In an effort to improve efficiency while addressing challenges like workforce adaption and data privacy, Kansas’s state and local agencies are working to harness AI and GenAI for their IT modernization efforts. This article explores the opportunities and challenges AI presents for government leaders.
State and local government agencies quickly have begun to include artificial intelligence (AI) and generative artificial intelligence (GenAI) in IT modernization plans. Agency leaders are both optimistic and cautious about AI’s impact on their organizations: AI solutions can streamline agency operations and enhance human capabilities but implementing them adds myriad considerations to agency IT modernization strategies. Here we explore the AI challenges state and local agencies face and six best practices to guide government AI adoption strategies.
Workforce and Privacy are Top-of-Mind for Agency Leaders
It will come as no surprise to attendees of the NASCIO 2025 Mid-Year Conference that AI dominates the agenda. State technology leaders discussed topics including data literacy, AI ethics, and the broader push for human-centered design in government, with many identifying privacy and impacts on workforce as their top AI concerns.
Despite initial concerns about how generative AI will impact state and local technology workers, a recent report from NASCIO and McKinsey & Co. found that most CIOs were optimistic about the ability of AI to free up tech workers from repetitive tasks and streamline agency operations. At last year’s conference, Shawnzia Thomas, CIO of Georgia, told Government Technology that AI has significant potential to reduce procurement times from 18 months to three years to just six months. However, more training, upskilling, and cultural change are required to prepare the public sector workforce for AI innovation. Continuous learning programs may help agencies prepare staff to use AI solutions now and innovate with them as the technology develops. At the same time, leaders will need a strategy to foster a work environment that is open to AI adoption.
While agency employees are concerned with impacts on their jobs, constituents are more worried about data security and privacy. State and local governments hold large amounts of sensitive data and personally identifiable information (PII), which is managed according to record series laws that govern how data should be protected and discarded. However, complying with these laws is complex. If regulations are not followed, a GenAI model could unintentionally locate and train on data that should have been disposed of. For constituents who already have hesitations about the amount of PII held by government agencies and their plans for AI, this could result in an erosion of trust. Agencies must therefore ensure their data complies with record series laws before bringing AI into their operations. Said Christopher Bramwell, Utah’s Chief Privacy Officer, “If your personal data is not ready for AI, you are not ready for AI.”
Best Practices to Unlock AI’s Potential
State agency leaders aren’t alone in feeling these pressures. A new report from TCS – Tata Consultancy Services revealed that executives and technology leaders across industries are facing similar challenges. The study’s findings regarding these challenges, the current state of AI, and goals for future use informed six best practices that can guide state and local agencies’ AI implementation strategies.
1. Focus on the value, not the technology
As public and private sector organizations continue to adopt AI solutions, agencies may feel pressure to implement an AI system as quickly as possible and find uses for it as they learn how it works. Instead, agencies should “start with a top-down vision to enhance value, and then engineer and orchestrate activity-specific purposive AI agents,” recommends Dr. Harrick Vin, Chief Technology Officer at TCS. By starting with a strategy based on use cases, agencies can put AI to work supporting the mission.
2. Adopt a value chain-based approach
There are two main approaches to AI implementations: horizontal scaling and value chain scaling. Horizontal scaling applies an AI solution to a specific task or function across the agency, such as data analysis. The other approach, value chain scaling, takes one value chain, such as finance, and identifies and applies AI solutions based on that value chain’s needs. With this approach, each department within an agency can use AI where they need the support most.
3. Make the agency and culture AI-ready
Preparing for AI will likely require significant changes to agency workflows, which can cause concern for both leadership and staff. Fostering a culture that embraces change, experimentation, and continuous learning is critical to AI adoption and success. Change management processes can help to build an understanding of how new technology will enhance employees’ jobs, not replace them. Willingness to embrace change will make AI implementation easier and also smooth the process of preparing agency infrastructure and data for AI.
4. Develop higher-level relationships with constituents
As agencies begin to use AI solutions to interface directly with constituents, they need to ensure that the technology enhances the customer experience, rather than detracting from it. While part of that goal relies on the technology, improving customer experience with personalized, proactive connections requires knowing who the constituents are and what kind of experience they want to have. This information can come from public forums and meetings, surveys, or post-interaction feedback.
5. Don’t go it alone
AI implementation is complex, and handling the work in-house can be daunting. Leveraging external expertise and resources can help relieve agency IT teams of some of that burden. Partnering with a technology service provider gives agencies access to specialized skills and knowledge and allows IT teams to focus on their core competencies and strategic objectives. Beyond partnering with external experts, open-source AI tools and frameworks offer a wealth of resources that agencies can leverage to reduce development time and costs.
6. Plan for success, not scarcity
Opinions on how AI will affect the workforce are split, with some concerned that implementing AI will eliminate their job functions. However, the impact of AI may depend on agency strategy. The TCS study found that among respondents reporting greater AI success, four in five focused on growth and innovation rather than cutting costs. By planning for AI to support employees, agencies can set themselves up to create new jobs and enhance human capabilities.
Conclusion
AI, when combined with strategic thinking and creativity, has the potential to enhance agency capabilities and deliver consistent value to constituents. These best practices, inspired by human-centered design principles, can guide state and local agencies to create an AI strategy capable of adapting to mission demands and technological advancements.
