Gartner Predicts at Least 80% of Governments Will Deploy AI Agents To Automate Routine Decision-Making by 2028

At least 80% of governments will deploy AI agents to automate routine decision-making, enhancing efficiency and service delivery by 2028, according to Gartner, Inc. a business and technology insights company.

"Government CIOs are under growing pressure to embed AI into decision-making capabilities rapidly and responsibly," said Daniel Nieto, Sr. Director Analyst at Gartner. "The rise of multimodal AI, alongside conversational and agentic systems, has expanded what public organizations can automate, understand, and anticipate."

However, one of the most persistent barriers to AI value in government is fragmentation. According to a Gartner survey of 138 respondents from government organizations worldwide between July and September 2025, 41% of respondents cited siloed strategies and 31% cited legacy systems as key challenges to adopting and implementing digital solutions.

"Technology modernization alone has not resolved these issues," said Nieto.

Governance Must Shift From Models to Decisions

As AI transitions from experimentation to being deeply embedded in decision-making, governance approaches must also evolve. Traditionally, AI governance has centered on managing models, data, and algorithms.

However, decision intelligence (DI) shifts this focus toward the governance of decisions themselves, for example on how they are designed, executed, monitored, and audited. This shift in governance is especially critical in government, where public legitimacy relies on transparency and fairness.

The Gartner survey found that 39% of respondents cited improved service and citizen satisfaction as primary reasons to invest in building citizen trust. DI offers a structural foundation for operationalizing this trust by making decision pathways explicit and auditable.

"By governing decisions, rather than just isolated AI components, governments can better balance automation with human judgment, particularly in high-stakes or rights-impacting contexts," said Nieto. "Regulated industries and governments cannot rely on opaque 'black box' systems for consequential decisions. DI elevates explainability from a technical requirement to a governance imperative."

Because of the need for transparency in decision-making, Gartner predicts that by 2029, 70% of government agencies will require explainable AI (XAI) and human-in-the-loop (HITL) mechanisms for all automated decisions that impact citizen service delivery. XAI and HITL designs are foundational to public-sector DI. These mechanisms ensure that decision logic can be inspected, explained, and challenged. Because of XAI and HITL, humans also retain authority over exceptions, appeals, and high-risk cases and accountability is preserved even as automation increases.

Citizen Experience Becomes a Qualitative Measure of AI Value

While efficiency remains important, citizen trust in the government's ability to provide effective services is becoming a key driver of digital transformation. Fifty percent of government respondents cited improved citizen experience as one of their top three priorities.

"As AI and decision intelligence increasingly automate and streamline service delivery, the traditional notion of 'citizen experience' evolves," said Nieto "When citizens receive what they need from the government automatically, direct interactions may decrease making trust in the system's reliability, fairness, and transparency even more critical. Because trust is so imperative in these situations, the predictive capacity to anticipate potential needs that could reshape how government digital services are delivered."

DI enables governments to redesign decision flows across citizen-facing services, shifting from reactive, process-driven interactions to proactive and personalized engagement. This not only improves consistency and reduces delays but also enhances perceived fairness and builds public trust, even as direct contact with government staff becomes less frequent.

• ข่าวo:member
• ข่าวo:loc
• ข่าวen
• ข่าวo:it