Abstract
In a globalised, post-Covid business world increasingly shaped by artificial intelligence (AI), Tom Pfefferkorn argues that management education must move beyond theory towards practice excellence – a model that blends experiential learning, value-based judgement, and reflective dialogue. This chapter introduces the Practice Excellence Network (PEN) at Edinburgh Business School, which connects students, academics, and businesses through consultancy-style projects addressing real organisational capability gaps.
Drawing on psychology, strategic management, and control systems theory, Pfefferkorn establishes an interdisciplinary framework for understanding how learners develop intuitive and analytical judgement. Attention-based theories explain how humans make value-based choices; organisational capability theory identifies the structures that enable performance, and control systems theory (Simons, 1995) reveals how social and technical mechanisms shape responsibility, alignment, and engagement.
A cross-case analysis of 28 student-led consultancy projects shows that two control mechanisms are central to practice-enhancing management education: maximising learner responsibility and building triadic alignment and trust between students, supervisors, and business clients. Granting students ownership of projects enhances confidence and judgement, while establishing shared goals and open communication fosters empowerment and engagement.
While AI can process information efficiently, Pfefferkorn contends that human value-based judgement remains essential for defining the right problems. PEN positions AI as a complementary tool, enabling learners to combine reflective reasoning with technological capability.
The chapter concludes that practice-enhancing management education develops future leaders who think about the right things, not merely in the right way – integrating ethical reflection, analytical rigour, and AI-informed insight to generate sustainable organisational value.
Drawing on psychology, strategic management, and control systems theory, Pfefferkorn establishes an interdisciplinary framework for understanding how learners develop intuitive and analytical judgement. Attention-based theories explain how humans make value-based choices; organisational capability theory identifies the structures that enable performance, and control systems theory (Simons, 1995) reveals how social and technical mechanisms shape responsibility, alignment, and engagement.
A cross-case analysis of 28 student-led consultancy projects shows that two control mechanisms are central to practice-enhancing management education: maximising learner responsibility and building triadic alignment and trust between students, supervisors, and business clients. Granting students ownership of projects enhances confidence and judgement, while establishing shared goals and open communication fosters empowerment and engagement.
While AI can process information efficiently, Pfefferkorn contends that human value-based judgement remains essential for defining the right problems. PEN positions AI as a complementary tool, enabling learners to combine reflective reasoning with technological capability.
The chapter concludes that practice-enhancing management education develops future leaders who think about the right things, not merely in the right way – integrating ethical reflection, analytical rigour, and AI-informed insight to generate sustainable organisational value.
| Original language | English |
|---|---|
| Title of host publication | Hybrid Working and AI |
| Subtitle of host publication | The Impact on Organizational Development |
| Editors | Julian Randall, Bernard Burnes |
| Place of Publication | New York |
| Publisher | Routledge |
| Chapter | 3 |
| Pages | 40-53 |
| Number of pages | 14 |
| ISBN (Electronic) | 97911041089843 |
| ISBN (Print) | 97911041089810, 97911041089827 |
| DOIs | |
| Publication status | Published - 23 Mar 2026 |
Keywords
- practice excellence
- judgement
- business performance
- attention
ASJC Scopus subject areas
- Business, Management and Accounting(all)
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