In today’s rapidly evolving landscape of learning and development (L&D), the integration of artificial intelligence (AI) and machine learning (ML) stands as a transformative force. Vishant Jain, Director of Talent Management at Ascendion, sheds light on the profound impact of AI and ML in revolutionising L&D practices. Through insightful discussions, Jain explores how these technologies contribute to personalised learning experiences, streamlined content creation, and data-driven decision-making, ultimately shaping the future of talent development and organisational success.
How can AI and ML contribute to the effective processing of tedious tasks within L&D?
AI/ML is transforming L&D by providing data-driven insights, personalised learning experiences, and optimised content creation. By embracing these technologies, organisations can develop a skilled and engaged workforce, ultimately driving business success. Organisations are leveraging AI/ML to evaluate the current L&D initiatives by enhancing analytics and reporting on learning and performance data. The objective is to derive actionable insights from past activities and anticipate future business outcomes. This data can give comprehensive reports on the effectiveness and return on investment (RoI) of learning, helping both coaches and leaders get a deeper understanding of workplace learning and how to embrace it effectively. AI and ML can be used for content creation and curation, where Gen AI helps create personalised & Interactive learning materials, such as adaptive quizzes and interactive simulations, freeing up L&D professionals to focus on strategic initiatives. AI-powered grading engines automatically assess assignments and provide immediate feedback, allowing trainers to focus on individual coaching and support. ML algorithms can also handle administrative tasks like scheduling, enrollment, and tracking progress, streamlining administrative processes, and saving time.
How might the integration of AI and ML enhance the personalisation and customisation of learning experiences for individuals?
AI can tailor learning paths based on individual needs, skills, and business goals (adaptive learning), ensuring that participants are challenged and engaged while avoiding redundant content. AI can personalise the learning environment by adjusting the learning pace, format, and delivery method to suit individual preferences, such as auditory, visual, or kinesthetic learning styles. ML algorithms can recommend relevant learning resources and development opportunities based on individual learning styles, interests, and career aspirations, giving way for personalisation. AI can analyse the learner data to recommend relevant learning resources, including courses, articles, and videos, that match their individual interests and skill gaps.
AI can generate self- paced bite-sized learning modules that fit seamlessly into busy schedules and accommodate diverse employee learning preferences. Such micro modules make learning more accessible and convenient.
How can the utilisation of AI contribute to the streamlined and efficient creation of content in various domains?
In content creation AI offers significant advantages in terms of efficiency, quality, cost, and creativity. AI is revolutionising content creation across various domains by automating tasks, optimising workflows, and generating creative outputs. Ascendion’s in-house C-AI bot is a prime example of how AI is streamlining workflows and generating high-quality content. The C-AI (AI bot) accessible through Microsoft Teams, automates routine tasks like data analysis and report generation. This frees up the employees’ time for more strategic work. C-AI also analyses vast amounts of data to identify patterns and trends, provides insights and suggestions leading to more efficient decision-making and content creation.
How are conversational business analytics used to enhance real-time data analysis to optimise L&D strategies?
Conversational business analytics refers to analysing and extracting insights from natural language conversations, between customers interacting with businesses through various conversational interfaces like chatbots and virtual assistants. According to Gartner, by 2026, conversational artificial intelligence deployments within contact centers will reduce agent labor costs by $80 billion.
In L&D, CBA can be integrated with learning platforms to gather real-time feedback from learners through chatbots or virtual assistants. This feedback can be analysed to improve content, identify areas for improvement, and assess learner engagement. CBA can analyse employee’s interactions with the L&D platform to identify their preferred learning styles, interests, and knowledge gaps helping personalise learning paths and recommend relevant content, courses, and resources. Analysing learner data helps to identify patterns and trends in performance, predict future performance and proactively address potential challenges. Ascendion’s METAL-AI, is our AI-enabled talent orchestration platform, leverages conversational business analytics (CBA) to enhance its effectiveness in various aspects of talent management.
METAL-AI revolutionises talent sourcing by enabling professionals to operate with heightened efficiency and effectiveness, resulting in enhanced talent acquisition results. Through features such as real-time insights, task automation, and data-driven decision-making, METAL-AI facilitates smarter workflows for talent sourcers. Its capabilities span from AI-powered candidate matching and personalised pre-screening to job description summarisation and predictive Boolean search, all aimed at optimising the sourcing process and driving superior talent acquisition outcomes.
Thus in these ways, organisations must be committed to AI-powered L&D programs to build a future-ready workforce, unlock individual potential, and drive organisational success.
