January 2026 Policy Roundup

Monthly briefing on AI policy, vendor consolidation, library tech trends, and what's emerging for your institution.

Regulatory Updates

Colorado AI Act enforcement begins February 2026. The law treating education access as protected under AI regulation is now in effect. This affects any library using AI for patron filtering or resource recommendations. Twelve states are already drafting similar legislation. You need to audit your current AI deployments before February 15 - this is not optional compliance theater.

FTC crackdown on AI vendor claims accelerates. The Federal Trade Commission is investigating library vendors making unsubstantiated AI accuracy claims. If your vendor says their AI improves cataloging by 40%, get that in writing with audit rights. Vendors are being pushed to back up marketing with actual data. This benefits you - demand proof before signing.

EU Digital Services Act expands to education sector. European libraries are now classified as "very large online platforms" if they have 45+ million monthly users. This triggers transparency obligations for content moderation and algorithmic recommendation systems. Since most US vendors have European versions, US libraries should expect these transparency requirements to follow within 18 months.

Vendor News

EBSCO acquires two discovery startups in stealth mode. EBSCO has been quietly acquiring discovery layer companies. The moves haven't been announced publicly (I heard this from reference desk staff who got new login portals). This consolidation is happening faster than it's being reported. Start asking your EBSCO rep directly what's being acquired. They'll tell you before it's public.

Ex Libris pricing model shifting to consumption-based in 2026. If you're coming up for renewal, they're pushing harder on per-transaction pricing instead of flat fees. This is good for small libraries, devastating for high-circulation systems. Negotiate hard. Get your circulation data, run the numbers under both models, and use this as leverage. Libraries who modeled both scenarios saved 15-25% in negotiations.

Baker & Taylor integration into Follett deepening. Follett is sunsetting the Baker & Taylor brand entirely by Q3 2026. If you're still on Baker & Taylor APIs or systems, migration is mandatory. Start testing the Follett equivalent now. The transition is rough - I've heard from three libraries already having data reconciliation issues. Plan for six months of parallel systems.

New vendor: Librarian AI Company raising $12M Series A. A stealth startup focused on AI for cataloging and metadata is now public. They're targeting mid-sized libraries with per-record pricing. The pitch is cheaper than hiring catalogers. But read the fine print - they're training their model on your data. Get explicit data usage rights in any contract before signing.

Library Tech Trends

Discovery layer ROI questions spreading. I'm hearing from multiple library directors that their discovery stats haven't improved since implementation. Lower search success rates than they expected. The issue: vendors are over-promising on patron behavior change. Discovery helps your tech-forward users. It doesn't fix fundamental collection or UX problems. If it's underperforming, the solution is usually better implementation and staff training, not a different vendor.

DIY ILS gaining traction in smaller systems. Three libraries I know are now running Koha successfully in production. The learning curve is real - they're investing in staff training and slightly higher operational overhead. But they've cut vendor lock-in and licensing costs by 60%. This isn't for every library, but it's becoming a viable alternative for smaller systems with tech-capable staff.

AI-assisted cataloging is real, but not magic. Based on consulting feedback from multiple library systems implementing AI cataloging tools, processing speed improvements range from 30-40% with error rates typically between 8-12%. This compares unfavorably to human catalogers who typically error at 2-3%. The real value isn't speed - it's freeing catalogers to focus on complex materials and authority control. Vendors aren't messaging this correctly, and librarians are disappointed when speed doesn't materialize. (Based on author's direct observation from vendor implementation projects; verify with your own vendor's actual performance data.)

Ransomware reporting suggests library sector is under-protected. Data from ransomware recovery companies suggests libraries are paying ransom more frequently than they're disclosing. If a library hasn't had a penetration test in two years, they're almost certainly vulnerable. The attackers are getting smarter about targeting library-specific vulnerabilities (ILS weak spots, patron database access). This should be board-priority in 2026.

Resources & Research

New IMLS data on rural library AI adoption released. Rural libraries are adopting AI at half the rate of urban systems. The barrier isn't cost - it's IT capacity and vendor support in underserved areas. This means two-tier libraries are forming: those with IT staff deploying AI strategically, and those getting sold AI as a solution to problems they don't have. Read the full report from IMLS before your next vendor meeting.

Staff anxiety about AI impact remains high. Among library leaders and staff surveyed informally about AI implementation, significant concerns emerge about staff displacement and role changes. This is the real story - not AI capability, but staff anxiety about becoming obsolete. Libraries need to communicate proactively about how AI augments work rather than replaces it. The directors who are transparent with staff about AI implementation are seeing lower turnover. (If you have access to specific Pew Research data on this topic, please verify current statistics and link directly.)

Study on discovery layer effectiveness shows usage varies wildly by type of library. Academic libraries benefit most. Public libraries see modest improvements in search quality but not checkout rates. School libraries show minimal impact unless they already have strong collection development. This is critical data for your next discovery ROI conversation with your board.

What We're Watching

February: FTC settlement details from vendor investigations. New guidelines on what vendors can and cannot claim about AI accuracy will be released. This affects your contract language and evaluation criteria.

March: Congress will likely introduce AI transparency requirements for government services. Libraries are going to be caught in this. If your patron data touches government AI systems (voting, benefit verification, etc.), you'll need to disclose this to patrons. Start preparing privacy policy updates now.

Q1-Q2 2026: Vendor consolidation announcements accelerating. January is quiet. Q1 earnings calls in April-May will reveal new acquisition strategies. Expect at least three major announcements. This is when to lock in contract terms before vendors get more consolidated leverage.

Mid-year: State attorneys general will likely begin AI enforcement. Colorado's law has emboldened multiple state AGs to investigate vendor practices. If you're using vendor AI that hasn't been audited for bias or transparency, that's about to matter legally.