Tech Trends: Why AI Is a Top Management Priority for State and Local Agencies in 2026

Moving State and Local Agencies From Pilots to Production

Early AI efforts often focus on experimentation — chatbots, analytics tools or isolated productivity pilots. But Alexander describes a future where AI becomes embedded into the fabric of how organizations run.

Cloud-native modernization, she says, is moving beyond simple “lift and shift” migrations toward composable, microservices-based architectures. That architectural shift enables AI to be built directly into workflows.

“These things accelerate model training, automate workflows and embed intelligence into every operation,” Alexander says.

For state and local governments, that could mean AI handling intake, routing and triage for high-volume processes — quietly improving turnaround times without requiring wholesale system replacements. Instead of standing alone, AI becomes a background layer that speeds up processes already in place.

The move from pilots to production also forces leadership questions: How will AI be governed? Who owns it? How will performance be measured? Those are not IT questions alone — they are executive decisions.

READ MORE: State CIOs examine pilot paralysis in AI projects.

Automating End-to-End Work With Agentic AI

One of the most significant shifts Alexander outlined is the rise of agentic AI — systems that don’t just recommend actions but take them.

“Agentic AI is software that acts, not just advises,” she says. “Think of a business process like closing the month or onboarding new customers. These are multistep workflows that used to need human oversight.”

In 2026, she says, AI agents will increasingly orchestrate entire workflows, intervening only when human judgment is required.

“We’re going to continue to see agents orchestrating tasks end to end, escalating when needed, and cutting cycle times dramatically,” Alexander says. “The impact: leaner operations, fewer errors and decisions in minutes.”

For government, this points to AI taking on repetitive, rules-based work that currently slows down services. Over time, that could reduce backlogs, standardize decision paths and free employees to focus on complex cases that require discretion.

An AI-Augmented Government Workforce

While some AI tools will run autonomously, Alexander says that much of the near-term value will come from supporting employees, not replacing them.

“Copilots and agents become standard gear in email, documents, code, analytics and services,” she says. “They draft, summarize and analyze, so people focus on judgment and creativity.”

That model aligns closely with the needs of state and local agencies, many of which are dealing with persistent hiring challenges. AI-powered assistants can help employees work faster, reduce manual documentation and manage large volumes of information.

But Alexander cautioned that productivity gains are not automatic.

“The main catch is reskilling your organization,” she says. “Organizations that invest in AI literacy and change management are going to see the biggest productivity lift and also a better employee experience.”

For public-sector leaders, that means training must accompany deployment. Without it, AI risks becoming shelfware rather than a transformative tool.

DIVE DEEPER: Let’s debunk some AI myths for government.

How AI Can Foster Trust, Oversight and Public Accountability

As AI becomes more embedded in daily operations, transparency and governance become critical — especially for public institutions.

“Regulations and expectations are rising,” Alexander says. “Boards are going to demand traceability: what data trained the model, why it recommended this and who approved it.”

She says leaders will need formal model registries, testing standards and audit trails to ensure systems remain explainable and defensible.

“The result is lower risk, stronger trust and faster approvals,” Alexander says.

For governments, that kind of transparency is essential. AI decisions may affect benefits eligibility, licensing, inspections and other high-impact interactions with the public.

Scaling AI Without Blowing Budgets

Cost is another factor pushing AI into the executive suite. AI systems are compute-intensive, and unmanaged deployments can quickly strain budgets.

“We all know AI is power hungry,” Alexander says. “Making it sustainable and affordable is a true competitive edge.”

She points to strategies such as rightsizing models, shifting inference to the edge and using greener infrastructure.

“Expect cost-to-serve metrics and carbon KPIs, better performance at a lower cost, and with less of a footprint,” she says.

For state and local agencies, a focus on efficiency is not optional. AI must demonstrate clear ROI to earn long-term funding.