If the 2010s were the era of “there’s an app for that,” today it’s “there’s an AI for that.” In 2026 and beyond, AI tools, agents, and capabilities are popping up across industries and use cases.
Yet the speed alone of AI’s evolution creates a hurdle for local government leaders, who are accustomed to operating the slower pace of the public sector, constrained by budgets, limited staff, and mandate to prioritize community safety above all else.
A 2025 EY survey of municipal leaders found that 78% of IT decision makers are “concerned about the lack of clear regulations / government standards for AI development.”
All the while, mandates to adopt AI are increasing, and local leaders, caught between the pressure to use AI and the imperative of doing so responsibly, are looking for appropriate AI use cases that ensure data governance and security while providing actual value.
For those who don’t know where to start, solving routine plan review workflow issues is a strong first use case for AI. Below, we look more closely.
Delays Start Before Review Even Opens
In conversations with customers, one issue that comes up again and again is that submissions arrive incomplete. Missing engineer stamps, incorrect scales, absent north arrows, or the wrong file formats force staff to pause higher-value work just to track down items that should have been provided at intake.
In fact, about 20–30% of first-cycle plan submissions are sent back for basic issues, and each resubmittal typically adds one to three days before reviewers can even start, meaning agencies can lose substantial time before plan review ever begins.
At the same time, applicants often can’t easily see where their application sits in the process, so they email or call for status checks, which only creates yet another administrative loop for staff.
One of the strongest near-term opportunities is using AI to remove this preventable, incomplete submission issue that slow teams down before any substantive review work begins.
Avolve AI Submit is designed to serve as a front gate to check incoming plans for missing sheet references and titles, detect and help populate common elements such as the north arrow and scale, and confirm there’s room for approval stamps before plans proceed through the review lifecycle.
By ensuring cleaner submission packages from the start, applicants get fast feedback so they can correct problems early and avoid unnecessary rejection loops. And review teams receive cleaner packages, helping projects move into review sooner and reducing interruptions once review is underway.
Built to Match Real Public-Sector Workflows
For AI to take hold in government, it has to reflect how agencies operate day to day. In practice, this means supporting human decision-making rather than replacing it.
Avolve’s approach is grounded in what we describe as intentional, people-first paradigm. Our goal is to reduce the tedious administrative back-and-forth while ensuring trained professionals remain in control of final decisions. We believe AI should augment, not replace, human expertise.
Ease of adoption also hinges on usability. If an AI tool feels disconnected, complicated, or requires employees to learn a separate process, it rarely becomes part of everyday operations. The most successful tools blend into the flow of work and surface the right information at the moment it’s needed.
AI in government carries a higher bar for accountability. Too many models operate as black boxes, making it hard for developers and users to understand how outputs are generated. But local agencies often need to explain how decisions are reached and maintain defensible records.
Avolve AI was shaped by direct customer feedback and our years partnering with more than 350 local government customers. The result is purpose-built Avolve AI that ensures the unique security and data governance requirements of local agencies while providing highly practical applications that provide meaningful time and resource savings today.
Backed by documented governance policies, enterprise-grade security, and well-defined data-handling standards, Microsoft Azure AI supports Avolve’s deliberate approach to augment human expertise and preserve records of judgment. As we evaluated partners, we tested multiple models against real plan review pain points. Microsoft Azure AI, already widely used by government organizations to meet stringent security and data governance expectations, proved to be the strongest option for accuracy and performance.
Next, we’re developing capabilities such as revision change detection, plan object counts (for example, doors, windows, and walls), and expanding AI into our mobile, field-enabled solutions.
As the first and only Esri Gold Partner offering plan review, we are excited to bring AI-powered GIS asset feature creation with Esri ArcGIS to further reduce manual work and bottlenecks for our end users, accelerating how quickly jurisdictions can update and act on spatial data.
Why This Matters for Public-Sector Teams
AI in local government will keep advancing, and we’re eager to see how it continues to mature. Today, plan submission remains one of the highest-impact places to introduce AI in the review workflow as improving completeness at intake creates downstream benefits across staffing, throughput, and even revenue-related timelines.
Ultimately, higher-quality submissions set up jurisdictions up for faster, more scalable, and more predictable community development.
Explore how Avolve AI addresses a highly preventable plan review bottleneck—and what it looks like to operationalize AI within your current workflow.
1. How can AI reduce delays in the plan review process?
AI reduces plan review delays by catching incomplete or incorrect submissions before review begins. Avolve AI Submit automatically checks for missing elements like sheet references, scales, and required fields, allowing applicants to fix issues early so reviewers can start with complete, compliant plans.
This prevents resubmittal cycles and keeps projects moving without unnecessary administrative back-and-forth.
2. What are the most common causes of plan review delays?
The most common cause of plan review delays is incomplete or incorrect submissions at intake. Basic issues, such as missing documents, incorrect formats, or absent plan details, often force resubmittals before review can even start.
About 20–30% of first-cycle submissions are returned for these reasons, adding 1–3 days per cycle.
3. What is Avolve AI Submit?
Avolve AI Submit is an AI-powered intake solution that validates plan submissions before they enter the review process. It automatically extracts sheet data, checks document types, and flags missing or incorrect elements in real time.
This ensures higher-quality submissions, reduces rework, and helps agencies begin reviews faster.
4. Is AI in government plan review secure?
Yes, AI in government plan review can be secure when built on compliant, enterprise-grade infrastructure. Avolve AI uses Microsoft Azure AI to support strong data governance, security, and auditability requirements.
This ensures agencies can maintain transparency and defensible decision-making while adopting AI.
5. Will AI replace human plan reviewers?
No, AI is designed to assist, not replace, human plan reviewers. It automates repetitive administrative checks so professionals can focus on complex evaluations like code compliance and safety.
Avolve’s approach emphasizes human oversight, ensuring final decisions remain with qualified experts.
6. Why is plan submission a good starting point for AI in government?
Plan submission is a strong starting point for AI because it addresses high-volume, repetitive errors that delay the entire review process. Improving submission quality early creates faster downstream reviews and fewer interruptions.
It’s a practical, low-risk way to introduce AI while delivering immediate operational value.
7. How does AI improve the permitting applicant experience?
AI improves the applicant's experience by providing instant feedback during submission. It identifies errors in real time, allowing applicants to correct issues before submission instead of waiting for rejection. This reduces delays, minimizes confusion, and creates a more transparent, predictable permitting process.