Riding the Technology Wave with Artificial Intelligence
Artificial Intelligence (AI) allows for efficient data analytics which is the fuel that runs digital-first systems and processes
Disruption, caused by the introduction of digital technologies, has become the new normal and companies are scrambling to transform themselves in order to survive. Large manufacturing facilities are digitalising their factories with Industry 4.0 technologies. Others, working in the services, retail, transportation and related sectors are moving to the cloud and digitalising their systems and processes in order to transform into flexible digital-first organisations.
The technology surge that is transforming how we live, work and play has many elements. But at the heart of it all lays one common thread: data and analytics. In a mobile-first world, with ubiquitous connectivity, sensors and applications are gathering ginormous amounts of data, particularly customer data. Data has been described as the new oil in the digital economy and companies that can harness their data are emerging as winners.
Artificial Intelligence (AI) and Machine Learning (ML) act as force multipliers for data analytics1. AI algorithms analyse complex data sets in a fraction of the time it used to take previously. They are self-learning, autonomous and are extremely good at data analysis. AI analytics, scaled to the cloud, makes it possible to generate actionable intelligence instantaneously and share it with stakeholders in real time.
The immediate benefit from this ability is productivity gain. A study by Microsoft and IDC2 shows that AI will accelerate the rate of innovation and allow for employee productivity improvements to nearly double current rates in the Asia Pacific region by 2021. The report also notes that for companies which have implemented AI initiatives, the top business drivers for adopting the technology are better customer engagement, higher competitiveness, higher margins, accelerated innovation and more productive employees.
AI is also being used to provide what is known as predictive maintenance in Industry 4.0 environments3. Instead of performing maintenance according to a fixed schedule, predictive maintenance uses algorithms to predict the next possible failure of a component or machines and alert maintenance crew to replace the parts before the machine or component stops working, thus affecting production and productivity.
Singapore in the forefront
The Singapore government has been at the forefront of AI adoption, not only in public services but also in creating a proper environment for enterprises to adopt AI technologies. As Singapore continues its journey to become a Smart Nation, the use cases for AI will grow even further. Singapore is also one of the first countries in the world to adopt a model AI Governance Framework for public consultation4.
The framework provides a readily implementable guidance to private sector organisations to address key ethical and governance issues when deploying AI solutions. This is an important step since the basic power of AI comes from the ability to shift through vast amounts of data, some of which may be confidential in nature.
While the benefits of AI adoption are clear there are many bottlenecks that need to be overcome before companies can seamlessly transition into a digital future. The Microsoft and IDC study, mentioned earlier, notes that despite clear benefits accruing from the adoption of the technologies, only 41 per cent of organisations in Asia Pacific today have embarked on their AI journeys. Companies need help from experts in their AI adoption.
One of the reasons for this is because AI adoption is not just a technology challenge. There are human and social aspects as well. Most AI projects disrupt the status quo and often employees see AI automation as a threat to their jobs. Many low skill, repetitive jobs will be automated by AI algorithms.
Last year the Singapore government launched an initiative to impart AI knowhow to more than 12,000 people under the TechSkills Accelerator (TeSA) initiative5. There are also other initiatives and programmes undertaken by tertiary institutions in collaboration with the private sector.
Re-skilling displaced workers, so that they are able to take up better and more productive jobs will be a major challenge. Retraining and reskilling is not just a company level issue. Considering the pervasive nature of AI and the digital disruption it is fuelling, it is more of a societal challenge in which various stake holders, ranging from companies, government and tertiary institutions need to be involved.
While training courses will provide for future-ready workers, the more immediate challenge that organisations face is to retrain and upskill existing workers so that they can undertake more value-added jobs. A good example is PSA. As the port operator introduces automated container cranes at the berths, it is reskilling their crane operators to handle the machines autonomously via a computer sitting in an office6. Once this become more widespread it will improve productivity as one operator, sitting in front of a screen, would be able to monitor more than one crane.
While AI is transforming industries, it is useful to keep in mind that it is cloud computing that gives the power to AI. The data is usually from a variety of sources and in different formats and this can only be brought to work together through cloud computing platforms.
AI applications and algorithms are becoming a part and parcel of our everyday lives. In this situation, it is crucial to have a strategy on how to adopt and use AI to gain competitive advantage and to maximise benefits. This applies not only to organisations but also to individuals who need to pick up the skills required to thrive in an AI-driven world.