Generative AI (gen AI) presents a sizable opportunity for US state governments to redefine services to residents and revamp service delivery. At the same time, it highlights new uncertainties. Given the technology’s potential risks, an effective strategy for state leaders may be to set risk guardrails and prepare to move at scale while piloting lower-risk applications along the way. Such an approach brings real-time application and learning to the process of setting up robust enterprise governance and an adoption road map.

Understanding this opportunity as well as its risks, governments and private sector groups are addressing measures for the appropriate and safe adoption of gen AI. In October 2023, President Joe Biden issued an executive order to govern the development and use of AI in a “safe, secure, and trustworthy” way.1
In the past year, many US state governments have followed suit by considering or introducing similar regulations, with the goal of improving state government operations while managing the technology’s downsides such as risks related to data security and privacy.2

Gen AI’s potential is unique. Unlike traditional AI, gen AI can work on unstructured data. This could enable organizations to leapfrog older approaches to digital innovation. States that lead the way in gen AI adoption may find themselves in the enviable position of achieving the following:

Some state leaders have already initiated efforts to set an adoption road map and organizing principles for gen AI. To name a few, the governors of California, Oklahoma, and Virginia have issued executive directives designed to address the operational, IT, workforce, and risk dimensions of gen AI.3
Given the speed of gen AI development and adoption, leaders are piloting early use cases and preparing for a broader rollout at the same time. In this article, we discuss a four-pronged framework that state leaders can use to create longer-term gen AI strategies for their governments. We also provide a step-by-step guide to help ensure these strategies address five critical areas: adoption, governance, technology and data, talent, and operations.

State leaders should be aware of some popular myths about gen AI as they plan for its implementation. One misconception is that gen AI is a technology of the distant future. In a recent McKinsey survey, one-third of respondents say their organizations are already regularly using the technology in at least one function.4
Another mistaken belief—that gen AI is the exclusive domain of tech experts—is countered by the fact that gen AI solutions are user-friendly because they can use natural language. Previous McKinsey research reveals that about 25 percent of C-suite executives say they personally use gen AI tools for work and 56 percent of workers report using gen AI tools.5
Executives may believe that gen AI adoption requires a complete overhaul of IT infrastructure. In reality, many “out of the box” gen AI tools are deployable with minimal one-time and recurring investments on top of the current infrastructure.6

Perhaps the most pervasive myth is that gen AI will only eliminate jobs. In fact, the career categories most exposed to generative AI could continue to add jobs through 2030, as gen AI accelerates shifts across occupations. Some occupations such as STEM jobs will likely increase by 23 percent during this period as organizations undergo major digital transformations. Other job categories, such as administrative and retail jobs, may decline.7
The automation of individual work activities could provide the global economy with an annual productivity boost of 0.2 to 3.3 percent from 2023 to 2040, with gen AI contributing 0.1 to 0.6 percentage points of that growth.8

Among the positive outcomes of gen AI, the following are particularly relevant to state governments:

As gen AI evolves, governments could consider four archetypes of gen AI tools to propel operational improvements (Exhibit 1):

Using the tools outlined in our framework, state leaders may wish to develop their gen AI strategies in five critical areas (Exhibit 2).

Pilot gen AI in carefully chosen use cases first. Consider focusing on a critical business need or pain point before rolling out an enterprise-wide application. Leaders, for example, may prioritize building a “virtual expert” that enables frontline workers to tap proprietary sources of knowledge and offer the most relevant content to customers. This has the potential to increase productivity as well as build enthusiasm and enable an organization to test gen AI internally before scaling to customer-facing applications. Questions to ask while establishing your adoption road map include the following:

Establish a cross-functional team to provide ongoing oversight, direction, and guardrails. Building a robust governance framework for gen AI adoption is an essential requirement. Few organizations appear fully prepared for widespread use of gen AI. In a recent McKinsey survey, only 21 percent of respondents say their organizations have established policies governing employee use of the technology in their work, and just 32 percent report that their organizations are addressing AI-related inaccuracies.14
A robust approach should mitigate for risks relating to inaccuracy, cybersecurity, regulatory compliance, privacy, and equity and fairness.

State leaders may consider setting up a cross-functional group of professionals—agency leaders; data scientists; and engineering, legal, and cybersecurity experts—to help govern enterprise-wide efforts. Such a group can integrate the diverse expertise and perspectives needed to explore and adopt gen AI safely. Relevant questions to ask while considering your governance approach could include the following:

Evaluate your infrastructure and data architecture needs. To generate value, these models need to work with the government’s existing systems and applications. Meaningful changes in architecture and data governance can take years to achieve for many state governments, so getting started now will be essential. Such efforts can be more cost-efficient when using gen AI tools themselves, including more efficient coding and technical debt reduction. McKinsey research shows that gen AI coding support and code translation for legacy systems can help software engineers develop code 35 to 45 percent faster, refactor (improve or update code) 20 to 30 percent faster, and perform code documentation 45 to 50 percent faster.15
When assessing technology readiness across your government, consider the following questions:

Prepare your leaders and plan for the effects of gen AI on recruitment, retention, and capability building. As gen AI functionality continues to be embedded in common word processing, email, and communications tools, it may have a profound impact on ways of working across the entire organization, including different working patterns and training needs. When planning for talent readiness across your government, consider the following questions:

Identify business processes for which gen AI may create the greatest value. Adoption of gen AI in state government business processes will affect entire workflows, often eliminating steps for staff and those being served. While the technology itself may be relatively straightforward to deploy, the business process implications may be more complex. Agency leaders could consider identifying all major business processes that could benefit from gen AI and sketching out road maps for adoption. Questions to consider include the following:

Gen AI is already here, and it will shape the future of work. The technology provides state governments with an opportunity to enhance services, streamline operations, and make data-informed decisions. Balancing the risks and rewards will require leaders to organize and build capabilities in new ways. By piloting gen AI while simultaneously organizing for scale, state governments can capture gen AI’s value for residents and employees while adjusting to risks along the way.