In 2013, the MD Anderson Cancer Center embarked on a “moon shot” project: utilising IBM’s Watson cognitive system, diagnose and prescribe treatment regimens for certain types of cancer. However, the project was halted in 2017 after expenditures exceeded $62 million—despite the fact that the system had not yet been utilised on patients. Simultaneously, the cancer center’s information technology group was experimenting with using cognitive technologies to perform significantly less ambitious tasks.
For example, making hotel and restaurant recommendations for patients’ families, determining which patients required assistance with bill payment, and resolving staff IT issues. The outcomes of these initiatives have been far more encouraging. The new technologies increased patient satisfaction, improved financial performance, and less time spent by the hospital’s care managers on repetitive data entry. Despite the moon shot’s failure, MD Anderson is commit to enhancing cancer therapy through the use of cognitive technology. That is, next-generation artificial intelligence—and is currently pursuing a number of new projects at its centre of competency for cognitive computing.
The distinction between the two techniques is critical for anyone considering undertaking an AI endeavour. According to our study of 250 executives who are familiar with their organisations’ usage of cognitive technology, three-quarters predict that artificial intelligence will significantly impact their businesses within the next three years. However, our analysis of 152 projects across nearly as many organisations suggests that extremely ambitious moonshots are less likely to succeed than “low-hanging fruit” efforts that improve business operations. This is unsurprising, as that has been the case with the vast majority of new technologies embraced by businesses in the past. However, the hype surrounding artificial intelligence has been particularly persuasive, and it has seduced certain enterprises.
In this post, we’ll examine the many types of artificial intelligence Malaysia already used as outline a framework for how businesses should begin developing their cognitive skills over the next several years in order to meet their business objectives.
Types of Artificial Intelligence
It is advantageous for businesses to view AI through the perspective of commercial capabilities rather than technological skills. In general, AI can help businesses meet three critical needs: automating business processes, getting insight through data analysis, and engaging consumers and workers.
Automation of processes.
The majority of the 152 projects we examined involved the automation of digital and physical tasks—typically back-office administrative and financial activities—through the use of robotic process automation technologies. RPA is more advanced than previous business-process automation solutions in that the “robots” (that is, server-side code) function similarly to a human in terms of inputting and consuming data from numerous IT systems. Among the tasks are the following:
transferring data from e-mail and call centre systems to systems of record—for example, updating customer files with address changes or service additions; replacing lost credit or ATM cards, involving the use of multiple systems to update records and handle customer communications; reconciling billing system failures to charge for services across multiple document types; and “reading” legal and contractual documents to extract provisions in accordance with applicable law.
RPA is the least expensive and easiest to adopt of the cognitive technologies we’ll explore here, and it often results in a rapid and significant return on investment. (It’s also the least “smart” in that these applications are not program to learn and improve, though developers are gradually adding intelligence and learning capabilities.)