AI and Cloud Convergence Define the Next Era of Public Sector Service Delivery — Granicus - August 20, 2025
By Staff Reports - August 20th, 2025
A Conversation with Bob Ainsbury, Chief Product Officer at Granicus
The convergence of artificial intelligence (AI) and cloud computing is redefining the way government agencies deliver services to their constituents. Over the past decade, cloud adoption has given public sector organizations the scalability, flexibility, and resilience needed to modernize systems and move critical functions online. But the rapid evolution of AI — particularly large language models and generative capabilities — is opening a new frontier in how services are designed, delivered, and experienced.
For Bob Ainsbury, Chief Product Officer at Granicus, the implications go well beyond technology. He sees the shift as a transformation in the very relationship between governments and the people they serve. While the mission of government remains constant — safeguarding public health, ensuring public safety, advancing education, and fostering economic prosperity — the means of achieving these goals is being reshaped by intelligent, cloud-enabled tools.
In a recent BizTechReports vidcast, Ainsbury outlined how this technological convergence is creating opportunities for innovation, efficiency, and improved citizen engagement, while also introducing new challenges in governance, security, and trust. His insights have been summarized and organized into four critical dimensions: Strategic Assessments, Operational Imperatives, Financial Implications, and Technology Development.
Here is what he had to say:
STRATEGIC ASSESSMENTS
BTR: How is the convergence of AI and cloud computing altering the public sector’s strategic landscape?
Ainsbury: Governments are the broadest service providers in the world and one of the largest publishers of information. Every interaction — from renewing a driver’s license to applying for benefits — depends on the ability to communicate information effectively and connect citizens with the right services.
Cloud computing gave us a foundation for modernizing those interactions by moving away from siloed, on-premises systems. It gave agencies scalability, elasticity, and the ability to deploy new solutions faster. But the introduction of AI, particularly conversational AI and intelligent agents, takes us a step further — from simply digitizing processes to orchestrating them in ways that feel intuitive and human-centered.
For example, a citizen might type “parking” into a city website search box. Traditionally, they’d get a list of links — many irrelevant — and have to hunt for the right information. AI can change that by asking, “Do you need to pay for parking, contest a ticket, or find parking availability?” It can immediately narrow the scope, understand the context, and connect the citizen to the right resource. This is the beginning of what I call “service orchestration,” and it fundamentally changes the strategic role of technology in government.
BTR: Beyond the citizen experience, how will this impact governance and policy?
Ainsbury: The policy environment is developing quickly. What’s notable is how similar AI regulations are across different jurisdictions — whether you’re in South Korea, Scotland, or the United States. Most are focused on the same foundational principles: transparency in how AI models work, responsible use of data, accuracy in outputs, and preventing harmful or fabricated content.
From a strategic perspective, agencies need to recognize that AI governance isn’t a side conversation — it’s integral to digital strategy. Policies around explainability, model training, and oversight will shape how AI is deployed and how much trust it earns from citizens. The agencies that align early with these principles will be better positioned to innovate without hitting compliance roadblocks.
OPERATIONAL IMPERATIVES
BTR: What operational changes should agencies be preparing for as AI and cloud converge?
Ainsbury: The first is rethinking the definition of “self-service.” For years, we’ve equated self-service with shifting the workload from the agency to the citizen — filling out long forms, navigating multiple portals, trying to figure out which department handles their request. AI can flip that model entirely.
Imagine a guided process where the system, not the citizen, is doing the heavy lifting. The AI asks clarifying questions, verifies eligibility, fills in known data, and flags missing information before the application is submitted. It doesn’t just point you to a form — it walks you through it, step by step, across different departments if necessary. That’s a fundamentally different operational model.
BTR: What makes that shift possible from a back-end perspective?
Ainsbury: Interoperability is key. We’re moving into an era of “agentic AI” — where different AI agents handle different tasks, like processing applications, managing payments, or scheduling appointments. For this to work, those agents need to hand off seamlessly to each other, maintaining context and security the whole way through. That means establishing standard protocols, robust identity management, and a governance framework that covers both technology performance and the accuracy of the content being delivered.
BTR: And the role of human employees?
Ainsbury: AI is not about replacing public servants — it’s about freeing them from repetitive, low-value tasks. When the system can handle routine inquiries, employees can focus on the cases that require human judgment, empathy, and creativity. In fact, as the technology matures, I think we’ll see “conversation-first” become the default mode of public service. Citizens will engage through natural dialogue — whether typed or spoken — and AI will handle the back-end orchestration. Humans will step in where their skills are uniquely valuable.
FINANCIAL IMPLICATIONS
BTR: What are the financial considerations agencies should be factoring into their AI–cloud strategies?
Ainsbury: The most immediate benefit is efficiency. Fewer manual touchpoints mean faster service delivery, which translates into lower operational costs. AI also reduces the rate of incomplete or incorrect submissions, which minimizes the time and money spent on rework.
But there’s also a broader value story. When citizens can access services more easily and accurately, compliance improves, and programs achieve better outcomes. That could mean more timely tax payments, higher enrollment in eligible benefit programs, or faster permitting processes that help local economies.
BTR: What about the risks?
Ainsbury: Poorly governed AI is costly — both in direct remediation expenses and in long-term reputational damage. If a system delivers inaccurate information, makes biased decisions, or uses outdated data, the legal and trust implications can be significant. That’s why governance is as much a financial safeguard as it is an ethical requirement.
Another point is vendor flexibility. Large language models are already on the path to commoditization. The real differentiator will be in how agencies integrate their own data, fine-tune responses, and apply governance policies. If you lock into a single vendor ecosystem, you risk paying a premium for something that becomes a commodity. Model-agnostic strategies protect against that and give agencies more leverage in cost negotiations.
TECHNOLOGY DEVELOPMENT
BTR: Where should agencies focus their technology investments to prepare for this convergence?
Ainsbury: Start with the foundation. A secure, scalable cloud infrastructure is the prerequisite for everything else. Without it, AI can’t operate effectively.
From there, begin with targeted, well-defined pilot projects. Choose use cases where the impact can be measured — like permit processing, benefits applications, or customer service triage. Success in these areas builds momentum and justifies further investment.
BTR: Security must be a top concern here.
Ainsbury: Absolutely. Security is non-negotiable. That means FedRAMP-aligned systems, strong identity verification, and safeguards against prompt injection or data poisoning. It also means ensuring the underlying data is current, accurate, and protected from manipulation — because AI is only as good as the data it’s working with.
BTR: And interoperability?
Ainsbury: It’s critical. As more AI agents enter the ecosystem, agencies will need architectures that allow these agents to communicate and collaborate securely. Without that, you end up with automation silos — efficient within a single department, but disconnected from the broader service experience. True value comes from creating a connected, citizen-first model where every part of the system works together.
BizTechReports Bottom Line
The convergence of AI and cloud computing represents one of the most significant shifts in public sector technology in decades. For Bob Ainsbury, the opportunity lies not in replacing the human element of government, but in using technology to elevate it — making services faster, more accurate, and more accessible.
The roadmap is clear: build on a secure cloud foundation, introduce AI through targeted pilot projects, maintain flexibility by staying model-agnostic, and enforce rigorous governance over both technology and content.
Done well, this convergence can help governments deliver on their core mission more effectively than ever before — improving citizen satisfaction, reducing operational costs, and increasing trust in public institutions. In an era where expectations are high and resources are finite, that’s a transformation worth pursuing.