The NFL has replaced its game-day incident tracking with an AI-powered reporting tool called the Game Ops Dashboard, built with Microsoft using Copilot Studio and Power Platform. The system, which classifies operational incidents automatically and consolidates reports, saves the league's Game Operations team an estimated eight to 12 hours per week in data entry and report generation.
Staging an NFL game involves thousands of operational tasks beyond the field. When something goes wrong - a stadium access issue, a malfunctioning microphone - staff record the incident and begin building a report. Previously, that meant working from an Excel spreadsheet during the game, with multiple staff entering notes that could be inconsistent.
The NFL worked with Microsoft to build the Game Ops Dashboard as a single source of truth for game-day incidents across venues, with the goal of improving post-game reporting and operational learning.
How the system works
The solution centers on a Power Apps application designed for the NFL Game Operations team. Staff select a specific game and log operational notes or incidents from a phone or tablet. They describe what happened, and AI automatically classifies the incident using a model in AI Builder within Power Automate.
The model selects from categories and subcategories - such as broadcasting, uniform policy, game presentation, or stadium facilities - and assigns the incident a predicted priority level and a confidence score. Those predictions draw on domain-specific knowledge about stadium operations, including historical data stored in Dataverse. Power Automate also pulls official game schedules and metadata via league APIs, so each incident is tied to the correct game, teams, venue, and kickoff time.
"As the season went on, we found ourselves making fewer and fewer manual corrections," said Quentin Autry, game operations associate at the NFL. "It was clear that the model was quickly learning and moving to near-perfect accuracy."
The tool also consolidates all incidents after each slate of games into a single standardized view. Built-in filtering lets staff tailor reports by category for NFL leadership or individual teams, and reports can be exported to Excel automatically.
An embedded AI agent built in Copilot Studio lets staff query data stored in Dataverse - pulling up the latest notes for a specific game or week, or filtering notes by category.
Why this matters for operations professionals
The NFL's approach is a practical example of using AI to tame a recurring operational problem: inconsistent manual data entry that produces unreliable reports. The key design choice was letting AI handle classification and routing while keeping humans in control of the final record.
For operations teams, the lesson is in the feedback loop. The model improved as the season progressed because staff corrections fed back into the system. That same pattern - start with historical data, let AI propose categories, and track correction rates over time - applies to incident reporting, ticket systems, and maintenance logs in any industry. The NFL also built its system on its own domain data, not a generic AI tool, which is why the classifications matched how the league actually works.
For operations professionals looking to apply similar methods to their own workflows, AI for Operations covers practical implementation approaches. Those managing teams through such a transition may also find value in AI for Operations Managers, which focuses on workflow optimization and team adoption.
"The platform has helped save the Game Ops team between eight-12 hours a week in data entry and report generation," said Greg Horrocks, senior coordinator of game operations at the NFL. "Additionally, we look forward to the unrecognised efficiencies the platform will provide us as a year-by-year database. This will allow the Game Ops team to be more informed in making policy changes each offseason."
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