How can the public sector meet the AI moment?

Public sector organizations are under increasing pressure to improve outcomes for residents while making every taxpayer dollar count. When deployed responsibly, AI presents a practical opportunity to meet that challenge. The technology holds the promise of helping governments reduce wait times, simplify everyday interactions, and free public servants to spend more time solving complex problems for the people they serve.

Achieving real impact, however, requires more than isolated pilots. Fully realizing AI’s potential demands organizations rewire by redesigning processes, ways of working, and operating models to make the technology part of everyday service delivery.

This series explores how leaders can move beyond experimentation and take practical steps to scale AI responsibly, delivering measurable improvements in efficiency, service quality, and the experience of the people they serve. Our ambition is to help the public sector dramatically improve their capacity to deliver for residents by providing leaders with the insights and confidence needed to accelerate that journey.

Rewiring the public sector: Seizing the AI moment

By Hrishika Vuppala, Tim Fountaine, Tim Ward, and Tony D’Emidio

AI has the potential to rewire how governments work, transforming public sector efficiency and effectiveness. But seizing the moment takes much more than a bolt-on approach.

Governments around the world face mounting challenges that threaten their ability to effectively deliver essential services. Barriers ranging from fiscal constraints to skilled-workforce shortages and high consumer expectations are forcing agencies to do more with less, while declining public trust reduces their ability to act. The need for efficiency and innovation has arguably never been greater.

Technology has long been heralded as a potential solution, promising modernization, streamlined processes, and productivity gains across the private and public sectors. Recent breakthroughs in AI give reason for optimism—especially the application of generative and agentic AI—around how the technology can improve productivity and other measures. Yet its impact on services to residents remains elusive¹ and many AI deployments seem stuck in “pilot purgatory,” not yet achieving results that reach residents or frontline workers as organizations struggle with data access, workflow integration, model risk, and high ongoing operating costs (Exhibit 1).

The public sector trails others in its AI maturity.

Can governments capture value from AI? It’s rarely, if ever, about the tools or technology alone. Unleashing the potential of AI in the public sector requires rewiring operations by redesigning workflows, adopting fundamentally new ways of working, and engaging the workforce to drive and scale adoption. Even the most advanced tools will underdeliver if they do not address structural issues, including outdated processes, fragmented decision-making, and misaligned workforce models.

That demands a proven, four-part approach undertaken simultaneously, not sequentially (Exhibit 2):

Crafting a strategy that leads with the mission outcome. The focus should be on meaningful outcomes for residents at an appropriate cost, not on tools or technologies.
Reimagining required workflows end to end. The time for piecemeal experiments or use cases is over.
Building the operating system around the technology. Careful change management will be needed to drive adoption that scales.
Keeping humans in the loop for any consequential action. There should be clear lines around what humans do and what AI does.

Rewiring government takes four moves made in parallel, not in sequence.

While AI is already part of people’s lives and has the potential to be a powerful force for good in the public sector, governments have very different regulatory regimes and degrees of organizational and public support. The road to rewiring can therefore be variable and lengthy. But the four-part approach detailed in this article can, when taken together, be a starting point to unlock AI’s potential to deliver better services to the public while building trust and resilience.

Understanding the public sector’s unique challenges

Governments face a structural mismatch between today’s demands and a workforce and technology architecture built decades ago. Fiscal pressures are mounting, with most advanced economies facing balance sheet constraints.² Public sector headcount is well below historical peaks in many countries.³ And confidence in public sector institutions continues to decline.⁴

In addition to these challenges, the public sector is simply different. Government agencies exist to serve residents by delivering critical services without a profit motive while balancing access, fraud prevention, due process, cost, and fairness. This is exactly where AI can do real harm if it is not applied correctly. Structural characteristics make AI in public sector services meaningfully different from AI in the private sector, as exemplified by the following:

Most governments’ procurement rules were not designed for outcomes-based contracting, and multiyear appropriations with rigid line items are often incompatible with AI capabilities that evolve in months.
Unified views of data common in private sector AI are much harder to replicate in public agencies, where resident, mission, and operational data are split across agencies for legitimate privacy and statutory reasons.
Workforce and role rigidity that can come from job classifications, collective bargaining, and merit system rules can stretch role redesign cycles to a year or more, even when leadership is aligned.
Decisions affecting individuals require explainability, reviewability, and auditability, raising the bar on rigor and human-in-the-loop oversight well above private sector norms.

What’s driving successful public sector AI efforts

The good news is that while these differences impose real constraints on the ability of governments to realize AI’s potential, they are surmountable. Examples throughout this article will show governments delivering on AI-enabled transformation today, not just in theory. And learning from these success stories is critical as an increasing number of countries seek to improve efficiency and effectiveness through technology: 95 countries today have national data and AI strategies, compared with less than 20 in 2020.⁵

In our experience, public sector AI programs that deliver value do the following:

Reimagine entire cross-functional, end-to-end processes (or domains) from the resident’s perspective. The agencies pulling ahead are choosing a whole cross-functional, end-to-end process workflow (such as a benefits journey, a licensing process, an inspection workflow, or an eligibility determination) and redesigning it from the resident’s perspective, with AI as one tool among several. McKinsey research finds that about 70 percent of domain-based programs reach production, compared with just 30 percent of programs led by individual use cases.
Start work without hesitation. The most common stall pattern is a two-year program to “get the data ready” before moving. Agencies making progress do the data work concurrently with the AI work, not sequentially, and apply AI to the deterministic parts of a workflow. In our experience, there is ample opportunity to make significant gains with data already available.
Make it clear AI is not about reducing head count. Workforces in many governments are already shrinking while demands and expectations increase. AI is a lever for existing workforces to meet the moment, empowering them to handle larger volumes while spending more of their time on judgment-intensive work only humans can do. This framing matters because public sector workforce skepticism is real: Only one in five public sector employees expects AI to meaningfully affect their daily work, and just 31 percent trust their employer to develop AI safely, compared with 71 percent across industries.⁶
Measure AI’s progress by outcomes, not by announcements. Beyond the dollars spent on the underlying technology, leaders should equally plan for a multiple of that on adoption, training, and capability building. Almost no public sector budget reflects this balance. And leaders measure success not by publicity but whether residents can tell the difference, through tangible progress such as shorter wait times, fewer rejections, faster emergency response, and clearer answers.

Where to start may matter as much as how. In a forthcoming companion piece, we will examine which government processes have the highest value at stake, as well as where the largest clusters of value lie. But what’s important for governments to understand today is the uniqueness of this moment in terms of the opportunity to rewire their operations—and how to go about it.

Seizing the moment: Four steps to rewiring government

Powerful advances driven by AI and growing openness to change have created a rare moment for government agencies to truly rewire processes at scale, unlocking meaningful productivity gains while improving service quality.⁷ Work could evolve into an AI-enabled partnership between people, agents, and robots as modern technologies, especially agentic AI, grow increasingly capable.⁸

In the public sector, that holds the promise of improving operational agility, unlocking innovation, and fundamentally transforming the resident experience by accelerating execution, enabling parallel processing, enhancing adaptability and personalization, introducing elasticity to operations, and strengthening resilience. Their capabilities will likely continue to improve rapidly as models iterate, data scales, and infrastructure matures. But these capabilities will only translate into more effective and efficient services when governments take a holistic approach to rewiring that prioritizes strategy and people together with technology. Otherwise, governments risk AI following a familiar pattern of investment failing to deliver expected or potential impact.

Encouragingly, policy signals are fostering innovation, and there is a growing demand to procure and implement AI and to follow best commercial practices. Governments worldwide are actively pursuing AI initiatives to enhance productivity and the quality of their services, from national initiatives in Singapore, the United Arab Emirates, and the United Kingdom to initiatives in US states such as California and Pennsylvania. A full government rewiring effort augments and builds on these policy steps with a comprehensive approach balancing cost, efficiency, and effectiveness while leveraging both people and technology. It also demands strong leadership, cross-sector collaboration, and a focus on resident-centered outcomes.

In our experience, four steps can maximize the odds of a successful transformation:

Step one: Crafting a strategy that leads with the mission outcome

Rewiring in action: Crafting a strategy that leads with the mission outcome

A national public customer-service organization with a large, distributed footprint undertook an AI-enabled transformation to improve service speed, payment accuracy, and staff productivity across millions of resident interactions each year. Rather than launching isolated pilots, the agency adopted a domain-led approach that prioritized high-impact areas such as self-service, payment integrity, fraud detection, and digital product productivity. It established dedicated cross-functional delivery teams, strengthened data and technology foundations, and embedded risk, assurance, and ethics controls from the outset. Within two years, the program improved developer productivity by approximately 30 percent and identified more than $1 billion in inaccurate or fraudulent payments. The transformation also reshaped how the organization delivered services nationwide, improving customer outcomes while enabling staff to focus on higher-value, more complex work.

A professional licensing department redesigned its physician licensing process, incorporating agentic AI to address long turnaround times, high rework rates, and a heavy manual burden. One key takeaway: Process mapping was not optional but more important than the technology itself. To operationalize the new workflow, the agency codified licensing rules into a structured digital rulebook, deploying AI agents to extract and organize application data, validate credentials against deterministic requirements, flag missing or inconsistent information at submission, and generate structured case summaries with explainable recommendations. Staff retained decision authority for complex or judgment-based determinations. By automating rule-based checks and surfacing issues earlier, the redesign (combined with AI adoption) has so far reduced potential send-backs by 40–45 percent, halved the number of review rounds, and reduced review timelines by 50 percent, down to 12 days—thereby improving speed and consistency while allowing experienced staff to focus on higher-risk decisions rather than administrative verification.

Technological investments are often made without a clear vision of the outcomes they are meant to achieve. Effective rewiring instead begins by defining mission-critical goals, whether it’s improving public health outcomes, reducing response times for resident services, or increasing operational efficiency. By focusing on outcomes first, governments can ensure that technology enables rather than distracts. This involves the following:

Prioritizing more than just cost cutting. Investments should focus on achieving an agency’s mission-critical goals, such as improving resident well-being or enhancing public safety, rather than solely reducing expenses. Leading with outcomes helps ensure technology is deployed thoughtfully, rather than as a “hammer looking for a nail.”
Being bold. Organizations with ambitious AI agendas are seeing the most benefit.⁹ Conversely, organizations that set low expectations often see only incremental change.
Starting with a clean sheet. Unlocking the full potential of AI requires more than plugging agents into existing workflows. Rather than just digitalizing what already exists, agencies can take a hard look at workflows to identify inefficiencies, redundancies, and bottlenecks. Rewiring requires reimagining workflows from the ground up, often leveraging human-centered design principles to ensure they meet the real (not just perceived) needs of both employees and the public.
Designing for continuous evolution. In an environment where AI capabilities evolve daily, waiting months to perfect a strategy before moving forward risks obsolescence. Leading organizations set guardrails and priorities early, then prototype quickly, learn in the field, and refine in waves. Governments can pair clear strategic direction with rapid pilots, continuous learning, and the flexibility to adapt as capabilities evolve, fueled by a funding mechanism that frees up investments concurrent with each wave.

Step two: Reimagining required workflows end to end

Rewiring in action: Reimagining required workflows end to end

A federal-level agency deployed an enterprise-wide dashboard for a healthcare system, standardizing key metrics and targets into a centralized platform. Yet while this enhanced performance-management structure enabled regular forums for reviewing, discussing, and acting on key operational metrics, it was not the end of work. The agency trained more than 1,000 leaders on the dashboard and held multiple performance management forums, supporting a standardized, consistent approach across the organization.

A public sector financial institution launched a modernization effort to strengthen service delivery and minimize downtime of mission-critical operations. Rather than pursuing a narrow infrastructure migration, leadership aligned on a clear modernization strategy, launched targeted lighthouse initiatives to demonstrate early value, and shifted to a more agile, product-oriented model that better integrated business and technology teams. The institution exited its legacy data centers, migrated roughly half of its applications to public cloud and the remainder to modern co-location environments, and strengthened its technology foundation with embedded security and resilience. It also expanded the use of low- and no-code platforms to accelerate development and supplemented existing talent with outside hires where needed. The result was more-resilient systems, faster time to market, and improved service delivery.

Generative and agentic AI may fundamentally reshape how public and private sector organizations advance their missions in the largest organizational paradigm shift since the industrial and digital revolutions.¹⁰ AI technologies are not simply new tools; they are redefining how work is structured, how decisions are made, and how services are delivered—with humans and agents working side by side. And because the capabilities themselves are evolving rapidly, the way government agencies operate and the skills people need must also change dramatically.

We see four key levers to drive organizational capabilities and ways of working:

Ensuring teams—inside and outside of IT—have the skills, capabilities, and opportunities to execute and innovate safely. Effective AI adoption requires more than access to tools. As agentic AI becomes embedded in day-to-day operations and agents increasingly support service delivery, humans could move from completing tasks to delivering outcomes. This would require specific skills, such as AI fluency, agent orchestration, deep problem-solving, and quality control. That may demand agencies rethink every aspect of their talent systems—from roles to career paths to incentives to leadership models—providing consistent, on-demand training to build strong digital skills at every level.
Adopting agile, product-based operating models with flat, cross-functional teams built for speed and scale. Agencies have an opportunity to move away from rigid, hierarchical structures to embrace agile, product-based operating models that concentrate skills, decision rights, and accountability. Critically, this is where the redesign of the work itself happens: McKinsey research shows roughly 60 percent of AI value comes from workflow redesign rather than layering models on top of existing processes.¹¹
Deploying scalable technology to reengineer workflows. Digital factories integrating physical production with digital technologies such as AI agents can be launched within 12–18 months to accelerate delivery, enable rapid experimentation, and reduce execution risk. As agentic systems mature, agencies will need integration patterns that go beyond traditional APIs alone. Emerging standards (such as agent-to-agent) can help agents collaborate, while protocols, such as model context protocol, and existing APIs can connect agents to enterprise systems, data, and tools.
Building a robust data foundation to scale AI across the organization. Research across the public and private sectors shows that data issues are a leading cause of AI project failure, whether due to poor data cleaning and management or simply insufficient data. This can be especially acute in the public sector, where sharing data across siloed, privacy-sensitive agencies presents a cultural and regulatory challenge. Establishing modern data architectures with clear ownership and federated governance can enable real-time decision-making. Critically, data work should occur concurrently with AI work, not sequentially.
Building financial operations discipline. AI run costs can rise quickly in agencies that don’t track them, and many agencies don’t. Government CTOs are increasingly asking how to forecast, allocate, and contain inference costs before they become the line item that ends the program. Building financial operations capabilities around AI early is cheaper than retrofitting them after the bill arrives.

Step three: Building the operating system around the technology

Rewiring in action: Building the operating system around the technology

A national unemployment agency serving roughly two million benefit applicants annually used paper-based forms for nearly 40 percent of applications, provided inconsistent guidance during filing, and still relied heavily on manual effort across multiple channels for processing. It launched a large-scale transformation to redesign its end-to-end benefits journey, with leadership establishing a clear North Star to become truly customer-centric, improve the employee experience, operate as a learning organization, and deliver measurable labor market impact. The agency then redesigned the customer journey around a new model that integrated AI, including mobile-friendly digital consultation and application channels, processing bots to reduce errors in complex edge cases, and more personalized and proactive communication with claimants. It measured outcomes across the entire process and delivered quick wins to demonstrate progress, establishing clear handover points to ensure accountability between design teams and operations and mechanisms to resolve strategic questions quickly and maintain momentum. The effort was framed not as a technology rollout but as a mission-driven redesign of service delivery, with AI embedded as a core enabler of consistency, speed, and capacity.

Responsible adoption of AI captures intended value through an intentional organizational change-management program that scales digital solutions while building organizational skills such as procurement, training, culture, and oversight. Risk and ethics are embedded into the foundation of the work, ensuring the government’s unique position and responsibility are upheld in ethical principles from ideation to post-deployment monitoring. This transformation requires the following:

Focusing on organizational change management. Building organizational capability is essential to capturing value from any technology investment, especially AI. This includes attracting skilled talent, ensuring public service remains compelling, systematically upskilling the existing workforce, and redefining roles to meet modern demands. It applies not only to IT but also to functions such as procurement, finance, legal, and hiring, where new tools and ways of working must be integrated into daily operations. Fully realizing the benefits is not inexpensive or automatic: McKinsey research shows that for every $1 spent on technology, $5 must be spent on change management to drive capability building, adoption, and buy-in successfully.¹²
Integrating human-centered design. Processes and systems should be designed in conjunction with employees to ensure usability, accessibility, and the ability to deliver on behalf of residents. Adoption will take time as humans learn how to partner with agents side by side, although a more intuitive design will ease the transition. Agencies can also co-develop new systems with the people and organizations who rely on their services. Agency leaders and administrators understand policy deeply, but they are rarely the end users—designing from a resident’s perspective improves clarity, reduces friction, yields more-effective outcomes, and strengthens legitimacy. Employees are also learning to revise work habits entirely, from utilizing AI to transcribe video calls and create meeting notes to embedding the technology to monitor their calendars, emails, and other common work tools. The intent is to use AI collaboration to both ease the administrative burden on employees and strengthen their higher-value tasks.
Providing real-time, data-driven governance, ensuring AI is implemented responsibly, ethically, and transparently. Public trust is foundational to effective governance. As with other technologies, leaders should prioritize data privacy, security, and fairness to build confidence in new systems. Government agencies should also build monitoring and evaluation directly into workflows, and leaders should set clear expectations for human accountability and oversight (such as technology leaders validating code outputs while program leaders verify sources and policy interpretations). Without intentional governance, agencies risk either accumulating unapproved tools with unverified outputs or absorbing added compliance, quality, and reputational risk as adoption grows.
Evolving procurement to avoid free-pilot traps and contracting for scalable outcomes. Government procurement was built to buy inputs—such as licenses, seats, and tidy demonstrations—but AI’s value only shows up when a redesigned workflow runs at scale. The “free-pilot trap” may deliver a no-cost or low-cost proof of concept that dazzles in a demonstration but then has to be rebuilt almost entirely to work in a scaled environment. The solution is to make procurement evolve in step with the technology—contracting for measurable, scalable outcomes and shared delivery risk so that the government contracts for impact that sustains rather than for tools that never leave the pilot stage.

Step four: Keeping humans in the loop for any consequential action

Even with a strong strategy, capable teams, and the right technology, risk issues can stall AI deployment in government. Agency leaders are right to ask hard questions about over-automation, accountability, and unintended consequences, and agencies pulling ahead design for risk management from the start rather than retrofit governance after deployment. Two moves matter:

Defining what requires human sign-off—by consequence, not category. A benefit denial, a license revocation, or a public safety dispatch is a consequential decision requiring a human in the loop. A grammar check on a draft response is not. Most agencies default to a human in the loop for everything because not enough work has been done to draw clear lines around what humans do and what AI does (Exhibit 3).
Making the human-in-the-loop decision a political safety net. Public trust in government AI rests on the ability of an affected resident to know that a human can review, override, and explain a decision. Designing for that capacity from the start—and saying so publicly—may be the single biggest determinant of whether an agency retains public trust.

Identifying government processes that require a human in the loop should be based on consequence, not category.

The path forward

The frustrations of navigating government bureaucracy have become a cliché for a reason. In our experience, public agency leaders are often equally frustrated by the challenges they face in transforming the business of government: legacy systems, the sheer scale of change required, the unique responsibilities of public service, and the risks inherent in any misstep.

AI presents a potential inflection point. But rewiring government is not just about modernizing systems or bolting on AI tools to existing processes. This moment is an opportunity for public agencies to truly transform, becoming more agile and efficient while better meeting people’s needs. Exhibit 4 shows how residents may experience a truly AI-native government.

A rewired government could meet residents’ needs at each stage of their lives.

We’re not suggesting it’s easy. Meeting the demands of this modern era is complex and challenging, but the rewards are clear. Governments can create agile, transparent, and resident-focused systems by starting with the mission, reimagining workflows from the ground up, building organizational capabilities to deliver advanced technologies, and empowering both people and technology (for a tactical leader’s to-do list, see below, “Ninety-day actions: Immediate steps for government leaders”). In the process, they can move beyond incremental improvements to achieve significant improvements in outcomes.

Ninety-day actions: Immediate steps for government leaders

The first step is often the hardest. Here are recommended actions public sector leaders may take to kick-start the transformation process and gain immediate momentum for change.

Publish the resident-facing outcomes you will measurably improve with AI by year-end. This includes wait and response times as well as accuracy and reduced fraud—not the number of pilots launched or executive orders signed.
Map two end-to-end workflows from a resident or frontline perspective. Look for steps that exist only because of legacy constraints, not because of mission need or legislative requirement.
Stand up one cross-functional pod. This pod should combine policy, technology, operations, and frontline staff to own a single end-to-end process. Give this group of five to eight people real decision rights.
Review your AI procurement pipeline for outcomes-based clauses. If every contract still pays for inputs (hours, licenses, or seats), you cannot share risk with vendors or capture value.
Preposition your data governance for agents, not just analytics. Identify the three data flows that an agent would need to act on and resolve the access, lineage, and audit questions now before the agent becomes the bottleneck.
Commit to a change management investment ratio. For every dollar approved for technology, secure a parallel commitment for adoption, training, and capability building.

ABOUT THE AUTHOR(S)

Hrishika Vuppala and Tim Ward are senior partners in McKinsey’s Southern California office, Tim Fountaine is a senior partner in the Sydney office, and Tony D’Emidio is a partner in the Washington, DC, office.

The authors wish to thank Ali Ustun, Anne Neville-Bonilla, Deidre Harrison, and Kelly Ungerman for their contributions to this article.