AI will
Presented for the Government Finance Officers Association of New Jersey NJ GFOA. Last updated: September 10, 2025
Artificial intelligence (AI) refers to systems that perform tasks requiring human-like judgment—pattern recognition, prediction, and language. Most modern AI relies on machine learning and, increasingly, large language models (LLMs) that generate text, tables, code, and summaries. For public finance, the NIST AI Risk Management Framework (AI RMF 1.0) is the go‑to reference for governance and controls (Govern → Map → Measure → Manage), offering a practical backbone to manage model risk, documentation, and oversight.
Platform landscape (what differentiates them):
Expect rapid adoption of multimodal copilots (text + tables + charts + PDFs), agentic workflows that chain steps (e.g., “pull GL, detect anomalies, draft variance memo”), and AI embedded in ERPs/budget suites. In New Jersey, the Governor’s Executive Order 346 established the State’s AI Task Force; the Administration has published updates and reports underscoring responsible, practical adoption for government services (Nov. 12, 2024 release).
Federal policy shifted on Jan 23, 2025 with the White House order “Removing Barriers to American Leadership in AI”, followed by OMB’s memo M‑25‑21. While directed at federal agencies, these documents influence vendor claims, procurement templates, and practices that counties/municipalities will encounter. Anchor your governance to NIST AI RMF and monitor New Jersey’s interim gen‑AI guidance (25‑OIT‑001) and OPRA updates (P.L. 2024, c.16).
Robotics, as a facet of embodied AI, is poised to transform government operations with applications like autonomous drones for infrastructure inspections, robotic assistants for public safety, and automated systems for waste management. These systems will integrate with existing AI frameworks, requiring robust governance to ensure safety, transparency, and compliance with standards like NIST AI RMF. New Jersey’s AI Task Force is expected to expand its guidance to cover robotics, addressing ethical deployment and public trust in these technologies.
Excel & financial modeling. Show Copilot in Excel generating/explaining formulas, proposing pivot tables, and spotting outliers; pair with the built‑in Analyze Data feature for natural‑language questions. See Copilot in Excel and Analyze Data.
Memos, audit summaries & reporting. In Word/OneDrive, draft from a prompt and summarize long documents into findings and next steps—ideal for audit follow‑ups, ACFR notes, and council briefings. See Draft with Copilot in Word and Summarize in Word. For email threads and attachments, see Outlook summaries.
Procurement, budget analysis & grants. Use AI to draft SOWs from templates, summarize vendor proposals, and extract key dates/deliverables from NOFOs into a checklist. Frame acquisition with GSA’s “Buy AI” resources and keep an eye on the 2024 Uniform Guidance revisions (effective Oct 1, 2024) that impact grants processes (see EPA’s summary overview and Grants.gov Quick Start).
Real municipal use today. Government Technology profiled Mt. Lebanon, PA’s AI‑enabled AP automation initiative (coding/electronic processing of invoices to increase efficiency), offering a replicable pattern for finance offices (article; vendor background: Stampli). For a county‑wide framework, see NACo’s AI County Compass toolkit (PDF update).
Hands‑on prompts attendees can try later (paste into your approved AI tool):
Excel (variance): From table "GeneralFund_Actuals_2024", compute MoM variance by department and flag any variance > 10% and > $50,000; draft a one‑paragraph narrative for the top 5 variances.Audit summary: Summarize the attached Single Audit findings into: finding, cause, effect, recommendation, management response; output a one‑page brief for the CFO.Procurement: Draft a scope of work for annual external audit services (GAGAS), including deliverables, timelines, independence requirements, and 3 evaluation criteria.Grants tracking: Extract key compliance dates, reporting requirements, and eligible/ineligible costs from this NOFO into a checklist with due dates.Policy alignment. Adopt a policy mapped to the NIST AI RMF, align with New Jersey’s interim gen‑AI guidance (25‑OIT‑001), and coordinate with your records custodian for OPRA (P.L. 2024, c.16). For acquisition, consult GSA’s Buy AI.
Data safeguards for finance teams. Prefer government editions: Microsoft 365 Copilot in GCC (see limitations & roadmap); use Azure OpenAI (FedRAMP High) or Claude via AWS Bedrock (FedRAMP High/IL4‑5). Enforce Microsoft Purview controls—DLP for Copilot (DLP policy location, sensitivity labels, Copilot privacy). If any workflow touches cardholder data, consider PCI DSS 4.x “future‑dated” requirements (mandatory after Mar 31, 2025): PCI SSC guidance.
We begin with ChatGPT and Gemini because they lead today’s GenAI landscape by usage.
Source of chart: Semrush (Aug 2025), via a16z Consumer. For informational purposes only.
Large language models, like ChatGPT, offer a wide array of capabilities. Depending on user expertise and needs, its applications can range from simple text generation to creating complex autonomous workflows.
Ideal for individuals taking their initial steps into the AI language model realm, ChatGPT offers:
As users advance their familiarity with the platform, ChatGPT can be utilized for:
Catering to technologically adept users and those seeking in-depth digital integrations, ChatGPT is capable of:
Embracing the capabilities of ChatGPT allows for tailored solutions across different expertise levels, ensuring that each user can harness its power to the fullest extent relevant to their needs.
The model evaluates many possible paths (faint, flowing lines). The most probable path lights up as an electrical pulse.
Large language models (LLMs) learn to predict the next token (word or sub-word) given the previous ones. With billions of examples, they internalize grammar, styles, facts, and patterns of reasoning. This simple objective—scaled up—explains why they can draft emails, write code, summarize budgets, and answer questions coherently.
Pretraining: learn general language patterns from large text corpora.
Alignment (RLHF / DPO): steer the model toward helpful, honest, and harmless behavior using human and AI feedback.
Finetunes & System Prompts: add role-specific skills (e.g., finance analysis) and operating rules for production use.
Modern LLMs don’t work in isolation. They can call tools (calculators, SQL/BI, GIS, search) and retrieve trusted documents (RAG) before drafting an answer. This makes outputs fresher, more verifiable, and auditable—crucial for government finance.
Many systems show step-by-step “chain-of-thought” or offer an extended-thinking mode. These can improve accuracy on hard problems, but recent research shows the written reasoning is not always a faithful record of the model’s internal process (it can omit sources or post-rationalize). Treat visible reasoning as a helpful explanation, not a guaranteed audit log; pair it with citations or verification when decisions matter.
🔗 Anthropic – Reasoning Models Don’t Always Say What They Think · “Faithfulness in Chain-of-Thought” (arXiv 2024)
New “reasoning” modes spend more tokens thinking before answering—often boosting math, coding, or multi-step analysis—yet more text isn’t automatically more faithful. Use when stakes or complexity are high; otherwise default to fast mode.
🔗 OpenAI – Reasoning Tokens · “Test-Time Scaling in Reasoning Models Is Not Effective for Knowledge-Intensive Tasks Yet” (arXiv 2025)
LLMs can be wrong, overconfident, or outdated. In production, combine them with retrieval, validation checks, constrained outputs, and human review. Where possible, prefer answers with sources and log the inputs, tools, and data used to produce each result.
Large Language Models (LLMs) often produce plausible but incorrect responses. Users criticize this as one of AI’s core weaknesses.
Like students on multiple-choice exams, LLMs are trained to guess when uncertain. Eliminating obvious wrong answers and then guessing improves accuracy on benchmarks because there’s no penalty for being wrong.
When sampling many outputs, strong agreement across answers suggests confidence, while variation signals uncertainty. However, models are not rewarded for expressing uncertainty, only for producing confident-sounding answers.
Hallucinations aren’t a bug—they’re a byproduct of training incentives. With small shifts in how we evaluate and reward models, future LLMs could become more reliable by openly admitting uncertainty.
AI “hallucinations” are not mysterious flaws but predictable side-effects of how LLMs are trained and tested. Like students on exams, they are rewarded for correct answers but not for honesty about uncertainty. This boosts benchmark scores but creates trust issues in real-world use. With new incentives—such as rewarding partial credit for “I don’t know” and penalizing confident wrong answers— future models can become more trustworthy and transparent.
ChatGPT (GPT-5): OpenAI’s latest flagship model designed to “think” deeper when needed and respond quickly when not—excellent at coding, analysis, and writing. Website: openai.com/gpt-5 · Developers: API post
Grok: xAI’s AI assistant designed to provide helpful and truthful answers, accessible on multiple platforms with free and premium usage options. Website: x.ai/grok · API: xAI API
Claude 4: Anthropic’s reasoning-first models built for long, complex tasks and agent workflows—great for structured planning and coding. Website: Introducing Claude 4 · Update: Opus 4.1
Gemini: Google’s multimodal “thinking” model family; used across Google Search (AI Overviews / AI Mode) and Google AI Studio / Vertex AI. Website: Gemini ecosystem · In Search: AI in Search
Copilot: Microsoft’s assistant across web, Windows, and Microsoft 365—chat, search, agents, and Office integration (Word, Excel, PowerPoint). Website: copilot.microsoft.com · For orgs: Copilot for organizations
Open LLM Leaderboard — compare open models and benchmarks in one place.
Explore some cutting-edge examples of AI-driven video generation, showcasing the exciting advancements in this field. This technology is poised to be a significant feature in the near future.
These examples highlight the creative and sometimes thought-provoking capabilities emerging from AI video generation models.
By 2024, ChatGPT's voice technology has reached new heights, providing users with natural, interactive voice experiences. This is powered by cutting-edge text-to-speech models that can generate lifelike voices from just a few seconds of real speech. OpenAI's collaboration with professional voice actors ensures authenticity and quality, making it a go-to for hands-free use cases, storytelling, and real-time conversation. Additionally, integrations with platforms like Spotify, through features such as Voice Translation, have further expanded its reach, allowing podcasters to translate content into multiple languages while retaining their original voice and style. These capabilities provide both creative and accessibility-focused applications.
ChatGPT's ability to interpret images has evolved significantly, utilizing its multimodal models, GPT-4 and GPT-4V, to analyze and engage with visual content like photos, screenshots, and documents. Users can now upload multiple images to troubleshoot issues, plan meals based on their fridge contents, or decode professional diagrams for work. This visual processing is further enhanced by features like the drawing tool, which lets users highlight specific parts of images for more accurate assistance. The system’s vision capabilities have been honed with real-world feedback and careful testing, especially in collaboration with services like Be My Eyes to assist visually impaired users, ensuring both utility and privacy.
Author: OPM.gov
Description: Responsible Use of Generative Artificial Intelligence for the Federal Workforce, outlining the benefits, risks, and recommendations for using GenAI in the federal workforce. It emphasizes the importance of safe, secure, and responsible usage, and provides guidance on best practices, potential applications, and continuous learning resources for federal employees.
Author:Dr. Alan R. Shark
Date:16th August 2023
Description: The article discusses the importance of developing an AI-ready workforce and outlines the challenges and strategies to achieve this goal. It emphasizes that while there is significant talk about the need for such a workforce, the focus should be on how to actually achieve it. The article highlights the unique nature of AI compared to other technologies and underscores the need to integrate AI into various disciplines, including public administration and policy.
Organization:National Institute of Standards and Technology
Description: A comprehensive framework that offers guidelines for assessing and mitigating risks associated with the use of AI.
Organization:Government of Washington State
Description: Offers interim measures for responsible AI implementation, with a focus on purposeful and ethical usage.
Organization: City of Seattle
Description: A case study on how Seattle is utilizing generative AI in its governance and administrative tasks.
Organization:City of Boston
Description: Provides guidelines that offer a roadmap for the City of Boston's efforts to responsibly integrate generative AI in various sectors.
The use of AI should support the work of our workforce to deliver better, safer, more efficient and equitable services and products to our residents.
Fact: Check and review all content generated by AI, especially if it will be used in public communication or decision making.
Why: While Generative AI can rapidly produce clear prose, the information and content might be inaccurate, outdated, or simply made up.
What to look for: Inaccurate information including links and references to events or facts.
Disclose:that you have used AI to generate the content. You should also include the version and type of model you used (e.g, Open AI's GPT 3.5 vs Google's Bard). You should include a reference as a footer to the fact that you used generative AI:
Author:RJ Wolcott
Date: June 21, 2023
Organization: WSU News & Media Relations
Description: Offers insights into the latest guidelines on AI and data stewardship, which can be a useful reference for government agencies.
Crafting the perfect prompt is crucial when leveraging ChatGPT for Municipal Finance. A well-designed prompt sets the stage for clear and relevant responses from the AI model. It should be specific, concise, and tailored to the desired information or task. By providing detailed instructions and context within the prompt, assessors can guide ChatGPT towards generating accurate and insightful outputs. A carefully crafted prompt ensures effective communication with the AI model, maximizing its potential in assisting with data summarization, analysis, research, and report generation for Municipal Finance purposes.
A prompt contains any of the following elements:
Not every prompt needs all four elements—the structure depends on the task. In practice, combining them strategically leads to the most reliable results.
Source: www.promptingguide.ai
Summary: GPT-5 goes beyond “generic workflows” and shines in agentic setups—better tool calling, instruction adherence, and long-context reasoning—so use that to your advantage.
OpenAI recommends the Responses API so reasoning persists between tool calls, enabling tighter multi-step plans and smarter follow-ups; you can dial the model’s “eagerness” with lower reasoning_effort and by stating concrete exploration rules (goals, stop criteria, uncertainty handling).
Pair these with the Cookbook’s prompt optimizer patterns to tune coding and frontend tasks, and remember: prompting isn’t one-size-fits-all—iterate quickly on clear criteria, measure, and refine.
<context_gathering>
Goal: Get enough context fast. Parallelize discovery and stop as soon as you can act.
Method:
- Start broad, then fan out to focused subqueries.
- In parallel, launch varied queries; read top hits per query.
- Deduplicate paths and cache; don’t repeat queries.
- Avoid over-searching. Run one batch of targeted searches if needed.
Early stop criteria:
- You can name exact content to change.
- Top hits converge (~70%) on one area/path.
Escalate once:
- If signals conflict or scope is fuzzy, run one refined batch, then proceed.
Depth:
- Trace only symbols you’ll modify or rely on; avoid deep expansion unless necessary.
Loop:
- Batch search → minimal plan → complete task.
- Search again only if validation fails or new unknowns appear.
- Prefer acting over more searching.
</context_gathering>
A flat, animated map of how to craft great prompts: gather context efficiently, then assemble the classic prompt equation.
These handbooks from OpenAI, Google, and Anthropic are the fastest way to level-up prompting and agent design. Each guide below includes a one-line summary and a direct link so you can open, skim, and use them right away.
Start with a general prompt guide to learn structure and patterns, then move to agent design for workflows and safety. For live demos, keep a budget, policy, or use-case doc open beside you and iterate prompts in short loops (draft → test → refine).
Prompt engineering begins with understanding how to effectively communicate with language models. The quality of results depends heavily on how well the instructions are structured and how much context is provided. A prompt serves as the interface between human intent and model execution, containing instructions, context, inputs, and output specifications.
Prompt engineering isn’t just asking a question—it’s designing a precise set of instructions so the AI can interpret intent correctly. Like programming, it requires iterative refinement: testing, evaluating, and improving prompts to consistently achieve reliable results.
By mastering prompt design, you can harness models like GPT-5 for tasks ranging from policy review to software generation. Good prompts reduce errors, increase reproducibility, and improve efficiency.
Concrete prompts you can copy, adapt, and use right away.
Prompt: “You are an expert in municipal finance. Review the following legislation [insert text or link]. Identify fiscal impacts, compliance requirements, and risks to municipal budgets. Summarize in 5 bullet points, and flag areas where further legal review may be needed.”
Source: dair-ai/Prompt-Engineering-Guide
Prompt: “Solve this budgeting problem using Tree of Thoughts reasoning. Generate three possible revenue strategies, evaluate pros/cons of each, and recommend the best option.”
Source: Prompting Guide - ToT
Context engineering goes beyond writing single prompts. It is the deliberate design of the full information payload that a model sees at inference time, including not only the instruction but also examples, retrieval results, memory, tools, and control flow.
As Andrej Karpathy explains, “Context engineering is the delicate art and science of filling the context window with just the right information for the next step.” (davidkimai/Context-Engineering)
“Context is not just the single prompt users send to an LLM. Context is the complete information payload provided to a LLM at inference time, encompassing all structured informational components that the model needs to plausibly accomplish a given task.” (A Systematic Analysis of Over 1400 Research Papers)
Instead of one long prompt, context engineering structures the task:
By orchestrating these layers, context engineering ensures precision, consistency, and traceability.
More Resources: davidkimai/Context-Engineering | DeepWiki
This master prompt is based on the GPT-5 Prompting Guide. Use it as a starting framework for any complex task — legislation review, financial analysis, planning, or coding. GPT-5 will first structure its reasoning, then deliver practical, actionable results.
From now on, act as my expert assistant with the full reasoning and capabilities of GPT-5.
When I provide you with a request (document, question, dataset, or code), always follow this structured workflow:
1. **Clarify & Plan**
- Restate my request in clear terms.
- Break it into logical parts or steps if it is complex.
- Outline a step-by-step plan before answering.
2. **Direct Answer**
- Provide a clear, focused response to my request.
- Highlight key facts, insights, or solutions.
3. **Step-by-Step Reasoning**
- Show the reasoning or calculations you used.
- If coding, explain the approach before giving code.
4. **Alternative Perspectives**
- Suggest at least one other way to approach or interpret the problem.
- Identify potential pitfalls or risks.
5. **Action Plan**
- End with a practical summary or checklist I can apply immediately.
- Use plain English and professional tone.
**Rules:**
- Never give vague answers.
- If the request is broad, break it into smaller parts.
- Always push your reasoning to 100% of your capacity.
- If specialized, act like a professional in that domain (teacher, coach, engineer, lawyer, doctor, municipal finance officer, etc.).
A simplified version of the GPT-5 best practices workflow. Use this when you want **quick, structured answers** without the full framework.
From now on, act as my expert assistant.
For every request, always provide:
1. A clear, direct answer.
2. A short step-by-step explanation of how you got there.
3. A practical action plan or summary I can use immediately.
Never be vague. Push your reasoning to the fullest, even in short form.
I want you to become my Prompt Engineer. Your goal is to help me craft the best possible prompt for my needs. The prompt will be used by you, ChatGPT. Please follow this process:
From now on, act as my expert assistant. Always provide:
Important:
Use this prompt when you want GPT-5 to carefully analyze legislation, long policy documents, or financial statutes. It’s designed to simulate the review process of an expert municipal finance professional.
How to use: Copy the prompt below, paste it into ChatGPT, and then provide your link, PDF, or full text of the legislation for review.
From now on, act as an expert municipal finance professional reviewing legislation.
When I provide you with a link, PDF, or long piece of legislative text:
1. Provide a clear summary of what the legislation does.
2. Highlight the financial and tax implications for municipalities.
3. Identify compliance requirements or risks for local government.
4. Suggest practical actions a municipal CFO, tax assessor, or finance officer should take.
5. Flag any ambiguous or unclear sections that may need legal interpretation.
6. Provide examples of how this legislation could impact municipal operations in New Jersey.
Always write in plain English, with structured sections:
**Summary → Fiscal Implications → Compliance/Risk → Action Plan → Open Questions.**
Use this directive to keep AI outputs factual, clearly labeled, and safe from speculation or overreach.
From now on, follow these rules when generating responses:
• Do not present speculation, deduction, or hallucination as fact.
• If unverified, explicitly say:
- “I cannot verify this.”
- “I do not have access to that information.”
• Label all unverified content clearly:
- [Inference], [Speculation], [Unverified]
• If any part is unverified, label the full output.
• Ask clarifying questions instead of assuming.
• Never override user-provided facts, labels, or data.
• Do not use these terms unless quoting the user or citing a verified source:
- Prevent, Guarantee, Will never, Fixes, Eliminates, Ensures that
• For LLM behavior claims, always include:
- [Unverified] or [Inference], plus a note that it’s expected behavior, not guaranteed.
• If you break this directive, immediately issue a correction:
> Correction: I previously made an unverified or speculative claim without labeling it. That was an error.
Use this prompt to turn long threads of emails or correspondence into a structured, attorney-ready or meeting-ready summary with clear sections, timelines, and next steps.
From now on, act as a professional assistant who summarizes email threads and correspondence.
When I provide emails, letters, or notes, create a structured summary in this format:
⚖️ Summary
1. Background
- Short, pointed context of the case or correspondence.
2. Timeline
- A clear chronological list of key dates and actions.
3. Current Status
- Where things stand right now.
4. Settlement Terms / Key Points (if applicable)
- List any proposed terms, agreements, or unresolved issues.
5. Next Step
- Practical, actionable steps to move forward.
Rules:
- Be concise but precise.
- No speculation—only use information in the correspondence.
- Write in professional, attorney-ready language.
- Highlight dates, deadlines, and commitments clearly.
- If used for internal notes/meetings, adapt the same structure for clarity.
This is always from SO we’re talking about — and how to craft prompts doesn’t have to be complicated. Sometimes simply saying “Explain this to me in plain English”, “Please summarize this”, “Please make this sound better”, or asking “How does this sound?” is good enough.
Example: Copy and paste 15 pages of the handbook.
Prompt: As a tax assessor, my town wants to make an agreement with a developer in an area they deem in need of rehabilitation.
Can you tell me my role in the process and what questions I should ask about the project to help me do my job better?
Tip: Prompt-engineer first (audience, tone, goal, evidence), then draft.
Prompt: How can I count characters in a cell in Excel?
Practical prompts and demonstrations for generating, explaining, and improving code with AI.
Option Explicit
Sub SaveAttachments(Item As Outlook.MailItem)
Dim olApp As Outlook.Application
Dim destFolder As String
Dim folderPath As String
Dim folderName As String
Dim attachment As Outlook.attachment
Dim i As Long
Dim promptName As String
On Error Resume Next
' Initialize Outlook objects
Set olApp = Outlook.Application
' Prompt for a folder name
promptName = InputBox("Enter the folder name: ", "Save Attachments")
If promptName = "" Then Exit Sub
' Prompt for a folder path
folderPath = InputBox("Enter the folder path to save attachments: ", "Save Attachments")
If folderPath = "" Then Exit Sub
' Create a folder with the prompted name
folderName = folderPath &\& promptName
MkDir folderName
' Loop through all attachments
For i = 1 To Item.Attachments.Count
Set attachment = Item.Attachments.Item(i)
' Save each attachment into the newly created folder
destFolder = folderName &\& promptName & _ & i & _ & attachment.fileName
attachment.SaveAsFile destFolder
If Err.Number <> 0 Then
MsgBox "An error occurred: " & Err.Description
End If
Next i
' Clean-up
Set olApp = Nothing
On Error GoTo 0
End Sub
Sub RunSaveAttachmentsWithoutReply()
Dim currItem As Object
Set currItem = Application.ActiveInspector.CurrentItem
If TypeOf currItem Is Outlook.MailItem Then
SaveAttachments currItem
End If
End Sub
Follow these step-by-step instructions to enable and use macros in Microsoft Outlook:
File > Options.Trust Center.Trust Center Settings.Macro Settings.Enable All.Customize the Ribbon.Main Tabs, click Developer.Developer tab.Visual Basic.Project1.Microsoft Outlook Objects.Insert and then select Module.Customize Ribbon.Choose commands from: and select Macros.New Tab button at the bottom and name the tab (e.g., "Macros", "Automate").A curated list of powerful AI tools for creators, professionals, and entrepreneurs.
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Google’s latest image editing & generation model inside Gemini / Google Photos. Edit with text prompts, combine images, and keep character consistency.
Try in Gemini | Feature overview | Model page (Gemini 2.5 Flash Image)
PDF Extract Kit: GitHub Repo
AI automates repetitive and time-consuming tasks, allowing municipal staff to focus on more strategic initiatives. For example, AI-powered chatbots can handle routine citizen inquiries 24/7, reducing the workload on customer service representatives and improving response times.
By automating processes such as data entry, invoice processing, and maintenance scheduling, municipalities can significantly reduce operational costs. For instance, implementing predictive maintenance through AI can prevent equipment failures, saving costs on emergency repairs.
AI provides advanced analytics and real-time insights into municipal operations. For example, AI-driven data analysis can help city planners identify traffic patterns and optimize road usage, leading to more informed infrastructure development decisions.
AI enhances the quality and speed of services provided to citizens. Automated systems can expedite permit approvals and licensing processes, making it more convenient for residents and businesses to comply with regulations.
A municipal finance department was manually consolidating financial data from multiple sources into Excel spreadsheets—a process that took approximately 50 minutes per dataset. By implementing an AI-powered macro, the department automated data cleaning, consolidation, and initial analysis.
The AI macro reduced the task completion time from 50 minutes to 15 minutes per dataset. Over the course of a fiscal year, this time savings amounted to hundreds of hours, allowing staff to allocate more time to financial planning and analysis.
AI automates data aggregation from various departments and historical records, enabling faster and more accurate budget creation and revisions. AI algorithms can predict future revenue and expenditure trends based on historical data, economic indicators, and demographic changes.
Real Example: A city used AI-driven forecasting tools to reduce the time required for budget preparation from several weeks to just a few days, allowing for more timely adjustments in response to economic shifts.
AI-driven systems can automatically categorize expenses, detect anomalies, and flag unauthorized expenditures. This reduces manual entry and review times, minimizing errors and preventing fraud.
Real Example: A county government implemented an AI expense management system that reduced manual processing by 70% and identified duplicate payments, saving thousands of dollars annually.
Utilizing predictive analytics, AI enhances the accuracy and speed of revenue forecasts by analyzing patterns in tax collections, property values, and economic indicators.
Real Example: A municipal tax office used AI models to predict property tax revenues more accurately, enabling better financial planning and resource allocation for public services.
AI can automate audit trails and compliance checks, scanning large volumes of transactions for compliance with regulations and internal policies, saving hours of manual review.
Real Example: A state government integrated AI into its auditing processes, reducing the time spent on compliance checks by 50% and increasing the detection of non-compliant activities.
AI automates the generation of financial reports and dashboards, pulling real-time data and presenting it in an easily digestible format. This reduces the time it takes to compile reports from hours or days to minutes.
Real Example: A city council adopted an AI reporting tool that automatically generated monthly financial statements and performance dashboards, enhancing transparency and allowing for quicker decision-making.
ChatGPT can act as a virtual reviewer, performing consistency checks and identifying potential errors or inconsistencies in valuation calculations. It can help assessors ensure the accuracy and reliability of their valuation work.
ChatGPT can help generate comprehensive property valuation reports. By synthesizing the collected data, analysis, and calculations, it can assist in preparing accurate and standardized reports that comply with relevant regulations and guidelines.
Link to Report Generation ExampleTaxVector houses thousands of pages of documents, including manuals, court cases, regulations, and applications for assessors, county tax boards, and anyone interested in diving deep into tax assessment knowledge.
TaxVector is an AI-powered application that allows you to have conversations with vast amounts of tax-related data. It's designed to make research faster, more intuitive, and more comprehensive than ever before.
- Quickly search and retrieve information from thousands of documents.
- Engage in interactive Q&A with tax-related documents to get direct answers.
- Gain insights from manuals, regulations, and real court cases.
Whether you are an assessor, part of a county tax board, or simply want to learn more about tax assessments, TaxVector will enhance your research capabilities by making information retrieval both faster and more accurate. Say goodbye to the days of combing through endless PDFs and welcome AI-assisted research at your fingertips.
Azure‑native leaderboards, outliers, and Local Finance Notice (LFN) RAG—built for CFOs and finance teams
New Jersey’s User‑Friendly Budget (UFB) dataset is rich but time‑consuming to compare across municipalities. This proposal delivers an Azure‑native analytics and AI copilot that produces peer‑fair leaderboards, an Outlier Explorer that asks credible “why” questions, Local Finance Notice (LFN) citation search, and transparent town scorecards—so meetings focus on decisions, not manual spreadsheets.
VECTOR for the knowledge base).Each score is normalized within peer groups to keep comparisons fair and transparent.
If this would help your team, let’s run a pilot—county‑wide or a cohort of 3–5 towns. I’ll bring the Azure‑native pieces; you bring your finance context.
Contact to Discuss a PilotConcluding our session on ChatGPT's transformative role in Municipal Finance, it's pivotal to stress the importance of security and data privacy. Though AI systems, like ChatGPT, offer immense benefits, they also come with inherent risks. It's our duty to consistently uphold stringent security measures, data audits, and maintain rigorous privacy norms.
Through our discussions, we've seen ChatGPT's unmatched potential for Municipal Finance. It processes vast data with unparalleled speed and accuracy, serving as a prime tool. While AI is designed to be accurate, it's essential to note that errors, though rare, can occur.
We're entering an era where AI transitions from merely assisting to partnering. AI models like ChatGPT excel in automating routine tasks, allowing us to concentrate on intricate, strategic challenges. Echoing Chris Lattner's sentiments, our growth is via delegation. Delegating responsibilities to AI greatly amplifies our efficiency, notably in Municipal Finance.
As the horizon of AI in Municipal Finance expands, staying updated with emerging trends is crucial. Adapting, learning, and embracing these novelties will determine our success. The key lies in our readiness to incorporate AI seamlessly, understand its nuances, and pledge to its ethical use.
In summary, AI models, especially ChatGPT, can be revolutionary in the tax domain, enhancing our accuracy and efficiency. As we proceed on this promising journey, our focus should always be on security, ethical use, and data privacy.
Your attention and participation have been invaluable. Thank you.
As you engage with our content, please note that 95% of what you've encountered, including this closing statement, was crafted using AI.