AI in Collection Development & Resource Management

 

By Niranjan Mohapatra, World Skill Center
Series: AI Transformation in Libraries (Part 4 of 10)

 Introduction

Collection Development (CD) and Resource Management (RM) are the strategic engines of library growth. Traditionally, these workflows rely on circulation statistics, user requests, expert judgment, and periodic assessments. However, with the explosion of digital content, datasets, open-access materials, and user-generated needs, these manual approaches often fall short.

Artificial Intelligence (AI) is now emerging as a transformative force—bringing predictive analytics, automated decision-making, and evidence-based strategies to CD & RM. AI supports librarians in building smarter, user-centered, and future-ready collections.

 

1. How AI Supports Modern Collection Development

1.1 Predictive Analytics for Demand Forecasting

AI models analyze:

• Circulation patterns
• Search logs
• Academic trends
• Course syllabi
• User demographics
• Subject-wise resource usage

to predict future needs.

For example:

• Anticipating demand for emerging technologies
• Adjusting budgets based on projected subject popularity
• Forecasting heavy-use titles during exam periods

AI moves CD from reactive to proactive.

 

1.2 Smart Selection & Recommendation Tools

AI-powered systems can scan:

• Publisher catalogs
• Open-access repositories
• Preprint servers
• Research feeds
• Book reviews
• Citation databases

and automatically recommend potential acquisitions.

Tools like:

• GOBI AI modules
• EBSCO AI selection assistants
• Generative AI-based book evaluation bots

help libraries evaluate quality, relevance, and expected usage.

 

1.3 AI for Collection Gap Analysis

AI compares library holdings with:

• Institutional curricula
• Research trends
• Benchmark collections
• Faculty publications
• Community interests

to detect:

• Areas of underrepresentation
• Outdated materials
• Subjects needing expansion

This supports data-driven collection diversity and inclusiveness.

 

2. AI in Resource Management

2.1 Automated Weeding & Retention Decisions

AI tools evaluate:

• Age of materials
• Usage statistics
• Citation relevance
• Duplication
• Digital availability

and generate suggestions for:

• Weeding
• Retention
• Digitization priority
• Offsite storage

This helps librarians streamline shelf management and optimize space.

 

2.2 Budget Allocation & Cost Optimization

AI analyzes:

• Subscription usage
• Download trends
• Cost-per-use statistics
• Vendor pricing models

and offers:

• Budget forecasts
• Renewal recommendations
• Alternatives for low-use, high-cost resources

This significantly improves ROI in library investments.

 

2.3 AI for License & Access Management

AI-based systems can:

• Monitor access logs
• Detect unusual usage
• Ensure license compliance
• Recommend optimal access models (single-user, multi-user, evidence-based acquisition, etc.)

This automates many routine RM tasks.

 

3. Enhancing Decision-Making with AI Dashboards

Modern CD & RM depend on clear insights. AI dashboards generate:

• Trend visualization
• Resource heatmaps
• User segmentation
• Subject growth analysis
• Acquisition impact metrics

Librarians gain a full 360° view of collection health.

 

4. Benefits of AI in CD & RM

✔ Evidence-Based Decisions

No more guesswork—AI provides solid data.

✔ Time Savings

Automated analysis reduces manual workload.

✔ Improved User Satisfaction

Collections evolve according to real needs.

✔ Budget Efficiency

Identifies the most cost-effective resources.

✔ Balanced, Inclusive Collections

AI identifies diverse voices and underrepresented subjects.

 

5. Risks & Ethical Considerations

❗ Data Bias

If AI is trained on biased datasets, recommendations may favor dominant subjects.

❗ Overdependence on Automation

Librarian judgment must remain central.

❗ Privacy Concerns

Usage data must be anonymized.

❗ Vendor Lock-In

AI-driven models may limit transparency.

 

Conclusion

AI is helping libraries build agile, responsive, and intelligent collections. Instead of reacting to past usage, libraries can now anticipate future needs and strategically allocate resources. AI does not replace the expertise of librarians—it amplifies their ability to build meaningful collections that serve dynamic academic and community landscapes.