From the Campus Operations edition of November 2, 2026
UC Berkeley opens its campus AI Use Case Library to the public, with reviewed staff submissions, ratings and a leaderboard, and says inclusion is not a risk review
University of California, Berkeley (Berkeley IT, AI Tiger Team) · Higher education IT and administrative operations · California, United States
- Publisher
- UC Berkeley Information Technology
- Original publication
- 2 November 2026 (Berkeley IT news post)
- Source retrieved
- 2026-11-03
- Event date
- 2026-11-02
What happened
Berkeley IT announced on 2 November 2026 that its AI Use Case Library is open to the public. Entries span Administration, Research, Teaching, Learning and Personal Productivity, and each records the goal, impact, AI tool, limitations and lessons learned, and a campus contact. Staff can rate entries (feeding a leaderboard) and submit their own; every submission is reviewed before publication and must exclude confidential, student or personal data. Berkeley states that inclusion is not an endorsement and not a risk review, and points users to its appropriate-use guidance before deploying a tool.
Why it matters
A low-cost pattern for spreading administrative AI practice across decentralized offices, while explicitly separating knowledge sharing from risk approval.
Evidence and measured results
Primary institutional announcement; it describes the library's structure, not the number of entries or any measured impact.
Limitations and uncertainty
No counts of entries, users or measured benefits are given; impacts in entries are self-reported by staff.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-11-02; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Staff AI use cases are scattered across offices and repeated without shared lessons.
- Stakeholders
- CIO
- AI program lead
- Administrative unit heads
- Discovery questions
- Where are staff AI use cases recorded today?
- Who reviews them, and how does a use case reach risk review?
- Which administrative areas are most active?
- Value hypothesis
- A reviewed catalog may reduce duplicated effort; Berkeley reports no measurement.
- Potential engagement
- A bounded design of a campus AI use-case catalog and its link to tool approval.
- Unsupported claims
- Time or cost savings
- Number of use cases or users
- Endorsement of any tool
Pre-sales engineering
Role takeaway
- Fit
- Campuses with decentralized administrative AI adoption.
- Architecture and integration
- Catalog with institutional SSO for ratings and a review queue; tie-in to tool approval not described.
- Prerequisites
- SSO
- Appropriate-use guidance
- Reviewer capacity
- Constraints
- Entries must exclude confidential, student or personal data
- Not a risk review
- Security
- Content screening for sensitive data at submission review.
- Proof of value
- Tag entries with approved-tool status
- Track which entries are adopted by other offices
Delivery
Role takeaway
- Work
- Launch and curate a reviewed AI use-case library for staff.
- Dependencies
- IT AI team
- Unit contacts
- Policy owners
- Ownership
- Berkeley IT AI Tiger Team.
- Skills and adoption
- Staff learn from named colleagues' documented uses.
- Governance checkpoints
- Editorial review before publication
- Separate risk review before deployment
- Acceptance criteria
- Proposed: every entry lists tool, limitations and contact
- Proposed: no sensitive data in published entries
- Risks
- Entries read as endorsements
- Stale entries
- Self-reported impact
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
A curated catalog with authenticated (Berkeley Google account) rating and an editorial review workflow; no integration with tool provisioning is described.
Governance
Who approves, reviews and stays accountable for outcomes?
Review of submissions is editorial; the post says it is not a risk review, so tool approval remains a separate process.
Security and privacy
What data, permissions and controls need testing?
Submitters are told not to include confidential, student or personal data in entries.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Peer-to-peer learning for staff; each entry names a campus contact.
Procurement
What should contracts, pricing and exit terms secure?
Not an endorsement of products; it may still steer demand toward the tools named in popular entries.
Operating model
Which teams own the service once it runs?
An AI Tiger Team curates the library and handles feedback.
Publication history
- 2026-11-02Campus Operations · Issue 553 resources
Stable resource ID: uc-berkeley-ai-use-case-library-public-20261102