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Theory-driven perspectives on generative artificial intelligence in business and management

Journal: British Journal of Management

Date: 2024

Author: Olivia Brown, Robert M. Davison, Stephanie Decker, David A. Ellis, James Faulconbridge, Julie Gore, Michelle Greenwood, Gazi Islam, Christina Lubinski, Niall G. MacKenzie, Renate Meyer, Daniel Muzio, Paolo Quattrone, M. N. Ravishankar, Tammar Zilber, Shuang Ren, Riikka M. Sarala, Paul Hibbert

Abstract:
The advent of generative artificial intelligence (GAI) has sparked both enthusiasm and anxiety as different stakeholders grapple with the potential to reshape the business and management landscape. This dynamic discourse extends beyond GAI itself to encompass closely related innovations that have existed for some time, for example, machine learning, thereby creating a collective anticipation of opportunities and dilemmas surrounding the transformative or disruptive capacities of these emerging technologies.

Link: Google Scholar

Background and Context

Current State of GAI

GAI is attracting significant interest from academics and practitioners, with rapid advancement in capabilities that are transforming business and management.

Research Approach

The British Journal of Management invited prominent scholars to contribute theoretical perspectives on GAI through intensive collaborative discussions.

Research Gap

Despite extensive discussion of GAI, prevailing discourse often lacks robust theoretical foundation, necessitating refinement of existing theories or development of new ones.

Three Key Insights Emerging from GAI Research in Workplace Settings

Insight 1 Dynamic & Emergent Not predetermined outcomes Workplaces become spaces where outcomes unfold through emergent processes rather than fixed paths Insight 2 Context Matters Relational social activities Technologies must be understood within broader contextual relationships and historical forces Insight 3 Human-GAI Groups New forms of teamwork Knowledge creation and problem-solving emerges within human-GAI groups disrupting traditional teams
  • The paper identifies three novel conceptual insights for understanding how GAI transforms workplaces and organizational processes.
  • GAI-laden workplaces feature dynamic and emergent outcomes rather than predetermined processes and fixed knowledge structures.
  • Context matters significantly, requiring researchers to examine GAI within broader social, historical, and institutional relationships.

Evolution of AI Transforming Professional Work Over Time

Early 2000s Decision Support Systems Knowledge Asymmetry Maintained Mid/Late 2010s Process Automation Systems Professional Autonomy Challenged Early/Mid 2020s Advisory Systems (GAI) Reduced Knowledge Asymmetries Increasing Impact on Professional Autonomy
  • Table 2 in the article shows three eras of technological progression in professional work environments.
  • Professional autonomy has been increasingly challenged as AI capabilities have evolved over time.
  • Current GAI systems are reducing knowledge asymmetries between professionals and clients, transforming traditional professional-client relationships.

Diverse Business Applications of GAI Across Industries

Business Applications of GAI Customer Experience Zalando, Instacart Business Operations DHL, Air India Marketing & Advertising Coca-Cola, Heinz Customer Service Mastercard, Salesforce
  • Table 1 in the article documents diverse business applications of GAI across multiple industries.
  • Organizations like Zalando and Instacart utilize GAI to enhance customer experience through virtual assistants.
  • Companies are deploying GAI across operations, customer service, and marketing to improve efficiency and capabilities.

Multiple Theoretical Perspectives Enriching GAI Understanding

Theoretical Lenses Examining Generative AI The Art in the Artificial GAI as compositional art Creating futures, not predicting Contested Imaginaries Historical context of tech adoption Competing societal narratives Organizational Teams Redefining team composition Human-GAI collaboration Professionalism Changing producer-consumer relationships Relational Knowledge Knowledge networks reshaping epistemic accountability Multiple Perspectives Enriching Theoretical Understanding
  • The article presents five distinct theoretical perspectives to interpret and understand GAI in business.
  • Each theoretical lens offers unique insights into GAI's implications for different aspects of business.
  • This multidisciplinary approach enriches understanding beyond any single perspective or theory.

Implications of GAI for Research and Management Practice

Research Implications Reframe team research theories Reconsider professional autonomy Explore relational knowledge networks Practice Implications Balance human-GAI teamwork Reimagine professional-client relations Manage emergent workplace dynamics Shaping Sustainable and Responsible GAI Integration
  • The article presents significant implications for both management research and practical business applications.
  • Research must adapt theories to address human-GAI teams, professional-client dynamics, and relational knowledge systems.
  • Organizations must develop practices for sustainable GAI integration that balance technological capabilities with human needs.

Contribution and Implications

  • The article bridges theory and practice by providing theoretical frameworks to understand GAI in business contexts.
  • It encourages rethinking traditional management theories to accommodate the transformative impact of GAI technologies.
  • The work emphasizes responsible and sustainable development of GAI that considers ethical and societal implications.
  • It highlights the need for cross-disciplinary approaches to study GAI's impact across different business domains.

Data Sources

  • Visualization 1 is based on the three key insights discussed throughout the article about GAI-laden workplace dynamics.
  • Visualization 2 draws from Table 2 in the article, which outlines the progression of AI in professional contexts.
  • Visualization 3 is derived from examples in Table 1, showcasing current business applications of GAI technology.
  • Visualization 4 represents the different theoretical perspectives presented by the various contributing scholars in the article.
  • Visualization 5 synthesizes the research and practice implications discussed in the conclusion section of the article.