Top 10 AI Prompts and Use Cases and in the Government Industry in Fayetteville

As Fayetteville's municipal leaders weigh generative AI for tasks like drafting external communications or transcribing meetings, North Carolina guidance stresses plain-language policies that define scope, protect confidential data, and require human fact-checking; the UNC School of Government recommends treating prompts and AI outputs as potential public records under G.S. 132-1 and avoiding entry of sensitive information into public tools (UNC School of Government guidance on generative AI in local government).

Practical next steps include vendor oversight and governance checklists tailored for North Carolina municipalities (Vendor oversight and governance checklists for North Carolina municipalities) and focused staff training - skills taught in Nucamp's 15-week AI Essentials for Work course - to make small, transparent pilots safer and more cost-effective for Fayetteville departments (Nucamp AI Essentials for Work syllabus (15-week bootcamp)).

Bootcamp	Length	Early-bird Cost	Syllabus
AI Essentials for Work	15 Weeks	$3,582	Nucamp AI Essentials for Work syllabus

“AI outputs shall not be assumed to be truthful, credible, or accurate.”

Selection began with a North Carolina–focused landscape scan - drawing on ncIMPACT's catalogue of proven and plausible municipal applications such as traffic-signal management, property appraisal, and gunshot detection - to surface use cases already discussed by peers (ncIMPACT report: AI uses in North Carolina municipalities); each candidate was then vetted against the N.C. State Government Responsible Use of AI Framework for privacy, assessment, and vendor-contract considerations (N.C. State Government Responsible Use of AI Framework and guidance) and practical governance tools like vendor oversight checklists for North Carolina municipalities (municipal vendor oversight and governance checklists).

Prioritization used four evidence-based criteria from the ncIMPACT “Relevance” rubric - impact, certainty, timeframe, and context - so the final top‑10 emphasizes high-impact, ethically governable prompts and use cases that can leverage standardized or open local data and existing vendor resources to accelerate safe pilots in Fayetteville.

The practical payoff: a short list focused on concrete deployments (e.g., analytics for signals, automated valuations, gunshot analytics) that align policy guardrails with operational feasibility.

Criteria	Value (ncIMPACT)
Impact	High
Certainty	Medium
Timeframe	Emerging
Context	All areas

Strategic planning in Fayetteville should center the N.C. State Responsible Use of AI Framework as the policy backbone, pairing an AI inventory and risk-based impact assessments with vendor oversight and plain‑language staff guidance so pilots avoid common pitfalls like inadvertent public‑records exposure or cloud vendor lock‑in (N.C. State Responsible Use of AI Framework).

Start with short, targeted pilots drawn from proven municipal use cases identified by ncIMPACT - traffic signal analytics, automated property valuations, and gunshot‑detection demos - and require documented privacy reviews and procurement clauses that reflect statewide privacy laws and retention requirements (ncIMPACT AI uses in North Carolina municipal examples).

Local guidelines should mirror UNC School of Government advice on generative AI - define scope, ban entry of sensitive data into public tools, and mandate human fact‑checking so outputs never substitute for official decision making (UNC School of Government generative AI guidance for local government).

Horizon	Priority Actions
Short‑term	AI inventory, pilot selection, privacy checklists
Medium‑term	Scale successful pilots, standardize data, vendor contract language
Long‑term	Regional partnerships, annual AI forum, workforce retraining

“AI outputs shall not be assumed to be truthful, credible, or accurate.”

ShotSpotter - a network of acoustic sensors that triangulate impulsive sounds and route candidates to human reviewers - has been piloted in Fayetteville to speed officer response and aid evidence collection, but adoption comes with tradeoffs that North Carolina leaders must weigh: an independent Edgeworth audit reports aggregate accuracy near 97% with false positives under 0.5% (Edgeworth independent audit of ShotSpotter accuracy), while local reporting and public forums highlight privacy worries, potential increases in officer workload, and mixed evidence that alerts translate into arrests or reduced violence (Carolina Public Press interview and community feedback on ShotSpotter).

Fayetteville's city page outlines the pilot's data-driven coverage plan and promises quarterly public updates and an independent evaluation to test operational value before longer contracts are pursued (ShotSpotter Fayetteville project page and pilot details); the concrete takeaway: even with high reported detection accuracy, the city's “so what” is accountability - use short contracts, require public metrics (response times, confirmed shootings, complaints), and budget for the extra canvassing and follow-up that the technology typically generates.

Metric	Value	Source
Aggregate detection accuracy	~97%	Edgeworth audit
False positive rate (2022)	≈0.36%	Edgeworth audit
Fayetteville one‑year contract	~$197–210K	Carolina Public Press / The Assembly

“ShotSpotter shouldn't be viewed as the panacea to gun violence here. It's one single tool that can be used in a broad array of different law enforcement approaches.”

Traffic‑signal modernization pilots can start small and governed: a nearby peer, Spring Hill, authorized a sole‑source purchase of six Miovision Scout Plus devices (Resolution 23‑193) and separately moved to seek Traffic Signal Modernization Program (TSMP) grant funding (Resolution 20‑185), illustrating a practical procurement path for municipalities (Spring Hill resolutions authorizing Miovision devices and TSMP grant).

Fayetteville leaders should treat any Miovision pilot as a bundled procurement plus governance exercise: require a short demonstration contract, baseline traffic metrics and public reporting, documented data‑retention and public‑records handling, and an independent post‑pilot evaluation so decisions rest on measurable performance rather than vendor claims.

Pairing that approach with North Carolina's Responsible Use of AI expectations and municipal vendor‑oversight checklists will reduce legal and operational risk while keeping the pilot eligible for state grant paths and clear enough to scale only if the corridor‑level gains justify it (North Carolina Responsible AI Framework for government agencies, Vendor oversight and governance checklists for North Carolina municipalities).

Metrics: Peer procurement - 6 Miovision Scout Plus devices authorized (sole source) (Spring Hill Res. 23‑193); Grant pursuit - Letter of intent to seek TSMP grant funding (Spring Hill Res.

20‑185).

For Fayetteville's property-tax and planning teams, an underwriting‑grade Automated Valuation Model (AVM) like HouseCanary's can deliver instant, scalable fair‑market estimates that complement traditional appraisals by combining 114M+ property records, 35 years of normalized transaction history, image recognition, and machine‑learning models to produce a point value, a high/low value range, and a confidence score - details that make valuations auditable and defensible for municipal use (HouseCanary Automated Valuation Model overview).

These outputs and diagnostic data points (comparables, value distribution, and a “Value Analysis” recommending next steps) let assessors triage large portfolios - automating routine updates where confidence is high and flagging low‑confidence outliers for field inspection - so the real payoff is measurable: faster updates, lower per‑parcel cost, and clearer justification for adjustments in local tax rolls (HouseCanary valuation data points explanation).

Metric	HouseCanary AVM
Property coverage	114M+ properties
Historical depth	35 years of normalized data
Key outputs	Value, high/low range, confidence score, diagnostic value analysis

Fayetteville can use an OpenAI‑powered constituent chatbot to handle routine requests - status checks, FAQs, simple permit guidance - so staff focus on complex cases, but only if the tool is procured and configured to meet North Carolina safeguards: use a state‑licensed or government‑grade instance (not a public consumer account), require state email accounts, disable chat history for high‑risk queries, log and document AI use for public‑records compliance, and build mandatory human review and fact‑checking into any outward‑facing response workflow (N.C. Department of Information Technology guidance on publicly available generative AI, UNC School of Government generative AI guidance for local government).

Municipal leaders should also evaluate secure procurement options: industry programs are now offering government‑grade ChatGPT and Claude instances with compliance features and onboarding support, which lowers technical risk for small IT teams (OpenAI for government initiative and secure deployment reporting).

The practical payoff: a single, well‑governed chatbot can speed routine service delivery while keeping transcripts auditable under G.S. 132‑1 and reducing repeated manual lookups - provided strict PII and procurement controls are enforced.

“will become part of the chatbot's data model and can be shared with others who ask relevant questions, resulting in data leakage.”

Microsoft Copilot can speed routine administrative work - meeting summaries, draft reports, inbox triage, and natural‑language Power Automate flows - yet Fayetteville must match capability to compliance: a Microsoft support thread (May 28, 2025) warned Copilot was not certified for NIST 800‑171 or CMMC Level 2 and wasn't available in government clouds at that time, meaning Copilot shouldn't process Controlled Unclassified Information (CUI) until official support exists (Microsoft Q&A: Copilot compliance with NIST 800‑171 and CMMC Level 2); days later Microsoft announced government‑cloud releases - Microsoft 365 Copilot in Office 365 DoD (IL5) and Copilot Studio agent builder for GCC with Purview and admin controls to help meet security and governance requirements - so the practical path for Fayetteville is clear: pilot Copilot only in government‑grade tenants, enable Microsoft Purview and strict admin settings, log all AI interactions for public‑records and auditability, and confine use to non‑CUI administrative workflows until formal NIST/CMMC attestations arrive (Microsoft blog: new AI capabilities for government environments (M365 Copilot in DoD, Copilot Studio for GCC, Purview)).

So what: without government‑cloud deployment and Purview controls, Copilot cannot legally or safely touch many municipal records - making cloud posture the gating factor for automating Fayetteville's back‑office tasks.

Date	Item	Implication for Fayetteville
May 28, 2025	Copilot not certified for NIST 800‑171 / CMMC L2	Do not use Copilot for CUI in consumer/cloud instances
Aug 14, 2025	GA: M365 Copilot in DoD (IL5); Copilot Studio for GCC; Purview available	Enable government tenant + Purview to consider broader administrative automation

Tableau's state-and-local toolkit - prebuilt dashboards, visual analytics, chatbots, and “Accelerators” for common government workflows - pairs effectively with custom machine‑learning models to produce auditable, public-facing budget forecasts and rapid what‑if visualizations that councilors and citizens can verify in minutes; by surfacing model diagnostics (confidence bands, comparable scenarios, and drill‑down datasets) staff can triage anomalies and focus inspections where automated forecasts show low confidence, delivering a measurable efficiency gain - roughly 30–60 minutes saved per routine fiscal query - while improving mid‑year responsiveness to revenue shortfalls (Tableau state and local government analytics solutions).

Fayetteville can accelerate pilots by partnering with local analytics talent at Fayetteville State University - faculty with optimization, predictive‑modeling, and data‑mining expertise - to build defensible models, embed them in Tableau Cloud or embedded analytics, and document data‑retention and public‑records procedures up front (Broadwell College of Business faculty analytics and staff directory); the “so what”: clearer, faster answers for policymakers and transparent evidence to support tax and spending decisions under G.S. 132‑1.

Name	Role	Relevant expertise
Dr. Burcu Adivar	Interim Associate Dean, Associate Professor of Management	Optimization, operations management, quantitative methods
Dr. Jiahua Edward Zhou	Department Assistant Chair	Data mining, data analytics
Dr. Majed Al‑Ghandour	Adjunct Professor	Predictive models, machine learning, business intelligence & data analytics
Dr. Johnson Owusu‑Amoako	Assistant Professor of Finance	FinTech, ML & big data in financial services, forecasting

“Typically, when a concerned citizen asks a question, they just want an answer. Policymakers not only want an answer, they want to see the data to verify it. With Tableau, we now save about half an hour to an hour for each question.”

TEAMMAIT - Trustworthy, Explainable, and Adaptive Monitoring Machine for AI Teams - is an NSF‑funded, interdisciplinary effort to build an interactive AI “teammate” that evaluates clinician performance, provides individualized, nonjudgmental feedback, and adapts to users so mental‑health workers can learn and sustain evidence‑based practices without constant human retraining; the $2,000,000 award (Georgia Tech allocated $801,660 over four years) supports three years of human‑centered design and a fourth‑year prototype trial, a timeline that makes the approach directly applicable to North Carolina school and community behavioral‑health programs looking to scale supervision, produce audit trails, and protect clinician wellbeing (Georgia Tech TEAMMAIT project overview, NSF TEAMMAIT project page); when paired with broader clinical research on AI's capabilities and limits in mental healthcare, TEAMMAIT's ethics‑first, interdisciplinary design offers Fayetteville a concrete “so what”: a governance‑friendly path to expand supervised care capacity, target scarce trainer time to high‑need cases, and generate the deployment data municipal leaders need to set responsible local policy (Systematic review of AI applications in mental healthcare (PMC)).

Grant	GA Tech allocation	Timeline	Primary functions
$2,000,000 (NSF)	$801,660 over 4 years	09/15/2023 – 08/31/2027	Evaluate clinician performance, deliver actionable feedback, adapt to user input

“The initial three years... understanding the nuances of their work, their decision-making processes, and the areas where AI can provide meaningful support.”

Records, privacy, and legal compliance for Fayetteville's AI deployments hinge on treating every prompt and transcript as a potential public record: NCDIT explicitly warns that information entered into publicly available generative AI “is considered ‘released to the public'” and may be subject to public‑records requests, so never upload PII, financial, health, or restricted data and prefer state‑licensed instances that have completed procurement and risk reviews.

Practical controls required by NCDIT include using state email accounts for AI tools, disabling chat history and opting out of training data for high‑risk queries, conducting a Privacy Threshold Analysis and documented AI risk assessment (and re‑assessing on major releases), and logging outputs for auditability to meet G.S. 132‑1 expectations; these steps make pilots defensible and keep citizen data from inadvertently leaking.

For detailed operational checklists, follow NCDIT's guidance on publicly available generative AI and the NCDIT AI Assessments page for PTA and assessment procedures.

Compliance Action	Why it matters
Prohibit PII in public tools	Prevents data leakage and PRA exposure
Use state‑licensed instances	Aligns with procurement, privacy & security standards
Conduct PTA / AI assessment	Documents risk, enables audits and accountability
Disable chat history for high‑risk use	Reduces training‑data exposure and retention risk

“will become part of the chatbot's data model and can be shared with others who ask relevant questions, resulting in data leakage.”

AI‑generated images, audio, and video intended for Fayetteville's public channels should follow a formal review pipeline that mirrors N.C. state guidance: require public information officer and chief information security officer sign‑off before publication, vet outputs for bias, offensiveness, and factual errors, confirm copyright and licensing, and embed clear citations inside images or as metadata for audio/video so provenance is visible to the public and audit teams (NCDIT guidance on using publicly available generative AI).

Use state‑licensed or procured instances (never upload PII into public tools), document every AI use for public‑records compliance, and tap local research capacity - e.g., recent N.C. A&T work on deepfake detection and AI impacts - to validate suspicious content before release (N.C. A&T research on AI and deepfake detection).

Pair this process with municipal vendor‑oversight checklists during procurement so the “so what” is concrete: embedded citations plus PIO/CISO review minimize retractions, legal exposure, and erosion of public trust when AI media is deployed (municipal vendor oversight and governance checklists for AI media).

Review step	Responsible role
Pre‑publication PIO & CISO approval	Communications + IT Security
Bias, accuracy & copyright vetting	Communications + Legal
Embed citations/metadata in media	Content producer
Use approved, state‑licensed instances; no PII	IT / Records
Document use and retention for PRA	Records Officer

“will become part of the chatbot's data model and can be shared with others who ask relevant questions, resulting in data leakage.”

Conclude with an actionable checklist: adopt North Carolina's living AI Framework as the policy backbone, run an agency AI inventory and Privacy Threshold Analysis before any pilot, and treat every prompt and transcript as a potential public record; prefer state‑licensed or government‑grade instances (use state email, disable chat history for high‑risk queries) rather than public consumer tools to avoid inadvertent data leakage (North Carolina AI Framework for responsible AI use, NCDIT guidance on using publicly available generative AI).

Require short demonstration contracts with vendor oversight checklists, documented risk assessments, human fact‑checking and public metrics for any safety‑critical pilot, and invest in staff readiness - start with the 15‑week AI Essentials for Work curriculum to equip teams with prompt‑writing, risk‑assessment, and procurement literacy so Fayetteville can scale only when pilots are auditable, lawful, and demonstrably cost‑effective (Nucamp AI Essentials for Work syllabus).

Next step	Why it matters
AI inventory + PTA	Identifies risk, scope, and public‑records exposure
State‑licensed instances; state email; disable chat history	Reduces data leakage and aligns with NCDIT procurement
Short pilots + vendor oversight + public metrics	Keeps accountability high and procurement flexible
Staff training (AI Essentials)	Builds prompt, governance, and auditing skills to run safe pilots

“will become part of the chatbot's data model and can be shared with others who ask relevant questions, resulting in data leakage.”