Library Study by Clarivate (pulse review)

According to Clarivate’s Five-Stage AI Maturity Model.  This is Where Libraries Stand Today…

The report establishes a clear framework for understanding AI adoption across five distinct stages:

1. No Plans (20% of public libraries)

These institutions have made a conscious decision not to explore AI, often driven by resource limitations or philosophical concerns about technology’s role in community service.

2. Not Actively Pursuing (35% of public libraries)

Awareness exists, but no concrete steps have been taken. This represents the largest cohort in public libraries, suggesting widespread paralysis between recognition and action.

3. Exploration and Evaluation (35% overall)

AI discussions are happening at leadership and staff levels, but implementation remains theoretical. This is the most common stage across all library types in 2025.

4. Initial Implementation (24% in U.S., up from 9% in 2024)

Pilot projects and planning stages characterize this group. The dramatic growth in the U.S. suggests movement, albeit slower than global counterparts.

5. Moderate to Active Deployment (10% combined)

Only a small fraction has achieved strategic, multi-use-case implementation, representing the true early adopters.

Critical Insight: The concentration at the exploration stage (35%) suggests libraries are stuck in “analysis paralysis”—aware of AI’s potential but unable to translate that awareness into action. This represents both a risk and an opportunity: institutions that break through this barrier will gain significant competitive advantages in service delivery and operational efficiency.

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The Great Regional Divide: U.S. Pessimism vs. Asian Optimism

Perhaps the most striking finding is the stark contrast in AI sentiment across regions:

United States: The Cautious Skeptic

• 77% of U.S. respondents are at early exploration stages or have no plans (vs. 68% global average)
• 36% express pessimism about AI’s five-year benefits (scoring 1-2 on a 5-point scale)
• Only 25% are optimistic (scoring 4-5)
• 31% of public libraries report low confidence in understanding AI (vs. 21% average)

Asia: The Confident Innovator

• 67% of Asian respondents are highly optimistic about AI’s benefits (scoring 4-5)
• Significantly higher implementation rates across all stages
• Greater institutional support and strategic planning

What Explains This Gap?

The report attributes U.S. pessimism to “broader geopolitical and budgetary pressures” rather than skepticism about AI’s actual capabilities. This is a crucial distinction: American librarians aren’t rejecting AI on technical grounds—they’re constrained by systemic factors beyond their control.

Three underlying factors emerge:

1. Funding Instability: 25% of respondents expect budget cuts of less than 10% due to geopolitical factors. U.S. public libraries, heavily dependent on local tax revenues, face particular vulnerability.
2. Political Uncertainty: The report notes “unprecedented times for policy and funding,” with libraries caught in broader culture wars about information access and institutional roles.
3. Risk Aversion Culture: U.S. institutions may face greater legal and reputational risks around privacy, copyright, and data security, creating institutional caution that Asian counterparts don’t experience to the same degree.

Future Implications: If this trend continues, we may see a “global AI divide” in library services, where Asian and European institutions leapfrog American libraries in service innovation, operational efficiency, and user satisfaction. This could fundamentally reshape international library leadership and best practice development.

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The Shifting Concern Landscape: From Skills to Money

One of the most significant year-over-year changes involves the hierarchy of barriers to AI adoption:

2024’s Top Concern: Lack of Expertise

Last year, skills gaps dominated librarian anxieties about AI implementation.

2025’s Top Concern: Budget Constraints (62%, up from 56%)

Money has overtaken expertise as the primary barrier, suggesting two possible interpretations:

Optimistic Reading: Libraries have made progress on the skills front through training and experimentation, moving the bottleneck to resource allocation.

Pessimistic Reading: Budget pressures have intensified to the point where skills development becomes moot—you can’t implement what you can’t afford to purchase or staff.

The Persistent Top Three Concerns:

1. Budgets (62%) – The new #1, reflecting broader economic pressures
2. Privacy and Security (65% for public libraries) – Unchanged from 2024, particularly acute for public-facing institutions
3. Lack of Expertise – Still top 3, but no longer #1, suggesting modest progress in AI literacy

Specialized Concerns by Library Type:

Academic Libraries: Research and academic integrity concerns dominate, with librarians positioning themselves as “integrity coaches” who help faculty and students navigate AI’s implications for scholarly work.

Public Libraries: Privacy and security remain paramount (65%), reflecting their role as trusted community institutions and their service to vulnerable populations.

Collection Librarians: This group shows the highest pessimism (35%) and greatest concern about job displacement—unsurprising given they’re “most directly navigating the realities of funding cuts and new policy demands.”

Critical Insight: The shift from skills to budget concerns suggests libraries are moving up Maslow’s hierarchy of AI needs—they’ve addressed basic awareness and are now confronting resource allocation realities. This is actually a sign of maturation, not regression.

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The Confidence-Implementation Correlation: A Virtuous Cycle

The report reveals a powerful positive feedback loop:

Librarians who implement AI → Report greater confidence → Pursue more advanced implementation → Become more optimistic

This creates a self-reinforcing cycle with profound implications:

The Early Adopter Advantage

Libraries in later implementation stages report:

• Higher confidence in AI understanding
• Greater optimism about five-year benefits
• More ambitious plans for expansion
• Better ability to articulate value to stakeholders

The Laggard Penalty

Institutions stuck in exploration face:

• Persistent confidence gaps
• Continued pessimism
• Difficulty moving from discussion to action
• Risk of falling further behind as the gap widens

Strategic Implication: The data suggests a “tipping point” exists between exploration and initial implementation. Libraries that cross this threshold enter a virtuous cycle of learning, confidence-building, and expanded deployment. Those that remain in perpetual exploration risk a vicious cycle of declining confidence and increasing irrelevance.

The 2026 Prediction: We’ll likely see bifurcation in the library sector—a growing gap between AI-mature institutions and those still discussing potential applications. This divide will manifest in service quality, operational efficiency, and institutional relevance.

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The Public Library Paradox: Community Focus vs. Technology Caution

Public libraries present a fascinating contradiction:

Mission Consistency

• 50% identify community engagement as their primary mission (up from 38% in 2024)
• Core values remain stable despite technological disruption
• Long-term focus unchanged by AI emergence

Technology Hesitation

• 54% have no plans or are not actively pursuing AI
• Only 20% are optimistic about AI’s five-year benefits (down from 26% in 2024)
• 31% report low AI understanding confidence
• 65% cite privacy and security as top concerns

The Service Gap

Public librarians describe serving populations with vastly different technology literacy levels: “We’re helping people learn how to use AI and in the same day we’re helping people learn how to type on a computer.”

The Paradox Explained: Public libraries face a unique challenge—they must simultaneously:

1. Serve as technology educators for their communities
2. Protect vulnerable populations from technology risks
3. Maintain trust as neutral, safe community spaces
4. Operate with limited and unstable funding

This creates institutional conservatism around AI adoption that academic libraries don’t face to the same degree.

Critical Question: Can public libraries maintain their community centrality if they lag significantly behind in AI adoption? Or does their caution actually strengthen their role as trusted guides through technological disruption?

The 2030 Scenario: Public libraries may evolve into “AI literacy centers” for their communities—not necessarily using the most advanced AI internally, but serving as educators and ethical guides for community members navigating AI in their daily lives. This would represent a strategic repositioning from technology adopter to technology interpreter.

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The Innovation Imperative: Skills for Tomorrow’s Librarian

The report reveals a striking consensus about future competencies:

Top Skills for Tomorrow’s Librarian:

1. Ability to innovate and evolve library services (49%) – Nearly half view innovation capacity as the critical skill
2. Integrating library services with curriculum (35%) – Particularly for academic libraries
3. Expanding digital resources and e-learning tools (33%) – Recognition of the digital-first future

Steve Powell’s Prediction: “In five years, the role of the librarian is going to be so much more digital. I think most librarians in five years are going to have far more IT skills than they have today.”

The Skills Gap Reality

Despite recognizing innovation as critical, libraries face significant barriers to skills development:

• Limited professional development budgets
• Lack of structured AI training programs
• Insufficient time for experimentation and learning
• Generational differences in technology comfort

The Training Paradox: Libraries need AI skills to implement AI, but need AI implementation experience to develop AI skills. Breaking this cycle requires:

1. Structured Learning Pathways: Not ad-hoc exploration but systematic training programs
2. Protected Experimentation Time: Staff need permission and time to learn through doing
3. Ethical Framework Development: Skills must be paired with responsible use principles
4. Cross-Institutional Collaboration: Sharing lessons learned to accelerate collective progress

Critical Insight: The report notes “learning doesn’t happen by itself”—a seemingly obvious statement that carries profound implications. Libraries that wait for organic AI skill development will fall behind those that invest deliberately in structured training, even when budgets are tight.

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The Conventional Tools Trap: Innovation Stagnation Risk

Yuan Zihan’s observation is particularly telling: “Current explorations are based on conventional, commercially available AI tools; innovative or unconventional AI applications have not yet been considered or explored.”

This reveals a significant risk: Libraries may be adopting AI without truly innovating with AI.

The Difference Matters:

AI Adoption: Implementing vendor-provided tools (chatbots, discovery systems, cataloging assistance)

• Lower risk, lower reward
• Incremental efficiency gains
• Maintains existing service models
• Easy to justify and implement

AI Innovation: Developing novel applications unique to library contexts

• Higher risk, higher reward
• Transformative service possibilities
• Reimagines library roles
• Requires experimentation and potential failure

Current Focus Areas:

The report shows libraries primarily pursuing AI for:

1. Enhancing staff productivity (53%) – Efficiency focus
2. Streamlining administrative processes (40%) – Operational improvement
3. Discovery and research assistance – User-facing but conventional

Missing Innovation Opportunities:

• Predictive collection development based on community needs analysis
• AI-powered community program design and evaluation
• Personalized learning pathway development
• Automated accessibility enhancement for diverse populations
• Predictive preservation and conservation
• Community data analytics for advocacy and funding

Strategic Warning: Libraries risk becoming AI consumers rather than AI innovators. While vendor tools provide value, they don’t differentiate institutions or create unique value propositions. The libraries that will thrive are those that move beyond implementation to innovation.

The 2027 Inflection Point: We’ll likely see a second wave of AI adoption in libraries—moving from “AI for efficiency” (current focus) to “AI for transformation” (future opportunity). Early movers on this second wave will define next-generation library services.

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The Academic Library Shift: Student Engagement Emerges

The report notes that academic libraries show mission evolution, with student engagement emerging as a priority. This reflects broader higher education trends:

The New Academic Library Role:

• Research integrity coaching: Helping faculty and students navigate AI’s implications for scholarly work
• AI literacy education: Teaching critical evaluation of AI-generated content
• Academic integrity support: Addressing concerns about AI-enabled plagiarism and data manipulation
• Curriculum integration: Embedding library expertise throughout the learning experience

David Runyon’s Framework: “Academic librarians can help advance research integrity by coaching faculty and students… if that comes at the price of manipulating your data, which could possibly lead to a retraction or damage to your scholarly reputation, you’re going to have a real hard time repairing that.”

The Opportunity:

Academic libraries are uniquely positioned to become AI ethics and literacy centers for their institutions—not just implementing AI tools but teaching critical AI engagement.

Critical Insight: Academic libraries may be experiencing a “relevance renaissance” through AI—positioning themselves as essential partners in maintaining research integrity and academic standards in an AI-augmented world. This could reverse decades of marginalization in some institutions.

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The Collection Librarian Crisis: Job Displacement Fears

The report highlights a particularly vulnerable group: collection librarians show:

• 35% pessimism about AI benefits (highest among library roles)
• Heightened concern about job displacement
• Direct exposure to funding cuts and policy changes

Why Collection Librarians Face Unique Challenges:

1. Automation Vulnerability: Collection development, cataloging, and metadata creation are precisely the tasks most amenable to AI automation
2. Budget Target: When libraries face cuts, collections budgets are often first to be reduced, directly impacting these professionals
3. Vendor Consolidation: AI-powered discovery and collection management tools from major vendors may reduce need for specialized staff
4. Changing Models: Shift from ownership to access, from physical to digital, fundamentally alters collection work

The Existential Question:

Will AI eliminate collection librarian roles, or will it free these professionals for higher-value work like:

• Strategic collection policy development
• Diversity and inclusion analysis
• Community needs assessment
• Vendor negotiation and evaluation
• Specialized subject expertise application

Historical Parallel: The introduction of integrated library systems in the 1980s-90s eliminated many technical services positions but also created new roles in systems management and digital services. AI may follow a similar pattern—not eliminating collection librarians but fundamentally transforming what collection work means.

The 2026 Reckoning: Collection librarians face a critical two-year window to redefine their value proposition around strategic thinking, community expertise, and ethical oversight—the things AI cannot replicate—or risk significant workforce reduction.

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The Geopolitical Factor: Collections Under Pressure

The report notes a fascinating and concerning trend: libraries are “actively removing content or widening the diversity of content as a direct reaction to current geopolitics.”

This reveals libraries caught in broader political tensions:

The Divergent Responses:

• Some libraries: Removing controversial content to avoid political pressure
• Other libraries: Expanding diverse content to resist censorship pressures
• 25% of respondents: Expecting budget cuts under 10% due to geopolitical factors

The Digital Content Dilemma:

“Respondents are moving in opposite directions: both toward and away from digital content, based on the perceived implications of content availability and future access.”

What This Means:

• Some libraries embrace digital for preservation and access
• Others retreat from digital due to licensing vulnerabilities and platform dependencies
• No consensus on best strategy for ensuring long-term access

Critical Insight: The geopolitical pressure on libraries extends beyond funding to fundamental questions about collections, access, and institutional independence. AI adoption is happening against this backdrop of institutional vulnerability and political scrutiny.

The Chilling Effect: Libraries may be more cautious about AI adoption not because of technical concerns but because AI implementation creates new vectors for political criticism—around bias, privacy, vendor relationships, and content moderation.

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Recommendations: A Strategic Framework for AI Adoption

Based on this analysis, libraries should consider a staged approach to AI implementation:

Phase 1: Foundation Building (Months 1-6)

Objective: Move from exploration to initial implementation

Actions:

1. Conduct AI Literacy Assessment: Identify current staff knowledge and skills gaps
2. Establish AI Ethics Framework: Develop institutional principles for responsible AI use
3. Identify Low-Risk Pilot Projects: Choose applications with high value, low risk (e.g., administrative automation)
4. Secure Leadership Buy-In: Present business case linking AI to strategic priorities
5. Allocate Protected Learning Time: Give staff permission and time to experiment

Success Metrics:

• 80% of staff complete basic AI literacy training
• Ethics framework approved by governance
• At least one pilot project launched
• Budget allocation secured for Phase 2

Phase 2: Strategic Implementation (Months 7-18)

Objective: Cross the confidence threshold through hands-on experience

Actions:

1. Expand Pilot Projects: Based on Phase 1 learnings, implement 3-5 targeted AI applications
2. Develop Training Programs: Create structured pathways for different roles and skill levels
3. Establish Evaluation Framework: Define metrics for assessing AI impact on services and operations
4. Build Vendor Partnerships: Engage with AI providers on library-specific applications
5. Create Innovation Space: Dedicate resources (time, budget, staff) to experimental projects

Success Metrics:

• 50% of staff report increased AI confidence
• Measurable efficiency gains in targeted areas
• Positive user feedback on AI-enhanced services
• At least one innovative (not just adopted) AI application

Phase 3: Mature Deployment (Months 19-36)

Objective: Achieve strategic, multi-use-case implementation

Actions:

1. Scale Successful Pilots: Expand proven applications across the organization
2. Develop Unique Applications: Move beyond vendor tools to library-specific innovations
3. Establish AI Governance: Create ongoing oversight for ethical use and evaluation
4. Share Learnings: Contribute to professional community through publications and presentations
5. Integrate AI into Strategic Planning: Make AI a core component of institutional strategy

Success Metrics:

• AI integrated into 5+ core service areas
• Staff confidence at 70%+ across all roles
• Documented ROI on AI investments
• Recognition as AI leader in library community

Critical Success Factors Across All Phases:

1. Budget Realism: Acknowledge that AI requires investment, even when budgets are tight. Make the case for AI as cost-saving over time.
2. Privacy Protection: Especially for public libraries, maintain trust through transparent data practices and strong privacy protections.
3. Equity Focus: Ensure AI implementation doesn’t exacerbate existing inequities in service or access.
4. Change Management: Address staff concerns about job displacement through retraining and role evolution.
5. Continuous Learning: Recognize that AI is rapidly evolving; what works today may be obsolete tomorrow.

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The 2030 Vision: Three Possible Futures

Based on current trends, three scenarios emerge for libraries in 2030:

Scenario 1: The Bifurcated Future (Most Likely)

• AI-Advanced Libraries (20%): Fully integrated AI across operations, innovative applications, recognized thought leaders
• AI-Adopting Libraries (50%): Implemented vendor tools, efficiency gains, but not innovating
• AI-Resistant Libraries (30%): Minimal adoption, struggling with relevance and efficiency

Implications: Growing gap between library tiers, with advanced libraries attracting more resources and talent, creating self-reinforcing advantage.

Scenario 2: The Democratized Future (Optimistic)

• Professional associations and library schools successfully create shared AI infrastructure and training
• Open-source AI tools reduce cost barriers
• Collective learning accelerates adoption across all library types
• Most libraries achieve moderate AI maturity

Implications: Libraries maintain relative parity, with innovation happening at community level rather than institutional level.

Scenario 3: The Disrupted Future (Pessimistic)

• Budget cuts and political pressure force library closures and consolidations
• Commercial AI services replace many traditional library functions
• Libraries retreat to core community space functions, abandoning information services
• AI adoption becomes moot as institutional missions fundamentally contract

Implications: Libraries survive but with dramatically reduced scope and influence.

Most Probable Path: A combination of Scenarios 1 and 3—bifurcation among surviving libraries, with some institutions closing or dramatically contracting. The libraries that thrive will be those that successfully navigate AI adoption while maintaining community trust and political support.

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Conclusion: The Cautious Optimism Imperative

The 2025 Pulse of the Library report reveals an institution at a crossroads. Libraries recognize AI’s potential (67% exploring or implementing) but face significant barriers (budget constraints, privacy concerns, skills gaps). Regional disparities suggest that context matters enormously—what works in Asia may not translate to the U.S., and vice versa.

The most encouraging finding is the confidence-implementation correlation: libraries that take the leap report greater optimism and pursue more advanced applications. This suggests that action itself builds capacity—that the way out of analysis paralysis is through experimentation and learning.

The most concerning finding is the concentration at the exploration stage (35%) and the growing pessimism among U.S. public libraries. If this trend continues, we risk a library sector divided between AI-empowered institutions and those left behind, unable to serve their communities with the efficiency and innovation that modern information work requires.

The Path Forward: Libraries must move from cautious exploration to strategic implementation, even in constrained environments. This requires:

• Leadership courage to invest in AI despite budget pressures
• Staff empowerment through structured training and protected learning time
• Ethical frameworks that maintain community trust while embracing innovation
• Collaborative learning across institutions to accelerate collective progress
• Innovation focus that moves beyond vendor tools to library-specific applications

The libraries that will thrive in 2030 are those that act in 2025—not recklessly, but deliberately, building the confidence and capabilities that the virtuous cycle requires.

Final Insight: The question isn’t whether libraries will adopt AI—that’s inevitable. The question is whether they’ll adopt it strategically, ethically, and innovatively, or whether they’ll be passive consumers of vendor tools, perpetually behind the curve. The 2025 data suggests we’re at the moment of decision. The choices libraries make in the next 12-24 months will determine which future they inhabit in 2030.

The pulse of the library in 2025 is steady but cautious. For libraries to thrive, that pulse needs to quicken—moving from careful consideration to confident action, from exploration to implementation, from adoption to innovation. The data shows it’s possible. The question is whether it’s probable.