Select Page

An effective response to the global crisis known as covid-19 requires a leadership responsibility that is flexible, transparent, and able to identify future needs, as well as communicate these needs thoroughly. This is formally known as adaptive leadership, and the following are some key identifiers to help guide one towards an adaptive leadership style.

Evidence-Based Learning and What To Do With It
As the covid crisis evolves, it can sometimes seem like old data referring to the virus and its impact become irrelevant. In light of this, leaders need to constantly assess the decisions they make and their impact while being aware that they will have to continuously adapt their methods as they go. This requires an identifiable process for building SOPs and a clearly defined demarcation separating success from failure. Also important is the need to build SOPs on evidence-based research and to set out a clearly defined course of action should the data change. This process of observing evidence-based claims, building a response based on them, and having guidance in place if aspects of the pandemic change help to ensure a state of preparedness as well as a smoother transition into future conditions.

Industry-Grade Field Testing
To ensure that a given SOP will be successful, it is essential to subject it to rigorous evaluation by simulating with it a multitude of possible scenarios. These simulations need to take into account uncertainties in the conditions of public health, the influence of government measures against the pandemic, the economic landscape, and industry-specific demand forecasting, as well as real-time observation of results and data. 

Streamlined Data Management
One of the challenges facing modern leaders during this crisis is the sheer volume of changing and sometimes contradictory data. This kind of atmosphere may urge leaders to resort to risk-aversion behaviors in order to ensure some degree of safety.

To help resolve this, there needs to be a clearly defined and concise process for data processing, research, and decision making based on the observed data that is well communicated and understood by all. This way, communication can be clear about what a hypothesis is being based on, what is being done and why, how a decision was made, and accountability can be taken for identified errors.