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Abstract

Biotechnology organizations increasingly combine omics, laboratory, process, clinical, surveillance, and supply-chain data with artificial intelligence, yet analytical sophistication does not by itself produce defensible strategic action. Biological variability, incomplete causal knowledge, scale-up discontinuities, off-target effects, model opacity, cyberbiosecurity exposure, and emergency time pressure can interrupt the path from evidence to decision. This conceptual article asks how data analytics and AI can be integrated with Strategic Decision Intelligence across biomanufacturing, gene technologies, and medical countermeasures. A critical synthesis of 20 verified academic sources is used to develop the Biotechnology AI-Integrated Strategic Decision Intelligence (Bio-AISDI) Framework. The framework has seven connected layers: biotechnology data and information; analytics; AI and advanced computation; biological knowledge and explainability; safety, biosecurity, and resilience; biotechnology strategic decision intelligence; and human governance and action. A governed feedback loop connects biological and operational outcomes to new evidence, model revision, knowledge updating, and organizational learning. The synthesis indicates that AI creates strategic value when recommendations preserve provenance, expose uncertainty, respect biological and regulatory constraints, and remain contestable by responsible experts. Six propositions relate governed data, hybrid modeling, explanation, embedded safety and security, platform interoperability, and human-AI complementarity to decision quality. The contribution is an integrative decision architecture that connects the three biotechnology domains without erasing their distinct evidence standards or governance obligations.

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Citation: Naresh Gumala (2026) AI-Integrated Data Analytics for Strategic Decision Intelligence in Biotechnologies. Epistora J. Artif. Intell. & Intell. Syst. 1(1), 1-12. Article EJAIS-110