From the Citizen Engagement & Services edition of October 30, 2026
Pennsylvania Office of Open Records survey: 36% of agencies saw AI in at least some requests, 39% could not tell, and only 31% use redaction software
Pennsylvania Office of Open Records (OOR) · State and local public records (Right-to-Know Law) · Pennsylvania, United States
- Publisher
- Pennsylvania Office of Open Records
- Original publication
- 2026-10-19 (PDF creation date; no date printed in the report)
- Source retrieved
- 2026-10-31
What happened
The OOR surveyed 4,066 registered agency open records officers and received 1,265 responses (about 31%); respondents reported 89,559 requests in 2025, which the OOR extrapolates to roughly 285,000 to 288,000 statewide. 36% said AI was used in at least some requests, 39% could not determine whether AI was used, and 25% observed none. Nearly 90% received at least some commercial requests and 55% said all or most requests were commercial. Only 31% use electronic redaction software. The OOR now sees over 500 appeals a month, up from a 2024 average of 269, and passed its 2025 total of 3,970 appeals by the end of September.
Why it matters
A statewide, quantified look at the public records workload that AI tools are targeting from both sides: requesters using AI and agencies still redacting by hand. Most respondents are townships, boroughs and school districts, so it describes small local agencies well. Other states' laws and fee rules differ.
Evidence and measured results
Self-reported survey with a 31% response rate; AI categories are respondent perceptions, and the report notes the high-volume agencies that skew the average while some large agencies did not respond. Appeal counts come from OOR's own docket.
Limitations and uncertainty
Perception-based AI measures; partial response; one state; no date printed in the PDF (date taken from file metadata).
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-31; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Records officers face rising AI-assisted and commercial request volume with mostly manual redaction.
- Stakeholders
- Agency open records officer
- Solicitor or legal counsel
- Township or borough manager
- School district administrator
- Discovery questions
- How many requests and appeals did you handle in 2025, and how many involved commercial requesters?
- Do you redact electronically, and who checks redactions?
- Do you have a written policy on automated or bulk filings?
- Value hypothesis
- Electronic redaction and tracking could reduce manual effort and appeal risk; size of benefit is unmeasured here.
- Potential engagement
- A records workflow assessment for a group of small agencies, scoped to tracking, redaction and policy.
- Unsupported claims
- That AI requests caused the appeal spike on their own
- Any time savings from redaction software
- Results outside Pennsylvania
Pre-sales engineering
Role takeaway
- Fit
- Fits request tracking, electronic redaction and document search for small local agencies.
- Architecture and integration
- Connect request intake, document repositories and redaction with a review step and an audit log.
- Prerequisites
- Scanned or digital records
- Request log
- Redaction review procedure
- Constraints
- Right-to-Know Law timelines and exemptions
- Many agencies still fulfill on paper
- Small budgets
- Security
- Redaction failures expose personal data; keep audit logs of who redacted and reviewed each release.
- Proof of value
- Measure redaction accuracy on a sample of past releases
- Compare hours per request before and after on a pilot group
Delivery
Role takeaway
- Work
- Introduce request tracking, electronic redaction with review, and automated-filing policy for small agencies.
- Dependencies
- Legal counsel
- Records software vendor
- County or regional shared-service sponsor
- Ownership
- Agency open records officer, with solicitor oversight.
- Skills and adoption
- Staff need training on redaction tools and on handling commercial and automated requests.
- Governance checkpoints
- Policy review by counsel
- Redaction quality check before go-live
- Annual comparison with the next OOR survey
- Acceptance criteria
- Proposed: 100% of releases pass second-person redaction review
- Proposed: hours per request tracked monthly against baseline
- Risks
- Redaction errors
- Staff not adopting tools
- Fee policies challenged on appeal
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?
With two-thirds of agencies apparently redacting manually, basic electronic redaction and request tracking come before any AI review layer.
Governance
Who approves, reviews and stays accountable for outcomes?
The OOR suggests policies on fees for complex datasets, standards for automated filings and processing rules, within current law; it flags commercial requests for possible legislative scrutiny.
Security and privacy
What data, permissions and controls need testing?
Manual redaction at scale raises the risk of releasing personal information; redaction tooling and review checks matter.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Respondents describe requests taking more time and legal review, stretching limited staff; the OOR plans training on redaction tools.
Procurement
What should contracts, pricing and exit terms secure?
Small agencies may need shared or state-provided redaction and tracking tools rather than individual purchases.
Operating model
Which teams own the service once it runs?
Records work is shifting toward handling commercial and automated volume; intake rules and fee policy are as important as tools.
Publication history
Read preserved resource versions (JSON)Stable resource ID: pa-oor-2026-aoro-survey-ai-requests-redaction-20261019