Understanding cellular decision-making processes offers valuable insights that can benefit artificial intelligence (AI) development. By studying how cells compute information, make choices based on environmental cues, and exhibit adaptive behaviors without central control mechanisms, researchers can inspire new approaches to AI design. Concepts such as habituation, associative learning, fear conditioning observed at the cellular level could inform algorithms that enhance machine learning capabilities or autonomous decision-making systems. Furthermore, mimicking biological principles of decentralized computation seen in cells could lead to more efficient AI architectures capable of self-organization and adaptation to changing conditions.