NGA HHS Institute 2025: Making Innovative Tech Work for Health & Human Services – National Governors Association

Report on the Integration of Artificial Intelligence in Health and Human Services for Sustainable Development

Executive Summary

This report summarizes key findings from the “Making Innovative Tech Work for Health & Human Services” session at the 2025 NGA Health and Human Services Institute. The discussion focused on the strategic implementation of Artificial Intelligence (AI) to enhance service delivery, mitigate workforce strain, and achieve cost efficiencies. A significant emphasis is placed on aligning these technological advancements with the United Nations Sustainable Development Goals (SDGs), particularly those concerning health, decent work, innovation, equality, and strong institutions.

Navigating AI Implementation: Myths vs. Realities

The successful integration of AI requires a clear understanding of its current capabilities and limitations. The panel addressed common misconceptions to ground strategies in reality, ensuring that technological adoption is both responsible and effective, in line with the principles of sustainable development.

Myth 1: Comprehensive Mastery of AI

• Assertion: Technology leaders and state officials possess a complete understanding of AI’s impact and design.
• Reality: The field is in a state of continuous development. Most states and territories are adopting AI through pilot programs, reflecting an iterative learning process aligned with the principles of responsible innovation under SDG 9 (Industry, Innovation, and Infrastructure).

Myth 2: AI as a Panacea

• Assertion: AI can solve any problem presented to it.
• Reality: AI is a tool dependent on the data it is provided and requires well-defined use cases. Its application must be carefully managed to ensure accuracy and prevent misinformation, a critical step in building trust in public institutions as per SDG 16 (Peace, Justice, and Strong Institutions). As noted by panelist Nikhil Deshpande, AI is a specialized tool, not a source of universal wisdom.

Strategic Recommendations for AI Deployment in Alignment with SDGs

Panelists offered tangible recommendations for states and territories to deploy AI solutions effectively within their health and human services agencies, ensuring that technological progress contributes directly to sustainable development outcomes.

1. Assess Workforce Impact to Promote Decent Work (SDG 8)

   It is imperative to analyze the roles of key personnel (e.g., caseworkers, legal staff) to identify how AI can supplement their duties, reduce administrative burdens, and enhance job quality. This approach supports SDG 8 (Decent Work and Economic Growth) by focusing on technology that augments human capabilities and improves working conditions.

2. Automate Repetitive Tasks for Institutional Efficiency (SDG 16)

   Deploy AI to manage tedious and time-consuming tasks, thereby improving the efficiency and effectiveness of public institutions, a key target of SDG 16. This allows human capital to be redirected toward more complex, value-added activities. Practical applications include:

   • Automating information gathering from policy manuals to better support case managers.
   • Extracting data from PDF documents to streamline administrative workflows.

3. Adopt Intentional and Sustainable Procurement Practices (SDG 9 & 10)

   Effective AI procurement is foundational to successful and equitable implementation. Procurement practices must be strong, focusing on outcomes rather than outputs. This strategy supports SDG 9 (Industry, Innovation, and Infrastructure) by fostering responsible technological adoption and SDG 10 (Reduced Inequalities) by ensuring that solutions are designed to serve all communities effectively.

4. Focus on Outcomes to Advance Health and Equality (SDG 3 & 10)

   Design technology solutions by prioritizing the intended impact on citizens. An outcome-based approach ensures that AI applications in health and human services directly contribute to SDG 3 (Good Health and Well-being) and SDG 10 (Reduced Inequalities) by creating more personalized, accessible, and equitable service delivery. This focus also clarifies the return on investment for legislatures and other stakeholders.

5. Cultivate Multi-Stakeholder Partnerships (SDG 17)

   Foster collaboration among diverse stakeholders to develop robust and inclusive AI solutions. Engaging community members, workers, philanthropic organizations, and technical experts is essential for innovative and human-centered development. This aligns with SDG 17 (Partnerships for the Goals), which emphasizes the importance of multi-stakeholder partnerships to achieve sustainable development objectives.

Analysis of SDGs in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

• SDG 3: Good Health and Well-being: The article focuses on improving the delivery of health and human services through technology, which is central to ensuring healthy lives and promoting well-being for all.
• SDG 8: Decent Work and Economic Growth: The discussion on mitigating workforce strain for roles like caseworkers and automating tedious tasks directly relates to promoting decent work and improving working conditions.
• SDG 9: Industry, Innovation, and Infrastructure: The core theme is the application of innovative technology (AI) to upgrade public service infrastructure, making it more efficient and effective.
• SDG 16: Peace, Justice, and Strong Institutions: By aiming for “seamless service delivery” and advocating for “strong procurement practices,” the article addresses the need to develop effective, accountable, and transparent institutions at the state level.
• SDG 17: Partnerships for the Goals: The recommendation to “Build a village” by involving community, philanthropic voices, and technical experts highlights the importance of multi-stakeholder partnerships to achieve development goals.

2. What specific targets under those SDGs can be identified based on the article’s content?

• SDG 3: Good Health and Well-being

  • Target 3.c: Substantially increase health financing and the recruitment, development, training and retention of the health workforce. The article’s focus on using AI to “mitigate workforce strain” and “supplement the duties” of caseworkers supports the retention and effectiveness of the human services workforce.

• SDG 8: Decent Work and Economic Growth

  • Target 8.5: By 2030, achieve full and productive employment and decent work for all. The article discusses using technology to automate “tedious tasks” and mitigate “pain points of human services work,” which contributes to improving the quality and decency of work for employees in this sector.

• SDG 9: Industry, Innovation, and Infrastructure

  • Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries… including… encouraging innovation. The article is a direct example of this, advocating for states and territories to deploy “artificial intelligence solutions” to upgrade the technological capabilities of their health and human services agencies.

• SDG 16: Peace, Justice, and Strong Institutions

  • Target 16.6: Develop effective, accountable and transparent institutions at all levels. The call for “strong procurement practices” and designing solutions around “outcomes, not outputs” aims to build more effective and accountable public service institutions that can demonstrate their return on investment (ROI) and consumer impact.

• SDG 17: Partnerships for the Goals

  • Target 17.17: Encourage and promote effective public, public-private and civil society partnerships. The recommendation to “Build a village” by bringing in “community and worker voice, philanthropic voices, and technical experts” is a direct call for the formation of multi-stakeholder partnerships to develop innovative solutions.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

• Indicators for Efficiency and Institutional Strength (SDG 9 & 16)

  • Cost- and time-efficiency: The article explicitly mentions driving “cost- and time-efficient solutions.” Progress could be measured by tracking the reduction in operational costs and the time taken to deliver services after implementing AI solutions.
  • Seamless service delivery: This implies measuring consumer/citizen satisfaction with health and human services. An increase in satisfaction scores would indicate progress.
  • Outcome-based performance: The emphasis on designing solutions around outcomes suggests using metrics that measure the actual “impact” on consumers, rather than just the number of services delivered (outputs).

• Indicators for Workforce Well-being (SDG 3 & 8)

  • Reduction in workforce strain: Progress could be measured through surveys on employee job satisfaction, burnout rates, or tracking the amount of time saved on “tedious tasks” like “pulling information out of PDFs.”
  • Workforce retention: An improvement in the retention rate of caseworkers and other human services staff would be a key indicator of reduced strain and better working conditions.

• Indicators for Partnerships (SDG 17)

  • Number of multi-stakeholder collaborations: Progress can be measured by the number of partnerships formed between state agencies, community groups, philanthropic organizations, and technical experts like the U.S. Digital Response, as suggested in the article.

4. Table of SDGs, Targets, and Indicators

Source: nga.org