Cloud–AI Convergence Reshapes How Governments Serve Citizens – Granicus - August 19, 2025
By Staff Reports - August 19th, 2025
Federal/national, state and local governments are entering a pivotal stage in their modernization journeys as the convergence of artificial intelligence and cloud computing begins to alter how public services are designed, delivered and experienced.
For decades, agencies have adopted new technologies in incremental steps (digitizing forms here, moving data to the cloud there) often without fundamentally changing the processes that underpin service delivery. The rise of AI, however, has brought a new kind of pressure. Its potential to synthesize vast datasets, understand natural language and even take action on behalf of a user is pushing agencies to rethink the very architecture of their operations.
“The shift to AI and machine learning is disruptive,” said Bob Ainsbury, Chief Product Officer at Granicus, in a recent BizTechReports vidcast. “Government’s role doesn’t fundamentally change. It's still about public health, safety, education and economic strength. But the way services are provided clearly will.”
From Digitization to Orchestration
This shift, he explained, is happening on three levels at once: in the way agencies operate internally, in how they interact with citizens, and in the policies that govern the use of emerging technologies.
On the operational front, the integration of AI into cloud platforms is enabling agencies to move beyond basic digitization toward service orchestration. Cloud adoption gave governments more scalable infrastructure and wider access to shared applications, but much of the work still relied on traditional web forms and static information pages.
AI changes that equation by making it possible to connect disparate datasets, anticipate citizen needs and guide them through processes conversationally.
“Governments are arguably the most broad service providers in the world. They are certainly the largest publishers of information,” Ainsbury said. “AI, especially when it sits directly on top of the data, can make it possible for agencies to take a question from a resident — something as simple as ‘parking’ — and immediately ask clarifying questions and route them to the right solution.”
Citizen-Centered Interactions at Scale
For public employees, this evolution shifts the focus away from repetitive, rules-based work and toward more complex cases where human judgment and empathy are critical. “The promise for AI-enhanced government is that workers will be able to spend less time on the noise and more time on what makes cities, states and countries thrive,” Ainsbury said.
The next leap forward, Ainsbury said, will come from “agentic AI.”
“It will lead to systems that can not only answer questions but complete transactions on behalf of constituents. Intelligent assistants may soon help homeowners determine that a new fence requires a permit, ask the necessary eligibility questions, fill out the form, and hand off to a payment agent to finish the process,” he said.
The convergence of AI and cloud could finally deliver on the promise of a “single front door” to government services. Instead of navigating multiple agency websites and chatbots, residents will be able to ask one question and be routed to the right place. “Residents don’t want a health department chatbot or a parks chatbot,” Ainsbury noted. “They want to ask a question and get to the right place, no matter which department that involves.”
Trust, Transparency, and the AI Governance Challenge
This redefinition of self-service reverses a long-standing frustration in digital government. In the past, self-service often meant pushing more of the work onto citizens. With AI-powered cloud services, the system becomes a partner in completing the task, flagging missing information before submission and keeping the process on track.
But with greater capability comes greater responsibility — especially around trust. “You can’t just point a government chatbot at a large language model and hope for the best,” Ainsbury cautioned. Agencies must ensure responses are accurate, policy-compliant and reflective of the agency’s voice.
That means tightly governing the source material, keeping it current, and protecting it from manipulation. In the context of government and regulated industries, this includes compliance with the Federal Risk and Authorization Management Program (FedRAMP) — the U.S. government’s standardized approach to security assessment, authorization, and continuous monitoring for cloud products and services.
FedRAMP-aligned security ensures that cloud-based systems meet rigorous federal cybersecurity requirements, providing a high level of assurance for sensitive data. Combined with robust defenses against prompt injection and strict content controls, these safeguards are no longer optional, they are essential for maintaining trust, meeting compliance obligations, and protecting both agency operations and public confidence.
Staying Model-Agnostic in a Fast-Moving Landscape
Maintaining trust and confidence, however, is challenged by the pace at which emerging technologies are evolving. That said, Ainsbury noted that AI regulation initiatives across geographies have made impressive progress with efforts to keep up.
“What’s happening on the regulatory front in South Korea is remarkably similar to what’s happening in Scotland and the United States,” he said, pointing to shared concerns about transparency, data use, training methods and preventing hallucinations.
For federal agencies in the United States, that means aligning with Office of Management and Budget guidance as well as coordinating sector-specific privacy rules; for state and local governments, it may mean adopting model policies to accelerate safe adoption. But meeting these regulatory requirements is only part of the equation. Agencies also need to think strategically about the technology choices they make — not just from a compliance standpoint, but in terms of long-term flexibility and control over their AI capabilities.
To this end, Ainsbury recommends that agencies stay model-agnostic. “Large language models are becoming commodities,” he said. “Enhancing agency performance will increasingly come from how you integrate your own data, apply retrieval-augmented generation, fine-tune outputs and enforce your policies.”
Measuring Success by Service Outcomes
In the long run, for AI to be truly successful in the public sector, more than technical metrics like uptime or response time will have to be measured. Outcomes that are relevant to constituents is what will matter. “Time-to-resolution, citizen satisfaction, and workforce impact is what needs to be measured. It’s one thing to say we deployed a new platform,” Ainsbury said. “It’s another to say that this platform helped thousands of citizens to access critical services faster and more easily.”
Looking ahead, Ainsbury sees a pragmatic path forward. Agencies can begin by reinforcing their cloud foundations, piloting AI in well-defined use cases where the benefits are clear, and scaling from there. Privacy and security must be embedded from the start, and citizen feedback should shape the evolution of services.
Over time, the default mode of government could become “conversation first,” with AI handling routine inquiries and humans focusing on the most complex, sensitive and high-value interactions.
Granicus is helping agencies navigate this transition by providing cloud-based platforms designed specifically for public-sector engagement and service delivery.
“Our solutions integrate secure AI capabilities with FedRAMP-authorized infrastructure, enabling agencies to deploy conversational interfaces, automate routine tasks, and personalize citizen experiences without compromising compliance or data protection,” he said. “By aligning technology adoption with policy mandates and citizen needs, we are working with the public sector to develop more accessible, transparent, and responsive government services.”