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Maryland AI

Strategy

On this page

Executive summary​

  • The State of Maryland is on a journey to build its capacity to responsibly, ethically, and productively leverage AI to increase government effectiveness and improve constituent outcomes.
  • This memo outlines the State’s high-level 2025 AI Enablement strategy and, as required by the AI Governance Act of 2024 (SB818), includes plans for studying opportunities, risks, and next steps associated with the use of AI in State services in a variety of key domains.

AI enablement strategy

​​​​AI Enablement refers to the State’s efforts to ensure its components are set up for success in leveraging AI and Machine Learning (ML) technologies to improve constituent outcomes and managing/guiding these technologies’ myriad potential halo effects, with the capabilities to do so responsibly, ethically, and productively. AI is not a panacea or short term fix for systemic issues, but it is a rapidly evolving new platform technology with real potential as a critical tool in agency quivers. To that end, this work is a collaboration between many bodies, including the AI Subcabinet, the Governor’s office, the Department of Information Technology (DoIT), and working teams across many agencies.

2024: Foundation-building​

​​​​The Governor’s AI Executive Order - PDF was signed in January 2024, and the AI Governance Act (SB818) was passed and made effective as of July 2024. These pieces of policy kicked off th​e State’s work in earnest. Since then, the AI Subcabinet, Department of Information Technology (DoIT), and stakeholders across the State began cohering foundational building blocks to increase the State’s capabilities.

​​​​We can split these foundational activities into the “responsible” and “productive” use of AI. Under “responsible use,” this included disseminating interim GenAI guidelines; collecting the first state-wide AI inventory; making available free AI training​ for the state workforce; and crafting a “v1” state-wide AI intake process. Under “productive use”, this included running initial proofs-of-concept (PoCs) to start “learning by doing” (examples include PoCs at the Department of Environment, MDThink, and MD’s Open Data Portal); kicking off a state-wide AI Community of Practice; building partnerships to provide the state with expertise and advisory 3 support; and hiring for a new AI Enablement team at DoIT to get in place relevant expertise and “incubate” initiatives.

​​​​A number of agencies also set up their own working groups and innovation teams, in order to think through domain-specific policy, adoption, and use cases, and experimented with commercial-off-the-shelf GenAI tools like ChatGPT, Claude, and Gemini. Throughout, we engaged with industry, civil society, academia, and federal/county/municipal governments across Maryland, the US, and abroad to ensure our approaches are rooted in evolving best practices, without re-inventing the wheel.

​​​​2025: Experimentation and momentum-building

​​​​In 2025, we will build momentum on these efforts, clarify operating models, and in particular, seek to increase the pace of experimentation, iteration, and adoption. Our high-level strategy can be encapsulated as follows.

  1. Mature the State’s AI governance capabilities
    1. ​Current use of AI in the State is relatively minimal, and existing risk management processes have been sufficient as the de facto approach. However, as the volume of AI requests and solutions in use and production increase in 2025 onwards, the unique risk management needs for AI systems during intake, procurement, deployment, and monitoring will increase in tandem.
    2. Ensure adherence to the principles outlined in the Governor’s AI EO, as well as existing legal and policy requirements, by operationalizing a more formal AI governance framework and associated artifacts via the State’s software intake and procurement processes. We will streamline and integrate that framework into existing risk management processes and iterate from a minimum viable “v1” version to greater maturity over the year.
    3. Agencies will build more sector-specific guidelines on top of, or integrated within, this process as needed.
  2. Strengthen the State’s data foundations
    1. ​A strong data foundation is the core requirement for effective AI experimentation, adoption, and governance, with trusted and reliable outputs.
    2. Formalize the State’s data governance processes and ensure agencies have reliable data for testing and scaling AI solutions. This includes ensuring agencies have the tools and resources necessary to test risks associated with using datasets for AI, providing alternative datasets (i.e., synthetic data) to protect the security and privacy of state data, and cataloging data that is allowed/restricted for use with AI solutions.
    3. Establish an Authoritative Data Sources program to prioritize critical state datasets, outline the standards for “AI-readiness”, and ensure identified datasets meet those standards, in order to power experimentation and adoption.
  3. Build momentum around experimentation & adoption
    1. ​Move from the more “opportunistic” experimentation approach of 2024 to a more structured approach, leveraging clearer governance processes, evaluation frameworks, a newly formed state AI Enablement Team, clearer agency goals around leveraging AI, and fit-for-purpose procurement pathways.
    2. Establish and execute a variety of experimentation channels across the State to expedite learning and time-to-impact. These may range from pilots of commercial-off-the-shelf (COTS) AI tools to determine return-on-investment (ROI), common use cases, and areas for future investment; to focused hackathons; to a portfolio of more structured Proof-of-Concept (PoC) procurements focused on specific, prioritized, high-impact problem statements; to workshops helping agencies understand what is possible with AI and design prototypes; to a statewide sandbox environment to encourage more decentralized experimentation.
    3. Build clear playbooks to help agencies move from successful experiments to scaled solutions in production, including greater clarity on build vs. buy decisions and avoiding lock-in.
    4. In parallel, evaluate options around technical infrastructure, architecture, and platform strategy to underpin future growth.
  4. Increase the state’s “AI IQ”
    1. ​Establish and deepen programs and partnerships that increase the State’s AI literacy, talent, and available expertise.
    2. Increase the percentage of state workers taking AI courses the state makes available, and expand the types of courses available based on the broader workforce strategy noted in the critical domain deep dives.
    3. Launch a Maryland Data Academy to create opportunities for state employees to better understand how data influences and drives AI solutions. The Academy will provide opportunities for employees to engage with use cases, participate in a variety of training courses, and have access to a set of resources to support scaling the use of data responsibly.
    4. Build low-friction mechanisms for students and researchers at Maryland academic institutions to serve as interns, fellows, or short-term experts on agency AI projects, in order to increase opportunities for Maryland students while benefiting state services.
    5. Strengthen and expand the State’s AI Community of Practice as a collaboration and upskilling mechanism across state, county, and local government.
  5. Study and cohere state approaches to AI in critical domains
    1. ​​Develop an understanding of risks, opportunities, and approaches in key topic areas including those put forth by SB818.
    2. In aggregate, these studies and their outputs should provide a solid foundation to ensure the State is approaching AI-related impacts with a strong grounding in Maryland-specific trends and constituent perspectives.

​​​​Study Topics & 2025 Roadmap

​​​​As mandated and outlined by SB818, and to execute the “critical domains” portion of the State’s 2025 strategy, state agencies, together with relevant internal and external stakeholders, will study the topics noted below through 2025, with deliverables to be shared by December 2025 with the Governor and General Assembly. Outputs of these studies will depend upon findings, topic context, and resourcing but may include reports, prioritized recommendations, pilot initiatives, agency workstreams, or changes in policy.​

​​​​Relevant SB818 requirements:

“A plan to study the use of AI by the State workforce, including opportunities to upskill the workforce.”

​​​​“A plan to study the use of AI in workforce training.”

​​​​“A plan to study the hiring of talent with expertise in artificial intelligence, employment practices, and workforce development implications.”

The state government workforce

​​​​The State’s goal is to integrate AI and train its workforce in ways that enhance, rather than replace, human workers - while increasing the efficiency and efficacy of government service delivery. As outlined in the above 2025 AI Enablement strategy, the State will continue experimenting with GenAI tools in state employee workflows, and invest in tools where there is significant ROI and opportunities to improve constituent outcomes and reduce workplace drudgery. We will also build on existing efforts, such as the State’s partnership with InnovateUS, which provides free asynchronous AI trainings and workshops available to all MD state employees.

  • Plan
    • ​Collect and analyze industry analysis data to determine what public sector jobs will require which types of additional AI skills.
    • Meet with labor leaders and organizations to incorporate worker perspectives.
    • Clarify areas where AI should not be used or deployed in a more limited way.
    • Collect information about existing and emerging AI skills training programs to determine which meet the needs of the State.
    • Continue identifying and experimenting with use cases that can improve workflow and efficiencies and find the best routes to adoption.
    • Engage with federal bodies that have been successful in hiring AI talent, such as the DHS AI Corps, to adopt best practices for prioritized roles.
    • Leverage existing research, engage with experts, and outline where AI can be responsibly, ethically, and productively leveraged in State hiring, recruitment, and onboarding processes.
  • ​Stakeholders
    • Leads:
      • Dept. of Labor (MDL)
      • Dept. of Budget & Management (DBM)
    • Involve:
      • ​Dept. of Information Technology (DoIT)
      • MD Higher Education Commission (MHEC)
      • Labor unions
      • Relevant civil society organizations and academic researchers focused on AI workforce
      • Relevant technology companies and hyperscalers with insight into workforce implications
      • Civil Rights Commission
  • Projected Timeline
    • By June 2025:  Conduct industry and skills analysis
    • By Sept. 2025:
      • Gather information from state agencies on occupations identified in industry and skills analysis and also work with public sector labor representatives to determine the best way to collect information and identify potential impacted occupations.
      • Conduct partner outreach and discussions.
    • By Sept. 2025:  Analyze results and share recommendations.

​​​​The broader state workforce

​​​​In the coming decade, AI is likely to impact Maryland’s workforce in predictable and unpredictable ways. These impacts will manifest differently by industry, job type, demographic, and location. Our work seeks to develop the baselines from which to encourage promising 7 trends, mitigate downside risks, and ensure the availability of programs whereupon the workforce has tools to upskill and thrive.

  • Plan
    • Conduct an analysis to identify industries, skills, and occupations that are prevalent in Maryland that are at greatest risk of being impacted and those that are most likely to require workers to develop new or enhanced AI-related skills over the next 3-5 years.
    • Meet with labor leaders and experts to incorporate worker voices into AI policy analysis and determine needs around AI training programs.
    • Meet with groups representing underserved communities to incorporate their voices into AI policy analysis and determine needs around AI training programs.
  • Stakeholders
    • Owners:
      • MDL
      • MHEC
    • ​Involve:
      • Dept. of Commerce
      • Labor Unions
      • Community Colleges
      • Universities and academic researchers
  • Projected Timeline
    • ​By June 2025:  Conduct industry and skills analysis.
    • By Sept. 2025:  Conduct partner outreach (use labor market analysis to inform discussions).
    • By Dec. 2025:  Analyze survey results and partner input and share recommendations.

​​​​2026 and Beyond

​​​​Given the rate of change, predicting more than a year out is difficult. We are taking an iterative approach to most of the workstreams, which allows us to test and learn more quickly. The outputs from that test, learn, and iterate process, as well as the 2025 studies in key domains, will allow us to set future strategies rooted in real needs rather than theory. Assuming we are successful in executing our 2025 strategy, a number of themes are likely to increase in importance in 2026:

  • Ensuring the entire State workforce has negotiated, cost-effective access to modern, empowering AI tools in key, validated productivity categories to remove time, cost, friction, and drudgery from their workflows.
  • Improving our capacity to move successful PoCs and experiments to deployed, scaled systems “in production,” with robust monitoring, to power improvements in key domains while avoiding the fate of dozens of PoCs taking resources but not yielding benefits.
  • Shifting more focus to the underlying data - further increasing maturity in governance, management, security, privacy, labeling, and “readiness” - given its centrality and complexity in leveraging AI tools.
  • Developing the capability to responsibly build on open source models in addition to leveraging cutting edge closed models, with clear “build vs. buy” pathways.
  • Operationalizing more mature operations frameworks (e.g. - “AIOps”) as more State services adopt AI-powered components.
  • Ensuring we have the right frameworks and infrastructure to experiment with, adopt, and govern even more impactful near term opportunities like agentic systems.
  • Reducing silos between the State, academia, industry, startups, civil society, and other government entities in MD, to encourage a more robust, collaborative, and productive AI ecosystem.
  • Maturing the AI Enablement Team into a robust incubation shop to help the State adopt emerging technologies. Overall, success means adopting AI in ways that moves the needle on the Governor’s priority areas, in a manner that fully takes advantage of the wealth of intellectual, institutional, and people-powered resources across Maryland.

​​​​​Overall, success means adopting AI in ways that moves the needle on the Governor’s priority areas, in a manner that fully takes advantage of the wealth of intellectual, institutional, and people-powered resources across Maryland.