LEARN FROM
Our Resources
Executive Summary
Artificial Intelligence (AI) is becoming a critical force for driving efficiency, innovation, and strategic decision-making in government. Across the nation, state governments are harnessing AI to optimize budgets, enhance service delivery and drive data-driven decision-making. However, responsible AI adoption presents transformative opportunities when implemented methodically and used for specific tasks. For senior budget officers, the imperative is clear: capitalize on AI’s potential while ensuring outcomes based on AI seek to support a balanced budget which is in the public’s interest. This white paper offers a strategic roadmap for AI readiness in state budget offices, emphasizing governance, equity, security, high-quality data, and targeted investment. By prioritizing accuracy and integrity in data, agencies can drive responsible AI implementation that delivers lasting impact while maintaining public trust.
Budget Offices are at the Center of AI Readiness
State Budget Officers are more than financial stewards; they are architects of innovation, transparency, accountability, and strategic decision-making. As AI reshapes funding allocations, performance measurement, and resource distribution, Budget Officers must proactively harness its capabilities to enhance transparency, optimize investments, and drive smarter policy outcomes. This is vital now as State governments face economic challenges from external uncertainties — global supply chain disruptions, fluctuating interest rates, and federal policy shifts, including tariffs and trade tensions—that can destabilize revenue forecasts and planning.
As AI tools begin to influence funding decisions Budget Officers must:
- Lead strategic investment in data infrastructure and AI-driven initiatives to enhance efficiency, innovation, and decision-making.
- Dispel job displacement anxiety.
- Enforce accountability and equity in AI use.
- Ensure AI tools align with fiscal policy, legal frameworks, ethical standards, and robust security protocols to safeguard data integrity and protect against emerging threats.
AI readiness is not just a technology initiative; it is a leadership mandate for the budget community. At the same time, while AI is a powerful tool for improving efficiency, accuracy, and insight in budgeting, it is not a panacea—it depends on human judgment, policy expertise, and institutional knowledge to guide decisions and ensure accountability.
Benefits and Risks of AI in Government Budget Offices
Artificial Intelligence has the potential to transform budget decision making, delivering unprecedented speed and insight into fiscal analysis, resource allocation, what-if analysis, and performance tracking.
By leveraging AI, budget offices and agencies can make data-driven decisions with greater efficiency. However, these advancements come with significant risks that demand careful oversight upfront.
While AI technologies offer powerful tools to enhance efficiency, improve accuracy, and support decision-making in state budget offices, they are not a substitute for experienced human judgment. Overreliance on AI, without proper oversight, transparency, and validation, can introduce risks including data bias, misinterpretation of financial data, and unintended policy consequences. Agencies must ensure
appropriate safeguards, maintain human-in-the-loop practices, and establish governance frameworks before adopting AI at scale.
Therefore, successfully integrating AI with current budget practices requires a balanced approach—embracing innovation while implementing safeguards to ensure transparency, security, and equity.
As AI use increases in penetration, users will move from being “simple users” to “strategic collaborators”.
“Some people think of AI as a way to do the work they do not want to do. Top performers think of AI as a way to do the work they have always wanted to do.”
Major Risk Factors
As state government budget offices explore the use of artificial intelligence to enhance forecasting, reporting, and decision-making, it is essential to recognize that these technologies come with significant risks. While AI offers the potential for greater efficiency and insight, its implementation must be approached with caution and clear oversight. Key areas of concern include data quality, bias and fairness, transparency, privacy, and cybersecurity. Without careful planning and governance, AI can introduce unintended consequences that undermine trust, equity, and accountability in the budgeting process.
This discussion outlines the major risks associated with AI adoption and the considerations state budget offices must keep in mind to use these tools responsibly.
Data Quality and Integrity Risks
- Garbage In, Garbage Out: AI models are only as reliable as the data they process. Inaccurate or biased financial inputs can distort outcomes.
- Incomplete Data: Gaps in financial records may result in flawed assumptions or misguided funding allocations.
Poor data quality—such as outdated, inaccurate, or inconsistent information—can lead to flawed analyses and recommendations. AI models trained on incomplete or biased data may unintentionally reinforce inequities in funding or policy decisions. Without strong data governance and access controls, there is a heightened risk of unauthorized data access or manipulation.
Transparency and Explainability
- Black Box Decisions: AI-generated budget recommendations often lack interpretability, making it difficult to validate outcomes.
- Accountability Challenges: When AI drives critical financial decisions, organizations must establish clear guidelines on responsibility.
AI models being based on machine learning, operate”, operate as “black boxes,” making it difficult to understand how decisions are made or what data influenced an outcome. This lack of explainability can undermine trust among stakeholders, complicate audits, and make it challenging to justify budget decisions to legislators or the public.
Bias and Fairness
- Embedded Biases: AI models trained on biased historical data can reinforce systemic inequities.
- Unfair Resource Distribution: AI-driven budget allocation models prioritize efficiency and numerical optimization, which can inadvertently undervalue programs that do not have easily quantifiable benefits.
Bias and fairness are major concerns when implementing AI in state government budget offices. AI systems learn from historical data, which may reflect existing inequalities or institutional biases—such as underfunding certain communities, departments, or programs. If not carefully addressed, these biases can be embedded in AI models, leading to recommendations that unintentionally reinforce disparities in resource allocation or service delivery.
Cybersecurity and Privacy
- Sensitive Data Exposure: Budget offices manage confidential financial records that must be protected against breaches.
- Adversarial Attacks: Malicious actors can manipulate AI inputs to skew budgetary outcomes. AI systems often process sensitive financial and personal data, making them attractive targets for cyberattacks. A breach could expose confidential budgetary information or personal data of citizens, leading to identity theft, financial fraud, or loss of public trust. Moreover, as AI tools become more integrated into core financial operations, the potential impact of a successful cyberattack grows—potentially disrupting essential public services or compromising critical infrastructure. Ensuring robust encryption, access controls, and continuous monitoring is essential to safeguard these systems.
Over-Reliance on AI and Skill Gaps
- Loss of Analytical Expertise: Dependence on AI tools may erode traditional budget analysis skills.
- False Sense of Accuracy: AI can obscure uncertainty and give a misleading.
- Incorrect Application of Tools: Naïve users may be tempted to use a Large Language Model (e.g. ChatGPT) as a search engine, however this is not the intent of LLMs and they will frequently return inaccurate results. They are only meant to generate “probabilistic” strings of text.
There is a growing risk that state budget offices may become overly dependent on AI tools, assuming their outputs are infallible. This can lead to a reduction in critical oversight and human judgment, especially in complex or nuanced financial decisions where contextual understanding is essential. Compounding this issue is the fact that many public finance teams may lack the technical expertise to fully understand, audit, or troubleshoot AI systems. Without proper training and interdisciplinary collaboration, there is a danger that these tools could be misused or underutilized, ultimately undermining their intended benefits.
Legal and Regulatory Risks
- Compliance Challenges: AI adoption must align with open records laws, federal regulations, and funding guidelines.
- Auditability Concerns: AI-generated decisions can be difficult to review and justify during oversight.
State budget offices face a complex array of legal and regulatory risks when adopting AI technologies. The lack of a unified federal framework has led to a patchwork of state-level AI regulations, creating compliance challenges and legal uncertainty. A proposed federal moratorium on state AI governance could further complicate matters by preempting existing state laws, potentially leaving a regulatory vacuum without clear federal alternatives.
Additionally, questions of accountability and liability arise when AI systems make or influence budgetary decisions, especially if those decisions result in harm or inequity. Offices must also navigate strict data privacy laws and ensure that AI use does not inadvertently violate civil rights or anti-discrimination statutes
Ethical and Political Risks
- Public Perception: Citizens may be wary of AI playing a role in budget decisions, requiring clear communication and transparency.
- Equity Impacts: Without intentional safeguards, AI could worsen disparities in funding distribution.
The ethical and political risks of implementing AI in state budget offices are significant and multifaceted. Ethically, AI systems can unintentionally reinforce bias, make opaque decisions that are difficult to explain, and automate processes in ways that reduce human accountability, particularly troubling in public finance, where equity and transparency are paramount. Politically, the use of AI can trigger concerns over job displacement, loss of local control, and diminished public trust, especially if decisions appear to be made by unaccountable algorithms.
Integration and Maintenance Challenges
- Legacy Systems: Many budget offices rely on outdated technology that may not integrate seamlessly with modern AI tools.
- Ongoing Costs: AI implementation requires ongoing investment in updates, audits, and staff training.
Integration and maintenance challenges pose substantial risks for state budget offices adopting AI. Many government financial systems are built on legacy infrastructure that may not be compatible with modern AI tools, making integration complex, time-consuming, and costly. Data silos, inconsistent formats, and outdated software can further hinder AI deployment and performance. Even after initial implementation, AI systems require ongoing maintenance—including regular updates, model retraining, and performance monitoring—to remain accurate and relevant. Without dedicated resources and technical expertise, agencies risk system failures, inaccurate outputs, or degradation over time. These challenges can strain budgets, delay service improvements, and reduce confidence in AI’s value, underscoring the importance of long-term planning and cross-functional collaboration from the outset.
The Essential Pillars for Developing a Responsible AI Framework
The responsible adoption of AI by state budget offices rests on assorted foundational best practices that ensure its ethical, effective, and equitable use. Besides the common tenants of strong governance, transparency accountability, data integrity, one very vital task is workforce readiness.
One of the most pressing concerns surrounding AI adoption in state budget offices is the fear of job displacement and the potential erosion of traditional analytical skills. As AI tools become more capable of automating data analysis, forecasting, and even decision support, some public finance professionals worry that their roles may be diminished or replaced altogether. This fear can lead to resistance to AI adoption, low morale, and a reluctance to engage with new technologies.
Data Quality and Integrity Risks
- Garbage In, Garbage Out: AI models are only as reliable as the data they process. Inaccurate or biased financial inputs can distort outcomes
- Incomplete Data: Gaps in financial records may result in flawed assumptions or misguided funding allocations.
- Define roles for AI approval, monitoring, and auditing
- Create policies for AI procurement, documentation, and sunset clauses
Data Stewardship
- Implement a robust data governance framework
- Regularly audit datasets for bias and integrity
- Encrypt sensitive financial data and manage access by role
Model Transparency and Explainability
- Favor interpretable models (e.g., decision trees, regression) when feasible
- Document assumptions, logic, inputs, and limitations
- Require human review before major decisions are implemented
Building a Future-Ready Workforce
- Communicate AI’s purpose as a tool to enhance, not replace human expertise, helping staff focus on strategic and high-value tasks
- Provide training to help staff adapt and grow alongside new technologies
- Engage employees early in the process to build trust, gather insights, and foster a sense of ownership
- Integrate AI into Analytical Workflows
- Promote a culture where staff are trained to question AI outputs, explore alternative scenarios, and apply contextual understanding to complex budget issues
- Develop a structured plan with clear milestones, return-on-investment metrics, and sustainable funding strategies to support long-term AI adoption
Ethical and Equity Review
- Conduct impact assessments for equity implications
- Create and publish an AI Charter that establishes standards for transparency, accountability, and equity in AI-supported budgeting practices
- Track funding outcomes by demographic and geographic variables
- Involve affected communities in the review of AI use cases
Security and Compliance
- Regularly test AI systems for vulnerabilities
- Ensure compliance with laws on transparency, privacy, and procurement
- Set requirements for third-party vendors including audit rights
Capacity Building and Continuous Improvement
- Provide training to enhance staff AI literacy
- Launch targeted training programs to upskill analysts and budget managers, enabling them to work confidently with AI-driven tools and insights
- Start with pilot projects and scale based on result
- Use dashboards to monitor performance, fairness, and public feedback
By taking these steps, budget offices can lay the groundwork for responsible AI integration that delivers measurable value and strengthens public trust.
Performa: The Path to AI Readiness
State budget offices are at a pivotal moment. AI is transforming fiscal planning, performance measurement, and resource allocation—offering unprecedented opportunities to enhance efficiency and enable smarter policymaking. To fully realize these benefits, AI adoption must be strategic, with governance, transparency, and accountability at its core.
Performa’s Budget Intelligence Development System (BIDS) ensures states are AI-ready. By integrating and validating data from multiple systems, BIDS streamlines critical information flows—providing the solid foundation necessary for effective AI use. BIDS empowers budget leaders with the tools to make informed, transparent decisions while maintaining compliance with ethical and regulatory standards.
The BIDS AI platform’s flexible architecture integrates effortlessly with your existing systems, regardless of your ERP, general ledger, or budget software. New data is ingested in real time and instantly available for analysis based on user permissions.
Deploying BIDS AI within your agency means faster decision support, greater transparency, and the ability to surface powerful insights from data that’s been historically underutilized.
How can I get started?
The platform is designed to make onboarding easy. Our budget data model can be reconfigured via our easy-to-use launchpad tool, and then a template can be provided for you to upload an extract from your current system. Want to connect directly to sources? That is pretty easy, too. Our team can help you integrate with virtually any financial data source in a short time frame, providing unfettered access to generate more accurate and advanced results.