At the end of 2025, more than 2,060 gigawatts of generation and storage were waiting in U.S. interconnection queues. That is roughly twice the entire installed generating capacity of the country, sitting in a line that takes a median of over four years from application to commercial operation date. Most of those projects will never reach the grid: withdrawal rates exceed 70% in some queues. The engineers processing those studies are not slow or incompetent. They are working a manual, document-intensive process on a case load that has grown by orders of magnitude. This is the highest-leverage AI use case in the U.S. energy system, and it is not close.

Why the Queue Broke: Scale, Complexity, and the Study Bottleneck

Understanding why AI matters so much for interconnection queue processing requires understanding why the queue itself is in crisis. The root causes are structural and reinforcing.

First, the scale of applications has exploded. The transition to renewable energy requires enormous amounts of new wind, solar, and storage capacity to connect to the grid. Each of those projects must pass through an interconnection study process that was designed when a few dozen projects per year were the norm. Transmission-owning utilities and ISOs/RTOs are now processing hundreds or thousands of applications per year with staff sized for a different era.

Second, the physics have become more complex. Traditional interconnection studies evaluated the impact of a new generator on a relatively stable network. Modern studies must model the interaction of thousands of inverter-based resources (wind, solar, batteries) with each other and with the existing grid, account for significant uncertainty in which other projects in the queue will actually be built, and evaluate new grid code requirements for inverter behavior. The computational burden has grown substantially, and the engineering judgment required to interpret results has increased.

Third, the cluster study model that replaced the serial study model has created coordination complexity. When multiple projects are evaluated together in a cluster (because their combined impacts determine individual cost shares), a single withdrawal by one project can require the entire cluster to be resimulated. Withdrawal rates above 70% in some queues mean that most clusters must be restudied multiple times. Each resimulation takes months of engineering staff time.

Fourth, the documentation burden is large, and most of it is repetitive. A typical interconnection study report runs 50 to 200 pages, of which perhaps 20 to 30% requires unique engineering judgment and the rest is boilerplate: standard methodology descriptions, regulatory citations, template exhibits, and formatted results tables. Engineering staff spend a meaningful fraction of their study time on content that could, in principle, be generated much faster.

Where AI Has the Highest Leverage in the Queue

Not every part of the interconnection process benefits equally from AI. The applications fall into a rough hierarchy of leverage and maturity.

Intake and Completeness Checking

Before a project can enter the study queue, it must file an interconnection application that meets specific technical and administrative completeness requirements. These requirements are specified in each ISO's or transmission owner's tariff and typically include: evidence of site control, a specified point of interconnection, proposed technology and capacity specifications, electrical one-line diagrams, and payment of the required filing fee. Applications that are missing required elements must be rejected and refiled, consuming staff time on both sides with no progress on the actual study.

AI document review tools can check incoming applications against the completeness requirements in the applicable tariff faster than a human reviewer can, flagging missing elements, inconsistent specifications, and common format errors before the application goes to a technical reviewer. This is not a high-sophistication AI task; it is essentially a structured document parsing and compliance checking problem. But it saves real engineering time on a high-volume, repetitive task that currently consumes a meaningful fraction of intake staff capacity.

Study Report Drafting and Boilerplate Generation

The largest time-saving opportunity in interconnection study work is the generation of standard report sections. Methodology descriptions for power flow analysis, short-circuit analysis, and stability analysis are largely identical across studies at the same ISO. Regulatory citation sections repeat the same tariff provisions with project-specific variables substituted in. Results tables have defined formats. These sections can be templated and populated from structured model outputs, with AI generative tools assisting in drafting the narrative that connects the results to the standards.

The engineering team's scarce time should be concentrated on the sections that require judgment: interpreting model results that show a potential violation of a reliability criterion, determining the appropriate mitigation measure, specifying the scope of required network upgrades, and assessing whether the proposed solution is technically and commercially feasible. AI drafting assistance does not replace this judgment; it frees up time to exercise it by reducing the time spent on templated content.

Withdrawal Risk Scoring and Cluster Management

In a cluster study environment where withdrawal by any project triggers a resimulation, the ability to predict which projects are likely to withdraw before the resimulation is needed has high value. Projects withdraw for multiple reasons: financing falls through, site control is lost, the cluster study shows upgrade costs that exceed the project's economics, or the developer simply has too many applications in different queues and is winnowing the portfolio.

AI models trained on historical queue data can score each project's withdrawal probability based on observable features: time in queue (longer-queued projects withdraw more), cluster position and cost share characteristics, developer track record across queues, project type and size, and the gap between the project's proposed COD and the study-estimated realistic commercial operation date. A transmission planner who can identify the four most withdrawal-likely projects in a 12-project cluster before the next milestone can design the study process to prioritize those decisions and potentially save months of resimulation work.

The interconnection queue backlog is not a technology problem. It is a throughput problem. AI's job is not to replace the engineer's judgment; it is to eliminate the repetitive work that prevents the engineer from applying judgment where it matters.

Queue Analytics and System Planning Integration

The interconnection queue contains a forward-looking picture of the generation fleet that is likely to be built over the next 5 to 10 years. Processing this information into inputs for transmission planning, resource adequacy analysis, and IRP forecasting has historically been a manual task. AI tools can continuously process queue data (which projects are in study, which are executing agreements, which are withdrawing, which are approaching commercial operation) and produce updated generation mix scenarios for planning use.

This kind of queue analytics capability has grown in importance as the pace of queue evolution has accelerated. A transmission plan developed six months ago may be based on a queue picture that has changed substantially. AI-assisted queue monitoring keeps the planning inputs closer to current reality, reducing the risk that major infrastructure investments are made based on a generation mix that no longer reflects the actual development pipeline.

The 2,060 GW Context: Why This Is the Right Decade for Queue AI

The numbers justify urgency. At end-2025, the 2,060+ GW backlog represents more than the total existing U.S. generating capacity of approximately 1,200 GW. The median time from interconnection application to commercial operation date has more than doubled, now exceeding four years. FERC has taken regulatory action on large-load interconnection (loads over 20 MW) with its 2026 rulemaking, which will add a new category of load interconnection studies on top of the already-overwhelmed generation queue process.

The withdrawal rate makes the scale problem even more acute. When 70% or more of projects in a queue withdraw before completing studies, every unit of engineering time spent on ultimately withdrawn projects is a cost with no grid benefit. AI withdrawal prediction and triage capability that can identify likely-to-withdraw projects earlier is directly value-creating for the system, not just for any individual developer.

FERC has also been pushing for process reforms, including the transition to cluster study processes and the exploration of standardized agreements for smaller projects. These reforms can be implemented faster and more consistently when the underlying document processing and study workflow is assisted by AI tools that reduce per-study burden on already-stretched engineering teams.

Accountability in AI-Assisted Study Work

Interconnection study reports are legal and commercial documents. The transmission provider that issues a study report represents that it has followed the applicable tariff, applied the correct methodology, and produced results that are technically defensible. AI assistance does not change this accountability structure.

When AI generates a draft report section, the engineer responsible for the study must review it, verify that the content correctly reflects the model results, check that the regulatory citations are current and correctly applied, and sign off on the document. The model-generated draft is a starting point, not a finished product. Any error in the AI-generated content that is not caught by the reviewing engineer becomes the transmission provider's error, with potential tariff compliance and commercial liability implications.

This is why the "show your sources" discipline matters in this context more than almost any other. An AI tool that generates a report narrative without citing the specific model output, the specific tariff provision, and the specific study scenario from which each claim is derived makes the reviewing engineer's verification task much harder. A well-designed AI workflow for interconnection study support produces traceable draft content that the engineer can check against its sources efficiently.

A Worked Example: AI-Assisted Intake and Withdrawal Risk

Walk through a realistic scenario at a large transmission operator. The interconnection team receives 340 new project applications in a 60-day filing window. With their existing staff of 12 intake reviewers, each application takes an average of 3 hours to review for completeness, requiring a total of 1,020 staff-hours over the window, approximately 3 staff-person-weeks of effort.

With an AI document review tool configured against the tariff's completeness checklist, the same 340 applications can be screened in hours, not weeks. The tool flags 87 applications with potential completeness issues: 34 with missing exhibits, 28 with inconsistent data between the one-line diagram and the application form, and 25 with format errors in the technical specifications. These 87 are routed to a human reviewer for disposition; the 253 applications that pass the automated check move to technical review without the standard completeness hold.

Separately, the AI withdrawal risk model scores all 340 applications within the first cluster study cycle. The top-decile withdrawal risk scores cluster around 28 projects, four of which are in the same proposed cluster. The study manager reviews the withdrawal risk factors for those four: all four have developers with multiple simultaneous applications in other ISOs, two have sites with active land-use disputes visible in public records, and one has a proposed technology configuration that the developer has withdrawn in three previous applications. The study manager decides to sequence the first milestone payment deadline earlier for those four projects, concentrating the withdrawal decision before the cluster goes into full study mode. Two of the four withdraw, saving the cluster from a mid-study resimulation.

The engineering staff who were freed from completeness review paperwork spent that time on the technical review of the cluster study methodology, catching a potential voltage stability issue in the study scenario setup that would have required restudying the cluster if it had been discovered after the initial results were complete.

The Developer Perspective and the Information Asymmetry Problem

Most discussion of interconnection queue AI focuses on the transmission owner's perspective: how to process more applications faster. But the developer's perspective is equally important for understanding what AI can change. Developers with large portfolios of applications across multiple queues already conduct sophisticated internal queue analytics. They model which of their projects have the highest probability of making it to commercial operation given current queue conditions, cost share trajectories, and their own financing constraints. They time their withdrawals to minimize milestone payment losses and to concentrate their resources on the projects most likely to succeed.

A solo developer with one project in one queue does not have this capability. They are navigating a complex process based on information from their interconnection attorney and whatever public data the ISO publishes. The information gap between large portfolio developers and single-project developers creates an inequity in the queue process: large developers can optimize their queue management in ways that small developers cannot.

AI tools that provide queue analytics capabilities to all market participants regardless of size could reduce this information asymmetry. If a single-project developer can access the same type of queue position analysis and withdrawal probability information that large portfolio developers produce internally, the playing field becomes more level. This is not a technical challenge; it is a policy question about whether queue data should be more accessible and whether AI analytics tools should be considered a public good in the interconnection process.

The Role of Standardization in Enabling AI Queue Processing

One of the structural barriers to deploying AI across the interconnection queue is the lack of data standardization across ISOs. Each ISO uses different application formats, different data field definitions, different study methodologies, and different report templates. An AI intake checker trained on PJM's requirements will not work for CAISO without substantial reconfiguration. A study report drafting tool tuned to MISO's format will produce content that needs significant revision for SPP.

FERC's interconnection reform efforts have pushed toward more standardized processes, and the interconnection cluster study transition that multiple ISOs are implementing creates an opportunity for greater standardization. If the data formats, study methodologies, and report requirements converge across ISOs as part of this transition, the investment in AI tools can be applied more broadly. A completeness checking tool validated against the new standardized requirements works for every ISO that adopts the standard, not just the one it was developed for.

This is a regulatory opportunity that deserves attention from both AI developers and policy advocates: targeted standardization of specific data elements and report formats in the interconnection process would dramatically reduce the per-ISO implementation cost of AI tools and would accelerate adoption across the entire queue ecosystem, not just at the best-resourced ISOs.

Key Takeaways

• The interconnection queue crisis (2,060+ GW backlog, median 4-plus year study time, 70%+ withdrawal rates in some queues) is the highest-leverage AI opportunity in the energy sector, not because AI can replace engineering judgment, but because it can eliminate the throughput bottleneck created by repetitive, document-intensive work.
• The four highest-leverage AI applications in the queue are: intake completeness checking, study report boilerplate generation, withdrawal risk scoring for cluster management, and queue analytics for transmission planning input.
• Withdrawal prediction AI has outsized value in the cluster study model: identifying likely-to-withdraw projects before full study mode prevents the most expensive form of wasted engineering effort (a resimulation triggered by a withdrawal the team could have anticipated).
• AI-generated study content is a legal document and every claim must be verified against its source by the responsible engineer before the report is issued. The reviewing engineer's sign-off is not a formality; it is the accountability mechanism that makes AI assistance safe to use.
• FERC's 2026 large-load interconnection rulemaking adds a new category of load studies to an already-overwhelmed process; AI throughput tools are needed on both the generation and load study sides of the queue.
• Queue analytics AI that continuously tracks which projects are advancing and which are withdrawing provides more current input to transmission planning and IRP forecasting than the periodic manual snapshots that have historically been available.
• The interconnection queue problem is fundamentally a human throughput problem, not a technology problem. AI is the only tool available at the scale needed to meaningfully reduce the 2,060+ GW backlog without a decade-long hiring program.