MARC

LEADER 00000nab a2200000uu 4500
001 3252289617
003 UK-CbPIL
022 |a 1055-3096 
022 |a 2574-3872 
022 |a 1055-3104 
024 7 |a 10.62273/NRQW1204  |2 doi 
035 |a 3252289617 
045 2 |b d20250701  |b d20250930 
084 |a 50955  |2 nlm 
100 1 |a Samuel, Jayarajan 
245 1 |a Building AI Talent in Organizations – An Experiential Learning Approach 
260 |b EDSIG  |c Summer 2025 
513 |a Journal Article 
520 3 |a Rapid digitization in industries is transforming the way corporations conduct their core businesses and interact with their customers. The proliferation of data in these corporations and the ability to process them using the latest AI/ML techniques are compelling them to transform themselves into data-driven organizations. However, acquiring new talent with data science skills and/or reskilling existing employees with deep domain experience presents a major challenge. In this paper, we illustrate how a unique collaboration between industry and academia to impart AI and machine learning (ML) skills to domain experts contributed to furthering an organization's AI aspirations. Specifically, the collaboration has created a continuous learning environment that is conducive to active experimentation and reflective practice, both of which are essential to gaining actionable business insights. Our methodology can be applied to reskill workforces in the future in transformative new-age technologies while adding value to the organization at the same time. 
653 |a Teaching 
653 |a Students 
653 |a Collaboration 
653 |a Data acquisition 
653 |a Curricula 
653 |a Organizations 
653 |a Skills 
653 |a Machine learning 
653 |a Linear algebra 
653 |a Case studies 
653 |a Research methodology 
653 |a Artificial intelligence 
653 |a Data science 
653 |a Experiential learning 
653 |a Employees 
653 |a Workforce 
653 |a Subject specialists 
653 |a Algorithms 
653 |a Large language models 
653 |a Education 
653 |a Experiments 
653 |a Ability 
653 |a Learning environment 
653 |a Companies 
653 |a Customers 
653 |a Data 
653 |a Reflective practice 
653 |a Consumers 
653 |a Business 
653 |a Digitization 
653 |a Productivity 
653 |a Educational Opportunities 
653 |a On the Job Training 
653 |a Influence of Technology 
653 |a Reflection 
653 |a Entrepreneurship 
653 |a Learning Processes 
653 |a Learning Theories 
653 |a Observation 
653 |a Entry Workers 
653 |a Program Descriptions 
653 |a Program Development 
653 |a Mathematics Instruction 
653 |a Reflective Teaching 
653 |a Classrooms 
653 |a Labor Force Development 
653 |a Language Processing 
653 |a Professional Education 
653 |a Lifelong Learning 
653 |a Computational Linguistics 
653 |a Programming 
700 1 |a Nerur, Sridhar 
700 1 |a Mahapatra, RadhaKanta 
700 1 |a White, Brian  |u Ericsson Inc. Plano, TX 75024, USA 
773 0 |t Journal of Information Systems Education  |g vol. 36, no. 3 (Summer 2025), p. 277-287 
786 0 |d ProQuest  |t ABI/INFORM Global 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3252289617/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3252289617/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3252289617/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch