The age of artificial intelligence (AI) is here, and countries in the Asia Pacific (APAC) and Middle East zones have set their eyes on the prize. Start-up activity around AI is booming, and the region is set to overtake the rest of the world in AI spending over the next three years — reaching $15 billion by 2022, according to IDC.
Against this backdrop, we surveyed 590 senior executives across the region in late 2019 to understand their companies’ AI plans and actions. Our goal was to learn how APAC and Middle East businesses are employing AI, how this emerging technology is impacting business and how they are overcoming challenges to reap value from machine intelligence across various functional areas. (To learn more about the study, including its methodology, see our white paper, “How Companies Can Move AI from Labs to the Business Core.”)
While our research was conducted before the outbreak of COVID-19, we believe the pandemic only underscores the urgency around implementing AI — and doing so thoughtfully. Advanced forms of AI are critical to our understanding and treatment of infectious disease. Machine learning (ML) and deep learning are helping infectious disease scientists more quickly sequence and model treatment therapies and vaccinations, accelerating time-to-market and limiting unintended consequences. They are also helping researchers more effectively forecast and foretell the contours of pandemics. These capabilities are applicable to every business in every industry.
Despite significant progress, AI’s impact in APAC and the Middle East is today limited by the fact that businesses remain in experimentation mode (see Figure below). If this is a cloud, there is indeed a silver lining: organisations in the region can learn from the hard-won experience of businesses elsewhere. With this in mind, we prescribe the following procedures to shift AI from experimental mode to production mode.
At a given point in time, any two organisations will confront different problems and challenges. Achieving AI maturity involves moving along the maturity curve iteratively, adopting the necessary tools and creating new algorithms. Appropriate options will be defined by where organisations find themselves on the digital maturity curve.
The cultural rewiring of a business begins at the top and should be approached with the end goal of becoming a data-driven organisation in which human creativity thrives around AI. To that end:
Embed ethics up front to build responsible AI applications. As AI is expected to be pervasive, touching every aspect of the organisation, building ethics into the fabric of AI technology is essential. Companies must embed a focus on ethics from the initial development stages, and must never relinquish that focus, even as AI apps themselves evolve and learn. Governance models are critical in ensuring that ethical aspects of AI don’t get overlooked. AI’s current limitations in the form of built-in biases and an inability to handle complex situations are well documented. As businesses advance their AI efforts, ethics and governance become more and more critical to their deployments.