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The Development of AI-Enabled Smart Machines in Singapore

Most aspects of everyday lives as consumers or employees have been embedded by Artificial Intelligence (AI) based systems. The further advancement and increased diffusion of AI capabilities pose risks of job replacement and even concerns of what this all means in terms of being human. Singapore Management University’s (SMU)Business Partnerships unit and International Trading Institute delved into the issue of “Working with AI-Enabled Smart Machines”.

University professors and thought leaders documented 30 examples of people doing their everyday work in real-world business settings in partnership with AI-enabled smart machines. These case studies will be used in their co-authored book The Future of Work Now: People Collaborating with Smart Machines.

The case studies covered a range of industry settings including insurance and financial services, knowledge work across other service sector industries, healthcare, factory floor production, and field operations across multiple industries.

One example cited was from one of Singapore’s banks who had massive migration to data analytics starting in 2010 and their follow-on progression into using machine learning. The system was able to draw on the bank’s existing data sources and external data to evaluate the probability of fraud or financial crime.

Before using this new system, the majority of the time spent by the bank employees who were doing transaction surveillance was on data amalgamation and sorting through the alerts generated by the prior generation of rule-based systems. The latter is an earlier type of AI application, with most of these alerts being false alarms.

With the new machine-learning-based system analysing and evaluating the rule-based alerts, the transaction surveillance employees can now focus directly on the alerts identified by the system as having a high or medium probability of being an actual problem.

The employee’s work time is allocated more efficiently, as they no longer need to look at large numbers of false alarms. Additionally, they no longer need to manually amalgamate all the supporting information use to evaluate each alarm as that background data access and integration work was automated as part of the machine learning application.

Another case study was on Southeast Asia’s largest e-commerce platform.  They are a digital-native born company whose business is based on AI-enabled data analytics. The case study highlighted the role of their product managers. These are the people who orchestrate the complex process of developing, phasing in and scaling new Shopee e-commerce platform capabilities and feature enhancements.

The product manager’s challenge is to do this in a way that meets business goals, satisfies customer needs, deals with the constraints and problems faced by the technology teams developing the new AI-enabled capabilities and features, and addresses the many conflicting requirements and trade-offs that arise.

The case study highlighted that while product managers are overseeing the processes of bringing AI-enabled capabilities and features of the platform to market, the nature of their role is so multi-faceted and complex that very few of their engagement management, negotiation, coordination, and decision-making tasks can be automated by these same type of AI capabilities. This product manager example illustrates one of the important ways in which human roles are required to manage the implementation of AI-based change efforts within a complex company setting.

The threat is not about AI taking away human jobs. The real threat is when people choose not to team with AI. Organisations need to learn how to capitalise on what AI can do, go beyond just thinking about simple labour displacement and manpower cost savings, and find ways to use the technology to create value in ways that lead to new demand and correspondingly to new employment opportunities.

As reported by OpenGov Asia, AI is becoming more sophisticated at doing what humans do, but more efficiently, quickly, and cheaply. Scientists from Singapore’s Nanyang Technological University (NTU) and clinicians from Tan Tock Seng Hospital (TTSH) have used artificial intelligence to create a new method of screening for glaucoma.

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