Sandeep Bhargava, SVP, Global Services and Solutions, Public Cloud Business Unit, Rackspace Technology, on the hurdles and opportunities facing APAC manufacturers venturing into the world of AI and cloud computing.
Change is on the horizon for the Asia-Pacific’s buzzing manufacturing sector. What was once a slow path to modern infrastructure and applications is now a rapid embrace of robotics, cloud and edge computing, AI – including generative AI – and digital twins.
The IDC estimates that 60% of Asia-Pacific organisations will utilise automation by 2027 to augment operational roles, thus boosting employee engagement. Overall, manufacturers are shifting towards digital business models to grow revenue streams, raise operational efficiency, better manage risks, and fortify operational resilience.
But modernisation is not without challenges. Venturing into the world of AI and cloud computing, manufacturers in the Asia-Pacific have to consider the hurdles and opportunities shaping the future of manufacturing.
Leveraging emerging technologies
Manufacturers that want to effectively leverage these innovations and drive end-to-end transformation must shift to an integrated approach via the cloud. This means building upon the skillsets that they have gained so far in their transformation journeys, including in big data and analytics.
More specifically, manufacturers need to find ways to master data – which is a cornerstone of intelligent manufacturing. The use of expanded Internet of Things (IoT) devices has resulted in businesses having access to vast amounts of data that can truly transform their operations. However, translating high volumes of data across disparate systems into actionable insights poses a significant challenge.
Organisations can tap into AI’s vast capabilities to enable comprehensive data integration to overcome this challenge. Tailored centralised dashboards backed by this level of integration can provide real-time notifications throughout the operation process. When used in tandem with digital twins, the enterprise-wide visibility provided will empower manufacturers to make more informed and effective business and operational decisions.
Manufacturers that successfully tap into these capabilities will be able to process data obtained from across the enterprise, customise internal and external tools, and enhance visibility for better decision-making. AI and cloud are the pillars of efficient data and knowledge transfer. With a robust feedback loop, organisations can outline a roadmap that enables agility and resilience.
An example of a manufacturer that has successfully tapped into the AI advantage is Cerapedics, a leader in biologic bone grafting.
To scale, the company built a smart factory with IoT-enabled production. They connected both digital and analogue sensors via a cloud environment, which provided real-time insights, enabling predictive maintenance and creating opportunities for expansion in the future. The process included rigorous data collection to ensure compliance with regulatory requirements.
Embracing these changes to make a strategic shift will drive manufacturing in Asia-Pacific into the future, unlocking the full potential of the industrial IoT and enabling novel operational paradigms.
Challenges on the horizon
Although modernisation – particularly driven by AI – is widespread across enterprises, there are still major security concerns. Rackspace’s Technology FAIR 2024 AI Research report found that only half of the respondents surveyed say their organisations are compliant with data management and retention policies. Additionally, 55% of respondents are wary of cybersecurity risks presented by AI.
Another major concern is ethics, with over half of respondents citing preoccupation with the responsible and ethical use of AI as part of their AI governance strategies. When asked to describe Responsible AI, 60% said it pertained to data privacy while 52% said accountability. Transparency was cited by 50% of respondents. This underscores the importance of laying the governance groundwork first to ensure that AI risks are properly mitigated and managed.
Crucially, the number of people concerned with AI governance is low when compared to AI adoption rates, indicating that there is a potentially misplaced sense of security and trust that IT leaders have in AI systems. This is something that should be immediately addressed.
When it comes to talent, Rackspace Technology found that companies are hustling to fill the gaps in machine learning and data engineering (43%), software development, data analytics (40%) and data governance (38%). The problem is that most organisations don’t offer formalised training programs. Only about 40% of organisations have training programs for AI while about 58% say they plan to implement something in the future.
Fortunately, these aforementioned challenges are not without solutions. The onus is on manufacturers to make smart investments and strategies.
Manufacturers must shift to the cloud and connect with experts who can customise tools and solutions that fit their business and organisational needs. Collaborating with the right partners will enable manufacturers to create a solid foundation for manufacturing innovation.
