Streamlining Federal Regulation to Save Small Businesses with AI

Executive Summary

Federal regulations impose an estimated $3.079 trillion annual cost on the U.S. economy, equivalent to 12% of GDP and larger than the entire economies of Canada or Italy. Compliance costs from these towering regulations force businesses to hold off on hiring, raising wages, and investing in growth.

Recent advancements in AI technology can power regulatory reform through automating language analysis, tracking regulatory dependencies, calculating real-time compliance costs, and mapping the entire regulatory landscape in a machine-readable format. This four-layer AI system can reduce the largest drivers of regulation-induced stress: complexity, overlapping rules, contradictory language, and costs. Regulatory reform will go from a political process with piecemeal analysis to a data-driven, evidence-based capability.

Immediate Actions Required:

• $20M Federal Regulatory Intelligence Initiative to deploy AI analysis tools across pilot agencies
• OMB directive requiring AI-powered impact analysis before new small business regulations
• $100M Congressional appropriation for full federal system deployment
• Legislative mandate for machine learning analysis of all small business rules

Expected Results: $10B in measurable compliance cost savings by 2027, 5% increase in small business formation by 2027, and 15% reduction in overlapping regulations by 2028.

Problem Analysis

Large U.S. businesses (with more than 100 employees) spend approximately $12,800 per employee annually on federal regulations, excluding state and local regulations. Yet, while sizable corporations can spread regulatory compliance costs across thousands of employees, small businesses (fewer than 50 employees) face $14,700 per employee annually, 14.8% more per employee than large companies. This burden becomes even more pronounced in heavily regulated sectors like manufacturing. Small manufacturing businesses incur an estimated $50,100 annual per employee cost for compliance, whereas large manufacturing businesses incur $24,800. In this sector specifically, small businesses are paying 102% more per employee than their larger counterparts. Federal regulations are the most burdensome rules for 50% of small businesses, more than state and local regulations combined. Small businesses report complexity as the largest cause of difficulty when complying with regulations, followed by compliance cost and contradictory rules. These difficulties have forced 50% of small businesses to delay hiring, 46% to postpone growth strategies, 41% to raise consumer prices, and 34% to freeze employee salary increases.

Reducing regulatory burden by eliminating redundant and contradictory rules, revising language for simplicity, and measuring new regulation impact in real-time would decrease direct and indirect costs for businesses of all sizes. Small businesses would especially benefit from a simpler regulatory environment because 90% of them lack the staff to monitor regulations (unlike large firms with dedicated compliance teams) and 70% of regulatory compliance falls directly on small business owners. In its first 100 days, the second Trump administration lifted $48.1 billion in regulatory burden off the backs of American businesses, displaying the massive impact streamlining federal regulation can have on the economy when met with an active administration. This trend can be accelerated with new AI-powered tools and automation. By streamlining regulatory burdens, we can expect increased hiring efforts, higher wages, decreased prices for consumers, unlocked innovation, and a measurable increase in new business formation.

Technical Solution

Building on AFPI's Office for Fiscal and Regulatory Analysis mission to make government policy easier to understand and change for the American people, sophisticated AI tools can speed up the analysis to find contradictory or redundant regulations, estimate compliance costs, advise on regulations that can be safely cut without any dependencies, and map the entire federal regulatory landscape allowing both lawmakers and businesses to navigate its complexity with ease. This comprehensive approach consists of four integrated components that transform regulatory reform with data-driven optimization.

Layer 1: Federal Regulatory Map

Create embeddings from all federal regulations, alongside compliance requirements, affected sectors, implementation costs, expected costs, and expected benefits, and store these in a vector database. Through this “map” that charts out all federal regulations in a machine-readable format, users could run similarity search queries. Through a web interface integrated with the vector database, these similarity searches would assist users in identifying which other regulations are like a given regulation they are searching with.

Layer 2: Regulatory Chat

Integrating a general-purpose, open-source, privately hosted Large Language Model (LLM) on top of the Federal Regulatory Map through Retrieval Augmented Generation (RAG) would unlock two new benefits: the LLM could simplify the language for regulation that a user searches for, and users could “talk to regulation”. Simplifying language would reduce the complexity burden facing small businesses. Allowing users to “talk to regulation” would provide a chatbot-like experience for lawmakers and small businesses to inquire further about a regulation they are interested in, ask about potential impacts or issues, and find related laws and regulations. Regulatory Chat would be exposed to the public and private parties through a web portal.

Layer 3: Regulatory Impact Dashboard

Create a dashboard to calculate and display current regulatory burden metrics such as “Cost per Business”, “Cost per Employee”, and “Total Cost on Economy” broken down by sector and size. These metrics would be derived from identifying the individual direct and indirect costs of each regulation. This would be done through economic models using regression analysis methodology like the NAM 2023 study and regulatory impact analysis models. By storing information on a per-regulation basis, users would have access to identifying how each individual regulation affects the broader economy.

Alongside economic impact, a “Complexity Score” would be calculated for all current and new regulations. This score would be derived from readability metrics (Coleman-Liau/Flesch Reading Ease) and regulatory-specific factors (cross-references to other regulations, number of exceptions, and compliance steps). Regulatory-specific factors can be identified through querying Regulatory Chat connected to the Federal Regulatory Map. Complexity Score would be calculated as a weighted combination where regulatory complexity factors account for 60%, readability metrics 40%, scaled to a 1-10 range. The Dashboard can then display complexity scores for each regulation and high-level metrics like “Average Complexity Score” and “Max Complexity Score”.

This will provide a real-time view of the entire Federal Government’s impact on the economy through regulation. Combined with the “Federal Regulatory Map” to query current federal regulations and cross-reference with metrics found in the Dashboard, agencies could automate the process of identifying overlapping regulations and low-benefit/high-cost regulations for elimination.

Layer 4: Regulation Dependency Graph

All federal regulations will be added into a graph database where each regulation (referred to as a “node”) connects to all regulations it depends on and all regulations that depend on it. Graph databases allow for nodes to connect to other nodes through unidirectional or bidirectional “arrows”. Natural language processing algorithms with minimal human oversight will automatically identify dependencies by analyzing regulatory text for cross-references and citations with the Federal Regulatory Map / Regulatory Chat. This tool would serve to help lawmakers and agencies see which regulations they cannot cut, because other regulations depend on it. Conversely, it could also identify regulations that have nothing depending on it and can be safely cut without causing confusion or gaps. It would also be used to find “sibling” regulations, which can then be further analyzed to see if they overlap and could be streamlined. Individual users could use the Dependency Graph, but an AI Agent can be created to scour the graph to find instances of overlaps and contradictions.

Suppose the AI identifies that small manufacturers face overlapping reporting requirements from three different agencies about employee safety incidents. The dependency graph reveals:

• DOL Regulation Y: Requires injury reporting (depends on "injury classification standards" in Regulation Z)
• OSHA Regulation X: Requires safety incident reporting (depends on same "injury classification standards" in Regulation Z)
• EPA Regulation W: Requires environmental incident reporting when injuries involve chemicals (depends on same "injury classification standards" in Regulation Z)

All three regulations share the same foundation (Regulation Z), but they're creating a triple reporting burden for businesses. The AI could now propose to keep the foundation intact, but consolidate the three reporting requirements into a single process that satisfies all three agencies.

Policy Recommendations

The current administration has a critical political window to deliver permanent regulatory reform. These recommendations leverage AI to safely eliminate regulations while providing real-time monitoring of regulatory burden, reducing government workforce requirements through faster, data-driven approaches.

Executive Actions (Immediate Implementation)

• Federal Regulatory Intelligence Initiative beta ($20M): Deploy Federal Regulatory Map, Regulatory Chat, Regulatory Impact Dashboard, and Regulatory Dependency Graph to systematically identify elimination-safe regulations.
• Office of Management and Budget (OMB) AI Analysis Mandate: Require agencies to use AI system to assess cost and identify potential overlaps before proposing new small business regulations.

Legislative Actions

• Congressional appropriation ($100M over 3 years): Full four-layer AI system development and deployment across the federal government.
• AI Regulatory Analysis Act: Statutory requirement for machine learning impact analysis on all small business rules.

Success Metrics

• $10B compliance cost savings identified by 2027
• 15% reduction in overlapping regulations by 2028
• 20% reduction in small business compliance burden by 2028
• 5% increase in small business formation within 18 months

Implementation

Rollout Phases

• Phase 1 (Months 1-6): Deploy beta four-layer AI system using $20M executive appropriation across pilot agencies for testing and refinement.
• Phase 2 (Months 7-18): Launch public Regulatory Chat, implement OMB AI Analysis Mandate, achieve $10B cost savings identification.
• Phase 3 (Years 2-3): Execute $100M Congressional appropriation for full federal deployment and AI Regulatory Analysis Act implementation.

Resource Requirements

Technical: Cloud infrastructure and AI development team
Coordination: OMB oversight and agency integration
Oversight: Government Accountability Office (GAO) evaluation and Congressional reporting

Success Monitoring

Real-time tracking through Regulatory Impact Dashboard with quarterly Congressional progress reports and third-party validation of cost savings.

References

National Small Business Association. (2025). NSBA Small Business Regulations Survey. https://www.nsbaadvocate.org/_files/ugd/fec11a_d132369606b34957a0262d7d8eca561b.pdf

Small Business Administration. (2025). Summary of First 100-Days Accomplishments.  https://advocacy.sba.gov/wp-content/uploads/2025/04/First-100-Days_FINAL.pdf

Competitive Enterprise Institute. (2024, June 5). Regulations hit small businesses and low-income households hardest. CEI Blog. https://cei.org/blog/regulations-hit-small-businesses-and-low-income-households-hardest/

Crain, N. V., & Crain, W. M. (2023). The Cost of Federal Regulation to the U.S. Economy, Manufacturing, and Small Business. National Association of Manufacturers. https://www.nam.org/wp-content/uploads/2023/11/NAM-3731-Crains-Study-R3-V2-FIN.pdf