AI Product Leadership Academy · Strategy and delivery track · Enrolling now

AI Product Manager & Product Owner
From Product Fundamentals to AI Product Leadership

Learn to discover the right problem, define an evidence-backed AI product, turn strategy into an executable backlog, evaluate quality and risk, launch responsibly and lead continuous product improvement.

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
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
Direct answer

What does an AI Product Manager & Product Owner do?

An AI Product Manager & Product Owner connects customer value, business viability and responsible AI delivery. Product management leads discovery, market choice, strategy, economics, launch and growth. Product ownership turns that direction into a Product Goal, ordered backlog, acceptance evidence and release decisions. Business-analysis practices provide the stakeholder evidence, process understanding, requirements discipline and traceability needed by both roles. This program develops the combined capability while keeping each decision right clear.

The delivery ownership chain From evidence to evaluated release — one owner.
01DIRECT
  • Inherit & challenge BA evidence
  • Product Goal & near-term outcome
  • AI suitability — the solution ladder
  • Metric tree & learning roadmap
02ORDER
  • AI PRD acceptance standard
  • Human-control & autonomy matrix
  • One integrated backlog — 10 workstreams
  • AI Definition of Ready / Done
03RELEASE
  • Representative evaluation & release gates
  • Governed rollout — pilot / hold / rollback
  • Incidents & product operations
  • Production learning → next increment
Product Owner scope
  • The Product Goal, backlog transparency, ordering and decision logic
  • The behavioural and human-control standard an increment must satisfy
  • Controlled rollout decisions within delegated authority
  • Production evidence translated into backlog choices
And owning the release decision no demo can substitute for.
Product Manager scope
  • Customer discovery, market context and opportunity selection
  • Product positioning, strategy, roadmap and outcome metrics
  • Economics, packaging, go-to-market and adoption choices
  • Growth and portfolio decisions informed by production evidence
And proving the product deserves to be built, launched and scaled.
How this course differs · 2026

“Using AI in product work” and “leading an AI product” are different jobs.

Job 1 · Use AI as a Product OwnerProductivity

Use AI assistants responsibly to accelerate Product Owner work — every output checked against source evidence, policy and human judgement.

  • Feedback clustering & refinement preparation
  • Story & acceptance-criteria drafting
  • Dependency, gap & decision-alternative analysis
  • Stakeholder summaries, release notes & retro synthesis
Job 2 · Be the Product Owner of an AI product★ The durable differentiator

Own delivery decisions for predictive features, GenAI assistants, RAG knowledge products, tool-using agents and bounded agentic workflows.

  • Set the Product Goal & order one integrated backlog
  • Accept AI PRDs — behaviour, grounding, controls, fallback
  • Gate releases on representative evaluation evidence
  • Turn production incidents & drift into the next increment
The solution ladder — reject unnecessary AI
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 from analysis and delivery into AI product leadership.

Business Analysts & Sr BAs Product Owners & Scrum professionals Product Managers & Product Analysts QA / UAT leads moving to product Functional & implementation consultants Project & delivery professionals Founders & functional leaders Platform pros (ServiceNow · Salesforce · Workday)

Prior experience: no coding or advanced maths required. Two or more years in business, technology, delivery or a relevant domain is recommended; you build enough fluency in data, APIs, RAG, agents and evaluation to make credible product decisions with specialists.

What you will be able to do

Shape the strategy — then lead delivery with evidence.

Convert evidence into directionTurn validated business evidence into a Product Goal and a measurable near-term outcome.
Choose the lightest solutionDecide between process change, rules, search, predictive ML, GenAI, RAG, a bounded agent — or no AI.
Order one integrated backlogPrioritise by value, evidence, risk, dependency, data readiness, cost and learning priority.
Accept AI PRDsReview behaviour, grounding, tools, permissions, uncertainty, fallback and human handoff.
Talk credibly with specialistsDiscuss data, models, RAG, agents, APIs, identity, latency, cost and reliability without pretending to approve architecture.
Gate releases on evaluationDefine quality dimensions, representative coverage and thresholds; read reports, traces and feedback.
Decide rollout with authorityMake a defensible pilot, expand, hold, rollback or redesign decision within delegated authority.
Run product operationsRespond to AI-quality incidents and convert production learning into backlog changes.
Course curriculum

Twenty modules. From product fundamentals to AI product leadership.

Four connected stages combine product management, product ownership and business analysis: establish the foundations, discover and choose the right opportunity, define and deliver the product, then evaluate, launch, operate and lead it responsibly.

01–04 Foundations 05–09 Discovery & strategy 10–14 Definition & delivery 15–20 Evaluation & leadership
01

Product Thinking, Value and the AI Product Lifecycle

Foundation
Understand how products create value and why AI products require continuous discovery, evaluation and lifecycle responsibility.
Topics
  • Products, projects, platforms and services
  • Customer, business and public value
  • Outputs, outcomes and unintended effects
  • Product lifecycle from discovery to retirement
  • Continuous discovery, delivery and evaluation
Applied studio & portfolio evidenceAI Product TeardownProduct Lifecycle Map
02

Product Manager, Product Owner and Business Analyst Decision Rights

Foundation
Connect the three disciplines without blurring their accountabilities across strategy, analysis, backlog and release decisions.
Topics
  • Product Manager, Product Owner and BA accountabilities
  • Product vision, Product Goal and backlog
  • Decision rights, delegation and escalation
  • Product trio and extended AI product team
  • RACI, RAPID and decision logs
Applied studio & portfolio evidenceProduct Leadership CharterDecision-Rights Matrix
03

Business Analysis Foundations for Product Leaders

Foundation
Use disciplined analysis to uncover needs, align stakeholders and trace product decisions to evidence and value.
Topics
  • Change, need, solution, value, stakeholder and context
  • Enterprise, strategy and solution analysis
  • Elicitation and evidence quality
  • Assumptions, constraints and dependencies
  • Traceability from need to outcome
Applied studio & portfolio evidenceNeed–Context–Value BriefStakeholder Evidence Map
04

AI, GenAI, Retrieval and Agentic Product Foundations

Foundation
Build the technical fluency to make credible product choices across models, context, retrieval, tools and agents.
Topics
  • AI, ML, GenAI and foundation models
  • Tokens, context, prompts and embeddings
  • RAG, grounding and citations
  • Tools, workflows, memory and agents
  • Build, buy, configure or combine
Applied studio & portfolio evidenceAI System Concept MapBuild–Buy–Partner Decision
05

Customer, Market and Stakeholder Discovery

Discover
Find a consequential problem using customer, market, workflow and stakeholder evidence rather than opinion alone.
Topics
  • Interview design and research ethics
  • Users, buyers, administrators and affected non-users
  • Jobs, pains, gains and switching behaviour
  • Market alternatives and competitive intelligence
  • Evidence synthesis and confidence
Applied studio & portfolio evidenceDiscovery PlanOpportunity Insight Report
06

Process Analysis and Human–AI Workflow Discovery

Discover
Understand the real work, exceptions and failure demand before deciding where AI should participate.
Topics
  • SIPOC, service blueprints and value streams
  • Tasks, decisions, queues and handoffs
  • Knowledge sources and decision inputs
  • Human–AI task allocation
  • Current-state time, quality, cost and risk
Applied studio & portfolio evidenceCurrent-State Service BlueprintBaseline Measures
07

Problem Framing, Outcomes and AI Opportunity Qualification

Discover
Convert discovery into a bounded product opportunity and defend whether AI—and what level of autonomy—is justified.
Topics
  • Problem, outcome and assumption framing
  • Root cause versus visible symptom
  • Desirability, viability, feasibility and responsibility
  • AI suitability and non-AI alternatives
  • Autonomy, reversibility and kill criteria
Applied studio & portfolio evidenceAI Opportunity & Autonomy CanvasOpportunity Scorecard
08

Product Vision, Strategic Choices and the Product Wedge

Strategy
Choose who to serve, what to win on and how a focused first product creates room for responsible expansion.
Topics
  • Product vision and strategic narrative
  • Ideal customer and beachhead segment
  • Product wedge and expansion logic
  • Differentiation and defensibility
  • Strategic guardrails and non-goals
Applied studio & portfolio evidenceProduct Strategy One-PagerStrategic Choice Register
09

Outcomes, Product Goal, Metrics and Learning Roadmap

Strategy
Translate strategy into measurable direction and sequence learning rather than promising a feature list.
Topics
  • Product Goal and outcome hierarchy
  • North Star, input and guardrail metrics
  • Baseline, target, threshold and cadence
  • Outcome-based roadmaps
  • Risk-first learning milestones
Applied studio & portfolio evidenceOutcome TreeMetric Dictionary & Learning Roadmap
10

Human-Centred AI Experience and Trust Design

Define
Design AI interactions that communicate capability, uncertainty, control, escalation and recovery.
Topics
  • User mental models and AI onboarding
  • Conversation, copilot and workflow patterns
  • Sources, explanations and uncertainty
  • Confirm, undo, edit, retry and safe refusal
  • Accessibility, handoff and service recovery
Applied studio & portfolio evidenceExperience BlueprintHuman–AI Prototype
11

AI Product Briefs, PRDs and Requirements Architecture

Define
Specify intended and prohibited behaviour in a way that product, design, engineering, evaluation and governance can test.
Topics
  • Lean product brief and AI PRD
  • Functional, non-functional and policy requirements
  • Stories, scenarios and acceptance criteria
  • Prompt, context and response contracts
  • Fallback, refusal and traceability
Applied studio & portfolio evidenceAI Product Brief & PRDRequirement Traceability Map
12

Data, Knowledge, RAG, APIs and Architecture for Product Decisions

Define
Make informed trade-offs about the data, knowledge and system foundations required by the proposed experience.
Topics
  • Data rights, quality, lineage and freshness
  • Knowledge lifecycle and content ownership
  • Retrieval, chunking, metadata and grounding
  • APIs, events and systems of record
  • Latency, cost, reliability and auditability
Applied studio & portfolio evidenceContext & Data ContractSystem Context Diagram
13

Backlog Architecture, Story Mapping and Prioritisation

Deliver
Turn the Product Goal into one coherent, ordered path to usable value and fast learning.
Topics
  • Themes, capabilities, journeys and backlog items
  • Story mapping and vertical slices
  • Enablers, spikes, data and governance work
  • Value, risk, dependency and learning priority
  • Refinement and acceptance conversations
Applied studio & portfolio evidenceStory MapOrdered Backlog & Decision Log
14

Agentic Product Design, Tools, Permissions and Human Handoffs

Deliver
Define safe product boundaries for systems that use tools, retain state or take consequential action.
Topics
  • Workflow automation versus agentic autonomy
  • Goals, instructions, tools and state
  • Least privilege, identity and approvals
  • Prompt injection and memory poisoning
  • Human review, handoff and recoverability
Applied studio & portfolio evidenceAgent SpecificationTool & Permission Matrix
15

AI Evaluation Engineering and Acceptance Evidence

Evaluate
Replace subjective demonstrations with representative datasets, explicit rubrics, regression checks and release thresholds.
Topics
  • Failure taxonomy and evaluation objectives
  • Golden datasets and representative cases
  • Deterministic checks and rubric graders
  • Groundedness, task success and agent trajectory
  • Slice analysis, adversarial tests and release gates
Applied studio & portfolio evidenceEvaluation Plan & Golden DatasetRelease Recommendation
16

Experimentation, Product Analytics and Decision Quality

Evaluate
Design experiments and product measures that change decisions instead of merely reporting engagement.
Topics
  • Product hypotheses and stopping rules
  • Prototype, concierge and Wizard-of-Oz tests
  • Funnels, cohorts, retention and workflow analytics
  • Quality, cost, latency and override measures
  • Qualitative evidence and learning logs
Applied studio & portfolio evidenceExperiment BriefAnalytics Plan & Decision Memo
17

Responsible AI, Security, Privacy and Product Assurance

Govern
Turn policy and regulation into owned lifecycle controls, evidence and residual-risk decisions.
Topics
  • NIST AI RMF and AI impact assessment
  • ISO/IEC 42001 management-system concepts
  • Privacy, fairness, accessibility and transparency
  • LLM and agentic security risks
  • EU AI Act classification and assurance evidence
Applied studio & portfolio evidenceAI Impact AssessmentRisk, Control & Assurance Pack
18

AI Economics, Pricing, Packaging and Go-to-Market

Launch
Create a viable product whose value, cost-to-serve, packaging and route to adoption are understood.
Topics
  • Value pools and willingness to pay
  • Token, model, retrieval and review economics
  • Cost-to-serve and margin sensitivity
  • Pricing metrics and packaging choices
  • Positioning, procurement and launch readiness
Applied studio & portfolio evidenceUnit Economics ModelGTM & Packaging Plan
19

Adoption, Launch, Observability and AI Product Operations

Operate
Launch deliberately, detect changing behaviour and turn incidents and production learning into product improvement.
Topics
  • Change impact, enablement and adoption
  • Controlled rollout, canary and rollback
  • Product, model, retrieval and tool observability
  • Production evaluation and drift detection
  • Incident response, revalidation and value review
Applied studio & portfolio evidenceLaunch & Adoption PlanObservability Scorecard & Runbook
20

AI Product Leadership, Portfolio Strategy and Board Defence

Lead
Integrate the full lifecycle and defend customer value, evidence, economics, delivery, risk and the next investment decision.
Topics
  • Product leadership versus feature management
  • Portfolio choices and investment allocation
  • Product reviews and operating cadences
  • Executive storytelling with evidence
  • Capstone synthesis and individual defence
Applied studio & portfolio evidenceExecutive Product NarrativePortfolio Recommendation
Applied throughout — one continuous Digital Edify AI Admissions & Learner Success capstone connects discovery evidence, product strategy, Product Goal, experience, PRD, architecture, backlog, evaluation, assurance, economics, launch and executive defence. Coding is not required; technical fluency is developed to support credible decisions with specialists.
Tools you'll master

The AI product leadership toolkit, one real product.

Ji
Jira
AD
Azure DevOps
Ln
Linear
No
Notion / Confluence
Mi
Miro / FigJam
Fi
Figma
MS
Model studios
ADK
Agent SDKs / ADK
LG
LangGraph
CS
Copilot Studio
AS
AI Agent Studio
AF
Agentforce
LS
LangSmith
Lf
Langfuse
Pf
Promptfoo
Px
Phoenix
Ex
Excel / Sheets
SQ
SQL demos
PB
Power BI
GV
Governance templates
Real-time projects

You don't collect templates. You lead one product.

One continuous capstone runs across all 20 modules. Each stage adds inspectable product evidence and ends in a decision review.

Hero project

Digital Edify AI Admissions & Learner Success Product

Lead one focused product wedge — grounded program discovery, counsellor assist, lead qualification and handoff, enrolment support or learner-risk intervention — from discovery evidence and strategy through evaluated release, operations and executive review.

  • 01Discovery evidence, current-state workflow and AI opportunity decision
  • 02Product strategy, Product Goal, outcome scorecard and learning roadmap
  • 03Experience prototype, PRD, architecture, agent specification and ordered backlog
  • 04Evaluation, assurance, economics, launch and proceed / constrain / pivot / stop defence
20 modulesProduct strategyEvaluationBoard defence
Enterprise

AI Suitability & Product-Risk Canvas

Compare AI, rules-based and process-change alternatives; choose a defensible autonomy level; and record a product-risk decision an investment committee can audit.

Solution ladderSuitabilityRisk decisionReject AI
Evaluate & release

Evaluation & Release Decision Pack

Build representative normal, edge, adversarial, multilingual and sensitive cases; analyse failures and traces; and defend a pilot, conditional rollout, hold, rollback or redesign decision.

Golden datasetTracesThresholdsRelease gates
Project

Your product decision trail in a controlled project environment.

Build one connected portfolio from need and workflow evidence to market strategy, product definition, evaluation, assurance, economics, launch and production learning—then defend it before a review board.

Download the real world project
Full scope, product wedges, review gates, required evidence and assessment rubric.
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
Backlog architecture · AI PRDs · Evaluation gates · Rollout · Product operations
"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 owner's craft — increments run gate by gate, refinement that gets to real decisions, and release 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.

ServiceNow logo

Digital Edify graduates bring practical portfolio evidence and a structured approach to AI-enabled delivery.

Aakash Mehta

Aakash Mehta, Partner Programme Lead

Deloitte logo

We've worked with Digital Edify alumni who bring useful project context and evidence-led delivery practices.

Anita Sharma

Anita Sharma, Senior Manager, Deloitte

Mphasis logo

The PO programme is comprehensive — Product Goal, backlog, plus AI evaluation gates. Grads come pre-trained for enterprise.

Rahul Bhatt

Rahul Bhatt, Solutions Lead, Mphasis

TCS logo

Their PO track produces owners who run production-grade backlogs and evaluation gates on day one. Genuinely rare.

Deepak Pillai

Deepak Pillai, Senior Architect, TCS

Accenture logo

What sets Digital Edify apart is the evaluation-gate layer baked into the PO track. Our enterprise clients ask for exactly this profile.

Suresh Menon

Suresh Menon, Practice Lead, Accenture

Infosys logo

Their product fundamentals are rigorous, and the capstone with real workshop artifacts is what closes interviews for us.

Vikram Iyer

Vikram Iyer, Director, Infosys

Wipro logo

Digital Edify's PO grads get increments to evaluated release twice as fast in the first 90 days. Our internal metrics back this up clearly.

Lakshmi Nair

Lakshmi Nair, VP Engineering, Wipro

Cognizant logo

Best AI product pipeline we've sourced from in India. Their projects are production work, not toy code.

Karthik Subramanian

Karthik Subramanian, Engineering Director, Cognizant

Capgemini logo

Strong product and evidence foundation. Their graduates bring useful project context to enterprise engagements.

Arun Joshi

Arun Joshi, Practice Director, Capgemini

IBM logo

We've worked with Digital Edify alumni across analytics and AI delivery teams, where practical fundamentals and clear evidence matter.

Sanjay Verma

Sanjay Verma, Talent Director, IBM

LTIMindtree logo

ITOM + Predictive Intelligence is exactly the talent gap we've been struggling to close. Digital Edify is filling it for us reliably.

Anjali Desai

Anjali Desai, Practice Head, LTIMindtree

Tech Mahindra logo

Their PO track delivers owners who navigate backlogs, evaluation and rollout on customer engagements unsupervised.

Ramesh Iyer

Ramesh Iyer, Senior Manager, Tech Mahindra

Cyient logo

Digital Edify graduates have joined digital delivery teams with practical analysis, process and agent skills.

Geetha Pillai

Geetha Pillai, Talent Acquisition Lead, Cyient

Microsoft logo

Digital Edify grads who blend agent specification with evaluation evidence land production-ready on day one. Rare combination, well-trained.

Priya Reddy

Priya Reddy, Talent Lead, Microsoft

Program certifications

An Agent‑Ready credential, not a participation trophy.

Digital Edify · Institute Certificate
Applied Certificate — AI Product Manager & Product Owner
Presented to
Spandana Bala
For demonstrating the ability to convert validated evidence into a Product Goal, order a multi-workstream AI Product Backlog, establish behavioural and evaluation standards, make a governed rollout decision and use production evidence to drive the next increment. Attendance alone does not earn the role-level credential.
Manikanta Kona
CEO · Digital Edify
AGENT
READY
2026
01
Skills-focused institute credential
Issued by Digital Edify after reviewed project evidence and an individual defence. It is separate from external vendor and regulatory credentials.
02
Project artifact included
Your assessed portfolio records the product problem, strategy, decision trail, evaluation evidence, assurance controls and executive defence — proof, not a promise.
03
Enhanced skill validation
Graded across module studios, individual artifacts, evaluation and assurance, the team capstone and individual board defence. Evaluation, product assurance and the individual defence are compulsory gates.
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 Owner Own the Product Goal, ordered backlog and release decisions for one AI product team.
AI Product Manager Lead customer discovery, market choice, product strategy, economics, go-to-market and growth.
GenAI / Agentic Product Manager Lead assistants, copilots and agentic products across strategy, controls, launch and lifecycle value.
AI Business Analyst Connect needs, workflows, requirements and evaluation evidence to responsible AI product decisions.
Associate Product Manager (entry) Support discovery, analytics, roadmap decisions, experiments and product operations.
Product Owner — AI Applications Order data, experience, model, evaluation and governance work in one backlog.
AI Product Analyst (entry) Analyse product evidence, evaluation results and adoption for AI products.
Associate / Junior Product Owner (entry) Honest early-career targets while building applied product evidence.
Product Operations Analyst Run monitoring, incident triage and improvement backlogs for AI products.
AI Product Lead (progression) Guide product strategy, portfolio choices, operating cadence and accountable AI investment decisions.

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 product 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.

SB
Spandana Bala
AI Product Owner
Hyderabad · India
Now at · Infosys
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 Owner
Seattle · United States
Now at · Accenture
MM
Mujahed Mohammed
Sr Product Owner
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 follows the same continuous AI Admissions & Learner Success capstone, review gates and individual board defence 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. You are assessed on product decisions, requirements quality, backlog logic, evaluation evidence, human controls and rollout judgement — not on building production models. The course develops enough technical fluency to work credibly with engineers, data teams, architects and evaluators.
How is this different from a normal Product Owner course?+
A conventional Product Owner course focuses on product value, Agile practices and backlog management. This programme adds AI suitability, probabilistic behaviour, RAG and agent requirements, human-control boundaries, representative evaluation, controlled rollout, AI incidents and product operations.
Why does this course combine AI Product Manager and Product Owner?+
The responsibilities are distinct but closely connected. The AI Product Manager owns customer discovery, market choice, product strategy, economics, launch and growth. The AI Product Owner turns that direction into the Product Goal, backlog, acceptance evidence and release decisions. Many employers combine the scopes, so this programme develops both while making the decision boundaries clear.
Is this a good next step after Modern Business Analyst?+
Yes. The curriculum incorporates the strongest business-analysis practices—needs, context, stakeholders, workflows, requirements and traceability—then adds market strategy, Product Goal accountability, backlog ordering, AI product economics, evaluation, launch, operations and portfolio leadership. Previous BA experience helps, but it is not mandatory.
Will I build an AI product?+
You will lead a production-style capstone rather than merely watch demonstrations: discovery evidence, product strategy, Product Goal, experience prototype, AI PRD, integrated backlog, evaluation dataset, assurance evidence, economics, launch plan and improvement cycle. Optional studios can include configured or low-code prototypes, but coding is not the core assessment.
Will I learn prompt engineering?+
You will learn prompting and context at the level needed to understand product behaviour and validate AI-assisted Product Owner work. The stronger emphasis is on requirements, evaluations, controls and product decisions.
What happens when an AI system passes average quality but fails for one group?+
The course teaches segmented evaluation, affected-stakeholder analysis, error severity, guardrail metrics and incident escalation. A good overall average does not justify rollout when a critical segment or zero-tolerance invariant fails.
Who is the complete 20-module program designed for?+
It is designed for Business Analysts, Product Owners, Product Managers, Product Analysts, Scrum and delivery professionals, consultants, domain specialists, founders and functional leaders. Early-career learners can also join when they are comfortable with structured problem solving and business communication.
What prior experience is required?+
No coding or advanced mathematics is required. Experience in business, technology, delivery or a relevant domain is useful but not mandatory. The first four modules establish product, analysis and AI foundations before the program moves into strategy, delivery, evaluation and leadership.
What certificate will I receive?+
Learners who satisfy the artifact, evaluation, product-assurance, capstone and individual-defence requirements earn the Digital Edify Applied Certificate — AI Product Manager & Product Owner. This is a Digital Edify institute credential based on assessed course work; it is not an official Scrum, IIBA, ISO, OpenAI or regulatory certification.
What roles can freshers honestly target?+
Early-career targets are AI Product Analyst, Associate or Junior Product Owner, AI Business Analyst and Product Operations Analyst. AI Product Manager and full AI Product Owner titles are progression targets supported by applied evidence and workplace experience.
Does the programme guarantee placement?+
No. Digital Edify provides portfolio guidance, role mapping, resume and interview preparation, and introductions where available. It does not guarantee interviews, offers, salaries, companies, locations or timelines.

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.

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