United States DoD Artificial Intelligence Growth Opportunities
This Frost & Sullivan research service focuses on US Department of Defense (DoD) artificial intelligence (AI) budget spending and contracts. Artificial intelligence tools are used across an array of DoD applications to enhance mission accomplishment and safety, including logistics, maintenance, healthcare, administration, networks, intelligence analysis, unmanned vehicles, and training.
A military digital transformation is underway, and it leverages advanced commercial IT technologies such as embedded hardware, mobile networks, security, sensors, cloud computing, AI, and a variety of other innovative products helping to improve day-to-day operational efficiency and reduce costs.
The study includes research, development, test, and evaluation (RDT&E) as well as procurement activities, operations and maintenance (O&M) activities, and other services. The programs listed are from the FY2022 DoD budget request and contract activity is for FY2021. The DoD Artificial Intelligence budget request covers Army, Navy/Marine Corps, Air Force/Space Force, and joint service spending requests. The base year for financial spending is 2020.
- Strategic Imperatives
- Why is it Increasingly Difficult to Grow?
- The Strategic Imperative 8™
- The Impact of the Top Three Strategic Imperatives on the US DoD Artificial Intelligence Industry
- Growth Opportunities Fuel the Growth Pipeline Engine™
- Growth Opportunity Analysis—DoD Artificial Intelligence Market
- Purpose and Overview
- Trends and Challenges
- Market Segmentation
- Technology Application Examples
- Growth Drivers
- Growth Restraints
- Program Funding by Department
- Program Funding by Type
- Representative Programs
- Representative Industry Participants
- Representative Contracts
- Growth Opportunity Universe—DoD Artificial Intelligence Market
- Growth Opportunity 1: Artificial Intelligence for Missile Defense
- Growth Opportunity 2: Artificial Intelligence for Networking
- Growth Opportunity 3: Artificial Intelligence for Manned-Unmanned Teaming
- Conclusions and Future Outlook
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