An Artificial Intelligence (AI)-Based Decision-Making Framework for Crisis Management

Introduction

A crisis is a highly visible, unexpected, and potentially disruptive event that puts an organisation's existence in jeopardy (Bundy et al., 2017). While such environments present organisations with a unique set of challenges, it is critical to remember that every organisation, large or small, global or local, faces the threat of a crisis, which can be caused by either man-made or natural disasters. In such situations, crisis managers are responsible for managing and leading the crisis (Cavico & Mujtaba, 2016). Effective crisis management necessitates timely and candid communication with employees and stakeholders, as well as ensuring that decisions and actions taken in response to those communications are consistent with the message spread. As a result, managers must devise a process for arriving at sound, reasonable, and satisfying decisions that can then be translated into actions. This is easier said than done, however, because decision-making processes are frequently fraught with uncertainty, complexity, and ambiguity (Jarrahi, 2018). In uncertain environments, such as those encountered during organisational crises, making critical decisions becomes a win-or-lose proposition. Rapid decision-making is required, as every second counts during a crisis. Additionally, a trade-off exists between decision speed and accuracy. Alternatively, managers must be capable of making both rapid and qualitative decisions. As a result, rational decision-making is defined as the systematic execution of a series of logic steps, including problem recognition, alternative evaluation, and actual selection of the prioritised alternative. The dynamic nature of the business world, combined with the plethora of choices available to the customer, means there is no space for reactiveness as decisions need to be rapidly made and highly accurate (Davenport & Harris, 2017).

Humans are also intelligent; they can recall previously memorised information and apply it to decision-making. However, content may occasionally be incomplete (Banerjee et al., 2018), owing to knowledge gaps and limitations in our short-term memory, such as memorising numbers or lists. Today, we also have additional forms of assistance, many of which are facilitated by computers and many of which are facilitated by mathematics. The impact of technology on stored knowledge and, consequently, decision-making processes has become apparent, and technology intelligence is now expanding at an alarming rate. Additionally, because humans take longer to gather information, they take longer to make decisions. As a result, there are now semi-autonomous decision-makers operating in increasingly complex and diverse contexts (Jarrahi, 2018). These semi-autonomous decision-makers are a subset of the engineering science known as “machine learning” or Artificial Intelligence (AI).

The most recent global crisis was the coronavirus (also known as the coronavirus) pandemic, one that had a tremendously harmful impact on the global ecosphere, influencing both the financial, medical, logistics and political terrain all over the world. The coronavirus crisis was officially declared a worldwide pandemic by the WHO on March 11, 2020 and is still ongoing. This pandemic has damaged human life as it was known, impacted orgainsational and country performance/reputation, threatening physical and mental health, safety and well-being of citizens globally. According to the Worldometers (2021), there have been over 200 million cases of the virus, resulting in nearly five million deaths globally, as at September 2021. A situation of this magnitude necessitates the need for a crisis management plan that is robust, resilient and dynamic, which can result in social change (Dess et al., 2014). Within this perspective, therefore, there is the proliferation of scholarly efforts and various discussions relating to crisis management, some of which had occurred in the past – for example, the Hong Kong flu in 1968 and the Swine flu outbreak in 2009 (Butler, 2009; Peckham, 2020). In another perspective, there are other crises that have occurred in the past decades, relating to natural disasters, terrorist attacks at country level, economic crises (e.g., the 2008 world economic recession), and many other such crises or disasters, some of which might be in a less critical perspective.