Summary
To address contemporary challenges in talent management and employee retention within companies, our Talent AI module presents a range of innovative internal tools focused on artificial intelligence. These tools are designed to optimize employee evaluations, develop comprehensive profiles, and customize professional development. Here’s an overview of these key features:
Employee Evaluation AI Agent: We modernize the traditional employee evaluation process by transforming it into an advanced and thorough procedure, thanks to our AI tool. By conducting periodic interviews with employees and their supervisors, this tool gathers valuable data on performance, skills, and areas for improvement, while capturing employees’ aspirations and professional goals.
Creation of Digital Employee Profiles: Based on data collected by the Employee Evaluation AI Agent and internal HR data about the employees, Talent AI will generate structured digital profiles for each employee, highlighting their strengths, weaknesses, ambitions within the company, and training needs. These enriched profiles will facilitate personalized talent management, helping HR managers align internal career opportunities with employee aspirations and skills.
AI Coaching Agent: Based on employees’ training needs and company data, Talent AI will create a Transformer that can analyze the employee’s digital profile and offer training recommendations for continuous improvement, to achieve their ambitions within the company, or to address questions regarding their work processes.
Internal Recruitment AI Agent: Talent AI is developing an internal profile recommendation tool, based on digital profiles and the requirements of new positions. This will allow HR to quickly identify internal candidates who demonstrate the ambition and skills to take on these challenges—before seeking external recruitment.
Training Creation System: Talent AI offers a system for creating internal training. HR and departmental representatives can create explanatory videos targeting the weaknesses of the company’s employees. With the help of an AI system, the company will analyze the explanatory videos and create training sessions with multiple-choice questions and scenarios to guide employees on demand. This data will also be utilized by the AI Coaching Agent. This system is intended for a later phase of the project and is only mentioned in this document for illustrative purposes.
In summary, Talent AI systems provide a comprehensive solution for updating human resource management by making employee evaluations more effective, developing detailed profiles, customizing training, providing personalized virtual coaching, and identifying internal candidates for future company needs. This innovative approach not only optimizes HR processes but also contributes to creating a dynamic and engaging work environment where every employee has the opportunity to realize their potential.
Business objectives :
The Talent AI project addresses a crucial issue in the talent supply chain within companies, an often underestimated component of the overall logistics of organizations. Current challenges in human resource management—such as quickly identifying internal talents for key positions, optimizing career paths, and retaining employees—constitute major issues that directly affect the fluidity and efficiency of the talent supply chain.
Talent AI is committed to transforming these processes by integrating AI tools to optimize and personalize talent management. By improving employee evaluations and developing detailed profiles, the project enables companies to better understand and leverage their human capital. This approach enhances organizational responsiveness and agility in adapting to evolving skills, thereby contributing to a stronger and more flexible talent supply chain.
Furthermore, by providing personalized recommendations for professional development and facilitating internal mobility, Talent AI helps retain valuable talent, thereby reducing the need for costly and time-consuming external recruitment. In summary, this project addresses the talent logistics issue by making companies more competitive and resilient through optimized management of their primary asset: their employees.
Expected outcomes include improved recruitment efficiency, reduced administrative errors, increased employee satisfaction, development of intelligent career recommendations, mentorship, and training, as well as the ability to analyze and predict HR trends accurately. This should lead to an overall improvement in HR efficiency and experience, notably through:
- A 25% or more reduction in the time required for HR data analysis through the automation of repetitive tasks, effective processing of large data volumes, rapid identification of patterns and trends, and predictive analysis. This objective draws inspiration from cases such as Unilever, Google, and a McKinsey case study. On average, SMEs would spend about 10 to 15 hours per week on these tasks, or more during reporting periods or when strategic decisions need to be made. A time reduction of 2 to 3.75 hours per week would therefore amount to approximately 25%.
- Recruitment needs prediction with over 80% accuracy through historical data analysis, demand factor assessment, predictive modeling, and the adaptability and continuous learning of Talent AI. Setting an 80% accuracy for recruitment needs prediction by AI may seem ambitious, but advances in AI technologies suggest that it is not entirely unachievable. However, reaching this level of accuracy significantly depends on several factors, including data quality, specific AI techniques employed, and how these technologies are integrated into existing recruitment processes. AI models, such as those based on natural language processing (NLP) and machine learning, can analyze vast amounts of data from resumes and other candidate interactions to make informed predictions about candidate suitability. These models can identify patterns and trends from historical recruitment data, which can help predict the success of future hires with considerable accuracy. Several challenges will need to be addressed, such as ensuring data diversity and representativity to avoid algorithmic biases. Thus, while an 80% accuracy rate is a demanding target, ongoing advancements in AI, coupled with strategic implementation and continuous refinement of these technologies, make this goal realistic.
- Identification of employee retention trends with over 60% accuracy through big data processing, complex pattern identification, predictive analysis, and continuous improvement of Talent AI. This objective is inspired by the publication “Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-Making,” suggesting that the KNN (K-nearest neighbors) algorithm can be used to classify employees based on their similarity to previous cases of employee departures. By identifying employees who share characteristics with those who have left the company, it is possible to predict turnover risks and intervene accordingly. Using historical data and patterns of employee behavior, AI can forecast who is likely to leave the company. These models will learn from existing data and can make predictions with accuracy that often exceeds traditional methods based solely on intuition or experience. We can also rely on case studies like RetainTalent, which achieved over 90% accuracy, which could be realized in future phases of the project
Proposed solution :
The Talent AI solution stands at the forefront of innovation in artificial intelligence, leveraging the advanced capabilities of models like GPT-4, BERT, and KNN algorithms to revolutionize human resource management. By relying on these cutting-edge technologies, Talent AI aims to deliver concrete and personalized solutions to the various challenges of talent management.
GPT-4 for Personalization and Coaching: GPT-4, with its exceptional ability to understand and generate natural language, will be at the heart of the AI Coaching Agent. This model will be tailored to provide personalized advice on professional development, relying on anonymized data to ensure confidentiality. GPT-4 will facilitate rich and meaningful interactions with employees, effectively guiding them through their career paths within the company.
- Input: Available training data, company data, employee digital profile.
- Output: Professional development recommendations/training provided to employees to guide them in their internal career paths based on the analysis of their digital profiles.
BERT for Profile Analysis and Evaluation: BERT, recognized for its deep contextual understanding of text, will be used to analyze employee evaluations and structure digital profiles. This model will enable precise insights to be extracted from complex textual data, thereby facilitating the evaluation of skills, performance, and aspirations of employees anonymously.
- Input 1: Employee evaluation data collected from periodic interviews between employees and the Employee Evaluation AI Agent, capturing performance, skills, professional aspirations, and development goals of each employee.
- Input 2: Internal HR data on employees, including work history, completed training, certified skills, and past performance, providing a foundation for creating comprehensive digital profiles.
- Output: Digital employee profiles highlighting strengths, weaknesses, ambitions, and training needs, facilitating personalized talent management. Analyses generated from employee evaluation data provide insights into individual performance, strengths, and weaknesses.
KNN for Internal Recruitment: The KNN algorithm will be employed in the Internal Recruitment AI Agent to identify the best candidates for open positions based on similarities between anonymized employee profiles and job requirements. This approach ensures precise and equitable recommendations while preserving the confidentiality of personal data.
- Input: Data on vacant positions, job requirements, organizational objectives, and employee digital profiles used for internal recruitment and professional development recommendations.
- Output: Recommendations of internal candidates for vacant positions generated based on similarities between employee profiles and job requirements.
Large Action Models (LAM): Talent AI will integrate LAMs, advanced systems that mimic human behaviors and actions to perform specific tasks in the HR field. These models, specialized in talent management, will allow for intelligent and contextual automation of HR processes, providing tailored solutions for employee development, evaluation, and internal mobility
- Input: Data on existing HR processes, including employee evaluations, performance data, and professional objectives.
- Output: Intelligent and contextual automation of HR processes, tailored solutions for employee development, evaluation, and internal mobility based on simulated human behaviors and actions by the LAM.
Data Security and Privacy: The solution will be deployed via a secure interface, such as that offered by Microsoft, ensuring optimal data protection. The data used will be generated by each company and will be sufficient for the needs of this project. All information processed by the models will be anonymized before processing, ensuring that the Transformers only access essential information to accomplish the defined tasks without ever compromising personal data. Furthermore, clients will have the option to implement Radar Talent AI locally, keeping all data within their infrastructures.
- Input: All data mentioned above transitioning in Input and Output.
- Output: Data is anonymized and processed securely to ensure confidentiality and protect sensitive information.
In summary, Talent AI leverages a strategic combination of GPT-4, BERT, and KNN, along with the innovation of LAMs, to provide a highly secure, efficient talent management platform tailored to the specific needs of businesses and their employees. This advanced technical approach, focused on data security and anonymization, positions Talent AI at the forefront of AI solutions for human resources.
What sets apart the systems developed by Talent AI, despite the use of existing technologies like GPT-4, BERT, and KNN, lies in their innovative integration and specific adaptation to the challenges of human resource management. The major innovation is in the deep personalization and automation of HR processes, ranging from employee evaluations and the creation of digital profiles to training and internal recruitment. These systems utilize artificial intelligence not only to analyze and understand complex employee data but also to generate personalized and relevant interactions that address individual needs and career goals, all while ensuring unprecedented data security and confidentiality. The “novel” aspect is thus found in the holistic and integrated approach that transforms raw data into strategic insights and concrete actions, propelling talent management into a new era of digital intelligence.
CIMEQ has conducted a proof of concept (POC) for an intelligent chatbot aimed at modernizing certain activities within HR departments, utilizing a centralized data warehouse (users, roles, responsibilities, permissions, tools, clients), a platform for managing HR activities, and user interaction application solutions that integrate external data sources. Their mandate is to explore the possibilities of integrating Microsoft Copilot as an intelligent chatbot for specific usage scenarios like those described above for the Employee Evaluation AI Agent and the Coaching AI Agent. They are currently analyzing use cases, quality and security requirements, functional requirements, and specific scenarios for the prototype. They are exploring features of Microsoft Copilot that would be useful for developing the prototype to support the specific use cases targeted by the project.
Scope
The Talent AI project aims to transform the landscape of talent management within businesses through a series of innovative deliverables and technologies. Each solution is designed to address a specific challenge in human resource management, thus providing concrete and effective answers to encountered problems. Here are the main deliverables arising from the project:
Employee Evaluation AI Agent:
- Problem: Traditional evaluation processes can be subjective and often lack precise, easily usable quantitative data for subsequent HR processes.
- Solution: An automated system that utilizes AI to conduct detailed evaluations of employees.
Creation of Digital Employee Profiles:
- Problem: The difficulty of consistently tracking and analyzing employees’ professional development and skills.
- Solution: Dynamic digital profiles that consolidate detailed information on skills, performance, and aspirations of employees, facilitating their management and development.
Coaching AI Agent:
- Problem: Lack of personalized guidance for professional development and career opportunities within the company.
- Solution: A virtual coach powered by AI, offering personalized advice and career path recommendations based on the analysis of digital profiles and company data.
Internal Recruitment AI Agent:
- Problem: Difficulty in quickly identifying internal talents for promotion opportunities or new assignments, often missing opportunities to retain employees who could thrive if HR were aware of their ambitions.
- Solution: A recommendation tool that analyzes digital employee profiles to identify the best internal candidates for vacant positions, thus promoting internal mobility and talent retention.
Secure HR Data Creation and Management Pipeline:
- Problem: Growing concerns about data security and privacy in HR systems.
- Solution: A secure platform for managing HR data, ensuring data anonymization and local processing to protect sensitive information.
These deliverables, ranging from automated evaluations to AI coaching, integrate into a coherent ecosystem aimed at optimizing talent management. Talent AI thus offers innovative solutions to address the current and future challenges of human resource management, placing AI technology at the service of professional development and organizational efficiency.
To meet contemporary challenges in talent management and employee retention within companies, our Talent AI module presents a range of innovative internal tools focused on artificial intelligence. These tools are designed to optimize employee evaluations, develop comprehensive profiles, and customize professional development.
In summary, Talent AI harnesses the power of advanced AI models to provide concrete solutions for optimizing talent management. By enhancing employee evaluations, creating comprehensive digital profiles, delivering personalized coaching, facilitating internal recruitment, and ensuring data security, this innovative project revolutionizes the approach to human resource management in the modern era.
