JOURNAL ARTICLE

Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality.

  • Published In: Organization Science (INFORMS), 2026, v. 37, n. 2. P. 403 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Dell'Acqua, Fabrizio; McFowland III, Edward; Mollick, Ethan; Lifshitz, Hila; Kellogg, Katherine C.; Rajendran, Saran; Krayer, Lisa; Candelon, François; Lakhani, Karim R. 3 of 3

Abstract

This article investigates the concept of a "jagged technology frontier" to describe the uneven effects of artificial intelligence (AI) capabilities on knowledge work, where AI assistance improves performance on some tasks but degrades it on others within the same workflow. Conducted in collaboration with the global management consulting firm Boston Consulting Group (BCG), the preregistered randomized experiment involved 758 highly skilled management consultants performing realistic consulting tasks either without AI, with GPT-4 AI assistance, or with GPT-4 plus a prompt engineering overview. Results show that for tasks within GPT-4’s capability frontier—spanning creative, analytical, writing, and persuasive activities—AI use increased task completion by 12.2%, improved quality by over 30%, and reduced completion time by about 25%. Conversely, for a complex managerial task designed to lie outside the AI frontier, AI assistance reduced correctness by 19 percentage points despite improving the persuasiveness and coherence of responses, highlighting risks of overreliance on AI in tasks beyond its current capabilities. The study underscores the importance of human judgment in discerning when AI augmentation is beneficial and calls for organizational strategies that consider the jagged nature of AI’s capabilities across knowledge workflows.

Additional Information

  • Source:Organization Science (INFORMS). 2026/03, Vol. 37, Issue 2, p403
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2026
  • ISSN:1047-7039
  • DOI:10.1287/orsc.2025.21838
  • Accession Number:192562421
  • Copyright Statement:Copyright of Organization Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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