Top 10 AI Prompts and Use Cases and in the Government Industry in St Paul

AI matters for Saint Paul because it can scale everyday services - cutting permit and benefits backlogs, improving 311 response, and freeing staff for higher‑value work - while also creating new training and career pathways for residents through programs like the City's Tech for All initiative (Saint Paul Tech for All initiative for students, career pathways, entrepreneurs, and community) that focuses on students, career pathways, entrepreneurs, and community.

Minnesota guidance warns that these gains come with legal and ethical responsibilities: the League of Minnesota Cities stresses compliance with the Minnesota Government Data Practices Act and careful handling of nonpublic data when using AI (League of Minnesota Cities guide to AI and cities: Cities and Artificial Intelligence).

Practical upskilling - such as courses that teach prompt writing and workplace AI use - helps municipal teams adopt tools responsibly; pilots can turn processes that once took days into near‑real‑time responses, changing how residents experience city government.

Program	Details
AI Essentials for Work	15 Weeks; Learn AI tools, write effective prompts, apply AI across business roles.
Cost	$3,582 early bird; $3,942 regular. Paid in 18 monthly payments, first due at registration.
Syllabus	AI Essentials for Work syllabus and course outline (Nucamp)
Register	Register for the AI Essentials for Work bootcamp (Nucamp registration)

“AI is not about replacing city workers at all. Instead, it augments them so that they can focus on other value-added activities to serve the public.”

Selection for the top 10 prompts and use cases started with risk-first criteria drawn from the NIST AI Risk Management Framework - so every candidate was "mapped" for purpose, data flows and stakeholders, "measured" for bias, privacy and operational impact, "managed" with concrete controls, and considered under ongoing "govern" oversight before inclusion; the Diligent guide to the NIST AI Risk Management Framework informed those checkpoints (Diligent guide to the NIST AI Risk Management Framework) and recent NIST guidance on generative AI and federal expectations (per Executive Order 14110) informed higher‑stakes choices like benefits eligibility or predictive policing prompts (see GT Law's summary of NIST AI risk-management guidance: GT Law summary of NIST AI risk‑management guidance).

Practical tests emphasized measurable benefits for St. Paul - e.g., whether a pilot could convert multi‑week backlogs into near‑real‑time responses - while keeping vendor dependencies, monitoring burden, and cross‑department governance front and center so municipal teams can pilot safely and scale responsibly.

RMF Function	How it guided prompt/use‑case selection
Map	Define purpose, data sources, affected residents and legal touchpoints
Measure	Assess bias, accuracy, security and operational impact
Manage	Specify controls, human oversight and vendor checks
Govern	Assign ownership, monitoring cadence and reporting requirements

“By calibrating governance to the level of risk posed by each use case, it enables institutions to innovate at speed while balancing the risks - accelerating AI adoption while maintaining appropriate safeguards.”

A citizen‑facing chatbot built on Rezolve.ai conversational AI platform brings the same GenAI strengths that modernize employee service desks into public services: seamless Microsoft Teams and web channels, a ChatGPT‑like conversational interface, and an automation studio that can auto‑resolve a large share of routine requests so staff can focus on complex cases - Rezolve.ai reports it can auto‑resolve 30–70% of IT & HR tickets and cut first response time “from hours to seconds,” which translates into faster answers for residents chasing permits or 311 updates.

Integration breadth matters for a city: Rezolve.ai connects to 1000+ apps and supports out‑of‑the‑box automation and triage (password resets, status checks, ticket routing), while privacy controls and certifications (SOC 2, HIPAA, ISO 27001) help protect sensitive data when adapting the bot for municipal workflows; see the Rezolve.ai Microsoft Teams service desk overview and Rezolve.ai integrations pages for implementation details and typical go‑live steps.

For St. Paul, a careful pilot - start small, route complex cases to humans, measure ticket deflection and resident satisfaction - could convert multi‑week waits into near‑real‑time service without replacing the human touch.

“Rezolve.ai allows our staff to get help 24x7 365 days a year from any device. This can free up support staff for more in depth support.” - Nate. RIT Support Specialist

For St. Paul's permitting and licensing teams, Ocrolus brings intelligent document automation that turns sprawling paper stacks and emailed PDFs into decision‑ready data - think identity, proof of address, bank statements and paystubs parsed automatically so clerks can verify residency, spot missing fields, or confirm income without manual re‑typing.

Ocrolus combines OCR with machine learning and a human‑in‑the‑loop to raise extraction accuracy from OCR's rough baseline (~85%) to about 99%, and it handles low‑quality uploads (cell phone photos, JPEG/PNG/TIFF) so even a crumpled utility bill can be converted into structured fields in minutes, not days.

Built‑for‑integration APIs, tamper‑detection and cash‑flow analytics support fraud checks and scalable throughput (millions of pages weekly), and most clients reach full production within a month - making it practical to pilot ID checks or license renewals without long vendor lock‑in.

See the Ocrolus intelligent document automation overview and the Ocrolus FAQ for specifics on supported IDs, formats, and fraud detection: Ocrolus intelligent document automation overview, Ocrolus FAQ on document automation and fraud detection.

Document types	Relevant capability
IDs (US passports, driver's licenses)	Identity extraction and verification
Proof of address (utility bills)	Address extraction from low‑quality images
Bank statements & paystubs	Income/cash‑flow analytics for eligibility checks
Image formats (JPEG, PNG, TIFF, cell phone photos)	Robust OCR + ML with human review for hard cases

“Ocrolus technology elevated our bank statement analysis capabilities to the next level.” - Jim Granat, President of SMB Lending and Senior Vice President, Enova International

Automated legal drafting and review can quiet the busiest parts of St. Paul's legal workflow - drafting standard agreements, redlining vendor contracts, and turning long statutes or leases into clear, bullet‑point briefs - by using well‑crafted prompts and legal‑specific tools that act like a sharp junior associate; Casepeer's roundup shows how ChatGPT can draft initial agreements, summarize case law, and speed document analysis, while Spellbook and similar products add clause libraries and in‑Word drafting to make outputs negotiation‑ready.

See the Casepeer guide to ChatGPT prompts for lawyers (Casepeer guide to ChatGPT prompts for lawyers) and the Thomson Reuters guide to writing effective legal AI prompts (Thomson Reuters guide to writing effective legal AI prompts).

Minnesota teams should bake jurisdiction, audience, and required format into every prompt (e.g., “Summarize Minnesota statute X for a municipal clerk; list key compliance dates and citation”), anonymize privileged facts, and treat AI drafts as starting points to be validated against local law - practical prompt design and firm guardrails keep efficiency gains from turning into ethical or accuracy risks.

“Artificial intelligence will not replace lawyers, but lawyers who know how to use it properly will replace those who don't.”

Keeping St. Paul's parks, community centers and city fleet running smoothly is a perfect fit for Elise's AI-driven maintenance platform: it automates work‑order creation from resident reports, triages and de‑escalates non‑emergencies with how‑to guidance, and routes true emergencies 24/7 so crews focus on what matters most - preventing a flooded ballfield or getting a snowplow back on the road during a freeze.

Elise's Maintenance App adds real‑time visibility (work‑order timelines, photos, geo‑fenced clock‑in), automatic technician assignment and bilingual resident chat, and clients report measurable wins - like a 26% drop in emergency call volume and 533 maintenance hours saved in Q1 2025 - making it practical for municipal pilots that aim to cut downtime and payroll overhead.

Pairing Elise's operational automation with AI predictive‑maintenance insights from fleet guides (which turn sensor feeds and telematics into early‑warning alerts) helps St. Paul move from reactive repairs to scheduled fixes, lower costs and higher uptime across parks and vehicles; see Elise's maintenance overview and the AI fleet maintenance guide for implementation ideas.

Capability	Relevant Benefit
Streamline work‑order creation & triage	Faster diagnosis, fewer incomplete requests
24/7 emergency routing & de‑escalation	Ensures urgent responses while reducing unnecessary dispatches
Geo‑fenced clock‑in, photos & timeline	Real‑time visibility for crews and managers
AI predictive alerts from telematics	Predict failures, reduce downtime and lower repair costs

“EliseAI's maintenance tool is fully integrated and easy for our maintenance team members to use - it's been really amazing for them.” - Emily Mullies, Multifamily Marketing & Operations Support

For St. Paul, fraud detection and tax compliance for benefits and procurement should blend tried‑and‑true internal controls with modern analytics and staff protections: require segregation of duties and monthly bank reconciliations, run anonymous tip lines, and tighten card and check controls so no one person touches every step of a transaction (see the NYS Office of Mental Health internal controls checklist for fraud prevention NYS Office of Mental Health internal controls checklist for fraud prevention).

Pair those controls with advanced analytics - predictive modeling, anomaly detection and network analysis - to surface stair‑stepping refund schemes or supplier rings that can siphon millions over years, a blind spot documented in tax‑agency cases (see the SAS guide to tax fraud detection and prevention SAS guide to tax fraud detection and prevention).

Benefits teams should also prioritize employee training, secure payroll and W‑2 handling, and participant communications, while considering identity‑protection offerings and monitoring for signs of tax fraud to reduce exposure and speed recovery (see BDO guidance on reducing fraud risk in employee benefit plans BDO guidance on reducing fraud risk in employee benefit plans).

Together these measures let St. Paul move from firefighting to proactive detection - catching anomalies early so dollars meant for residents stay in the community.

Control	Why it matters
Segregation of duties	Prevents a single actor from executing and concealing fraudulent transactions
Monthly bank reconciliations	Independent review catches unauthorized disbursements and sequence gaps
Anonymous tip line	Most frauds are first reported by insiders; hotlines reduce losses
Advanced analytics	Flags anomalies and networks of related filings/vendors that rule‑based checks miss

For St. Paul, a Skyline‑style, data‑driven approach to incident forecasting could turn scattered signals into actionable forecasts - Skyline AI's platform shows how scraping public records and unconventional inputs (Fannie Mae/Freddie Mac files, FBI crime figures, local weather and even mobile‑device patterns) feeds machine‑learning models that surface early warning signs for neighborhood change and risk (Risk.net profile of Skyline AI's methodology and property-deal forecasting, Skyline AI official platform overview and capabilities).

Complementing that approach, peer research demonstrates crime‑prediction models that can forecast incidents about a week ahead with high accuracy by working at fine spatial tiles (~1,000‑foot squares) and learning temporal patterns - useful for resource planning, hotspot prevention, and public‑health analytics in Minnesota's cities (University of Chicago crime‑forecasting study on algorithmic prediction and bias).

Any St. Paul pilot should pair these predictive gains with strong governance and bias audits: the same models that spot a rising cluster can also mirror enforcement disparities, so human oversight, transparency and community input must be baked into deployment - think of it as a “digital twin” that warns officials without replacing judgment, not a magic wand that tells officers where to swarm.

“We created a digital twin of urban environments. If you feed it data from what happened in the past, it will tell you what's going to happen in future.”

When St. Paul buys AI tools, procurement is as much about contracts as cost - rigorous due diligence, clear definitions and firm data rights turn a promising demo into a safe city service.

Start by mapping inputs, outputs and training data so the agreement answers who can reuse citizen data and whether a vendor may absorb municipal prompts into its models (Byte Back's guide walks through those input/output and IP pitfalls: Key Considerations in AI‑Related Contracts).

Insist on AI‑specific clauses: audit and transparency obligations, model‑card disclosures, SLAs for accuracy and availability, and explicit prohibitions on using city Production Data to train third‑party models (Vorys' customer‑side primer highlights ownership and licensing for training/enhanced data: Data Ownership & Licensing).

Layer in governance - periodic bias testing, breach notification, and indemnities tied to IP and regulatory compliance - and require a kill‑switch or termination right if a vendor's third‑party dependencies create legal exposure.

Think of it like locking the city's vault: without explicit limits, a single uploaded spreadsheet can quietly become part of a vendor's model overnight, so contracts must translate technical risk into enforceable protections (see contracting best practices for vendor due diligence and governance: Contracting for AI Technologies – Top Best Practices).

Contract area	Key protections
Due diligence	Vendor risk assessment, model provenance, references
Data ownership & use	Define inputs/outputs, prohibit training on city data unless agreed
IP & outputs	Ownership/licensing of outputs; vendor warrants no third‑party infringement
Compliance & audit	Audit rights, regulatory compliance clauses, model cards
Liability & SLAs	Service levels, indemnities for IP/data breaches, clear termination rights
Governance & bias	Bias testing, monitoring, human‑in‑the‑loop and reporting obligations

For St. Paul's municipal teams, a Rezolve.ai internal agent can turn scattered SharePoint files, past tickets, intranet pages and HR handbooks into a single, searchable GenAI layer that answers staff and resident questions inside Microsoft Teams - auto‑resolving routine L1 requests so clerks and caseworkers can focus on complex decisions rather than repetitive lookups.

Rezolve.ai's knowledge management ingests PDFs, websites and documents, applies role‑aware, geography‑specific context, and pairs granular permission controls and audit trails with enterprise security standards, helping Minnesotan departments meet strict data‑handling expectations; see the platform's Knowledge Management overview and its integrations with Microsoft Teams and SharePoint for implementation details.

Practical wins are straightforward: with most HR queries being repetitive, the agent supplies accurate, hallucination‑controlled answers, surfaces trending knowledge gaps for continuous improvement, and feeds analytics that show where training or policy updates will have outsized impact - imagine a frontline clerk getting a concise, citation‑backed answer from a month‑old ordinance rather than paging through folders.

Built‑in governance, multi‑channel access (Teams, Slack, web, mobile) and agentic automation make Rezolve.ai a pragmatic pilot choice for Saint Paul's next step in digital service delivery.

“One of the things that lead us to Rezolve.ai was seamless integration with MS Teams. We moved away from the old format. Our users can now open tickets with just one icon click” - Team Management, The Minnesota Timberwolves

Construction monitoring in St. Paul's public projects can move from guesswork to a trusted visual record with OpenSpace's AI‑driven reality capture: crews simply attach a 360° camera to a hard hat, walk the site, and OpenSpace maps images to floor plans so as‑built views are ready in about 15 minutes - turning weekly site visits into instant remote inspections and faster RFI/punch‑list resolution.

The platform's Progress Tracking (powered by Disperse) tracks hundreds of visual components across schedules, flags early‑warning issues, and integrates with tools teams already use - Procore, Autodesk Construction Cloud, PlanGrid and Revizto - so verification for billing, QA/QC, and coordination becomes visual and auditable rather than paper‑bound.

That combination of rapid capture (25,000 sq. ft. in ~10 minutes), AI alignment to BIM, and enterprise security (SOC 2, FedRAMP Moderate) helps municipal owners and contractors spot rework risks early, reduce travel costs, and keep projects on schedule; see the OpenSpace reality capture platform and the OpenSpace Progress Tracking overview for implementation details and case studies.

Capability	Key fact
Capture speed	~25,000 sq. ft. in 10 minutes (hard‑hat 360° capture)
Processing turnaround	Images & mapping ready in about 15 minutes
Progress tracking	Track 700 visual components across 200+ schedule tasks
Integrations & security	Procore/Autodesk/PlanGrid/Revizto; SOC 2 & FedRAMP Moderate

“You can take 100 photos of every room manually, and I guarantee that the one photo you need won't be there. That's where OpenSpace is great. It captures sites thoroughly and fast, organizes imagery without any effort, and makes it searchable later when you need it.” - Chris O'Neil, Senior Manager Digital Engineering

HouseCanary‑style models bring automated valuation models (AVMs), ZIP‑level Home Price Indices and monthly forecasts into city budgeting so St. Paul can move from guesswork to evidence‑driven resource allocation: real‑time estimates and granular HPIs (state → MSA → ZIP) help finance teams forecast property tax yield, stress‑test capital plans, and spot neighborhood affordability shifts months before they show up in permit or sales data - HouseCanary reports proprietary forecasts that explain more than 95% of historical price variability and offer risk flags (flood, FEMA, crime, volatility) and affordability time series down to the ZIP code.

Integrating a Data Explorer/API into budget modeling gives analysts automated inputs (current values, HPI forecasts, risk indicators and value‑distribution context) so scenarios - revenue shortfalls, tax-base changes, or targeted housing investments - are anchored to reproducible, auditable data rather than anecdote.

For Minnesota this means better timing for levy planning and targeted relief in vulnerable neighborhoods, with the added benefit that automated tools have been shown to reduce appraisal bias in some studies; see HouseCanary's Data Explorer and its market analysis for API details and metrics to plug directly into municipal forecasts.

Capability	How it supports city budgeting
AVM & up‑to‑date valuations	Real‑time property values for revenue estimates
HPI forecasts (state/MSA/ZIP)	Monthly forecasts to model near‑term tax base shifts
Risk & affordability metrics	Identify neighborhoods needing targeted spending or relief
API / Data Explorer	Feed automated scenarios into budget models and dashboards
Proven accuracy	Proprietary forecasts explaining >95% of historical variability

“At HouseCanary, we have built and developed industry‑leading valuation technology to provide highly accurate, objective property information for all.” - Jeremy Sicklick, HouseCanary

The sensible next step for Saint Paul is to stitch pragmatic pilots to a clear governance backbone and an honest public conversation: start small - pilot a citizen chatbot for low‑risk, public data and a document OCR flow for permit intake - while mapping every data input to the Minnesota Government Data Practices Act so staff avoid accidentally feeding moderate or high‑risk records into third‑party models (see the League of Minnesota Cities' guidance on AI and data practices).

Pair those pilots with an adaptive AI policy, vendor contract clauses, and continuous monitoring so approvals, audit trails and a kill‑switch are standard, not optional; the GovAI Coalition and ICMA offer practical templates and playbooks that local teams can reuse and adapt.

Make training a citywide priority - role‑based upskilling (notably prompt writing and workplace AI use) prepares clerks and managers to treat AI drafts as starters, not final answers - while planning community outreach that explains risks and benefits in plain language to build trust.

Remember the operational upside: with right‑sized safeguards, pilots can convert multi‑week backlogs into near‑real‑time responses and free staff for higher‑value work - an outcome that's as practical as it is transformative.

For hands‑on staff training, consider a structured program like Nucamp AI Essentials for Work bootcamp registration to build prompt and governance skills across the organization.

“We all do better when we all do better.”