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Amogha Sudarshan

PhD Candidate, Trinity Business School

Email: ASUDARSH@tcd.ie
LinkedIn: https://www.linkedin.com/in/amogha-sudarshan/
Substack  https://ammava.substack.com
Google Scholar https://scholar.google.com/citations?user=1d8hSecAAAAJ&hl=en

 

 


 

Doctoral Thesis Summary

I am a business researcher, educator, and former machine learning engineer interested in how people work, decide, and learn alongside artificial intelligence. My research brings management and organisation studies into conversation with psychology, information systems, and responsible technology design. I examine artificial intelligence not simply as a technical artefact, but as a force that can shape the information, emotions, judgement, and practical conditions through which people make decisions.

My doctoral research at Trinity Business School investigates the use of artificial intelligence in managerial decision-making, with particular attention to the relationship between cognition, emotion, intuition, and human agency. Alongside my doctoral work, I teach business and research methods, supervise postgraduate research, and develop learning experiences designed for intellectually ambitious and professionally diverse learners.

Research

My work is motivated by a practical question: what does it mean for organisations when artificial intelligence participates in the formation of managerial judgement? I am especially interested in the point at which AI moves beyond a discrete tool or recommendation and begins to structure the categories, forecasts, options, and evidence through which a decision is understood.

My current work develops a systematic review of AI-aided managerial decision-making. It brings together decision theory, organisational research, and studies of AI to examine how intelligent systems affect both intuitive and deliberative judgement. A central contribution is the idea of AI as a potential substrate for decision-making. Rather than merely assisting a manager at a single point, an AI system may influence what becomes visible, measurable, plausible, or open to consideration before the manager reaches a final choice.

I also study how employees adopt AI tools in the workplace. This research examines the cognitive appraisals and emotional responses that lead people to accept, resist, or object to AI, while recognising that adoption is shaped by the specific business setting in which a tool is used. Across this work, I seek to produce research that is theoretically rigorous, empirically grounded, and useful to organisations seeking to introduce AI in ways that retain meaningful human understanding and agency.

Selected Research Projects

AI in managerial decision-making

Doctoral research, Trinity Business School

This programme investigates how AI enters managerial decision processes and how it interacts with two central human faculties: cognition and intuition. It examines whether contemporary AI systems should be treated only as tools and advisers, or also as an upstream layer that shapes the informational conditions of judgement. The work advances a functional perspective on AI, focusing on what systems do in a decision rather than only on how they are technically built.

Beyond simple control: variable autonomy in workplace AI

Mixed-methods research on AI adoption

This project examines AI tools that operate at different levels of user control, from fully autonomous systems to tools that remain closely directed by people. It combined practitioner interviews with a survey of 442 workplace AI users and multilevel analysis. The research finds that individual appraisals and emotions are more important than autonomy level alone in explaining adoption outcomes, while the level of autonomy can intensify the relationship between negative emotion and objection. Its practical implication is that organisations should not use one generic adoption strategy for every AI tool.

Drivers and barriers of AI adoption across business functions

Delphi study, presented at the Bled eConference

This research brought together researchers and practitioners to identify and prioritise the factors that support and hinder workplace AI adoption across organisational management, innovation, and marketing, finance, and human resources. It shows that the factors encouraging adoption are not simply the reverse of those producing resistance, and that priorities differ across business contexts. The project makes a case for AI adoption research and implementation that are more sensitive to the realities of particular functions, users, and organisational objectives.

AI adoption and learning in educational settings

Research and pedagogical work, Grenoble École de Management

At Grenoble École de Management, I contributed to an Erasmus KA220-HED-funded experimental study on AI adoption among children. I also designed and delivered teaching in digital business, helping students understand the practical journey from identifying a business problem to developing and applying an AI-based sales-price prediction approach. This work strengthened my interest in AI literacy, the design of learning experiences, and the social conditions that enable people to engage critically and confidently with intelligent technologies.

Teaching Excellence Award

I was awarded the Teaching Excellence Award at Trinity Business School in 2026. The award recognises my commitment to thoughtful, engaging, and student-centred teaching. I design learning experiences that combine intellectual rigour with practical relevance, helping learners develop confidence in working with complex ideas, evidence, and professional challenges.

Teaching and Learning

I am a lecturer at Griffith College, where I teach subjects including Business Research Methods and Brand Storytelling. My teaching combines conceptual depth with practical application, helping students develop the analytical confidence to frame good questions, evaluate evidence, communicate ideas clearly, and connect academic learning to the professional world.

I have designed course materials, assessments, and digital learning journeys for diverse cohorts, including mature and career-changing learners. I also supervise master's dissertations and support learners through the often challenging process of turning an initial interest into a focused, credible, and well-executed research project. My approach is informed by adult learning psychology and by a conviction that rigorous education should also be accessible, purposeful, and respectful of learners' time and experience.

I am currently completing a research-based MA in Learning, Education and Development at Griffith College. My master's research considers the learning burden experienced by adult full-time workers, including cognitive load, time pressure, and competing demands, and how learning design can better support sustained engagement.

Professional Background

Before moving fully into higher education and research, I worked in applied AI and technology projects. As a machine learning engineer at Aiddition Technologies, I worked on real-time classification using mmWave radar data and contributed to the development of an embedded-systems product deployed in physical retail settings. At HugoByte AI Labs, I supported the delivery of technical projects and produced user documentation, API documentation, white papers, and guides that made complex AI-enabled systems more accessible to technical and non-technical audiences.

Earlier, at Live-in-Labs at Amrita University, I was part of a research team supporting a UNESCO-funded community drinking-water initiative across 1,000 villages in India. The role involved project coordination, stakeholder engagement, participatory design workshops, and training on water conservation and technology use. This experience continues to inform my interest in technology that is designed with people and communities rather than merely deployed to them.

Across research, teaching, and industry, I work with quantitative and qualitative methods, including survey design, Delphi studies, systematic review, multilevel modelling, structural equation modelling, and research communication. I use R, SPSS, Qualtrics, and digital presentation and learning-design tools to turn complex questions and evidence into clear, useful outputs.

Outside research and teaching, I enjoy a good coffee and writing, preferably together.

Research Interests

  • Artificial intelligence and managerial decision-making
  • AI adoption, resistance, and organisational implementation
  • Human autonomy, user control, and responsible AI
  • Cognition, emotion, intuition, and human-AI collaboration
  • Digital innovation and technology-enabled work
  • Research methods and evidence-based management
  • Adult learning, learner experience, and educational design

Education

  • PhD Candidate, Trinity Business School, Trinity College Dublin (ongoing)
  • MA in Learning, Education and Development, Griffith College Dublin (ongoing)
  • MPhil in Business Management, Grenoble École de Management
  • MTech in Artificial Intelligence Engineering, Amrita University
  • BE in Electronics and Communication Engineering, Visvesvaraya Technological University

Connect

I welcome conversations and collaborations on AI and decision-making, responsible technology, business research, higher education, and research translation.

LinkedIn: https://www.linkedin.com/in/amogha-sudarshan/

Substack: https://ammava.substack.com

Google Scholar: https://scholar.google.com/citations?user=1d8hSecAAAAJ&hl=en