Managing expiring knowledge: Temporal dynamics of business information

In the current business environment shaped by artificial intelligence, the relevance of information is increasingly limited by time. This paper examines the concept of expiring knowledge, defined as information whose usefulness decreases as conditions change. We identify four main types of expiries: event triggered, continuous decay, context dependent, and structural. A temporal knowledge framework is proposed to classify business knowledge according to how quickly it expires and how frequently new knowledge is created. Case examples from finance, technology, public policy, and healthcare illustrate how expiry patterns differ across sectors. The study also presents strategies for managing expiring knowledge, including temporal tagging, decay monitoring, context aware reframing, structural decoupling, and automated invalidation. These approaches aim to help organizations move from static knowledge storage toward systems that are responsive to time. By aligning knowledge management practices with the changing value of information, organizations can reduce risk, improve decision making, and maintain strategic advantage in the era of artificial intelligence.