Benchmarking Cross-Sector AI-KM Readiness in Indian Defense: Statistical Insights, Barriers, and a Policy Roadmap for Indigenisation

Author : A. Sharma, S. Sidharth

Abstract :Background: AI is transforming defense Knowledge Management (KM), but readiness for its adoption—and its real impact on defense indigenisation—remains underexplored in the Indian context. Objective: This paper benchmarks organizational AI-KM readiness across Indian Tri-services, DRDO, DPSUs, academia, and private industry, identifying critical strengths, bottlenecks, and priorities. Methods: Using a mixed-methods design grounded in recent peer-reviewed frameworks, we combined a quantitative survey (N=20; Q1–Q2 2025) and qualitative interviews, mapping questions to five readiness dimensions. Rigorous thematic analysis, ANOVA, t-tests, and effect size calculations were performed. Results: Technological infrastructure scored highest (mean: 3.15/5), while data & knowledge management lowest (2.87/5). Group differences were not statistically significant (ANOVA p > 0.17), but moderate effect sizes suggested meaningful trends. Notably, academia outperformed Tri-services in People & Expertise (t=2.53, p=0.045). Five qualitative barriers emerged: policy-practice disconnect, legacy inertia, securityvs-sharing, collaboration silos, and skills gap. Limitations: Modest sample size limits generalizability; conclusions are exploratory. Implications: Tailored crosssector AI-KM training, leadership-driven digital culture, and interoperable infrastructure are urgently needed to close readiness gaps and advance India’s indigenisation goals. This is the first Indian, cross-sectoral, empirically benchmarked study of AI-KM readiness in defense ecosystem

Keywords :AI Readiness, Knowledge Management, Defense Indigenisation, Mixed Methods, India.

Conference Name :International Conference on Computational Intelligence and Communication Networks (ICCICN-25)

Conference Place Pune, India

Conference Date 23rd Aug 2025