Social media and news outlets have been headlining global mask shortages, toilet paper shortages, mixed messages about the virus spread, and photos of supermarkets with empty shelves one day, and overflowing with an oversupply of rice another day. These examples clearly indicate how unprepared our governments and industries are in tackling disruptions caused by serious disasters.
It is true that Artificial intelligence (AI) and data science tools can track and analyse data/information faster than a virus can travel. Machine-learning techniques and natural-language processing are now being used to review and analyse numerous news reports, patient records, social media, public health data, and related scientific articles to map the coronavirus outbreak and thereby understand its movement patterns and spreading behaviour (the age, gender, and location of those most at risk).
What makes AI and data science not very effective is the fact that the accuracy of these analytics and the actions to take based on the findings are reliant on the judgment of individual decision makers – be they policymakers or industry executives. How an individual interprets and applies the results produced by the analytics is influenced by their background, experience, personality traits, and cognitive biases.
Analytical models such as AI-powered optimisation models are often used to generate a set of “feasible solutions” to problems with multiple objective. Each of these feasible solutions may partially satisfy some of the objectives. A decision maker can then choose the most preferred solution from those feasible solutions.
Choosing a solution from a set of feasible solutions is reliant on the judgement of a decision maker. However, humans are prone to cognitive limitations which often causes information to be misused and decisions to deviate from that of a ‘rational’ decision maker. Understanding how human behaviour and cognition influence and interact with the design and implementation of analytics and AI-powered tools is rapidly-evolving area of academic research.
‘Personalised analytics’ brings together the best of two worlds – data science and human judgement – to empower policymakers and executives with information on how to better predict and tackle disruptions. Personalised Analytics are analytical models that can be customised to an individual’s background and personal characteristics. Individuals have ‘customised’ access to relevant data and information that can help them make more informed decisions.
Australia relies on AI and data analytics to tackle the productivity slowdown facing our industry. Approximately 78 percent of organisations who responded to a recent executive survey stated that analytical models and AI-based tools have empowered their managers in making some of the executive decisions. The food industry, for example, identifies analytics as the key to advancement in productivity with a potential boost of A$20.3 billion in the value of food production.
There is no doubt that the increasing amount of data and continuing maturation of AI and data analytics can help our executives and policymakers make more informed decisions. But the shift toward the development and adoption of personal analytics is essential to help integrate “analytics” and “intuition” to achieve what neither individuals nor machines could reach on their own.
Fahimnia, B., Pournader, M., Siemsen, E., Bendoly, E., 2019. Behavioral Operations and Supply Chain Management . Decision Sciences, Volume 50, Issue 6, Pages 1127-1183
 Loucks, J., Davenport, T., Schatsky, D., 2018. State of AI in the Enterprise (2nd Edition). Deloitte Insights October 2018.
 Perrett, E., Heath, R., Laurie, A., Darragh, L., 2017. Accelerating precision agriculture to decision agriculture – analysis of the economic benefit and strategies for delivery of digital agriculture in Australia. Australian Farm Institute.