Enterprise AI Analysis
Patterns in management research on artificial intelligence: A longitudinal analysis using structural topic modeling
The field of management research on applied artificial intelligence (AI) is growing rapidly as the technology is maturing in industrial applications. Due to the nascent state of the scientific discourse, it is hard to discern thematic focal points or how well managerial and technical topics are integrated. Based on 10,036 publications over 25 years, we map the topic landscape of AI-related management research, longitudinal patterns of topics, and structural changes of topic networks and research communities. Our model identifies 71 unique topics, indicating a strong but myopic focus on technological capabilities and applications. The lagged response to technological paradigm shifts indicates a double pacing problem. Network structures of thematic research communities reveal increased centralization and interconnections, suggesting the field's role in transferring basic AI research to industrial implementation. However, topics in technology management of AI seem to be separated from recent advances in AI. We propose mechanisms to foster an integrative discourse on applied AI that allows management research to act as a sense-giving institution. This includes focusing on fundamental technological characteristics instead of applications and strengthening the role of journals as discourse intermediaries.
Executive Impact: Key Metrics at a Glance
The study uncovers crucial insights into the evolving landscape of AI-related management research, revealing the scope and focus of current discourse.
Technology Management Discourse
Business Intelligence Discourse
Prediction/Optimization Discourse
Pattern Identification Discourse
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Technology Management
Business Intelligence
Prediction/Optimization
Pattern Identification
Technology Management Focus
This theme encompasses 17 topics (23.3% of discourse) covering human-AI interactions, organizational adoption, digital transformation management, and online data analytics. Technical focal points include computing resources and knowledge-based systems. Longitudinally, most topics in this theme show increasing relevance as hot topics, revivals, or evergreens, indicating growing attention to AI adoption and implementation issues.
Business Intelligence Focus
The largest theme at 33.2% of discourse, comprising 26 topics covering firm performance, quality management, resource efficiency, marketing, finance, project management, and healthcare applications. Technical topics focus on decision-support systems and text mining techniques. Hot and revival topics indicate a shift toward in-depth consumer behavior analysis, particularly using online text data. Topics on emissions, sustainability, and healthcare AI applications also show positive trends.
Prediction/Optimization Focus
The PO theme encompasses 13 topics (23.1% of discourse) addressing algorithm architectures and AI system tasks centered on regression/prediction and optimization. The theme includes specific industrial applications in logistics, production, and energy efficiency. Evergreen and revival topics reveal a strong emphasis on statistical prediction, particularly in recent years. Hot topics indicate a growing focus on machine learning methods and applications in transportation and energy.
Pattern Identification Focus
The PI theme comprises 14 topics (20.4% of discourse) centered on classifying and clustering data. It includes specific pattern recognition for visual data in automated systems and text data for recommendation systems in contexts like online retail. The theme addresses state-of-the-art technological architectures like convolutional neural networks and learning procedures such as reinforcement learning. A significant focal point involves improving transparency and relationships within training data or selecting data subsets to optimize AI system performance.
10,036
Publications Analyzed over 25 Years
71
Unique AI-Related Management Topics Identified
Enterprise Process Flow
Data Retrieval (10,036 publications)
→
Data Pre-processing (text cleaning, n-grams)
→
Topic Extraction (STM algorithm)
→
Topic Labeling & Clustering (71 topics)
→
Longitudinal Trajectory Estimation
→
Topic Network Construction
Double Pacing Problem in AI Research
The analysis reveals a 'double pacing problem' where management research responds to technological paradigm shifts in AI with substantial delays. This reactive pattern undermines its sense-giving potential by struggling to provide timely guidance during critical periods of techno-transformation. It highlights a disconnect between recent AI advances and technology management topics, which are often still based on outdated AI paradigms.
| Paradigm | Key Characteristics | Management Research Trend |
|---|---|---|
| Symbolic AI Systems (e.g., KBS, DSS) |
|
|
| Connectionist AI Systems (e.g., Neural Networks, Deep Learning) |
|
|
Bridging the Gap: AI in Business Intelligence
The Business Intelligence theme particularly connects well with recent AI advances. Topics like sentiment analysis, text mining, and areal mapping show positive trends and are increasingly influenced by computer science and environmental science journals. This integration suggests that data-rich business areas are more adept at adopting and applying modern AI, acting as a crucial intermediary between basic AI research and industrial implementation. However, other areas show less integration, leading to potential segregation between cutting-edge AI and traditional management research.
Calculate Your Potential AI ROI
Estimate the significant financial and operational benefits your organization could realize by strategically implementing AI.
Estimated Annual Savings
Total Annual Hours Reclaimed
Your AI Implementation Roadmap
A structured approach ensures successful AI integration, from strategy to sustainable impact.
Phase 01: Strategic Assessment & Alignment
Evaluate current operations, identify AI opportunities, and align with business objectives. Define clear KPIs and build a cross-functional AI task force.
Phase 02: Pilot Program & Iteration
Launch a focused AI pilot project in a controlled environment. Gather feedback, measure performance, and iterate based on initial results.
Phase 03: Scaled Deployment & Integration
Expand successful pilots across relevant departments. Ensure seamless integration with existing systems and robust change management protocols.
Phase 04: Performance Monitoring & Optimization
Continuously monitor AI system performance, refine algorithms, and explore new use cases to maximize long-term ROI and competitive advantage.
Ready to Transform Your Enterprise with AI?
Schedule a complimentary, no-obligation strategy session with our AI experts to discuss how these insights apply to your unique business context.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat