Managing Artificial Intelligence

Analysis

Why This Paper Matters

"Managing Artificial Intelligence" arrives at a moment when organizations are moving AI from experimental pilots into core operational and strategic processes. The authors argue that this shift marks a new age of information technology management, one in which managers must communicate, lead, coordinate, and control an ever-evolving frontier of computational advancements that reference human intelligence. Rather than treating AI as a single technology, the paper frames management as an ongoing negotiation with a moving target, which is a useful corrective to static, checklist-style governance approaches.

The paper's central conceptual move is to identify three related, interdependent facets that managers must decide about: autonomy, learning, and inscrutability. Autonomy concerns how much decision authority is delegated to AI systems; learning concerns how systems improve from data and experience; and inscrutability concerns the opacity of many AI models. Because these facets interact, decisions about one constrain the others. For example, increasing autonomy may amplify the consequences of inscrutability, while aggressive learning may undermine stability and accountability. This framing gives practitioners a compact vocabulary for trade-offs that are often discussed in isolation.

As a special-issue editorial in MIS Quarterly, the paper also serves a field-building function. It situates seven exemplar studies within the proposed framework and argues that information systems scholarship is uniquely positioned to study the organizational and managerial implications of AI. Given its 889 citations, the paper has become a reference point for researchers seeking to connect technical AI developments with management theory.

Technical Contributions

• A definition of managing AI as communicating, leading, coordinating, and controlling an evolving frontier of computational advancements.
• A three-facet framework of autonomy, learning, and inscrutability as interdependent management decisions.
• A historical account of how the frontiers of AI have shifted over time.
• An organizing schema for the seven exemplar studies in the special issue.
• A forward-looking agenda for information systems research on future AI frontiers.

Results

The paper is conceptual and reports no quantitative metrics, benchmarks, or comparative experiments. Its "results" are analytical: it demonstrates how the frontiers of AI have shifted with time and explains how the seven included studies help illuminate management at those frontiers. The value lies in the coherence of the framework and its ability to organize diverse research, not in empirical measurement.

Significance

The paper's significance is primarily agenda-setting. By naming autonomy, learning, and inscrutability as the core management facets, it gives scholars and practitioners a shared lens for analyzing AI governance, accountability, and organizational change. It also legitimizes AI management as a domain of information systems scholarship, encouraging research that goes beyond model performance to examine how AI is led, coordinated, and controlled in organizations. Its high citation count reflects its role as a foundational framing reference, though its conceptual nature means its claims await empirical testing.