Artificial Intelligence Challenges for Knowledge Innovation Cycles

ABSTRACT This article focuses on knowledge innovation and its contemporary reshaping, with particular attention to the growing role of AI in this process. This article first examines the general structure and phases of knowledge innovation cycles, aiming to identify AI's role within them. The discussion then focuses on the first phase, innovativeness, as the phase in innovation cycles that may still require human action, even when other phases of knowledge innovation cycles turn fully digital. This article then elaborates on AI's deepening engagement in innovation cycles, notably in the first phase of the innovation generation cycle. Finally, two implications of AI‐based transitions are addressed: The potential damage to human innovation processes within organizations and the need for broader changes in the academic teaching of cross‐disciplinary tools for knowledge management.

Authors

Institutions

Publication Details

Journal
AI & Innovation
Published
2026-05-23
DOI
https://doi.org/10.1002/aiv2.70007
Primary Topic
Artificial Intelligence Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial Intelligence Challenges for Knowledge Innovation Cycles

Aharon Kellerman
AI & Innovation
Artificial Intelligence Applications
article

Artificial Intelligence Challenges for Knowledge Innovation Cycles

Aharon Kellerman
article en

Abstract

ABSTRACT This article focuses on knowledge innovation and its contemporary reshaping, with particular attention to the growing role of AI in this process. This article first examines the general structure and phases of knowledge innovation cycles, aiming to identify AI's role within them. The discussion then focuses on the first phase, innovativeness, as the phase in innovation cycles that may still require human action, even when other phases of knowledge innovation cycles turn fully digital. This article then elaborates on AI's deepening engagement in innovation cycles, notably in the first phase of the innovation generation cycle. Finally, two implications of AI‐based transitions are addressed: The potential damage to human innovation processes within organizations and the need for broader changes in the academic teaching of cross‐disciplinary tools for knowledge management.

AI & Innovation
University of Haifa (IL)
Industry, innovation and infrastructure
Openalex Percentile: Top 3%
Artificial Intelligence Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.