SPECIAL NOTICE FOR ADVANCED AI/AA ANALYTICS | W911NF 17 S 0003 ADVANCED AI AA ANALYTIC

Opportunity Information: Apply for W911NF 17 S 0003 ADVANCED AI AA ANALYTIC

The Department of Defense (U.S. Army Materiel Command), through DEVCOM’s Data and Analysis Center (DEVCOM DAC) and under the Army Research Laboratory (ARL) Broad Agency Announcement W911NF-17-S-0003, issued a Special Notice seeking proposals focused on "Advanced AI/AA Analytics." The core purpose of the opportunity is to fund applied research that builds a rigorous analytical and simulation-based framework the Army can use to properly assess, test, and evaluate artificial intelligence and assistive automation (AI/AA) systems before and during integration into Army operations. In practical terms, the Army is looking for a credible, repeatable way to understand how AI-enabled or automation-enabled capabilities perform, what they are good at, where they fail, and how they affect overall mission outcomes when paired with human operators.

A central requirement is that the proposed framework cover live, virtual, and constructive (LVC) simulation approaches. That means the effort should not be limited to one type of modeling or test environment. Instead, it should be able to represent AI/AA systems in realistic Army operational situations across a spectrum: live testing where feasible, virtual simulations that replicate platforms and environments, and constructive simulations where forces and behaviors are represented computationally at scale. The government’s intent is to create a toolset and methodology that can model the AI/AA system inside operational contexts that matter to the Army, rather than evaluating algorithms in isolation. This emphasis reflects a need to examine the full technology-human operator ensemble, including how people interact with AI/AA, how trust and workload shift, and how performance changes under stress, uncertainty, and mission complexity.

The desired framework is meant to directly support a range of analysis types that DEVCOM DAC performs for Army leaders. Specifically, the framework should enable direct measurement of system behavior and outcomes, trade-off analyses (for example, comparing alternative AI/AA designs, levels of autonomy, or sensor/compute configurations), and performance and effectiveness analyses that translate technical performance into operational value. It should also produce data suitable for feeding higher-level force-on-force models, which are often used to explore campaign-level or large-unit outcomes. Additionally, the Army highlights sustainability analysis, signaling interest in understanding not only how AI/AA performs in ideal conditions, but also how it can be supported over time, including implications for maintenance, updates, data requirements, retraining, logistics, and operational readiness.

A major deliverable embedded in the notice is the development and delivery of metrics, along with the underlying variables that drive those metrics. The government is not just asking for generic scorecards; it is asking for experimental, calculated, or modeled measures that have high explanatory value. In other words, the metrics should meaningfully capture the most important determinants of performance and effectiveness for AI/AA technologies when used by human operators in realistic scenarios. This suggests an expectation that proposers will define what "good" looks like in operational terms, connect those outcomes to measurable drivers (such as decision accuracy, timeliness, robustness to degraded data, operator workload, error rates, mission success contributions, resilience under adversarial conditions, and similar factors), and provide a defensible method for measuring or estimating them within LVC environments.

From a funding and administrative perspective, the opportunity is categorized as discretionary and uses a cooperative agreement instrument, which typically implies substantial government involvement during performance (for example, collaboration, iterative reviews, or shared technical direction consistent with the award’s terms). The listing indicated an award ceiling of $45,000,000 with an expectation of a single award, pointing to a large, integrated effort rather than a collection of small independent projects. The CFDA number provided is 12.431 (a defense R&D-related assistance listing). The posting dates in the source data show it was created March 10, 2021 with an original closing date of April 12, 2021.

In summary, this Special Notice targets an applied research effort to build an Army-relevant evaluation ecosystem for AI and assistive automation: a comprehensive LVC-enabled analytical and simulation framework, backed by well-defined, high-value metrics, that allows DEVCOM DAC and senior decision makers to quantify performance, compare alternatives, understand operational impacts, generate inputs for broader force models, and make more confident choices about integrating AI/AA systems into Army organizations and missions.

• The Department of Defense, Dept of the Army -- Materiel Command in the science and technology and other research and development sector is offering a public funding opportunity titled "SPECIAL NOTICE FOR ADVANCED AI/AA ANALYTICS" and is now available to receive applicants.
• Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 12.431.
• This funding opportunity was created on Mar 10, 2021.
• Applicants must submit their applications by Apr 12, 2021. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
• Each selected applicant is eligible to receive up to $45,000,000.00 in funding.
• The number of recipients for this funding is limited to 1 candidate(s).
• Eligible applicants include: Others (see text field entitled Additional Information on Eligibility for clarification).

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