The Town of Gray is quietly turning a policy conversation into practical public service: after adopting guidelines for generative AI this summer, the town’s communications and IT director has begun using Microsoft Copilot to produce faster, more accessible meeting recaps, is hosting public training, and is helping shape how a small municipality balances efficiency, transparency, and risk in the age of large language models. (pressherald.com)
Small towns face two simultaneous pressures with artificial intelligence: resource constraints that make automation appealing, and governance gaps that leave residents exposed if tools are misused. In Gray, Director of Communications and IT Kyle Hadyniak has framed the solution as pragmatic and cautious — “use it correctly and responsibly,” he told local media — and has moved to operationalize AI rather than ban it outright. Gray’s municipal website and local reporting show the town adding both accessibility tools to its public portal and public-facing AI education while rolling out internal guidelines for staff. (graymaine.org)
Why this matters: municipal governments are information hubs. They collect resident requests, publish meeting minutes, manage permits, and field questions about services. When AI can summarize meetings, draft public notices, or help staff translate technical documents, it promises to improve timeliness and reach. But the technology also raises privacy, accuracy, and liability concerns — especially when output is generated from meeting audio or internal documents, or when a model’s training data and telemetry practices are unclear.
Key benefits Gray reports:
Why this matters for municipal trust: by inviting residents to learn how AI is used and to see examples, the town signals transparency. That reduces the “black box” feeling many constituents have when new tech is introduced into government workflows.
This reflects a broader trend in Maine and other states: rather than attempting hard bans, many municipalities and school districts are establishing tiered rules that classify AI tasks by risk (low/medium/high) and attach approval processes accordingly. That distributed, risk-based governance lets small governments gain efficiencies while protecting sensitive processes. (bangordailynews.com)
Examples worth noting:
Recommended controls:
At the same time, several non-negotiables remain: robust data safeguards, contractual protections around vendor use of municipal content, mandatory human review, and transparent communication with residents. Gray’s success will be measured not only by how efficiently it publishes meeting recaps, but by whether residents feel informed, safe, and able to contest AI-generated content that affects them.
If carried out transparently and prudently, Gray’s model can serve as a replicable blueprint for towns that must do more with less — but must also safeguard privacy, accuracy, and democratic accountability in the process. (pressherald.com)
Conclusion
Municipalities like Gray are on the front lines of how communities adapt to generative AI: operational gains are real and immediate, but the technology’s risks demand deliberate governance. Gray’s approach—policy-first adoption, staff training, enterprise tooling, and public workshops—checks many of the boxes needed to use AI responsibly. The next steps are procedural: tighten procurement, document practices, and maintain an open dialogue with residents so that efficiency does not come at the expense of transparency, privacy, or trust. (pressherald.com)
Source: WMTW https://www.wmtw.com/article/use-it-correctly-and-responsibly-gray-it-director-discusses-ai/66064630/
Background
Small towns face two simultaneous pressures with artificial intelligence: resource constraints that make automation appealing, and governance gaps that leave residents exposed if tools are misused. In Gray, Director of Communications and IT Kyle Hadyniak has framed the solution as pragmatic and cautious — “use it correctly and responsibly,” he told local media — and has moved to operationalize AI rather than ban it outright. Gray’s municipal website and local reporting show the town adding both accessibility tools to its public portal and public-facing AI education while rolling out internal guidelines for staff. (graymaine.org)Why this matters: municipal governments are information hubs. They collect resident requests, publish meeting minutes, manage permits, and field questions about services. When AI can summarize meetings, draft public notices, or help staff translate technical documents, it promises to improve timeliness and reach. But the technology also raises privacy, accuracy, and liability concerns — especially when output is generated from meeting audio or internal documents, or when a model’s training data and telemetry practices are unclear.
How Gray is using AI today
Meeting recaps and communications: Copilot as a “copilot”
One of the clearest, immediate uses Gray has adopted is turning recorded Town Council and board meetings into draft recaps. Because the town records meetings on Microsoft Teams — which includes Copilot integrations for enterprise customers — Hadyniak can produce a bullet-point summary with timestamps and speaker attributions, then edit and publish a polished news article much faster than by manual transcription. He describes Copilot as an assistant, not a replacement for human judgment. (pressherald.com)Key benefits Gray reports:
- Faster turnaround on meeting coverage and public notices.
- Improved accessibility by creating shorter, scannable summaries and timestamped records.
- Staff time freed for higher-value community engagement and follow-up work.
Public education and community workshops
Gray is making education part of the rollout. The Gray Public Library is hosting a workshop titled “How to Use Generative AI Safely & Productively” led by Hadyniak, open to residents and aimed at demystifying tools such as Microsoft Copilot, Google Gemini, and ChatGPT. Local podcasts and municipal channels are also being used to raise awareness about practical uses and pitfalls. (graypubliclibrary.com)Why this matters for municipal trust: by inviting residents to learn how AI is used and to see examples, the town signals transparency. That reduces the “black box” feeling many constituents have when new tech is introduced into government workflows.
Operational uses beyond communications
Gray’s communications emphasize broader goals for the department: equipping staff with modern tools, improving accessibility on the website, and managing software procurement responsibly. The town’s official site lists Communications & IT as responsible for both technology adoption and public-facing communications — which positions the director to coordinate policy and practical deployment. (graymaine.org)Policy and governance: the framework Gray has chosen
A guided-adoption model, not an outright ban
Gray adopted an internal policy this summer that governs how staff may use generative AI. Public reporting indicates the policy sets expectations for training, data handling, and human review: staff receive training on Copilot before they are issued enterprise licenses, and employees are told to treat AI outputs as draft material requiring human validation. Hadyniak and town leadership emphasize that AI will assist but not replace human decision-making. (pressherald.com)This reflects a broader trend in Maine and other states: rather than attempting hard bans, many municipalities and school districts are establishing tiered rules that classify AI tasks by risk (low/medium/high) and attach approval processes accordingly. That distributed, risk-based governance lets small governments gain efficiencies while protecting sensitive processes. (bangordailynews.com)
Core elements municipal AI policies should include
From Gray’s approach and comparable municipal practices, a robust local AI policy typically covers:- Data classification rules — what counts as sensitive (e.g., resident financial data, protected personal information) and may not be sent to third-party models.
- Authorized tooling — approving enterprise-grade deployments (e.g., Microsoft Copilot tied to town accounts) versus forbidding free consumer instances for official business.
- Human-in-the-loop requirement — a hard rule that AI outputs are drafts that require verification by a staff member.
- Training and certification — mandatory staff training modules before use.
- Procurement clauses — contract language requiring audit rights, data deletion and non-use for model training where necessary.
- Reporting and escalation — logs, incident response procedures, and review boards for high-risk deployments.
Strengths: why Gray’s measured adoption is notable
1) Practical accessibility gains
Producing accurate, timestamped recaps from meetings quickly makes town governance more accessible to residents who cannot attend or who need condensed summaries. Gray’s use of Copilot for meeting summaries is a textbook win for civic communications: more transparency, faster publishing cycles, and better searchability of public records. (pressherald.com)2) Staff amplification rather than replacement
Gray has emphasized that AI is a tool to augment staff capacity — a crucial narrative for preserving public trust and reducing fear of job loss. Small-town administrations frequently operate with minimal staff; tools that responsibly offload repetitive tasks can improve response times and free officials for direct constituent service. (pressherald.com)3) Public-facing education
Hosting open workshops and participating in regional podcasts provides two advantages: it demystifies the technology for residents and it creates an audit trail demonstrating the town’s commitment to transparent, responsible use. Gray’s public events are a pragmatic way to build consent and civic literacy. (graypubliclibrary.com)Risks, unknowns, and areas that need careful management
While Gray’s direction is sensible, municipal deployments of generative AI create a suite of risks that must be continually managed.A. Accuracy and “hallucination”
Generative models can produce confidently phrased text that contains factual inaccuracies. When AI drafts meeting recaps or public notices, even small errors can misrepresent policy, create legal exposure, or undermine trust. Gray’s policy of human review mitigates this risk, but continual vigilance and publishing correction protocols are essential. (pressherald.com)B. Data privacy and inadvertent disclosure
Municipal meetings sometimes include personally identifiable information (names, addresses, sensitive case details) or operational security information. Sending meeting audio, notes, or internal documents to external AI services without proper contractual safeguards risks privacy breaches and could run afoul of state or federal laws. Gray’s use of enterprise Copilot is preferable to consumer-grade tools because it can be governed under municipal accounts and contractual terms — but procurement language and data-handling audits must be explicit and enforced. (maine.gov)C. Telemetry and model training concerns
Many AI vendors collect telemetry or use prompts to improve models. Municipalities must insist on contracts that prohibit the vendor’s use of municipal prompts or uploaded content for training without consent. The absence of robust audit rights or contractual guarantees creates long-term exposure. Where contract terms are unavailable or unclear, towns should avoid sending sensitive data to models. (bangordailynews.com)D. Security: phishing and spoofing risk
The real-world context in Gray underlines another hazard: AI is being used offensively as well as defensively. A realistic AI-generated phishing email spoofing Gray’s letterhead was reported in early 2025, illustrating that automation amplifies both municipal functions and criminal abuse. That incident highlights the need for clear resident advisories on transactional practices and strict controls on how residents can be asked to send money or share data. (themainewire.com)E. Equity and bias
Automated summaries and translation tools can inadvertently privilege certain voices or misinterpret accents and speech patterns. Municipal officials must audit outputs for representational fairness and provide channels for corrections when residents feel misrepresented.Practical checklist for towns considering a similar path
Based on Gray’s experience and best practice guidance emerging from municipal associations, here is a pragmatic deployment checklist a small town can follow:- Create a cross-departmental AI steering group (communications, IT, legal, HR).
- Classify municipal data and forbid sending sensitive categories to external consumer models.
- Prioritize enterprise or on-premise solutions with contractual protections over consumer-grade tools.
- Implement mandatory training and certification for staff before issuing model access.
- Require human review and a sign-off workflow for any AI-generated public-facing content.
- Publish an AI transparency statement for residents explaining what tools are used, why, and how to request corrections.
- Harden phishing awareness and transactional policies for residents (e.g., “the Town does not accept payments online/by phone”).
- Maintain procurement clauses granting audit rights and prohibiting vendor reuse of prompts for model training.
How Gray compares with other Maine municipalities
Gray is not alone. Across Maine, towns and school districts are taking diverse approaches — from drafting formal policies (Winthrop) to building internal guidelines and restricted pilots (Camden, MSAD districts). The Maine Municipal Association is actively producing resources for local governments, and the state has convened an AI Task Force to develop statewide recommendations. The landscape is one of experimentation with a strong tilt toward managed adoption rather than prohibition. (centralmaine.com)Examples worth noting:
- Winthrop considered one of the earlier municipal AI policies, which specified that staff must personally review AI outputs and prohibited replacing human decision-making without oversight. (centralmaine.com)
- Camden has guidelines that categorize use by risk level, requiring different approvals for low-, medium-, and high-risk tasks. (bangordailynews.com)
- The Maine Municipal Association provides training and courseware for public professionals aiming to use generative AI safely. (innovate-us.org)
The editorial and legal tightrope: transparency, liability, and records retention
Public records laws and open-meeting statutes add complexity for towns using AI. Generated summaries are derivative work, and determinating the official record still falls to the municipality; towns must decide what constitutes the official minutes and how AI-derived content is archived. Additionally, legal liability for inaccurate or defamatory AI output requires clear internal controls and a documented review process.Recommended controls:
- Always preserve original recordings and meeting transcripts as the canonical public record.
- Log AI prompts and model outputs associated with official communications (for auditing).
- Maintain a corrections policy that allows residents to request amendments to AI-assisted summaries.
Where Gray should focus next: recommendations
To move from pilot to mature practice, Gray should consider the following prioritized actions:- Strengthen procurement language: ensure Copilot/enterprise contracts include non-training clauses and audit rights.
- Publish a condensed AI transparency note for the town website that explains what tools are used, what data is shared, and how residents can request corrections.
- Institute a periodic review of AI outputs for accuracy and bias, led by the steering group.
- Expand resident-facing guidance about transactional fraud and phishing — making it clear how the town will and will not request funds or personal data. The earlier reported AI-generated phishing incident in the area reinforces the need for this communication. (themainewire.com)
- Collaborate regionally: small towns often lack in-house legal and procurement resources; shared contracts or cooperative purchasing with nearby municipalities or the Maine Municipal Association could reduce risk and cost. (innovate-us.org)
Final assessment: measured adoption with accountability
Gray’s path — adopting an internal policy, using enterprise Copilot for meeting recaps, and actively educating the public — represents a pragmatic, modern approach for a small municipality balancing innovation and public trust. The move preserves staff roles, improves accessibility, and demonstrates civic leadership in AI literacy.At the same time, several non-negotiables remain: robust data safeguards, contractual protections around vendor use of municipal content, mandatory human review, and transparent communication with residents. Gray’s success will be measured not only by how efficiently it publishes meeting recaps, but by whether residents feel informed, safe, and able to contest AI-generated content that affects them.
If carried out transparently and prudently, Gray’s model can serve as a replicable blueprint for towns that must do more with less — but must also safeguard privacy, accuracy, and democratic accountability in the process. (pressherald.com)
Conclusion
Municipalities like Gray are on the front lines of how communities adapt to generative AI: operational gains are real and immediate, but the technology’s risks demand deliberate governance. Gray’s approach—policy-first adoption, staff training, enterprise tooling, and public workshops—checks many of the boxes needed to use AI responsibly. The next steps are procedural: tighten procurement, document practices, and maintain an open dialogue with residents so that efficiency does not come at the expense of transparency, privacy, or trust. (pressherald.com)
Source: WMTW https://www.wmtw.com/article/use-it-correctly-and-responsibly-gray-it-director-discusses-ai/66064630/
