Empowering HR Decisions through Explainable Agentic Intelligence and Predictive Analytics

The current landscape of human resources faces significant challenges in streamlining job documentation and job evaluation processes, which are critical for consistent, efficient, and defensible compensation-related decisions. Laulima Consulting Inc., as the partner organization, aims to overcome this by developing an AI-powered tool that supports the end-to-end creation and evaluation of job descriptions. The core innovation challenge or improvement priority that Laulima needs to solve is to move beyond traditional, potentially manual, and inconsistent methods of job documentation and evaluation. This project proposes to address this by integrating market intelligence, conversational AI, and standardized frameworks to ensure greater consistency, efficiency, and defensibility in human capital decisions, specifically concerning job level and pay grade determination.

This project will help Laulima Consulting Inc. address these challenges in a way that extends significantly beyond typical day-to-day business operations by creating a standardized and scalable AI agent. This agent will not only synthesize internal inputs with external market intelligence to generate customized job descriptions but will also leverage a natural language AI agent for interactive validation and refinement of job content with managers. Critically, the project involves automating point factor job evaluation methodologies, where AI interprets job content against selected criteria, assigns point values, and provides justifications for transparency and auditability. This level of AI-based factor evaluation, combined with a flexible evaluation framework, dynamic visualizations, and consistency checks, significantly enhances HR’s strategic decision-making capabilities, moving beyond manual tasks to provide data-driven insights and a defensible system. Solving this complex problem requires specialized expertise in Natural Language Processing (NLP), Large Language Models (LLMs) and Agentic AI systems for content analysis and conversational AI, as well as data aggregation and benchmarking methodologies. Additionally, it necessitates advanced software engineering for building configurable and scalable systems, and deep domain knowledge in human resources, including job evaluation and compensation frameworks.

Faculty Supervisor:

Emad Mohammed

Student:

Partner:

Laulima Consulting

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Wilfrid Laurier University

Program: