Federal Artificial Intelligence Landscape, 2021
Published: April 17, 2020
Deltek’s Federal Artificial Intelligence Landscape, 2021 report explores policy, budget, and contracting trends in the adoption and use of artificial intelligence, machine learning and related technologies by federal agencies.
As mission scope and complexity expand and data proliferation continues to grow, federal agencies are looking to emerging and advanced technologies to maximize the utility and impact of their massive data stores. Artificial Intelligence (AI), machine learning and other automation solutions have emerged as key strategic priorities for agencies needing to improve mission effectiveness, stretch workforce capacity, and drive operational efficiencies. The Department of Defense and civilian agency early adopters, such as Department of Energy and NASA, have been pursuing AI R&D developments for years. The Departments of Health and Human Services and Homeland Security, the Veterans Administration and others have been ramping up their exploration of AI to meet diverse mission needs.
Interest in AI has gained significant momentum as national policies emphasize its importance to national security and economic growth. Federal agencies are discovering the diverse applications and outcomes that AI capabilities offer. The use of AI across federal agencies will continue to accelerate as policies, budgets, technology and expertise continue to align and mature.
Critical Insight for Vendors
Deltek’s Federal Artificial Intelligence Landscape, 2021 assesses the state of artificial intelligence (AI) adoption within the federal government. The report takes an in-depth look at the factors shaping the strategic and budgetary priorities governing artificial intelligence procurement.
The report provides:
Deltek’s Federal Artificial Intelligence Landscape, 2021 report is delivered in PowerPoint® format, including a PowerPoint® Executive Briefing, and an Excel® data workbook.
Deltek’s Federal Artificial Intelligence Landscape, 2021
Trends and Drivers
Conclusions and Recommendations