As the IDC Government Insights team developed this year’s IDC FutureScape: Worldwide Smart Cities and Communities 2026 Predictions, one trend became clear: cities of all sizes are rapidly adopting LLM-driven AI tools. As cities confront tighter budgets, rising public needs, and the accelerating pace of AI adoption, two predictions stand out: One prediction on Agentic AI and workflow orchestration, and the other on unlocking the value of government data through fine-tuned large language models (LLMs).
Together, these prediction signal a shift from technology-as-a-tool to technology-as-a-teammate (or as “a personal intern”)— where intelligent systems collaborate with humans to simplify complexity, bridge data silos, and elevate service delivery. For mayors, CIOs, and innovation officers, this is more than automation, it’s a reimagining of how government works.
Agentic AI Connects the Dots Across City Systems
By 2027, 65% of cities will deploy AI agents across systems and data to orchestrate end-to-end workflows and reduce workloads while addressing risks of misuse and overreach and “process debt.”
Local governments have long wrestled with what IDC has termed “process debt” — the accumulated workflow inefficiency of fragmented systems, redundant data entry, and manual workarounds. Agentic AI changes that equation. Unlike traditional AI models built for narrow tasks, AI agents can understand goals, coordinate across systems, and execute full workflows — from processing applications to reconciling budgets to automating permit approvals.
But this evolution demands groundwork. Before AI agents can drive real impact, state and local governments must map workflows, clean data, and redesign processes that currently constrain efficiency. As we often discuss, automating broken processes “rarely delivers better outcomes.” Instead, success depends on combining automation with human oversight, workforce readiness, and transparent governance.
Human + Machine Collaboration
Agentic AI will shift how public sector teams work — not by replacing people in the near-term, but by augmenting their capacity. Entry-level clerical roles may evolve, but new opportunities will emerge for “AI process managers,” ethics officers, and cross-agency data specialists. IDC emphasizes that HR must be a strategic partner in this transformation, guiding reskilling and maintaining morale during rapid change.
The payoff? Smarter workflows, faster decisions, and lower service delivery costs. When AI agents manage the repetitive, city staff can focus on what humans do best — strategic decisions, innovation and empathetic human interactions.
Unlocking the Hidden Value of Government Data
By 2026, 50% of state and local governments will invest in fine-tuning LLMs on data the models have never seen, unlocking value from decades of protected records and siloed systems.
Every city sits on a goldmine of data — from zoning and traffic to health, housing, and economic development. Yet much of it is trapped in systems that don’t talk to each other; not only that, this data is private and has not been used to train the LLMs that are serving up GenAI results.
The next wave of Smart City innovation will come from fine-tuning LLMs on this untapped data. Cities will begin training models on internal records — with strict governance — to capture local context and institutional knowledge. The result: AI systems that “speak government”, understand regulatory nuances, and generate insights and recommendations grounded in real municipal operations. This provides faster insights for planning decisions and actions that support mission outcomes.
From Locked Archives to Living Intelligence
Imagine an AI system trained on decades of urban planning documents, council minutes, and building permits. It could summarize past precedents for new zoning requests, detect policy inconsistencies, or surface patterns in infrastructure maintenance failures. Or consider a model fine-tuned on social services data — capable of predicting which households may need early intervention to prevent homelessness.
These capabilities hinge on one foundation: responsible data governance. IDC advises governments to invest in “AI-ready data” — standardizing formats, labeling metadata, and implementing data governance technologies to ensure security and trust. As models become more specialized, leaders must also modernize infrastructure, upgrading government clouds and integrating intelligent computing power to support large-scale inferencing.
Bringing It Together: The Convergence of Agentic AI and Data Intelligence
The two predictions are two sides of the same coin. Agentic AI depends on data liquidity; data intelligence depends on intelligent orchestration. Together, they form the digital nervous system of the future city.
As IDC’s broader FutureScape 2026 report underscores, the Smart City of the near future is not just connected — it’s context-aware. AI agents will move seamlessly across departments, drawing on fine-tuned LLMs to provide decisions informed by a city’s own history and conditions.
The FutureScape highlights key trends:
- Agentic AI is the next leap in digital government, transforming automation into orchestration across workflows.
- Fine-tuned government LLMs will unlock decades of hidden data, fueling more contextual and accurate decision-making.
- Responsible governance is the foundation — without ethical frameworks, AI progress can erode rather than build trust.
- The future is collaborative: Humans define intent and context; AI executes and optimizes — together delivering public value faster and smarter.
Guidance for City Leaders
Smart City success depends not just on adopting AI, but on designing for agility, responsibility, and inclusion. Based on Predictions 1 and 6, here are three critical actions:
- Modernize the Data Core
Build secure, interoperable data platforms that connect siloed systems. Invest in metadata management, data lineage, and ethical AI governance frameworks that prepare your data for fine-tuning and automation. - Pilot Agentic Workflows in High-Impact Areas
Start small but strategic — automate processes where the value is measurable (e.g., licensing, fleet maintenance, or procurement). Use sandboxed environments to test AI agents safely before scaling. - Center People in the Process
Partner with HR to redefine job roles and develop AI literacy. Transparent communication and change management are essential to maintain public trust and employee confidence. - Design for Accountability and Transparency
Incorporate audit trails, explainable AI, and citizen feedback loops. The legitimacy of AI-driven decisions will determine long-term success more than the sophistication of the technology.
The FutureScape 2026 predictions make one thing clear —when agentic AI and data governance converge, cities can be better proactive orchestrators of well-being, equity, and sustainability.
Cities like Boston, Singapore, and Barcelona are already using AI-powered urban planning platforms to integrate policy, climate, and citizen feedback — showing how government-specific data can supercharge innovation responsibly. These early movers demonstrate what’s possible when leaders treat AI not as a black box but as a civic partner.
As Smart City leaders plan their 2026 strategies, now is the time to evaluate your readiness for agentic AI and data-driven transformation.
Explore the full IDC FutureScape: Worldwide Smart Cities and Communities 2026 Predictions report, join the IDC Directions 2026 event series, and dive deeper into the IDC PlanScape: An Agentic AI Guide for Smart Cities to chart your next steps.
If your city is already advancing innovative, AI-enabled initiatives, consider submitting your project for the IDC 2026 Smart Cities and Communities North America Awards, now open for nominations.
