“Technology alone is not the answer.” For Ohio Chief Data Officer Raivo Murnieks, the central challenge of AI is not deploying new tools but building the governance, workforce capabilities, and organizational trust needed to use them responsibly and effectively.

That principle has shaped Ohio’s approach, with the State AI Council aligning with the State Data Governance Committee. Instead of treating AI as a standalone initiative, the state has established a CDO Council, strengthened data governance, expanded data and AI literacy programs, and brought technology, security, privacy, legal, and business leaders into the process. In this conversation, Murnieks discusses how Ohio has built alignment across agencies with diverse missions, the lessons learned from advancing data sharing, and the governance models helping the state prepare for the next wave of AI.

Q: How does the role of a State Chief Data Officer compare with its private-sector counterpart? What are the expectations of a State CDO, and who are the stakeholders and customers you ultimately serve?

The core role is quite similar in both sectors. The key differences lie in the approach and varying responsibilities associated with executing that role.

In Ohio, we have state agencies, boards, and commissions that directly or indirectly serve all Ohioans and businesses. The missions of those agencies vary based on state and federal regulatory requirements for data sharing and protection.

One expectation, therefore, is to establish and execute an enterprise roadmap for advancing data analytics and data management capabilities through a federated model that sets minimum standards while allowing these agencies to be more prescriptive based on their mission.

In 2023, we formally chartered our State CDO Council, composed of agency CDOs (as well as our State CIO, CISO, and CPO), to collaboratively align on our priorities.

The “State Chief Data Officer Archetypes: The Evolving Roles and Capabilities of CDO Offices” case study summarizes Ohio’s multi-tiered approach of CDO as a policy strategist, internal consultant, and governance steward.

Q: Public-sector organizations often serve diverse missions through multiple agencies, each with its own priorities and constraints. What were the first governance and organizational foundations you put in place to create alignment around data and AI?

For the past 10 years, Ohio’s enterprise technology and advanced data capabilities have evolved and modernized, and each iteration has been tightly coupled with governed security and NIST-related standards. However, it is only in the past three years that we have placed equal emphasis on processes and workforce.

This started with the establishment of new management roles responsible for developing an enterprise Data Governance Program and a Data Analytics Center. These programs, along with the InnovateOhio Data Analytics and LeanOhio programs, formally established the Office of Data and Efficiency.

The data governance manager helped establish the state’s data governance policy and works collaboratively with the Chief Privacy Officer and Chief AI Strategist to ensure alignment with data classification and AI policies, which they oversee, respectively. This alignment establishes an operating model for responsible enablement of data and generative AI.

The State CDO Council collaborated to establish a dedicated data position series. In turn, an enterprise data literacy and data professionals training program was established so that all staff, regardless of competency level, can develop the skills to keep pace with the fast-evolving technologies of today and tomorrow.

Q: Many government organizations want to explore AI but struggle with fragmented data ownership and inconsistent processes. How can CDOs build the trust and governance structures necessary to move from isolated initiatives to enterprise-wide progress?

Building trust and governance structures is a team responsibility. CDOs must collaborate with the individuals responsible for technology and infrastructure, security, privacy, and AI.

The State Data Governance Committee, which includes executive and agency representatives from these functions, sets the governance priorities and ensures the AI governance framework stays aligned and in sync with governance standards. We have developed and maintained positive working relationships, which have been critical to our success in achieving alignment at the executive level.

However, ensuring that this alignment filters down to the agencies, and especially business units within those agencies, presents an additional opportunity. We have traditionally shared information and consistent messaging through CDO, CIO, data security, and legal professional networks.

Most recently, we established several related enterprise communities of practice, which have been effective but still have not fully addressed ensuring consistent processes within agencies. As a result, building upon our formally established Organizational Change Management (OCM) standards and resources, we developed an OCM AI Readiness and Implementation Checklist for assessing, deploying, and maintaining AI solutions.

Q: Looking back at your experience, what people or process investments have delivered the greatest long-term value, and why?

The investments in collaboration and building trust among peers, as well as the ability to build and develop a highly functioning team, have established long-term value. In less than three years, we have established:

• A State CDO Council – agency CDO representation has grown from 16 to 30, with every state cabinet agency now represented. It has reinforced trust and improved agency collaboration, which helps us better serve Ohioans and stakeholders.
• A foundational data governance policy that level-sets and standardizes data governance roles. This was a critical first step before we rolled out our enterprise data catalog for all state agencies.
• The Data Analytics Learning Center, which includes a data and AI literacy program available to all state employees and a data professionals’ program for data practitioners and leaders. These programs have not only increased the capabilities of our workforce to stay aligned with evolving technologies, but also produced capstone data solutions that have improved state operations and services.
• A Statewide Data Sharing Compact, which will help ensure agency alignment in the responsible use and sharing of data as it pertains to data privacy, regulation, and data management.

Q: A recurring challenge in government is enabling data sharing while maintaining privacy, security, and public trust. What practical lessons have you learned about bringing stakeholders together to solve these competing priorities?

Ohio Revised Code 125.32 and Governor’s Executive Order 2019 15-D established our foundational enterprise InnovateOhio Platform, which enables secure data sharing and analytics across state agencies and programs to better serve the public.

This enterprise ecosystem supports and promotes secure data sharing to improve programs and services and to address fraud, waste, and abuse in state government.

Achieving this required collaboration and building trust across agencies and stakeholders to demonstrate that advanced data analytics can responsibly solve problems without compromising or exposing personally identifiable information. The DataOhio Portal further supports this initiative by allowing both public and secure data sharing.

Additionally, the Statewide Data Sharing Compact was accomplished through a collaborative workgroup that included the State CDO, Chief Privacy Officer, and more than a dozen agency privacy attorneys.

Q: AI literacy is becoming a necessity for employees at every level, not just technical teams. How should government organizations approach workforce education, and where should leaders focus their efforts first?

Through InnovateUS, Ohio has established required training for every state employee who is using generative AI to develop solutions. We have also partnered with our vendor community to provide hands-on training and a community of practice for AI practitioners and resource guides for staff using on-prem tools maintained within the state ecosystem.

In addition, we are launching a series of AI Data Literacy badges to the existing enterprise Data Literacy program.

Q: Government agencies often operate under significant budget, talent, and regulatory constraints. What strategies have proven most effective in driving meaningful change despite these challenges?

Many organizations struggle to mature their data and AI capabilities because they focus on technology before establishing governance, processes, and workforce readiness. One of the most common mistakes is trying to do too much too quickly.

A more effective approach is to align AI governance with existing governance structures and adopt a minimum viable governance model that encourages innovation while maintaining appropriate guardrails. An AI Center of Excellence can further support collaboration, governance, and skills development.

Another challenge is underestimating the ongoing costs and maintenance required to sustain AI solutions. Organizations should begin with small-scale pilots and sandbox environments while developing an AI toolkit that includes governance frameworks, use-case templates, procurement guidance, security and privacy requirements, and evaluation criteria.

Organizations also risk pursuing AI because of vendor enthusiasm rather than business needs. AI should be treated as a strategic business enabler.

Finally, adoption suffers when employees are not engaged early. Building AI literacy, providing training, establishing communities of practice, and maintaining transparency are critical to successful and responsible AI adoption.

Q: If you were advising a newly appointed state or local government CDO who wants to prepare their organization for the AI era, what would your first-year roadmap look like?

Build rapport with peers. Collaborate with the CIO, CISO, CPO, and AI Strategist to ensure alignment and determine which area of the organization is taking point to execute on maturity gap priorities.

Assess the current state and maturity level of the enterprise regarding governance, technology, and workforce, and engage with each line of business, including support divisions.

Don’t recreate the wheel – establish rapport with CDO peers in your industry and take what’s been developed and customize it to meet your needs.

Also, establish communication channels to ensure progress is communicated throughout the organization.

Concurrently work on people, process, and technology foundations.

People:

• Establish data and AI literacy
• Clearly define data roles
• Communities of practice/application labs

Processes:

• Establish/update data governance, AI, and data classification policies to ensure alignment
• Organizational change management playbook for AI solutions

Technology/Solutions:

• Establish sandboxes – proofs of concept before full deployment
• Develop system administrator rights procedures to properly govern technology when agentic capabilities are added