Vikas Sahani

Posted on Jan 15

Product Titans Runner-up: Agentic AI for Hyper-Personalized Learning

#ai #productivity

Runner-up Case Study: Agentic AI Learning Platform for India

Overview

This project was created as part of Product Titans: National Product Management Challenge, hosted on Unstop and organized by Book My Mentor.

I approached this as a real-world PM discovery case and built an end-to-end product case study for a Hyper-Personalized Learning & Skill Development Platform powered by Agentic AI, aligned to the needs and constraints of India’s learning and skilling ecosystem.

Result: Certificate of Excellence – Runner-up (Rank 2, Score 6.4)

Solo Team Name: North Star Hunter

Problem Statement

In India, learners don’t always struggle due to a lack of content. They struggle because learning is often not aligned to:

their current gaps
their pace
their language preferences
their desired outcomes (exam performance, job readiness)

This results in:

low retention
repeated learning cycles without progress
dependence on parallel systems such as offline coaching/tutoring

So the real product problem is not:

"Build another course platform."

It is:

Help learners achieve outcomes faster with clarity, guidance, and accountability.

Product Sense: Why AI (and why Agentic AI)?

This project intentionally avoids “AI for hype.”

The core question was:
Do we actually need AI here?

The evaluation led to a practical conclusion:

personalization is not only content-level (recommendation)
it requires diagnosis, planning, feedback loops, accountability, and adaptation
this is where agentic workflows can reduce friction and improve learning outcomes

Who This Helps (Personas)

I mapped key learner segments to ensure the platform works for real India-first contexts:

Tier-2 / Tier-3 value learners with budget constraints and limited time
Exam aspirants needing structured planning and gap identification
Working professionals seeking upskilling with outcome clarity
D2C power learners looking for optimized learning paths and progress tracking

Key Insights (Friction Points)

I analyzed the full journey from discovery to outcomes and identified recurring friction:

learners don’t know what to learn next
lack of structured feedback loops
low motivation and inconsistent habits
weak accountability mechanisms
mismatch between course completion and real outcomes

Proposed Solution (High-Level)

An agentic AI-powered learning platform that supports:

Skill gap diagnosis
Personalized learning plan generation
Daily/weekly accountability tracking
Adaptive feedback and course corrections
Outcome-based progress measurement (not vanity metrics)

Prioritization Framework

I used RICE prioritization to avoid feature overload and focus on what moves outcomes:

Reach
Impact
Confidence
Effort

This helped separate:

root causes (diagnosis, clarity, feedback loops) from
surface-level fixes (more videos, more quizzes)

Success Metrics (North Star + KPIs)

I avoided vanity usage metrics and designed a measurable success model.

North Star Metric

Verified Learner Outcome Rate

Supporting Metrics

activation rate (first meaningful learning action)
habit formation / weekly consistency
completion quality (not completion volume)
diagnostic-to-outcome conversion
retention linked to goal attainment

Responsible AI: Risks and Controls

Because this product is agentic AI-driven, I documented risks and governance controls:

explainability and transparency of recommendations
bias and unfair personalization risk
privacy and data protection principles
safe completion boundaries and human override
accountability (who owns decisions and how errors are handled)

The goal was to ensure AI improves outcomes without creating unsafe or misleading personalization.

Output Artifacts

This project includes:

problem framing and market gap analysis (India-first)
persona mapping
user journey mapping
RICE prioritization
North Star metric + KPI tree
experiment design and GTM sequencing
responsible AI risk analysis + mitigations

Learnings

This project was a major learning experience that strengthened my practical PM skills:

problem framing before solution
structured prioritization
measurable outcomes and experiment-first thinking
responsible AI and governance as core product design constraints

Disclaimer

This is an independent case study created for learning and evaluation purposes as part of the Product Titans challenge. It is not affiliated with or endorsed by any employer or platform beyond the official competition context.

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