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RUDI · Responsible Use of Digital Intelligence Survey Preview

Library AI Readiness — Insights Preview

A sample report showing how staff responses are turned into action: where comfort lives, where anxiety clusters, and where to begin training.

Cincinnati & Hamilton County Public Library
900 staff · 42 branches
Sample survey close: 10 May 2026
Sample report generated: 12 May 2026
Response rate
62%
558 of 900 staff
Avg comfort with new digital tools
5.8/10
Median 6 · σ 2.4
Use AI weekly or daily
31%
+18pp vs. monthly or less
Org maturity stage
Informal experimentation
Stage 2 of 5
Headline finding

Curiosity outpaces capability. Two-thirds of staff have tried AI at least once, but only 1 in 6 feel confident applying it to their daily work — and concerns concentrate around patron data, ethics, and energy use, not job loss.

Who responded

Q1 · Q2
Age range
Role / department
Read: Response is well-distributed across age groups, with the strongest sample from staff aged 35–54 (54% of responses). Programming & Direct Service (reference, children's, outreach) is the largest functional cohort — your training priorities should center on this group.

What AI staff have actually tried

Q3 · Q4 · Q5
AI chatbots
% of respondents who have used
AI agents (multi-step autonomous)
% of respondents who have used
Specialized AI tools
% of respondents who have used
Read: ChatGPT and Microsoft Copilot dominate exposure, but only 9% of staff have used an AI agent. Specialized tools (image generators, transcription, research assistants) are siloed in IT and Programming. Recommendation: standardize on one chatbot first; reserve agents for a later cohort.

How often, and how mature

Q7 · Q8
Frequency of personal AI tool use
Perceived organizational AI maturity
No use yet14%
Individual staff experimenting informally58%
Small pilots in one area19%
Several pilots across units7%
Operationalized in standard workflows2%
Maturity stage (weighted): Stage 2 — Informal experimentation

Comfort with new digital tools

Q9 · 0–10 scale
Distribution of self-reported comfort
By cohort
CohortAvg
Executive (n=12)7.1
Middle management (n=39)6.4
IT / Data (n=22)8.3
Programming / Direct Service (n=210)5.6
Operations / Admin (n=126)5.4
Circulation / Shelving (n=149)4.9
Read: The distribution is bimodal — a confident cluster (8–10) concentrated in IT and younger Programming staff, and a hesitant cluster (0–3) concentrated in Circulation and long-tenured Admin. A single foundations workshop will not serve both ends; plan for at least two tracks.

Top concerns about AI in the workplace

Q10 · multi-select, top 3
Read: Patron data privacy and energy/ethics lead. Misinformation & hallucinations land third — staff are worried about being misled in their own work, not just about handing wrong answers to patrons. Job displacement ranks 6th, well below national norms. Implication: training that opens with responsible-use framing (not productivity gains) will land better with this workforce.

What staff want help with first

Q11 · 60-day priorities
Read: Scheduling & substitute coverage tops the list — a high-friction monthly task that touches every branch. This is exactly the kind of bounded, painful workflow where a small AI pilot pays for itself in weeks.
Preferred learning format
Q12
Ongoing monthly bite-sized sessions are the runaway favorite (47%) — consistent with what other library systems request.
Verbatim themes from open responses
Q13
  • "I'd love to use it to help patrons, but I worry about giving them wrong information." — Reference, North Central
  • "Please don't make us use a tool that scrapes our patrons' search history." — Branch Manager, Anderson
  • "Show me how to make my Excel scheduling sheet less painful and I'm sold." — Operations, Forest Park
  • "What is the library's actual policy on this? Staff are asking and I don't have an answer." — Branch Manager, Main

Where readiness lives, by branch

Derived · Q2 × Q9 × Q7
Most ready
Forest Park7.4
Main (downtown)7.0
Anderson6.8
Blue Ash6.6
Mid-range
Madisonville5.8
North Central5.7
Westwood5.5
Cheviot5.3
Most hesitant
Mt. Healthy4.1
Symmes Township4.0
Reading3.8
Sharonville3.6
Index = (avg comfort × frequency-of-use weight). Branches with active maker spaces or younger demographics tend to score higher.

What we'd recommend doing with this data

Step 1 · 0–30 days
60-min Executive Foundations

Demystify AI for the senior leadership team (20). Cover what AI is and isn't, responsible use, and how to talk to staff about it. Outputs: shared vocabulary + draft governance principles.

Step 2 · 30–60 days
Pilot one painful workflow

Scheduling & substitute coverage was the #1 staff request. Build a small AI-assisted tool with one branch (Forest Park) — measure time saved, then decide whether to scale.

Step 3 · 60–120 days
Manager track + resource center

Train the 65-person management team. Stand up a SharePoint AI Resource Center: prompt library, approved-tools list, FAQ for staff and patrons.