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

Richmond stands at the crossroads of practical promise and real risk: Virginia has already published statewide AI standards to set ethical guardrails (Virginia IT Agency AI Standards for Artificial Intelligence) and the governor's executive order put $600,000 behind pilots to test AI in classrooms and agency systems, signaling serious state investment in safe rollout (Virginia Executive Order 30 on Artificial Intelligence funding and pilots).

At the same time the Youngkin administration is piloting “agentic” AI to scan Richmond's regulations for redundancies - an effort touted to streamline rules and even identify billions in savings - so city managers must balance efficiency gains with transparency, bias mitigation, and a human-in-the-loop review process (Youngkin administration pilot to scan Virginia regulations using agentic AI).

Practical training, like Nucamp's AI Essentials for Work 15-week bootcamp registration that teaches prompt-writing and workplace AI skills, can help Richmond's public servants turn policy into accountable operations.

Bootcamp	Length	Courses Included	Early Bird Cost	Registration
AI Essentials for Work	15 Weeks	AI at Work: Foundations; Writing AI Prompts; Job Based Practical AI Skills	$3,582	Register for Nucamp AI Essentials for Work 15-week bootcamp

“These standards and guidelines will help provide the necessary guardrails to ensure that AI technology will be safely implemented across all state agencies and departments. At the same time, we must utilize these innovative technologies to deliver state services more efficiently and effectively.”

To build a Richmond-ready list of the top 10 AI prompts and use cases, selection prioritized practical, mission-enabling deployments that Virginia agencies could realistically adopt: cases already deployed or close to deployment in the DHS/CISA inventory were weighted highest, especially those that automate repetitive analyst work, surface high-value alerts from terabytes of logs, or produce measurable outputs that humans then validate; the Cybersecurity and Infrastructure Security Agency's catalog (the CISA AI use case inventory (DHS AI use cases)) provided the primary taxonomy and deployment-status flags used to shortlist candidates.

Choices also favored systems with documented monitoring, testing, and human-in-the-loop safeguards - responding directly to GAO's findings on accountability and data provenance for DHS AI systems (see the GAO report on DHS AI accountability) - and were cross-checked against DHS's broader AI playbook and use case library to ensure alignment with federal safety and operational guidance (the DHS AI use case inventory and playbook).

The result is a pragmatic shortlist built on real-world deployments, clear risk controls, and performance metrics - so Richmond leaders get examples that aren't hypothetical but proven in practice, like flagging PII or anomalies rather than promising silver-bullet automation.

Use Case	Use Case ID	Deployment Status
Detection of Personally Identifiable Information (PII) in Cybersecurity Data	DHS-4	Deployed
CISAChat (internal generative AI)	DHS-2306	Deployed
SOC Network Anomaly Detection	DHS-2403	Deployed

“AI offers a once-in-a-generation opportunity to improve the strength and resilience of U.S. critical infrastructure, and we must seize it while minimizing its potential harms.” - Alejandro N. Mayorkas

Richmond's cyber planners should treat AI as a powerful force-multiplier that needs guardrails before widescale use: DHS is explicitly pushing an intentional, responsible roll‑out of AI for cybersecurity, and local agencies can follow that lead to avoid new attack surfaces (DHS intentional AI approach for cybersecurity); practical guidance from DHS's new playbook - summarized by security researchers - frames threats as attacks using AI, attacks against AI systems, and failures in AI design, and recommends a four-part mitigation framework (Govern, Map, Measure, Manage) that maps neatly to priorities for Virginia: governance and workforce training, asset mapping and visibility, continuous measurement through SIEM/SOAR telemetry, and sustained risk controls (Unpacking DHS AI guidelines for securing critical infrastructure - Check Point summary).

Federal advisories from NSA reinforce the playbook with concrete operational products - like SIEM/SOAR and log‑prioritization guidance - that Richmond IT teams can adopt to harden networks without hampering citizen services.

Mitigation	Purpose
Govern	Create a culture of AI risk management and accountability
Map	Understand AI use context and asset interconnections
Measure	Build repeatable systems to monitor AI risks
Manage	Implement and maintain risk controls to reduce harms

Richmond's health and benefits teams can look to the U.S. Department of Veterans Affairs as a concrete roadmap for using automation and AI to speed care and reduce backlogs: VA's Robotic Process Automation (RPA) and claims automation workstreams pushed claims processing from months to hours - UiPath reports claims now can be established in about 12 hours versus roughly 27 days previously - and more than 21.2 million claim packets have moved through automated pipelines that extract between 3.5 and 6.6 million pages per day; at the same time VA's Community Care workflows automated 99.9% of community care claims and ClaimsXM redirected roughly 133,000 employee hours to higher‑value work, showing how low‑code bots plus OCR/NLP can shrink wait times, cut error-prone manual steps, and free staff for complex adjudication (VA Robotic Process Automation and Claims Automation case study, UiPath Veterans Affairs claims automation case study).

For Richmond, the “so what” is simple: automations that respect privacy and human-in-the-loop review can deliver faster payments, better user experiences for constituents, and measurable staff time savings without ripping out legacy systems.

Metric	VA Result
Claims processing time	From ~27 days to ~12 hours
Claim packets processed	21.2M+
Pages extracted per day	3.5–6.6M
Percent established automatically	~75%
Community Care claims automated	99.9%
Employee hours redirected / saved	133,000 (ClaimsXM); 6.4M hours reported saved overall

For Richmond officials looking to modernize city logistics without reinventing the wheel, the U.S. Postal Service shows how AI can tangibly tighten supply chains: machine learning and predictive analytics power route optimization that dynamically reroutes drivers around traffic, weather, or a downtown parade, while robotics and cobots speed sorting centers and edge compute delivers near‑real‑time visibility into parcel flows - so fewer missed deliveries, lower fuel bills, and happier small businesses and residents; practitioners can read a practical roundup of 2024 postal automation trends in BlueCrest's postal optimization and parcel automation trends article at BlueCrest, dive into the mechanics of AI route planning in UpperInc's AI route optimization guide, and watch how the USPS CIO outlines phased AI adoption in public service operations in the USPS AI integration in public service video on FedScoop.

The clear “so what”: combine data, dynamic routing, and human oversight and Richmond can shrink last‑mile cost and latency without compromising privacy or service continuity.

Use Case	Impact for Richmond	Source
AI route optimization	Reduced mileage, faster deliveries, dynamic rerouting	UpperInc / Yellow Systems
Automated sorting & robotics	Higher throughput, fewer manual errors	BlueCrest / Escher
Edge compute & predictive analytics	Real‑time visibility and proactive resource allocation	ProfileTree / Route Fifty

“We've been engaged in leveraging many different capabilities around machine learning. And now, as you look at generative AI, we're looking at where we can bring that value into the Postal Service almost immediately,”

Project Maven's story - launched in 2017 to "automate Processing, Exploitation, and Dissemination (PED) of tactical ... Full‑Motion Video" - offers Richmond concrete lessons for bringing AI into government operations: Maven focused on computer vision that autonomously extracts objects of interest from massive video and imagery streams, using algorithms that could place boundary boxes around vehicles, buildings, and people to speed analyst workflows and compress the kill‑chain (Project Maven Department of Defense overview, West Point analysis of Project Maven and big data at war).

Early fielding exposed predictable tradeoffs - initial accuracy was rudimentary, data was fragmented across hard drives and silos, and human feedback was essential to improve models - so the program doubled down on human‑machine teaming, iterative field testing, and building a cloud‑enabled data pipeline rather than more automation for automation's sake.

For Richmond, the takeaway is practical: invest first in clean data flows, cross‑domain infrastructure, and human‑in‑the‑loop processes (and pair that with local workforce and cloud training pathways) so AI tools amplify skilled staff instead of creating brittle new failure modes (AI Essentials for Work bootcamp registration and syllabus).

Feature	Notes from Research
Launch	2017 (Algorithmic Warfare Cross‑Functional Team / Project Maven)
Primary focus	Computer vision to automate FMV PED and object detection
Early challenge	Data fragmentation, low initial accuracy, integration with legacy systems
Operational lesson	Human‑in‑the‑loop, field testing, cloud/data pipeline investment

“AI for intelligence.”

NOAA's practical use of AI - ranging from machine‑learning photo matches for the imperiled North Atlantic right whale to acoustic classifiers and satellite detections - offers a clear playbook for Mid‑Atlantic environmental monitoring: NOAA Fisheries teamed with data scientists (a Kaggle competition and the WildMe Flukebook deployment) to speed whale ID in a population now down to roughly 360 individuals with only about 70 reproductively active females, and researchers are automating seal counts with VIAME and decoding whale calls from vast hydrophone archives (NOAA Fisheries: Using AI to Study Protected Species).

At the same time NOAA is moving optics, cloud processing, and edge AI into active use - projects that deploy thousands of cameras (the Gulf survey fields ~2,000 cameras across some 3,000 nautical miles) and pair drones, satellites, and underwater sensors with automated image and video analytics to cut months of manual review into hours or days (NOAA feature: Optics Technology in Marine Research).

For Virginia and Richmond-area planners, the “so what” is tangible: adopt validated models, cloud workflows, and human‑in‑the‑loop review to monitor coastal species, improve hazard detection, and turn massive observational datasets into timely, actionable insights.

Metric	Value / Note
Estimated North Atlantic right whales remaining	~360 individuals
Reproductively active right whale females	~70
Gulf survey annual camera deployments	~2,000 cameras
Survey coverage (Gulf project)	~3,000 linear nautical miles

“Once a survey is complete, we will have collected thousands of hours of footage that must be reviewed. Many surveys deploy hundreds - or even thousands - of cameras. The sheer amount of data can be overwhelming.” - Dr. Matthew Campbell, NOAA Fisheries

Richmond already sits on the starting line for smarter streets - the city appears in vendor case studies for transit signal priority and traffic control system integration - so Los Angeles‑style adaptive signal and IoT approaches are a practical next step (Econolite Richmond transit signal priority and traffic control case study).

Proven models from California show what's possible at scale: LA's ATSAC network uses thousands of sensors and adaptive signals to shave intersection delays by more than 32% while prioritizing buses and emergency vehicles, and corridor pilots combining IoT sensors with machine learning can retime signals, manage ramp meters, and reroute traffic in real time (California AI and IoT traffic management for adaptive signals and sensors).

Other deployments - like machine‑learning systems in Pittsburgh that cut travel time by roughly 25% - show the “so what”: targeted investments in sensors, adaptive controllers, and operator training can keep Richmond's buses on schedule, reduce idling and emissions, and make downtown events and emergency response far more predictable (machine-learning traffic systems case study including Pittsburgh travel time reduction).

New York's FDNY experiments offer a concrete playbook for Richmond's public-safety leaders: NYU Tandon's C2SMARTER team built a “digital twin” of a West Harlem district to simulate why ambulance and engine response times have crept up (from about 6 minutes 45 seconds to 7 minutes 26 seconds over a decade) and to test fixes before touching real streets, while live routing pilots aim to give crews AI-driven, real‑time guidance that factors in traffic sensors, dispatch logs, Waze feeds, taxis, and social signals; AI even models how drivers react to sirens so suggested routes are realistic and safe.

Those pilots underline a practical “so what”: simulated testing plus human-in-the-loop routing can shave critical seconds at scale - literally the difference between life and death - so Richmond should consider phased digital‑twin and routing pilots that prioritize accuracy, operator oversight, and data sources that respect privacy and interoperability (see the NYU Tandon project and Smart Cities Dive coverage for technical and operational detail).

Metric / Item	Value / Note
FY 2023 average FDNY response time	7 minutes 26 seconds
Earlier benchmark (about a decade prior)	6 minutes 45 seconds
Digital twin pilot timeline	Project began Oct 2023; expected conclusion Sep 2024

“Every second that's saved may save a life.” - Jingqin Gao, C2SMARTER

U.S. Army pilots show how AI can make government training far more personalized and practical for public‑sector workers: by using AI to generate and adapt rich, whole‑of‑force scenarios - think a swarm of more than forty drones at Fort Irwin used to stress-test defenses - planners can compress months of scenario development into days and tailor exercises to specific mission sets or local staffing levels (AI Integration for Scenario Development: AI-driven Scenario Development for Military Training).

Early tests with platforms like Scale Donovan returned roughly 70–80% solutions for complex research tasks and helped identify missing civic details (sewage, medical, utilities) that matter to realistic training, while one trial estimated AI cut a task by about ten hours - so the “so what” is immediate: smarter scenario tooling frees trainers to focus on judgment, oversight, and localized policy rather than rote document assembly.

Practical adoption for Richmond's workforce will require constrained, government‑controlled datasets, human‑in‑the‑loop validation, and new roles - librarian-style prompt engineers and quality‑control managers - paired with civilian upskilling pathways such as the government AI training series already used to bring employees up to speed (GSA AI Training Series: Empowering Responsible AI for Government Employees).

“garbage in, garbage out”

Richmond's finance and administrative leaders can borrow a pragmatic IRS playbook for automating paperwork and improving fraud detection: the agency began installing chatbots and voicebots in December 2021 to automate rule‑based taxpayer interactions (notably for payment plans), and by September 2022 those voicebots had handled over 4.8 million calls with roughly 40% resolved without live escalation - illustrating how targeted automation can free staff for higher‑value reviews (IRS increases use of chatbots to automate taxpayer interactions).

At the same time, AI models are being applied to audit selection and refundable‑credit screening to help close a tax gap estimated at $688 billion in 2021, showing real revenue and compliance upside when models are used to prioritize risk (How AI is helping the IRS close the tax gap).

The “so what” for Virginia: deploy narrow, rule‑based assistants for routine transactions, pair model outputs with human review to catch edge cases and bias, and invest in documentation and modern data pipelines so automation boosts service rather than creates new escalation burdens - paired with local training pathways to upskill staff (Nucamp Full Stack Web + Mobile Development bootcamp for cloud and workforce training in Richmond).

Metric	Value / Note
Voicebot calls assisted (by Sep 2022)	~4.8 million
Percent resolved by voicebot without live escalation	~40%
2024 call outcomes for payment plans	22.5% completed on call; ~67% transferred to live agent
Estimated U.S. tax gap (2021)	$688 billion

“The voicebot is a huge success story for the IRS and collection.” - Darren Guillot

Richmond's policy teams can turn Federal Reserve research into concrete tools: the Richmond Fed's April 2025 Economic Brief shows that AI “news shocks” often lift stock prices and boost GDP, consumption, and investment almost immediately while total‑factor productivity (TFP) tends to creep up over years - a reminder that Virginia leaders should plan for fast‑moving expectations even as real productivity gains diffuse slowly (Richmond Fed Economic Brief: What Can News Shocks Tell Us About the Effects of AI? (Apr 2025)).

At the same time, Fed work on natural‑language processing and nowcasting demonstrates how unstructured sources - earnings calls, news, and payment data - can be parsed for near‑real‑time signals that inform local hiring, housing, and revenue forecasts, turning “mountains of words” into timely indicators useful for city budgets and workforce planning (Richmond Fed Artificial Intelligence research and podcasts).

The practical takeaway for Virginia: pair cautious scenario planning with fast, explainable AI‑driven indicators so short‑term market optimism doesn't outpace durable, inclusive growth.

Research item	Relevance for Richmond
Economic Brief: What Can News Shocks Tell Us About the Effects of AI? (Apr 2025)	Explains how AI announcements move macro aggregates quickly while TFP rises slowly - useful for fiscal and investment timing
Richmond Fed AI hub & podcasts	Showcases NLP/nowcasting and sectoral surveys that can supply faster local labor, payments, and housing signals

“Announcements regarding AI can have significant impact on the economy, even if the actual technology isn't enacted for a while.”

Richmond's immediate playbook is clear: treat the governor's new agentic AI pilot as a controlled experiment - one that pairs the Office of Regulatory Management's AI guidelines with strict human‑in‑the‑loop review, transparent metrics, and workforce training so gains don't come at the cost of fairness or auditability.

Practical next steps include adopting ORM's AI standards as governance guardrails, standing up a color‑coded dashboard to track pilot progress (the same technique Virginia uses to flag laggard agencies), and measuring outcomes against concrete targets - Youngkin has already pushed the state from a 25% reduction goal toward a new 35% target after earlier reforms cut nearly 89,000 requirements and, in one housing example, lowered construction costs enough to save buyers roughly $24,000.

Fund pilots thoughtfully (the 2024 order earmarked $600,000 for pilots), require public reporting, and close the skills gap with targeted training - Richmond staff can build prompt‑writing and workplace AI skills through programs like the Nucamp AI Essentials for Work bootcamp registration - so the city moves from promise to accountable practice without sacrificing service or oversight (Youngkin agentic AI pilot coverage (Virginia Mercury), Virginia Office of Regulatory Management AI guidelines, Nucamp AI Essentials for Work bootcamp registration).

Priority	Next step	Resource
Governance & transparency	Adopt ORM AI guidelines & public reporting	Virginia Office of Regulatory Management AI guidelines
Pilots & measurement	Run controlled agentic AI scans with dashboards	Coverage of Youngkin agentic AI pilot (Virginia Mercury)
Workforce & skills	Train staff in prompt design and oversight	Nucamp AI Essentials for Work bootcamp registration
Funding	Allocate pilot funds with audit requirements	$600,000 pilot funding (state executive order)

“We have made tremendous strides towards streamlining regulations and the regulatory process in the commonwealth. Using emergent artificial intelligence tools, we will push this effort further in order to continue our mission of unleashing Virginia's economy in a way that benefits all of its citizens.”