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

Billings can use AI to make wildfire strategy proactive, not reactive: Montana's PODs risk-based framework combines spatial analytics and machine-learning models to pre-draw control lines across USFS Region 1 - helping managers place shaded fuel breaks and decide where to suppress or allow fire for ecological benefit (PODs risk-based wildfire control lines framework).

Complementary research like the $20M SMART FIRES program is building AI-driven sensors and real-time models to monitor prescribed-fire behavior and smoke for better timing and community protection (SMART FIRES sensors and AI monitoring program).

Practical training for city staff - such as Nucamp's 15-week AI Essentials for Work bootcamp - can fast-track skills to write prompts, interpret models, and operationalize alerts so Billings plans before “smoke is in the air,” lowering suppression costs and protecting infrastructure (Nucamp AI Essentials for Work 15-week bootcamp registration and syllabus).

Attribute	Detail
Project	SMART FIRES
Award	$20,000,000
Timeframe	2023–2028
Lead	Robert Walker (MSU); partners include UM, Salish Kootenai College, NASA AERONET, USFS

“If we're ever going to get over the hump in fire management of being more proactive about allowing certain fires to burn and putting other fires out, you have to think about these things and plan for them before the fire happens.”

Selection focused on practical, low-friction AI work that Billings can adopt quickly: priority went to prompt templates and use cases that scale with no-code automation for small local government agencies in Billings so city teams can deliver citizen services without large IT budgets, while also highlighting workforce pathways - like cybersecurity and developer roles in Billings government - that protect jobs and make adoption sustainable; each prompt was tested against three local criteria (cost to implement, staff skill uplift, and legal compliance) with state rules in mind, particularly the Montana AI legislation overview (SB 212, HB 556) for Billings government, so recommended use cases reduce risk while delivering measurable savings and faster response times for Billings residents.

ChatGPT-powered virtual assistants can give Billings residents immediate, 24/7 help with routine needs - permit steps, utility billing questions, trash schedules, and even pre‑evacuation guidance during wildfire season - reducing call‑center wait times and letting skilled staff focus on complex cases; real deployments have cut public‑records fulfillment time in half and handled millions of queries, showing clear operating-cost and service-quality gains (municipal ChatGPT deployments and benefits for government services).

Use the OpenAI Prompt‑Pack for Leaders to copy‑paste tested prompts for FAQs, multilingual public announcements, and executive briefs - upload local reports to ground answers and require review before publication (OpenAI government prompt pack for leaders and executives).

Pilot small, document decisions, and align with Montana requirements (see local AI legislation overview) so automation improves response times without exposing personal data or creating new legal risks (Montana AI legislation overview (SB 212, HB 556)); the payoff is measurable: faster, consistent answers to routine requests and fewer overtime hours for staff.

Prompt‑Pack for Leaders

Virtual Assistant	Jurisdiction	Impact
CHARLIE	CRA - Canada	~5 million questions answered in first year
ALEX	ATO - Australia	1.4 million conversations (Jul 2020–Mar 2021)
ASK JAMIE	IRAS - Singapore	~15 million questions in 5 years

Predictive analytics for emergency departments - like the machine‑learning model for hospital‑admission prediction presented at the 40th International Symposium on Intensive Care & Emergency Medicine - offer a practical lever for Montana cities to sharpen emergency response by forecasting which 911 callers or ED arrivals will likely need admission (ED admission prediction machine‑learning study (ICU & Emergency Medicine)); VA research briefs similarly highlight how adding predictive machine‑learning to nurse triage phone lines can route higher‑risk patients to faster in‑person care or direct telehealth, reducing unnecessary ambulance trips and easing ED crowding (VA research brief on predictive machine‑learning nurse triage).

For Billings Fire Rescue, a staged pilot using local EMS and hospital data - paired with clear governance under Montana AI rules - can let dispatchers pre‑position resources along likely evacuation routes and trigger clinician alerts when hospital admission risk crosses a threshold, so scarce ambulances and staff arrive where they matter most during wildfire smoke or mass‑casualty surges; align pilots with the Montana AI legislation overview (SB 212 and HB 556) to control risk and preserve public trust.

Billings can cut congestion and local emissions by piloting adaptive signal control and AI‑driven sensors like Pittsburgh's SURTrAC, which reduced travel time by about 25%, braking by 30% and idling by more than 40% - results that translate to faster emergency response and lower tailpipe emissions during wildfire‑smoke episodes (Pittsburgh SURTrAC AI traffic system study).

A practical roadmap from smart‑city guides shows the same tech class (edge sensors, real‑time AI, adaptive signals) cuts travel times 20–25% and emissions up to ~20% when paired with local pilots, stakeholder agreements, and phased deployments; start with one arterial corridor or an evacuation route, measure idling and response times, then scale with vendor partnerships and staff training to keep costs manageable (Smart City traffic management guide for adaptive signals and edge sensors).

For small municipal teams, combine these pilots with no‑code automation and local training so Billings realizes measurable service improvements without large IT budgets (No-code automation solutions for small municipal agencies in Billings).

Metric	Reported Result
Travel time reduction (Pittsburgh)	~25%
Braking reduction (Pittsburgh)	30%
Idling reduction	>40%
Emissions reduction (smart traffic systems)	Up to ~20%

Montana social‑welfare programs can borrow from HSBC's AI approach - built with Google Cloud - to detect benefit fraud faster and with far fewer false positives: HSBC's AML AI screens over 1.2 billion transactions monthly, finds 2–4× more suspicious activity than legacy rules, and reduced alert volume by about 60%, cutting time‑to‑meaningful detection to roughly eight days (HSBC and Google Cloud AML AI case study: improved transaction screening).

For Billings, a scaled model that triages claims and flags behavioral patterns (rapid fund movements, repeated address changes, linked accounts) can let a small benefits team focus on high‑confidence investigations instead of chasing false leads - so the city preserves taxpayer dollars and shortens case resolution times.

Adoption must pair technical controls with policy: recent regulatory action against HSBC Australia underscores that weak governance erodes public trust, so align any pilot with Montana AI rules and oversight (ASIC action against HSBC Australia (2024 enforcement)) and consult the state AI compliance guide (Montana AI legislation overview (SB 212, HB 556)).

Metric	Result reported by HSBC
Transactions screened	~1.2 billion/month
Suspicious activity identified	2–4× increase vs rules
Alert volume	−60%
Time to detect suspicious accounts	~8 days

“Alleged failings were widespread and systemic; the bank failed to protect its customers.” - ASIC Deputy Chair Sarah Court

Document analysis is a core machine‑vision application that turns scanned forms, handwritten notes, and PDF case files into structured data - research overviews call it one of the main uses of machine vision and student projects demonstrate practical extraction pipelines (document analysis machine vision research overview, CUNY extraction pipeline project examples).

For Billings municipal offices, that means low‑cost pilots can automatically parse benefit applications, inspection reports, and identity documents into existing case‑management systems using no‑code automation and validated extraction rules - reducing repetitive keystrokes, cutting triage steps, and letting staff focus on exceptions and client outreach rather than data entry (no-code automation for Billings municipal agencies).

Start small with a single form type, require human review on edge cases, and align the pilot with Montana's AI compliance guidance so accuracy gains do not create privacy or legal risk - so what? Faster processing means residents get benefits and permits sooner while the city reduces backlog stress on a small workforce.

Policy teams in Billings can use McKinsey's digital‑twin approach to move decisions from intuition to evidence - building virtual replicas that run rapid “what‑if” scenarios across capital cost, transit modalities, accessibility, carbon emissions, and population growth so planners can spot tradeoffs before construction starts; McKinsey shows digital twins can improve public capital and operational efficiency by 20–30% and enable a practical “dig once” posture that reduces rework and community disruption (McKinsey digital twins for government ROI).

For smaller cities, start with a single high‑value use case - an arterial corridor, lift station, or transit hub - and iterate: build the data layer, emulate current operations, then simulate scenarios to quantify ROI and risk.

Practical guides show how real‑time sensors, bidirectional models, and simple emulation layers turn costly assumptions into testable choices (Simio digital twin guide for business leaders); so what? A modeled 20–30% efficiency gain means fewer budget overruns and faster delivery of services funded by federal infrastructure dollars.

Metric / Element	Value / Example
Expected efficiency uplift	20–30% (McKinsey)
First pilot advice	Single high‑value asset (corridor, lift station, transit hub)

“[Digital twin] contains three main parts: a) physical products in Real Space, b) virtual products in Virtual Space, and c) the connections of data and information that ties the virtual and real products together.” - Michael Grieves

Johns Hopkins' TREWS system shows how Billings-area hospitals and emergency departments could grab hours on a sepsis clock: in multi‑site studies TREWS identified sepsis accurately in 82% of cases, reduced mortality by about 20%, and - crucially - flagged the most severe patients nearly six hours earlier than traditional tools, a lead time where “an hour delay can be the difference between life and death” (Johns Hopkins Medicine article on TREWS sepsis detection, Johns Hopkins Hub overview of AI sepsis detection).

TREWS combines EHR history, current vitals, and labs to deliver bedside alerts and suggested actions and was deployed in partnership with major EHR vendors for easier integration - so a staged Billings pilot that connects local ED workflows to an explainable alert can speed antibiotic orders and triage decisions, reduce ED crowding during wildfire smoke surges, and free clinicians to focus on treatment rather than manual surveillance; the payoff is concrete: earlier detection, measurable mortality reduction, and operational relief for small regional hospitals (systematic review on deployment of sepsis prediction machine learning).

Metric	Result
Detection accuracy (study)	82%
Mortality reduction	~20%
Earlier detection (severe cases)	~6 hours
Study scale	~590,000 patients; 4,000+ clinicians

“It is the first instance where AI is implemented at the bedside, used by thousands of providers, and where we're seeing lives saved.” - Suchi Saria

Automating routine code snippets, document templates, and data‑wrangling scripts can turn a small Billings IT or benefits team into a force multiplier: real municipal examples show invoice processing dropping from about a week to 1–2 days when automation and AI‑assisted workflows are deployed (Oracle: AI in local government), and industry analysts estimate generative AI can deliver order‑of‑magnitude productivity gains when paired with role‑specific training and sandboxes (Route Fifty on AI training).

For Billings, practical pilots that combine no‑code document extraction, scripted code templates, and human‑in‑the‑loop review reduce repetitive keystrokes, shorten permit and benefits turnaround, and free experienced staff for complex casework; however, safeguards matter - research warns that poorly governed automation can increase worker stress and shift burdens onto residents, so pair tool rollout with clear oversight, staff reskilling, and an AI use policy aligned to Montana rules (Roosevelt Institute: AI and government workers).

Metric	Result / Source
Invoice processing turnaround	~1 week → 1–2 days (Mt. Lebanon example; Oracle)
Potential productivity uplift	Up to 10× (Deloitte estimate cited in Route Fifty)

“Failures in AI systems, such as wrongful benefit denials, aren't just inconveniences but can be life‑and‑death situations for people who rely upon government programs.”

The University of Michigan's Mcity driverless‑shuttle program - documented in a white paper and campus case study - offers a practical template for Billings to test autonomous microtransit: Mcity combined rich on‑vehicle data (microphones and cameras), extensively trained on‑board attendants, and third‑party rider surveys to learn how people and other road users actually react to shuttles (Mcity driverless-shuttle program white paper, Mcity driverless shuttle campus case study).

For Billings, the lesson is operational: pair clear human oversight and objective sensors with community surveys so pilots produce actionable consumer‑acceptance and safety insights rather than anecdote - an affordable first step is a short circulator route or senior‑transport pilot that uses the same data‑collection and training checklist to reduce operator uncertainty and build public trust (Mcity research and test facility overview).

Attribute	Detail (Mcity sources)
Pilot start	June 2018 (UM campus)
On‑vehicle data	Microphones and cameras to observe riders
Attendant training	~14 hours in test facility + two weeks on route
Survey partner	J.D. Power (rider acceptance analysis)

Estonia's experience shows that translation, accessibility, and inclusion aren't add‑ons but core ingredients of a resilient e‑government: Billings can reduce multilingual barriers and paperwork by combining strong digital identity, “once‑only” data exchange, and built‑in translation tools so residents access permits, benefits, and emergency alerts in the language they use most.

Estonia reached a milestone of offering every government service online, including complex workflows, which is a practical model for making services device‑ and location‑independent (Estonia's 100% digital government services); policy briefs stress interoperability and secure digital identity as replicable building blocks for U.S. states (lessons for U.S. e‑governance on identity and interoperability).

Estonia's Digital Agenda also highlights workforce and upskilling targets so local staff can run inclusive services without costly outsourcing (Estonian Digital Agenda 2030: digital skills & upskilling); so what? the Estonian model claims digital identity saves ~2% of GDP and interoperability frees roughly 1,400 years of work annually - concrete scale‑economies that Billings can pursue by starting with multilingual mobile forms and consented data‑sharing pilots to speed service access for Montana's diverse residents.

Metric	Estonia (reported)
Government services online	100% (Dec 2024)
Estimated GDP saving from digital ID	~2%
Interoperability time saved	~1,400 years/year

“Digitalising divorce reflects Estonia's commitment to making even the most complex life events simpler and more accessible.” - Enel Pungas, Estonian Ministry of Interior

To get Billings started safely and effectively, pair a small, measurable pilot with clear governance: use a regulatory‑sandbox approach to test one prioritized use case (e.g., virtual permitting, emergency dispatch triage, or document extraction) under controlled conditions and data‑sharing rules, as recommended in the FPF overview of regulatory sandboxes that documents cohort testing and real‑world data trials (Regulatory sandboxes for AI governance - FPF overview).

Mirror scalable federal practices for oversight - an AI Governance Board plus a technical Safety Team, centralized inventories, and lifecycle risk checks from the GSA AI Guide and compliance plan - so pilots produce reproducible lessons without legal surprises (GSA AI Guide for Government - AI governance and compliance planning).

Invest in one cohort of city staff with role‑specific training (for example Nucamp's 15‑week AI Essentials for Work course) to write prompts, validate outputs, and keep humans‑in‑the‑loop so the city captures early wins while meeting Montana's regulatory requirements (Nucamp AI Essentials for Work 15-week bootcamp syllabus and registration).

Start with a short, documented pilot (the sandbox playbook commonly runs 3 months to 2 years), require human review on rights‑impacting outcomes, and publish post‑pilot lessons so Billings converts a single pilot into citywide, legally compliant value.

Attribute	Detail
Program	AI Essentials for Work
Length	15 Weeks
Early bird cost	$3,582
Syllabus / Register	Nucamp AI Essentials for Work 15-week bootcamp syllabus and registration

“Alleged failings were widespread and systemic; the bank failed to protect its customers.” - ASIC Deputy Chair Sarah Court