Artificial intelligence innovation capability dynamic is a B.Tech project topic for Computer Science & Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Artificial intelligence innovation capability dynamic Project Details
| Abstract |
This project offers a hands‑on framework for modeling and measuring how artificial intelligence (AI) boosts an organization’s ability to innovate, using the dynamic‑capabilities view. The approach centers on the three pillars of dynamic capabilities: sensing, seizing, and transforming. The system applies machine‑learning and natural‑language‑processing (NLP) methods to three kinds of data: enterprise records, market signals, and internal resource allocations. It reads unstructured text from market reports and combines it with structured performance metrics to calculate a firm’s sensing capability. Next, classification and regression models predict future innovation outcomes. They also suggest how to reallocate resources, which represents the seizing and transforming capabilities. The framework gives engineering students a clear, step‑by‑step
way to build predictive‑analytics tools for strategic management. It shows how computational models can turn abstract organizational theories into concrete actions. The model is tested on simulated or publicly available corporate datasets. These tests measure both the direct and indirect effects of adding AI on overall innovation performance. In short, the implementation works as a decision‑support tool that links qualitative dynamic‑capabilities theory with quantitative machine‑learning practice.
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| Reference Paper |
Artificial intelligence and innovation capability: A dynamic capabilities perspective |
| Domain |
Artificial Intelligence |
| Sub-Domain |
Artificial Intelligence & Machine Learning |
| PDF Download |
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| Get Help |
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