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

For Toledo's city leaders and municipal staff, AI is no longer a remote possibility but a practical lever for faster service, smarter resource allocation, and lower costs - if implemented with guardrails.

Ohio's momentum, captured in the OhioX “State of AI in Ohio” report, signals a statewide push for startups, infrastructure, and collaboration that Toledo can tap into through local pilots and workforce training; at the same time, the State of Ohio's official AI policy lays out governance, procurement, and employee training guardrails that make responsible experimentation doable.

That mix of statewide leadership and local opportunity matters for everyday municipal functions - from front-counter records work to 311 requests - and points to a clear next step: equip city teams with practical prompt-writing and tool-use skills (see the AI Essentials for Work syllabus) so pilots deliver measurable, ethical improvements this fiscal year.

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

“Ohio is at the forefront of the innovative use of technology in the public sector and AI has great potential as a tool for productivity, as well as education, customer service, and quality of life,” said Lt. Governor Jon Husted.

Selection began with federal playbooks and practical constraints: prompts and use cases were chosen to align with the White House AI Action Plan (AI.gov) and federal guidance on safe, secure, and trustworthy deployment - prioritizing mission-aligned problems that are data-rich, sponsor-backed, and likely to yield measurable, ethical improvements this fiscal year (see AI.gov and the Digital.gov guidance for the policy and implementation context).

The GSA AI Guide for Government informed the operational filters: favor Integrated Product Team–friendly pilots, clear data governance, and routes to a central AI technical resource so projects don't stall in procurement or security review.

Practical selection criteria used across all items were impact × feasibility (impact, effort, fit), availability of labeled or easily accessible data, existence of an executive sponsor, and an evidence plan with KPIs and monitoring.

This approach follows the CIO's inventory findings about rapidly expanding federal AI use - so the list emphasizes small, high-confidence pilots that deliver early wins to build institutional momentum and responsible scale-up in Ohio's municipal context.

Smart traffic and camera-driven signal timing can turn stop-and-go frustration into measurable wins for Toledo: decentralized, schedule‑driven systems like SURTRAC - which cluster vehicles, share planned outflows with neighboring intersections, and optimize local green time - have delivered big improvements in pilot tests and are a model for corridor pilots here.

In a nine‑intersection deployment the approach cut travel times by about 25%, boosted average speeds ~34%, reduced stops ~31%, trimmed wait time ~40%, and lowered emissions ~21% - roughly 247 gallons of fuel saved per day and an estimated 588 metric tonnes of annual emissions avoided - showing both commuter time saved and cleaner air as concrete payoffs.

These gains come with tradeoffs that planners must model (coordination can sometimes reduce flexibility for minor movements), so pair camera/sensor feeds with microsimulation and the decentralized messaging architecture described in the SURTRAC patent to keep corridors moving without unintended side effects; see the SURTRAC system details and federal signal‑timing research for practical guidance.

Metric	Change / Value	Source
Travel time	≈ −25%	SURTRAC decentralized traffic control patent (US20140368358A1)
Average speed	≈ +34%	SURTRAC decentralized traffic control patent (US20140368358A1)
Number of stops	≈ −31%	SURTRAC decentralized traffic control patent (US20140368358A1)
Wait time	≈ −40%	SURTRAC decentralized traffic control patent (US20140368358A1)
Emissions	≈ −21% (≈588 metric tonnes/yr)	SURTRAC pilot summary and emissions results
Fuel savings	≈ 247 gallons/day	SURTRAC decentralized traffic control patent (US20140368358A1)
Signal timing best practices	Use microsimulation to evaluate corridor tradeoffs	FHWA signal‑timing research and microsimulation guidance (PB2007100024)

Toledo's Engage Toledo is already the city's 24/7 front door for service requests - call 419‑936‑2020, email engagetoledo@toledo.oh.gov, use the mobile app to upload a photo, or submit a request on the online portal - and the program's scale and turnaround metrics make it a prime candidate for careful AI automation like a 311 chatbot and text‑in service flow that preserve emergency channels while deflecting routine work.

After a rough history (residents once joked about the “419‑936‑Black‑Hole”), Engage Toledo now handles huge volumes - about 138,690 inbound calls in 2023 - with average calls around 3½ minutes, and leaders are evaluating a website chatbot and texting option to speed responses and keep trust high.

Thoughtful automation that routes urgent water/sewer or public‑safety calls to human teams, captures photos and geolocation from the app, and provides status tracking can cut staff handling time while keeping the transparency that built public confidence; see the Engage Toledo portal and a detailed profile of the call center's turnaround for implementation cues.

Metric	Value	Source
Inbound calls (2023)	138,690	Toledo Free Press Engage Toledo profile
Average call length	≈ 3½ minutes	Toledo Free Press Engage Toledo profile
24/7 phone	419‑936‑2020	City of Toledo Engage Toledo portal

“I believe that Engage Toledo has brought trust and confidence.”

Lucas County can turn routine claims reviews into a high‑value, low‑risk pilot by using targeted prompts and anomaly detection to surface the same red flags Ohio already defines as fraud - billing for services not delivered, duplicate billing, upcoding, dispensing generic drugs but billing for brand‑name, and other schemes that drain “millions of dollars” from the state's roughly $23 billion Medicaid program.

A focused “Lucas County claims fraud” prompt can prioritize provider patterns (duplicate claims, mismatched codes, unusual service volumes), flag high‑risk records for human audit, and create a clear escalation path to state authorities so investigations move quickly and with evidence.

Keep the loop tight: flagged cases should route to the Attorney General's Medicaid Fraud Control Unit or the Department of Medicaid reporting channels, and whistleblower protections and false‑claims definitions from Ohio's rules can guide internal policy and safe reporting so staff and residents aren't exposed to retaliation.

Item	Details / Source
Program scale	$23 billion annual Medicaid program - Ohio Attorney General Medicaid fraud overview
Report suspected provider fraud	Call 614‑466‑0722 or the Help Center at 800‑282‑0515 - Ohio Medicaid reporting suspected fraud help center
Definitions & whistleblower protections	False Claims / fraud, waste & abuse definitions and non‑retaliation rules - Ohio Administrative Code 3364-15-02 false claims and non-retaliation rules

Predictive models for EMS demand turn historical patterns into actionable prepositioning that keeps ambulances closer to likely calls - a practical step for Lucas County that complements local dispatch best practices and Ohio's broader public‑safety goals.

Benchmark research from BMC shows how daily ambulance demand forecasting can provide a validated baseline for workforce planning and incremental improvement, and operational pilots can layer weather, urban development, and real‑time incident feeds so units are staged where “seconds can save lives.” Pairing those benchmarks with the playbook in data‑analytics writeups for Fire & EMS helps translate forecasts into shift rosters, dynamic staging rules, and surge plans that reduce idle travel and improve on‑scene times without over‑committing scarce resources; see the EMS demand forecasting benchmarks and practical data analytics guidance for Fire & EMS, and consult local AI use cases for public safety when designing a responsible Lucas County pilot.

Item	Detail
Study	BMC study: Forecasting the daily demand for emergency medical ambulances (2023)
Published	11 July 2023 - BMC Medical Informatics and Decision Making, Vol. 23, Article 117
Access & impact	Open access; Accesses: 2705, Citations: 1, Altmetric: 2
Operational guidance	Operational data analytics guidance for Fire & EMS: Predicting emergency scenarios

For Toledo's procurement teams, a focused “Toledo procurement document extraction” prompt turns mountains of unstructured PDFs - contracts, invoices, receipts, bids - into immediately usable records so staff can stop hunting for renewal dates or payment terms and spend time on strategy instead of data entry.

Tools like Google's Document AI show how specialized parsers and OCR can extract invoice fields, expense items, and bank‑statement elements at scale (lowering procure‑to‑pay TCO by up to 60%) and enrich results with entity validation, while AI contract‑management features from vendors such as JAGGAER support conversational interaction with agreements and automated extraction of key clauses to shorten review cycles and highlight non‑standard terms.

Combined with procurement best practices - classifiers to split composite documents, invoice‑PO matching to flag exceptions, and RFP/bid parsing to accelerate sourcing - this approach yields faster approvals, fewer duplicate payments, and clearer audit trails; pilots can start with invoice and contract extraction, measure straight‑through processing gains, and expand to RFP triage and clause monitoring as confidence grows.

See the Google Document AI procurement solution for procurement patterns and JAGGAER contract automation solutions for practical examples of the stack and outcomes Toledo can adopt.

Benefit / Metric	Value	Source
Lower TCO for procure‑to‑pay	Up to 60%	Google Document AI procurement solution
Reduce bid/tender lead time	≈ 25% faster	Siemens AI bid and tender efficiency blog
Manual work / invoice automation	Up to ~30% work reduction; up to ~90% straight‑through processing reported	V7 Labs procurement software AI automation guide

Infrastructure monitoring in Toledo can move from reactive patches to proactive care by pairing satellite and drone imagery with field sensors and AI models that prioritize repairs where they matter most; studies show an integrated approach - very-high and high-resolution satellite analysis, drone validation, acoustic loggers, and ground‑penetrating radar - detects leak hotspots and reduces false positives, making targeted excavations more cost‑effective (see the SPIE leak‑detection framework).

For Ohio utilities facing aging pipes and climate stress - remember, every two minutes there's a water main break somewhere in the U.S. - high‑cadence satellite systems like the BBC‑reported Space Eye (fresh imagery every six hours from LEO micro‑satellites) promise faster, cheaper detections, while industry pilots have found satellites can narrow the search radius to meters so crews aren't digging blind.

Add AI to process moisture‑sensitive indices and sensor feeds, and Toledo can triage repairs, cut wasted water, and stretch crews farther with fewer truck rolls - an affordable, data‑driven step toward resilient local infrastructure that prioritizes the leaks most likely to become emergencies.

Method	Detects / Role	Key benefit
Satellite imagery + AI	Moisture/vegetation anomalies indicating leaks	Wide‑area monitoring, high revisit cadence
Drone (UAV) validation	Centimetre‑level confirmation of surface hotspots	Precise field targeting before excavation
Acoustic + GPR sensors	In‑pipe sounds and subsurface validation	Reduce false positives; prioritize excavations

A targeted Lucas County EHR outbreak detection prompt turns clinical records into a practical early‑warning system for local public health teams by querying diagnosis codes, lab results, and ZIP‑linked cohorts to surface clusters and prioritize triage - bringing the kind of near‑real‑time insight PCORnet demonstrated when it adapted EHR feeds for COVID‑19 surveillance.

PCORnet's lessons show EHRs can deliver comprehensive data soon after collection, support geographic cohorting (5‑digit ZIP coverage is widespread), and scale to hundreds of thousands of events, so a Lucas County pilot can focus on data quality, distributed queries, and privacy‑preserving linkages to Census or community metrics instead of sharing exact addresses; see the PCORnet surveillance review for practical design details at PCORnet surveillance review (PCD, 2024) - EHR-based surveillance design and findings.

Pair this with the CDC Field Epidemiology Manual playbook - predefine outputs, establish a data lead, integrate ELR and syndromic feeds, and automate situation reports - and Lucas County can spot local upticks and route alerts to clinics, EMS, or outbreak investigators so responses happen in days to weeks rather than months.

Item	Value	Source
Timeliness	Near‑real‑time; COVID updates biweekly–monthly in PCORnet	PCORnet surveillance review (PCD, 2024) - timeliness results
Geographic coverage	5‑digit ZIP available at ~84% of PCORnet sites	PCORnet surveillance review (PCD, 2024) - geographic coverage data
COVID‑19 surveillance (Oct 2022–Dec 2023)	887,051 adults; 139,148 children captured across PCORnet sites	PCORnet surveillance review (PCD, 2024) - cohort capture statistics

Building on the asset-monitoring and emergency-response pilots above, a Toledo Urban Development Simulation prompt uses a digital twin to let planners compare redevelopment scenarios, test infrastructure tradeoffs, and turn pilot lessons into repeatable practice rather than one-off reports that disappear after a project ends; the Journal of Information Technology in Construction paper proposing a Digital Twin Uses Classification System (DTUCS) is especially helpful because it diagnoses three common gaps - missing shared terminology, weak project documentation, and no use‑case taxonomy - and offers a three‑pronged remedy (Standardize‑to‑Publish, Detail‑to‑Prove, Classify‑to‑Reach) that makes cross‑agency reuse realistic.

The paper's bibliography even points to regional work (a Cincinnati bike‑share planning case), underlining Ohio relevance and how a Toledo prompt can require explicit knowledge capture so city teams can simulate outcomes, surface equity or traffic tradeoffs, and hand auditors a reproducible record.

For teams ready to move from ideas to measurable pilots, pair the DTUCS approach with a responsible AI pilot call-to-action to begin ethical, data‑driven simulations this fiscal year.

DTUCS Prong	Purpose	Source
Prong‑A: Standardize‑to‑Publish	Create shared DT terminology for city projects	Digital Twin Uses Classification System (DTUCS) paper on OUCI
Prong‑B: Detail‑to‑Prove	Document methods so pilots are verifiable	Digital Twin Uses Classification System (DTUCS) paper on OUCI
Prong‑C: Classify‑to‑Reach	Build a use‑case taxonomy for reuse across agencies	Digital Twin Uses Classification System (DTUCS) paper on OUCI

A Toledo permitting RPA prompt turns the city's repetitive permit intake tasks into measurable back‑office wins by automating data extraction, validation, routing, fee calculation and inspection scheduling so staff can focus on exceptions and customer service instead of copy‑and‑paste work; industry examples show RPA speeds application processing, minimizes errors, and standardizes approvals while bridging legacy systems and modern portals.

Use cases that map directly to Toledo include automating permit application processing and redaction for records requests, integrating permit fees with procure‑to‑pay flows, and auto‑creating inspection work orders from completed applications - each tied to clear KPIs (reduced handling time, fewer missed renewals, faster turnaround).

Vendors and consultancies recommend starting with high‑volume, rule‑driven processes and a Center of Excellence to govern scale, and local pilots can pair human review lanes with bots to keep control tight.

For practical playbooks and benefits see AIRPA's public‑sector RPA overview, Proven Consult's breakdown of eight government RPA uses, and Mazars' guidance on P2P automation for public services.

Use Case	Benefit	Source
Automating application & permit processing	Faster approvals, fewer errors	Proven Consult: 8 Uses of RPA for Government
Procure‑to‑Pay / invoicing	Lower costs, streamlined P2P	Driving public sector efficiency with RPA (Mazars/ForvisMazars)
Records / FOIA redaction & searches	Faster response times, compliance	Proven Consult: FOIA automation
HR & payroll onboarding	Reduced manual steps, improved accuracy	Robusta: RPA use cases in public sector

“RPA, your powerful lever, removes repetitive, mind-numbing and time-consuming tasks at your work, people feel more refreshed, are more engaged, and get more done when they can achieve a state of flow.”

Ohio cities can turn federal hydrology systems into a practical “NOAA flood forecasting & local leak detection” prompt by feeding National Water Prediction Service outputs and local sensor feeds into a single alert pipeline that prioritizes both riverine flood risk and likely infrastructure failures; NOAA's NWPS (Flood Hazard Outlook, National Water Model short‑range products and 12–18 hour rapid‑onset forecasts) and NSSL's MRMS/FLASH forecasts give the temporal backbone while USGS streamgage data provide ground truth, so models aren't just theoretical.

The payoff is concrete: FLASH's hydrologic models run continent‑scale simulations updated every ten minutes across about 11 million data points, turning scattered observations into near‑real‑time hotspots that can trigger drone flyovers or acoustic leak scans before crews are dispatched.

In practice, a Toledo pilot prompt would combine NWPS flood arrival times, MRMS precipitation inputs, and local telemetry to triage which streets, pump stations, or aging mains deserve immediate inspection - so responders get a narrowed, actionable search radius instead of chasing after noisy tips.

Start with daily automated briefs tied to threshold alerts and measure reduced truck rolls and faster repairs as the KPI for success; federal datasets and local streamgages make this a low‑regret integration for Ohio municipalities.

Tool / Source	Role	Key fact
NOAA National Water Prediction Service (NWPS) official site	Flood outlooks, NWM forecasts, product APIs	Includes Flood Hazard Outlook and short‑range high‑water arrival forecasts
NSSL MRMS & FLASH flood forecasting information	High‑resolution precipitation & flash‑flood modeling	FLASH hydrologic models update every 10 minutes across ~11 million data points
USGS streamgages river data and forecasts	Real‑time streamflow ground truth	Nationwide network (thousands of gauges) used in forecasts

Getting started in Toledo means pairing cautious governance with practical pilots: follow the national trend toward pilot-first executive action described in CDT's review of state AI orders, learn from local leadership - Toledo's own athletics department now requires AI training and has rolled out Microsoft Copilot to staff - and prioritize small, measurable experiments that protect civil rights while delivering quicker service and cost savings.

Begin with a single high-impact use case from this list (311 chatbots, procurement extraction, or flood/leak alerting), name an executive sponsor, define KPIs, and upskill teams before scaling; for practical training that city staff can complete on a 15‑week cadence, see the AI Essentials for Work syllabus from Nucamp.

A responsible pilot this fiscal year - focused on data governance, human review, and transparent reporting - turns policy momentum into local wins and ensures Toledo's public servants keep control while reaping efficiency gains.

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

“Many believe AI represents the Fourth Industrial Revolution – and it's no time to be timid.”