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

Cleveland's city agencies are already turning to AI to make government faster and more transparent - Urban AI now runs the City's data center of excellence, relaunching 311 and the Open Data Portal while earning a 2025 SAG award for GIS innovation (City of Cleveland Urban AI data center of excellence), and a planned pilot will outfit a city car with cameras and City Detect software to survey 1,264 miles of streets (one car could photograph every parcel in about a month) to spot dumping, graffiti, and structural hazards (Signal Cleveland report on city property condition AI photos).

Those local pilots sit alongside Ohio's statewide IT‑17 framework - an Ohio DAS policy that creates a multi‑agency AI Council, sandboxes, and procurement guardrails - making now the moment for municipal leaders to upskill staff in prompt-writing, data stewardship, and ethical oversight; practical training like Nucamp's 15‑week AI Essentials for Work helps nontechnical city teams use AI responsibly and measure impact (Ohio DAS IT‑17 state AI policy).

“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,” - Lt. Governor Jon Husted

Selection prioritized practical impact for Ohio agencies by following InnovateOhio's playbook: a five‑step policy‑to‑practice method that converts high‑level goals into measurable directives and resource maps, ensuring each prompt or use case can be operationalized in a school or municipal workflow (InnovateOhio AI Toolkit: policy-to-practice guidance for Ohio agencies).

Choices were screened against five criteria drawn from that toolkit - actionability, governance/readiness, equity & privacy risk, available implementation resources, and stakeholder demand - and cross‑checked with curated district examples and prompt templates from practitioner collections (AI resources for districts and teachers: curated practitioner templates).

Public interest and uptake guided prioritization: the statewide K–12 toolkit has been downloaded and viewed tens of thousands of times, signaling readiness for tools that deliver near‑term benefits like improved case triage, targeted interventions, or safer automated monitoring (AI Education Toolkit milestone and public adoption).

The result: ten prompts/use cases that map to policy steps, include vetted resources, and are ready for Cleveland pilots with built‑in governance checkpoints.

Ohio's Department of Administrative Services (DAS) is driving regulatory code modernization that moves AI from vague contract language into operational guardrails - establishing a multi‑agency AI Council, procurement checklists, and sandboxes so Cleveland agencies can test models, vet third‑party vendors, and require explainability before signing long‑term contracts; this matters because one short sandbox-to‑procurement path lets a pilot chatbot prove privacy, fairness, and measurable service-time improvements before a costly statewide rollout, reducing legal and vendor lock‑in risk.

Updated code templates should mandate vendor oversight checkpoints (data access, audit logs, incident playbooks) and ensure staff have the prompt‑writing and governance skills to enforce them - skills municipal teams can build from tailored playbooks and training resources such as a municipal AI starter playbook for Cleveland pilots (Municipal AI starter playbook for Cleveland AI pilots) and targeted upskilling in data literacy and AI oversight for government staff (Data literacy and AI oversight training for Cleveland government staff), enabling Cleveland to run accountable pilots that inform procurement rather than be constrained by it.

Ohio's IT‑17 policy sets a statewide operational framework for planning, procurement, security, privacy, and governance of AI - authorizing agency use while forcing concrete checkpoints: an AI Council Charter, an AI Governance Operations Chart, and an AI Procurement Checklist Template so Cleveland leaders can evaluate vendors with AI Comparison Criteria and require documented governance before scaling pilots.

The IT‑17 bundle (including a Generative AI Central Repository Template and a Use of AI FAQ) turns abstract risk into actionable tasks - agencies get templates for procurement, comparison, and governance that make a pilot's success measurable rather than anecdotal - so a Cleveland chatbot trial can show documented fairness and privacy checks before signing a citywide contract.

Learn the policy and download the artifacts from the Ohio DAS IT‑17 policy and resources page (Ohio DAS IT‑17 policy and resources) or review a municipal starter playbook for Cleveland pilots (Municipal AI starter playbook for Cleveland AI pilots (2025)); for questions contact DAS at 30 E. Broad St., Columbus, OH, phone 614‑466‑6511.

Cleveland agencies can use a governed sandbox to move experiments from ideas to procurement-ready pilots: Ohio's IT‑17 creates the Multi‑Agency AI Council and artifacts like a Generative AI Central Repository Template that formally authorize a statewide sandbox for testing use cases and workforce impacts (Ohio DAS IT‑17 policy and sandbox templates), while practitioner guidance from AWS shows how a Generative AI Sandbox on AWS uses an isolated account, Amazon Bedrock Studio, AWS IAM Identity Center, and AWS PrivateLink to keep model testing and sensitive traffic off the public internet and enforce access controls - letting teams compare LLMs, build retrieval‑augmented apps, and share vetted prompts across departments (AWS guide: Implementing a secure Generative AI Sandbox for the public sector).

Local pilots that validate privacy guardrails, explainability checks, and measurable service‑time gains inside the sandbox shorten procurement cycles and reduce vendor lock‑in, aligning Cleveland experiments with the state's oversight goals described in recent policy analysis (Policy analysis of Ohio's Multi‑Agency AI Council and sandbox).

Citizen-facing chatbots are already delivering measurable customer-service gains that Cleveland agencies can replicate under Ohio's IT‑17 governance: a federal example from CMS/OSFLO resolved 20% of incoming calls with its first model and projects 65% resolution at full implementation, a concrete efficiency win that can shorten wait times and free human agents for complex cases (Ohio IT‑17 AI governance policy and HHS chatbot example); the HHS AI use‑case inventory likewise lists “Help Desk Responses” and several chatbot pilots, offering vetted templates and lifecycle stages Cleveland teams can mirror to meet privacy, equity, and explainability checkpoints (HHS AI use‑case inventory for help desk responses and chatbot pilots).

For Cleveland, the bottom line is clear: a governed chatbot that reaches even the conservative 20% resolution rate immediately reduces call center load, and scaling toward the 65% projection creates budgetary headroom to reassign staff into higher‑value outreach, casework, or multilingual support.

University Hospitals Cleveland Medical Center is scaling medical‑imaging AI from lab to bedside - deploying Qure.ai's FDA‑cleared qXR‑LN as a “second read” on chest X‑rays to flag subtle pulmonary nodules for follow‑up and running a clinical trial to compare the algorithm's detection against radiologists' reads (UH press release on qXR‑LN deployment); this matters because national LDCT screening uptake is low (about 16 of 100 eligible people), so opportunistic nodule detection on routine X‑rays can materially increase early diagnoses.

UH's RadiCLE collaborative and Radiology research programs pair that deployment with validation pipelines and industry pilots - working with GE, AzMed (fracture detection), Siemens Healthineers, and triage partners - to move tools into clinical PACS, validate real‑world performance, and curate de‑identified datasets for reproducible trials (RadiCLE: UH's AI research collaborative).

With a lung‑screening program that evaluates ~1,000 patients/month (over 10,000 enrolled), UH can produce the outcome data Cleveland public‑health leaders need to decide whether imaging‑augmented screening lowers time‑to‑diagnosis and shifts more cancers to earlier, treatable stages.

“AI serves as an additional set of eyes for radiologists, enhancing detection by flagging lung nodules that may require further evaluation.” - Amit Gupta, MD

Ohio's TechCred program offers Cleveland agencies a fast, low‑cost path to build AI capacity: the state's “earn while you learn” model funds short (under‑12‑month) technology credentials, has awarded 2,758 AI credentials to 261 employers to date, and makes many trainings available online so municipal teams can quickly stand up prompt‑writing, data‑stewardship, or chatbot‑oversight roles without long payback periods (Crain's Cleveland article on Ohio AI innovation and government training).

Employers can receive reimbursements - commonly up to $2,000 per completed credential - so a modest TechCred application can cover training costs for a whole city unit, turning a pilot's governance checkpoints into deployable staff skills that meet IT‑17 procurement and audit requirements (Ohio TechCred workforce credentialing program).

“With TechCred everybody wins - employers get a more skilled workforce and their employees earn skills that give them more job security.” - Lt. Governor Jon Husted

InnovateOhio's K–12 AI Education Toolkit - launched by Lt. Governor Jon Husted and developed with the AI Education Project (aiEDU) - turns policy into ready-to-use assets for Ohio districts, offering introductions to AI for teachers and parents, superintendent policy templates, and explicit guidance on student privacy, data security, and ethics; the toolkit has been visited more than 30,000 times, a concrete signal that districts are actively seeking practical implementation help and that Cleveland school leaders can adopt vetted templates rather than invent local policy from scratch (InnovateOhio AI K–12 Toolkit announcement by the Lt. Governor, Ohio Department of Education AI Education Toolkit milestone and resource guide).

This resource shortens the timeline from pilot to districtwide policy by packaging governance checkpoints with classroom-ready materials, making it easier for Cleveland to meet state AI-readiness and procurement expectations.

“AI technology is here to stay... The predominant request was educators wanting help implementing the technology in the classroom. This toolkit is a resource for those who will prepare our students for success in an AI world. It continues our work to ensure Ohio is a leader in responding to the challenges and opportunities made possible by artificial intelligence.” - Lt. Governor Jon Husted

Fairness audits for public‑safety AI in Cleveland should be practical, repeatable checks that tie directly to procurement and daily operations: use a municipal AI starter playbook to define measurable test cases and acceptance criteria, require auditors with data‑literacy and AI oversight skills to run error‑rate and bias analyses, and include explicit multilingual performance tests so models don't replicate the processing delays that already hinder non‑English speakers accessing services.

Embedding audits in a governed sandbox produces artifacts - test datasets, audit logs, and remediation plans - that make vendor accountability and scaling decisions evidence‑based rather than anecdotal, and short, targeted training programs equip city teams to enforce those checkpoints across vendors and use cases.

Prioritizing language‑access checks and upskilling staff turns fairness auditing from a checkbox into a tool that preserves public trust while enabling safer, more equitable public‑safety deployments (Cleveland municipal AI starter playbook for pilot programs, data literacy and AI oversight training for government auditors, AI-powered translation tools to improve access to municipal services).

Operationalize Ohio's IT‑17 intent by building an incident response and monitoring playbook that turns policy into a repeatable municipal routine: adopt DAS's IT‑17 governance artifacts as the baseline for roles, logs, and vendor checkpoints (Ohio DAS IT‑17 cybersecurity policy), and use a tested incident‑plan template to define concrete timelines - 24‑hour containment and triage, a five‑member cross‑functional response team, and notification to affected parties within 72 hours - to limit exposure and preserve the integrity of resident data (Genie AI incident response plan template).

Include continuous monitoring hooks (audit logs, alert thresholds, and vendor escalation paths) so every sandboxed pilot or production model produces an evidence trail for audits and procurement; that matters because a documented 24‑hour containment plus timely 72‑hour disclosures converts abstract risk into action steps that protect Ohioans' data and make vendor accountability measurable rather than anecdotal.

A practical third‑party vendor checklist for Cleveland should insist that contracts explicitly align vendor deliverables with a municipal AI starter playbook, require built‑in language access (for example, translation tools that help non‑English speakers navigate benefits and reduce processing delays), and include vendor‑supported training so city teams build the data‑literacy and AI‑oversight skills needed to validate performance and hold suppliers accountable; tying procurement milestones to the starter playbook's templates and to measurable staff upskilling turns pilots into auditable, scalable services rather than one‑off experiments.

See the municipal starter playbook for Cleveland pilots, examples of translation tools improving access to services, and guidance on building data‑literacy and oversight skills for government staff (Municipal AI starter playbook for Cleveland pilots, Translation tools improving access for non‑English speakers in Cleveland, Data‑literacy and AI oversight training for government staff in Cleveland).

Next steps for Cleveland leaders are practical and short‑term: adopt Ohio's IT‑17 artifacts as the baseline for any pilot, run experiments inside the state‑authorized sandbox with Urban AI coordinating cross‑agency data and process standards, and fund rapid staff certification through TechCred so frontline teams and a five‑member incident response unit can meet procurement and audit requirements ( Ohio DAS IT‑17 policy and templates, City of Cleveland Urban AI initiative, Ohio TechCred workforce training program ).

Require vendor checklists, documented fairness tests, and the 24‑hour containment / 72‑hour notification incident cadence so pilots produce auditable evidence for procurement decisions - small, governed pilots that deliver measurable service‑time improvements will reduce legal and vendor lock‑in while building public trust.

“around 70% of the audit typically focuses on data-related questions.” - Ilia Badeev