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

Carmel's municipal AI strategy must align with Indiana's local service priorities and the evolving federal landscape - federal moves like the Biden EO and OMB guidance are already shaping how city leaders evaluate AI risks and workforce impact (Biden executive order and OMB AI guidance analysis).

Practical workforce reskilling is the immediate lever: targeted training helps staff write safe prompts, vet vendors, and oversee pilots (Municipal workforce AI reskilling programs case studies).

For teams ready to build in-house capacity quickly, a focused 15-week AI Essentials for Work bootcamp (syllabus and registration) gives prompt-writing and governance foundations that translate to operational control, not just vendor dependency (AI Essentials for Work 15-week bootcamp syllabus - Nucamp).

Attribute	AI Essentials for Work
Length	15 Weeks
Courses	AI at Work: Foundations; Writing AI Prompts; Job Based Practical AI Skills
Cost (early bird)	$3,582

Methodology prioritized prompts and use cases that align with Carmel's service priorities while meeting clear federal risk and governance signals: candidate ideas were first cross‑checked against the Department of Homeland Security's 2024 AI Use Case Inventory to flag any items “potentially impacting safety and/or rights,” then mapped to NIST's AI Risk Management Framework - including the Generative AI Profile - to ensure identifiable risk controls and testability, and finally evaluated for readiness against the compliance-plan expectations described in recent agency guidance reporting (OMB/NIST templates and agency postings).

The result: ten prompts that balance immediate operational value for Carmel - such as automateable citizen-service triage - with documented mitigation paths required by federal inventories and NIST risk practices, so city leaders get usable pilots that can be stood up without bypassing required oversight.

DHS 2024 AI Use Case Inventory, NIST AI Risk Management Framework (AI RMF), Analysis of agency AI compliance plans and the OMB template (Meritalk).

Selection Criterion	Why it mattered / source
Safety & rights impact	Flagged via DHS 2024 Use Case Inventory
Risk control & testability	Mapped to NIST AI RMF and Generative AI Profile
Governance & compliance readiness	Aligned with OMB/NIST compliance-plan expectations (agency postings)

A practical AI governance memo for Carmel municipal departments should translate recent federal directions into local, actionable steps: adopt a centralized oversight role (a Chief AI Officer or delegated lead), convene an AI governance board, and publish a short AI compliance plan and use-case inventory so decisions are auditable and grant- and contract-ready; OMB and subsequent guidance tie these elements to procurement expectations and risk controls (OMB policy memoranda archive (M‑24‑10, M‑24‑18)) and M‑25 memos now emphasize agency AI strategies, timelines, and minimum risk practices that municipalities should mirror where feasible (OMB M‑25 guidance on agency AI strategy and governance); require pre‑deployment testing and an AI impact assessment for any

high‑impact

use, embed human review and monitoring plans, and include procurement clauses limiting vendor reuse of non‑public city data to avoid lock‑in - one specific, actionable detail: follow the federal cadence by setting near‑term milestones (60/90/180 days) so vendors and grantors see a clear compliance posture when Carmel seeks FedRAMP‑ready cloud services or federal partnership funding.

Action	Target Timing / Note
Designate Chief AI Officer	~60 days (recommended)
Convene AI Governance Board	~90 days
Publish AI strategy / compliance plan	~180 days
High‑impact AI controls	Pre‑deployment testing, impact assessment, monitoring, human review

A pragmatic AI‑specific cybersecurity risk assessment for Carmel begins by inventorying deployed models, data flows (training/inference), hosting environments, and third‑party model providers, then mapping those assets to federal risk milestones so local remediation aligns with national practice: use the Sidley AI Monitor roundup of government guidance on managing AI-specific cybersecurity risks to frame model‑lifecycle controls and the Executive Order tracker for NIST deadlines and federal cybersecurity actions to prioritize fixes.

Focus test plans on model integrity (data provenance, poisoning/poisoning detection), supply‑chain assurance for prebuilt models, patch/patch‑validation cadence for model runtimes, logging/telemetry for forensic readiness, and explicit handoffs into existing incident‑response playbooks so that a discovered model vulnerability triggers the same reporting and containment workflow used for other city systems; one concrete deadline to anchor procurement and vendor SLAs: NIST's SSDF/consortium milestones this year inform what

secure software

assurances Carmel should require in RFPs.

Federal Milestone	Target Date
NIST consortium at NCCoE to develop secure software guidance (SSDF)	By Aug 1, 2025
Preliminary SSDF update / AI vulnerability guidance	By Dec 1, 2025
EO directive: integrate AI software vulnerability management into agency processes	June 6, 2025 (EO)

Protecting Carmel's confidential records starts with a clear, enforceable rule: forbid entry of non‑public city data into unsanctioned, public-facing generative AI and back that rule with technical, contractual, and training controls - deploy endpoint DLP and network filters on city devices, require agency‑level “maturity assessments” and a documented approval path before any AI project goes live, and insert vendor contract clauses that prohibit reuse of non‑public data and require model/data separation and provenance (the Indiana state AI policy and its maturity‑assessment + “just‑in‑time” notice requirement provide a practical template).

Pair these controls with mandatory employee training and concise “do's and don'ts” guidance, plus a sandboxed research environment or FedRAMP‑approved hosting for any work that must touch sensitive records so analysts can collaborate without leaking data.

These steps make the policy actionable for Carmel: a signed procurement clause and an approved maturity assessment become the gatekeepers that stop confidential inputs before they reach a public LLM. See Indiana's AI project policy and notices for implementation cues and why state leaders are prioritizing staff guidance and limits on unsanctioned AI use.

“From the state's perspective, the big fear is, ‘How do we ensure our 30,000 employees are not putting something into a generative AI engine that should be secure, proprietary or confidential,'” - Tracy Barnes, Indiana CIO.

Design the Carmel Parks & Recreation chatbot as a short, privacy‑first intake that routes common tasks while reserving high‑risk work for staff: open with a clear greeting and intent menu (Book facility; Report maintenance; Program info; Accessibility help), collect only the minimum fields needed to triage (first name, contact method, park/facility, preferred date/time or issue), and add an explicit “requires human review” flag for any booking that involves payment, minors, or medical accommodations so sensitive decisions never stay automated - this protects records and aligns with local training priorities; pair the flow with staff prompt‑writing and oversight training from municipal workforce reskilling programs for AI oversight (municipal workforce reskilling programs for AI oversight) and structure delivery using Integrated Product Teams to speed cross‑department testing and procurement readiness (Integrated Product Teams model for cross-department procurement); keep the script concise, include an automatic privacy reminder before free‑text entries, and map escalation paths so the chatbot qualifies as a low‑impact, auditable service under evolving federal expectations like the Biden EO and OMB AI guidance (Biden Executive Order and OMB AI guidance for municipal AI).

Repo	Watches	Stars	Forks
Dean/uri_nlp_ner_workshop	15	3	0

A practical, auditable test plan for a Carmel hiring screener begins by fixing what to measure: define job‑related success metrics and the protected groups to test, then require vendors to produce training data, feature provenance, and performance by subgroup so the city can run both classical adverse‑impact checks (eg, the four‑fifths rule) and intersectional slices that catch hidden disparities - steps recommended in employer and policy testimony before the EEOC (Gary Friedman EEOC testimony on navigating employment discrimination, AI, and automated systems).

Include mandated tests for accessibility and ADA accommodations (alternate assessment paths, timing, screen‑reader compatibility) as the EEOC guidance and expert testimony emphasize disclosure and reasonable alternatives for people with disabilities (Alex Engler EEOC testimony on accessibility and accommodations for AI assessments).

Require vendor self‑audits, an independent third‑party fairness audit, and publication of a short audit summary before procurement to mirror emerging federal practice and to demonstrate compliance if challenged (EEOC FY2023 report: AI & Algorithmic Fairness Initiative).

So what? - a single, repeatable audit that documents data lineage, subgroup results, and accommodation options both reduces legal risk and materially improves the diversity of hires by surfacing correctable model flaws before they affect hiring outcomes.

Test Phase	Key Check / Source
Define job metrics & subgroups	Set success criteria; test intersectional slices (Friedman)
Data & provenance review	Request training data details, feature sources (Friedman/Engler)
Statistical bias testing	Adverse‑impact (four‑fifths), subgroup performance (Friedman, EEOC)
Accessibility & ADA checks	Alternate workflows, disclosure, accommodations (Engler, EEOC)
Vendor & audit requirements	Vendor mitigation plan + independent audit summary (EEOC guidance)

For Carmel's RFP for a FedRAMP‑compliant enterprise chatbot, require explicit federal authorization and operational controls, plus vendor commitments that make the system auditable and safe for Indiana municipal data: mandate FedRAMP authorization for the hosting boundary, cryptographic separation of city data, a contract clause prohibiting vendor reuse of non‑public inputs, real‑time audit logging and an API for SIEM integration so SOC analysts can review model outputs (echoing federal pilots that pair AI alerts with human review in the GSA AI use‑case inventory), and interoperability with existing ticketing or virtual agent platforms to avoid siloed workflows; require delivery of a short, publishable audit summary, clear incident‑response handoffs into city playbooks, and vendor‑led operator training tied to a 90‑day IPT pilot so staff can validate controls and handoffs (pair this with local workforce reskilling plans to build in‑house oversight).

Include acceptance criteria that map to the city's compliance plan and a short vendor checklist (authorization, data segregation, logging, explainability, training) so procurement evaluates both security posture and operational readiness.

GSA AI use-case inventory for virtual agents and threat detection pilots, Municipal workforce reskilling programs for AI oversight in Carmel, Integrated Product Teams model for AI procurement and pilots in Carmel.

Use Case	Stage of Development
ServiceNow Virtual Agent (Curie)	Initiated
Elastic Machine Learning Threat Detection	Acquisition and/or Development
Login.gov (identity verification)	Implementation and Assessment

Draft an AI‑specific incident response playbook that folds AI failures into Carmel's existing IR pipeline: detect anomalies with prompt‑level logging and telemetry, escalate suspected prompt‑injection, data‑leakage or model‑poisoning events to a nominated SRO, contain by isolating affected model endpoints and revoking service keys, and trigger human review before any automated remediation - these steps mirror the UK playbook's advice on content filters, sanitisation and meaningful human control and the threat scenarios and mitigations described in recent convergence research (UK Artificial Intelligence Playbook - incident response guidance, Emerging technologies and their effect on cyber security - threat scenarios).

Operationalize the plan with vendor audit‑trail requirements, tabletop and red‑team exercises that simulate prompt‑injection and exfiltration, clear handoffs into forensic analysis, and a post‑incident assurance loop that updates red‑teaming scripts and content filters so the city reduces repeatable AI harms.

Phase	Key actions
Detect	Prompt logging, anomaly alerts, red‑team tests
Contain	Isolate endpoints, revoke keys, pause affected workflows
Recover & Review	Forensic analysis, human sign‑off, update controls and playbook

Probabilistic record linkage across health and housing databases in Indiana operates less as a single algorithm and more as a governed workflow: establish legal data‑sharing agreements and project charters, ingest agency extracts into a secure research enclave, apply probabilistic matching rules to connect records when unique IDs are missing, surface uncertain pairs for clerical review, then lock linked, de‑identified extracts for analysis and dashboarding so program teams can act without exposing raw PII; this is the model the Indiana Management Performance Hub used to create a population‑level dataset that supported COVID response and researcher access in a firewalled Enhanced Research Environment (ERE).

The “so what?” is concrete: MPH's ERE hosted 100+ users across 20 organizations and enabled targeted testing, vaccine outreach, and cross‑sector analytics (for example, linking housing indicators to infant mortality trends), showing how legally framed governance plus secure probabilistic linkage turns siloed records into actionable municipal insight.

AISP case study: Indiana Management Performance Hub data capacity and COVID‑19 response, BMC Medical Informatics article: INPC evolution to population health resources, Harvard Data‑Smart case study: Management Performance Hub model.

Metric	Value
BMC article accesses	1926
Citations	3
Altmetric	19

“The work MPH has put into building out the State's data infrastructure has allowed us and our partners to mobilize quickly in response to COVID-19 and use data to drive our decisions in a meaningful way,” - Indiana Chief Data Officer and MPH Director Josh Martin

Turn ChatGPT Enterprise adoption in Carmel into a measured change program: form role‑based cohorts (operators, reviewers, legal/comms) and require two “AI stewards” per department to complete a focused curriculum (the 15‑week AI Essentials syllabus used in municipal reskilling pilots) so staff gain prompt‑writing, data‑handling and human‑in‑the‑loop oversight skills; run 60/90/180‑day milestones that pair a 90‑day Integrated Product Team (IPT) pilot with endpoint DLP, SIEM logging, and a simple before/after productivity metric - bench against published findings (Forrester's enterprise adoption priorities and the Harvard/MIT studies showing large internal productivity gains) to decide scale‑up or roll‑back (AI Essentials for Work 15-week municipal reskilling syllabus (Nucamp), Forrester enterprise generative AI adoption analysis).

Make measurement concrete: require each pilot to report a primary KPI (e.g., document turnaround time or case triage time) and a compliance checklist tied to Indiana's maturity‑assessment expectations so procurement and records safeguards can be demonstrated to federal partners before wider rollout.

Phase	Timing	Purpose
Foundations & Steward training	15 weeks	Prompt craft, DLP, human‑in‑loop procedures
IPT pilot	90 days	Operational test, KPI measurement, vendor SLA validation
Scale / Compliance sign‑off	180 days	Publish compliance plan, procurement readiness

“From the state's perspective, the big fear is, ‘How do we ensure our 30,000 employees are not putting something into a generative AI engine that should be secure, proprietary or confidential,'” - Tracy Barnes, Indiana CIO.

Prepare a short, transparent public FAQ and press release that leads with purpose, safeguards, and measurable next steps: explain that the pilot will test AI to reduce administrative burden (example uses: drafting public notices, summarizing data, improving service triage), name a small, accountable cohort and timeline (Indianapolis' pilot began with 20 staff and informed its phased rollout), describe mandatory staff training and an approval path for any tool, and commit to clear data rules (no non‑public records in unsanctioned tools) plus an AI advisory review before scale - these elements reassure residents and comply with Indiana and federal expectations; link the announcement to local workforce reskilling resources so residents know the city is investing in staff oversight and skills (municipal workforce reskilling programs for Carmel government employees) and reference the Indianapolis policy pilot as a practical precedent (Indianapolis Public Schools AI pilot and draft policy overview).

For a memorable commitment, promise to publish a one‑page pilot report at 90 days showing at least one concrete KPI (e.g., time saved on routine communications) and the compliance checklist used to judge expansion - this turns abstract assurances into an accountable metric the public can track (Integrated Product Teams model for government AI pilots).

“There's still a lot to learn from a broader group of adult users before we're putting students in an environment that maybe doesn't match curriculum or what teachers are learning at the same time. We want to make sure that staff feel well equipped to determine what the boundaries are for use of AI in a classroom.” - Ashley Cowger

Carmel's immediate next steps are operational and measurable: formalize a 60/90/180‑day cadence that names AI stewards in every department and requires them to complete targeted training (a 15‑week AI Essentials for Work curriculum is a practical option), run a 90‑day Integrated Product Team (IPT) pilot with a single, auditable KPI and a public 90‑day report, and publish a simple use‑case inventory so procurement and oversight are transparent and grant‑ready (StratML provides a machine‑readable model for publishing plans and performance data).

Use the new GSA Multiple Award Schedules entry points to shortlist FedRAMP‑ready vendors quickly, but insist on contract clauses that prohibit vendor reuse of non‑public city data, require logging/SIEM integration, and tie acceptance to pre‑deployment testing and vendor audit summaries; these steps let Carmel buy faster without sacrificing the controls Indiana and federal guidance expect.

Anchor every pilot to the incident‑response and cybersecurity assessment steps already drafted, and make the 90‑day public report the “so what?” - a single, auditable metric (time saved or cases closed) plus the compliance checklist that convinces residents and funders the city is both ambitious and accountable.

AI Essentials for Work 15-week bootcamp syllabus - Nucamp, StratML machine-readable strategic plans and performance data, GSA MAS deal enabling easier government procurement of enterprise AI.

Attribute	AI Essentials for Work
Length	15 Weeks
Courses	AI at Work: Foundations; Writing AI Prompts; Job Based Practical AI Skills
Cost (early bird)	$3,582

“From the state's perspective, the big fear is, ‘How do we ensure our 30,000 employees are not putting something into a generative AI engine that should be secure, proprietary or confidential,'” - Tracy Barnes, Indiana CIO.