and evening classes running on IST and PST. Every online class ships
the same continuous Agentic CRM project, review gates and final viva as the on‑campus track.
Timezones
IST & PST
Format
Live + 1:1 mentorship
Admissions
ENROLLING NOW
FAQ
Questions we actually get — answered honestly.
Straight answers on prerequisites, the platform, certifications, and placement. If something's missing, book a 20-minute advisor call — no slides, no pitch.
Do I need coding or machine-learning experience?
No production coding is required. You will develop enough fluency in data, models, RAG, agents, APIs, identity, evaluation and observability to make product decisions and collaborate credibly with technical teams. Optional technical studios can add APIs, SQL, notebooks and simple evaluation scripts.
How is this different from the AI Product Owner course?
The AI Product Owner course focuses on Product Goal, team backlog, AI requirements, acceptance evidence, controlled rollout and the next increment. This programme focuses on customer segment, market choice, product strategy, product-system trade-offs, economics, pricing, go-to-market, adoption and growth.
Must I complete AI Product Owner first?
No. Existing Product Managers, founders, experienced consultants and customer-facing technologists may enter through a readiness assessment. AI Product Owner is the recommended progression for learners coming from Modern Business Analysis or delivery roles.
Can a fresh graduate become an AI Product Manager after this course?
A fresh graduate can build the same portfolio, but should initially target Product Analyst, Associate Product Manager, AI Business Analyst, Product Operations, Junior Product Owner or implementation roles. The AI Product Manager title usually becomes more credible with workplace product evidence.
Will I build a real product?
You will take one focused product wedge from customer and market evidence through prototype, architecture, agent controls, evaluation, governance, economics, GTM, roadmap and an executive investment decision. The capstone is production-style evidence, not only a presentation.
Is prompt engineering part of the curriculum?
Yes, at product-decision level. You learn how prompts, context, structured outputs, retrieval and tools affect behaviour, quality and cost. Prompting is one component of a much larger product system and is not the primary programme outcome.
Will I learn AI agents and multi-agent systems?
Yes. You will learn when an agent is appropriate, how tools and permissions should be defined, how autonomy should be bounded, how people and agents divide work, and how exceptions, auditability and recovery are designed. The programme does not train you as a production agent engineer.
How important are evaluations?
Evaluation is pass-critical. Every learner builds a representative dataset, rubric, trace review, error taxonomy, thresholds and a launch recommendation. Attractive demos do not replace repeatable product evidence.
Does the programme cover pricing and go-to-market?
Yes. You model the AI cost stack, cost per successful outcome, pricing and packaging options, pilot terms, enterprise buying, positioning, onboarding, adoption and product operations.
What is the time commitment?
The program uses live studios, critiques and review gates, with a Foundation route for learners who need product or AI foundations. An advisor confirms the current cohort schedule and expected commitment before enrolment.
What certificate will I receive?
Learners who meet the attendance, artifact, evaluation, governance, capstone and individual-defence requirements earn the Digital Edify Applied Certificate — AI Product Manager.
Does the programme guarantee placement?
No. Career support can include role mapping, portfolio reviews, resume and LinkedIn preparation, interview practice and introductions where available. Interviews, offers, salaries, employers, locations and timelines are not guaranteed.
Still have a question? Talk to an advisor — no slides, no pitch.
One million AI‑native professionals by 2027.
Let's put you in that number.
Book a 20‑minute advisor call. We'll map your current role to the right program, talk honestly about timelines, and walk you through a real class's project.
