Artificial Intelligence: AI Driven Talent Optimization

Sponsored by: EY

Business Context

Enterprises need agile talent strategies to stay competitive in a rapidly changing market. Traditional HR systems often fail to map evolving skills to open roles or provide actionable upskilling paths. This gap creates inefficiencies in workforce planning and limits career growth opportunities. AI agents can automate these processes at scale, but they introduce risks around bias, explainability, and data privacy. A successful solution must combine intelligent automation with strong governance and transparency.

Problem Description

Design an AI-driven talent strategy agent that:

·       Maps skills from employee profiles to open roles.

·       Generates personalized upskilling plans to close skill gaps.

·       Provides clear, human-readable explanations for recommendations.

·       Incorporates risk mitigation features, including:

o   Bias detection and fairness checks in role recommendations.

o   Explainable AI for why a role or learning path was suggested.

o   Data privacy safeguards for sensitive HR information.

The solution should balance accuracy, fairness, and transparency, while considering scalability for large organizations.

Desired Deliverables:

Working Prototype

·       Demonstrates skill-role matching and upskilling plan generation for at least 5 synthetic profiles.

·       Note: The solution must be used in an LLM-based approach.

Explainability & Governance

·       Explanation framework showing why roles and learning paths were recommended.

·       Governance packet including decision log, bias checks, and privacy safeguards.

Presentation & Demo

·       ≤10-slide deck summarizing problem, solution, architecture, and guardrails.

·       Short demo (video or live) showcasing functionality.

Success Criteria:

·       The solution must solve a real enterprise need and show clear value.

·       It must give accurate, meaningful role recommendations beyond keyword matching.

·       The upskilling plans must be practical, sequenced, and consider time and cost.

·       It must include transparent, human-readable reasons for all decisions.

·       The design must show bias checks and follow privacy-conscious principles.

·       It must address IT security risks and AI-related risks such as bias and misuse.

·       The solution must have robust, well-documented architecture.

·       It must deliver a clear user experience with strong presentation and storytelling.

TIMELINE

• February 16, 2026 (11:59 PM Eastern) - Preliminary submissions due 
  
• February 26, 2026 – Finalists notified
• March 27, 2026 – Final presentations (during SCLC) and winners announced

PRIZES

• First place: $2,000 USD
• Second place: $1000 USD
• Third place $500 USD

ELIGIBILITY REQUIREMENT

Only teams from current AIS Student Chapters are eligible to complete.   

• Unsure if your chapter is up-to-date with membership? See the list of current chapters.
• Interested in chapter membership? Learn more.

SUBMIT HERE

General Rules for Competitions

• Faculty can give insight and advice, but the competition submissions should reflect the students’ work.
• A team must consist of 2-4 students and include at least one undergraduate student. 
• Students can participate in multiple competitions but cannot be finalists in multiple competitions. If a student is a finalist in more than one competition, the student must choose which one to compete in at the finals held at SCLC 2026.

• Team names and all submitted materials must not include any information that identifies your university. If a team name or any part of the submission contains university-identifiable information, the entry will be disqualified.

Evaluation Rubric