Airspace AIML RFI_30JUL2025.pdf

U.S. ARMY – REQUEST FOR INFORMATION (RFI)

Date: 30 July 2025

Subject: Request for Information – AI-Enabled Airspace Management Solutions to Reduce Commander Cognitive Burden

DISCLAIMER

THIS IS A REQUEST FOR INFORMATION (RFI) NOTICE ONLY. THIS IS NOT A REQUEST FOR

PROPOSAL, QUOTE, OR BID. IT DOES NOT CONSTITUTE A SOLICITATION AND SHALL NOT

BE CONSTRUED AS A COMMITMENT BY THE GOVERNMENT. RESPONSES IN ANY FORM

ARE NOT OFFERS, AND THE GOVERNMENT IS UNDER NO OBLIGATION TO AWARD A

CONTRACT FROM THIS RFI. NO FUNDS ARE AVAILABLE TO PAY FOR PREPARATION OF

RESPONSES TO THIS RFI. ANY INFORMATION SUBMITTED BY RESPONDENTS TO THIS RFI

IS STRICTLY VOLUNTARY.

INTRODUCTION

The United States Army, in collaboration with Program Executive Office Intelligence Electronic Warfare & Sensors (PEO IEW&S) and Program Manager Next Generation Command and Control (NGC2), is issuing this RFI to gather industry feedback and identify innovative solutions leveraging artificial intelligence (AI) and machine learning (ML) technologies to support airspace management. This RFI is focused on addressing the cognitive burden faced by commanders in managing complex airspace operations and maintaining situational awareness in a rapidly evolving battlefield environment.

OBJECTIVES

1. “Fight Tonight” Near-Term Capability Demonstration Opportunities: Identify and experiment with "ready now" solutions to inform strategic objectives and system improvements for airspace management.

2. Long-Term Vision: Build toward an objective capability that integrates AI/ML tools into NGC2 for scalable and adaptive airspace management.

PROBLEM STATEMENT

The modern battlefield is increasingly complex, dynamic, and multi-domain, requiring seamless coordination across land, air, and cyber operations. Effective airspace management is critical to ensuring mission success, protecting friendly forces, and maintaining operational superiority. As the Army continues to integrate advanced technologies and expand its use of unmanned aerial systems (UAS), rotary-wing, fixed-wing, and emerging platforms, traditional airspace management methods are being challenged by the growing scale, speed, and complexity of operations.

Army airspace management is a critical enabler of operational success, ensuring the safe and efficient integration of manned and unmanned aircraft, fires, and other airspace users within the battlespace. However, the Army faces several unique challenges that demand innovative solutions:

1. Multi-Domain Operations: The Army operates in complex environments where airspace must be dynamically managed to support simultaneous operations across land, air, and cyber domains.

2. Proliferation of UAS and Emerging Platforms: The increasing use of UAS, loitering munitions, and autonomous platforms requires seamless integration into airspace management frameworks.

3. Contested and Congested Environments: Army operations often occur in contested airspace where adversaries may employ electronic warfare, jamming, or other countermeasures, as well as in congested areas with high volumes of friendly and neutral airspace users.

4. Dynamic Mission Requirements: Army airspace management must adapt to rapidly changing mission requirements, including the need for real-time deconfliction, airspace prioritization, and coordination with joint and coalition forces.

5. Integration of Fires and Effects: Effective airspace management must account for the coordination of indirect fires, air defense systems, and other effects to ensure mission success while minimizing risk to friendly forces.

6. Data-Driven Decision Making: Traditional airspace management systems often struggle to process and respond to the vast amounts of data generated during operations, limiting their ability to provide actionable insights in real time.

AI-enabled airspace management solutions have the potential to address these challenges by leveraging machine learning, predictive analytics, and automation to enhance situational awareness, optimize airspace allocation, and enable rapid decision-making.

Such systems can analyze real-time data from multiple sources, predict airspace usage patterns, and recommend proactive measures to improve safety, efficiency, and mission effectiveness.

Through this RFI, the Army seeks to explore the capabilities of AI-driven technologies in airspace management, including but not limited to:

Dynamic airspace allocation and optimization in support of multi-domain operations

1. Real-time conflict detection and resolution

2. Integration of UAS, loitering munitions, and autonomous platforms into Army airspace

3. Predictive analytics for mission planning and execution

4. Coordination of fires, effects, and airspace users

5. Resilience against adversary countermeasures, including electronic warfare and jamming

6. Environmental impact reduction strategies for long-term sustainability

The insights gathered will help shape the Army’s future airspace management capabilities, ensuring that it remains agile, adaptive, and ready to dominate the battlespace.

RESPONSE INSTRUCTIONS

Responses related to this RFI are due no later than 29 August 2025 at 5:00pm Eastern Time in the form of a white paper.

Submit responses via email to: usarmy.apg.peo-iews.mbx.linchpin@army.mil; Subject Line: “Airspace Management AI/ML Solutions RFI”.

The white paper should be no longer than seven pages (cover page is not included in this page count). This seven-page limit also does not include the optional standalone one-page response to Question 5 below (Demonstration Opportunity). The white paper must address the following information requests listed below.

The Government requests the following information from Industry:

1. Company Overview

a. Company name, NAICS code, and CAGE code

b. Company size and locations

c. Traditional or non-traditional defense contractor status

d. Points of contact and email addresses

REQUESTED INFORMATION

The Army is seeking information on commercially available or rapidly adaptable solutions that address any or all the above. Please provide responses to the following questions, organized by section:

1. Capability Description:

a. Describe your solution’s core functionality and how it leverages AI/ML to reduce commander cognitive burden. Be specific about the AI/ML techniques employed (e.g., supervised learning, reinforcement learning, natural language processing, etc.). Please address any integration challenges and ideas on how to address them. Please mention any critical data sources (e.g., radar feeds, Automatic Dependent Surveillance– Broadcast, weather data, mission plans) required and any desired data sources.

2. Deployment & Scalability:

a. How scalable is your solution? Can it adapt to increasing the numbers of airspace managed objects, expanding operational areas, and dynamic mission requirements? Provide examples of scalability in previous deployments, if applicable, or how your solution will handle increasing information volumes and users.

3. Maturity & Risk:

a. What is the Technology Readiness Level (TRL) of your solution? Provide justification to support your TRL assessment (e.g., test results, operational deployments).

b. What are the key risks associated with deploying your solution, and how are you mitigating those risks? Include risks related to cybersecurity, interoperability, and operational reliability.

c. Does your solution comply with Army and Department of Defense cybersecurity standards (e.g., Risk Management Framework, Zero Trust Architecture)?

4. Acquisition, Contracting, and Cost:

a. What is your approach to applying Agile software development, Development, Security, and Operations (DevSecOps), and continuous integration/continuous delivery (CI/CD) practices to develop and integrate new features into existing commercial software?

b. Summarize contracting methods the Government could use to maximize the benefits of these modern development approaches and ensure efficient user feedback for Agile development and software delivery.

c. Provide a rough order of magnitude (ROM) cost estimate. Include both initial acquisition costs and ongoing maintenance, training, support, and estimated feature enhancement cost. Describe your licensing model (e.g., perpetual license, subscription-based, usage-based, government purpose rights).

d. What evaluation factors would you recommend ensuring a low-risk, best-value solution that meets the Government’s requirements?

e. How would you include contract incentives to benefit the Government?

Please provide specific examples of incentives and explain how they would provide value.

5. Demonstration Opportunity – Joint Pacific Multinational Readiness Center

(JPMRC):

The Army is seeking interested industry partners to deliver a minimum viable product (MVP) for an AI-enabled airspace management solution that enhances UAS operations during JPMRC Exercise 26-01. The MVP must be operationally ready for deployment to the 25th Infantry Division by November 2025 and capable of addressing some of the unique challenges of UAS management in contested and congested environments.

Partner Interest and Support Needs (Provide a standalone one-page response to the following):

a. Describe the proposed MVP and provide a graphic (if different than what was described in question 1).

b. Identify the cost and resource needs (e.g., funding, data, time, personnel) required to deliver the MVP on time.

c. Do you foresee any challenges in meeting the November 2025 timeline? If so, what mitigations would you propose?

d. What level of Army support (e.g., access to data, testing environments, subject matter expertise) would be required to develop and deploy the MVP?

e. Are there specific funding mechanisms or contractual arrangements that would facilitate your participation?

SUMMARY

This RFI is an information-gathering effort to identify industry capabilities for AI/ML solutions that address the cognitive burden in airspace management. The Army seeks to collaborate with industry partners to deliver innovative and scalable solutions that enhance situational awareness, deconflict airspace, and enable effective UAS management.

QUESTIONS

Questions regarding this announcement shall be submitted in writing by e-mail to usarmy.apg.peo-iews.mbx.linchpin@army.mil; Subject Line: “QUESTIONS: Project Linchpin Airspace Management AI/ML Solutions RFI”. Verbal questions will NOT be accepted. Responses to questions will be posted on https://sam.gov; accordingly, questions shall NOT contain proprietary or classified information. The Government does not guarantee a response to questions received after 22 August 2025. Interested parties are invited to subscribe to the website to ensure they receive any important information updates connected with this RFI.