AI-Driven Strategies for ADHD Inclusion in Organizations: A Path to Sustainable Development

T. S. Saranya, Sandeep Kumar Gupta (), Darisha Kharmih, Anupama, T. L. Ashalatha and Gurpreet Kaur
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T. S. Saranya: Head of the Institute, AIBHAS, Amity University Bengaluru
Sandeep Kumar Gupta: Mohan Babu University
Darisha Kharmih: Independent Researcher
Anupama: Banaras Hindu University, Department of Sociology
T. L. Ashalatha: Independent Researcher
Gurpreet Kaur: Noida International University

A chapter in Proceedings of the International Conference on Artificial Intelligence in Management for Business and Industrial Growth (AIMBIG 2025), 2025, pp 412-426 from Springer

Abstract: Abstract This paper is discussed how AI can influence the opportunities for neurodiverse people, mainly with ADHD for entering into an inclusive working system. Through supporting person with ADHD a traditional system is mostly unable to navigate while also considering the distinctive the strengths and limitations that such persons bring into the work place. Apart from being adaptive planning systems or the virtual assistance or the providing the real time feedback that bring better about the working conditions, these artificial intelligence technologies evaluate neurodivergent employees. The artificial intelligence, it redefines the talent management to be more data driven, efficient, and also agnostic to the standard: like the recruitment, assessment, and also workforce planning that all stand under its influence. This paper. It also connects the transformation to the UN sustainable development goals, particularly which is the SDG8; decent work and also the economic growth and also the SDG 10 which includes the reduction of inequalities by ensuring the quality of chances for employment. Whereas however, it would be promising changes that are equally independent on the cultural shift in the organizations that prioritize inclusiveness and equity.

Keywords: ADHD; Artificial Intelligence; Sustainable Development Goals; Workplace Adaptation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.2991/978-94-6463-898-1_28

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