By Adi Pendyala
In the post-pandemic world, Industrial Artificial Intelligence (AI) will come of age as a driving force and enabler of innovative operational applications for capital-intensive industries. Fuelled by significant volatile market forces, this new normal will be anything but business as usual. Combined with shifts in new technologies such as 5G and even 6G networks, the transformational impact of Industrial AI is unprecedented. Companies need to adopt a business-first mentality and applying AI technology to domain-specific industrial challenges, by focusing on critical outcomes. With workforce shifts and a resulting loss of domain expertise, there is an increased adoption of AI, Industrial IoT and automation across these industries.
5G networks can provide accelerated industrial connectivity to potentially one million connections per square kilometre. This is expected to support implementation of advanced industrial IoT applications, as large networks of sensors can be deployed to collect data across distributed assets and plants. This data is crucial for the application of artificial intelligence (AI) in analysis and decision-making.
The implementation of 5G in Malaysia, an integral part of its National Fiberisation and Connectivity Plan, will see the Malaysian government injecting RM15 billion to roll out the network over the next 10 years, beginning end 2021. 5G is set to add US$ 3.12 billion (RM 12.7 billion) to Malaysia’s GDP by 2025 with 39,000 new jobs.
This trend further exacerbates the need to automate operational knowledge in data-intensive environments, where there is a greater need for intelligence-rich applications. However, a lack of in-house data science skills continues to be a key barrier to AI adoption. Industrial businesses can mitigate this challenge with the convergence of AI, data science, purpose-built software, as well as domain expertise – to overcome skills shortage and deploy built-for-industry AI applications.
Benefitting from Industrial AI
Embedded AI applications enable users to carry out domain-specific operations with increased accuracy, quality, reliability, and sustainability across the industrial asset lifecycle. The biggest benefit of Industrial AI is a significant increase in productivity, as well as benefits in sustainability. According to a recent study by Accenture, 5G-enabled factories can see up to 20% to 30% in overall productivity gains, including improvements of 50% in assembly time and 90% in defect detection.
The next-generation asset optimization solutions that have embedded AI can overcome resource barriers, which significantly reduces the need for organizations to recruit many data scientists, and yet successfully tap into the business value across their industrial data assets. Efficiency and value capture will become cornerstones in this economy. For example, with a relatively fragile supply chain, industrial companies cannot risk errors and machine failures slowing them down – as such, access to and analysis of real-time data will become ever more critical.
Predictive and prescriptive maintenance technology, with Machine Learning (ML) and Industrial AI, can identify precise failure patterns to predict equipment degradation weeks or even months in advance. This ensures that action can be taken to mitigate unplanned downtime, as well as safety and environmental risks. Companies are implementing specific process metrics that consider emissions and resource use, as well as efficiency enhancements through digital technology. The International Energy Agency (IEA) has found that Industrial AI and digital solutions can boost energy efficiency by up to 30% for industrial operations. Multi-dimensional optimization, predictive analytics, and other digital solutions also help in meeting sustainability goals.
Artificial Intelligence of Things – AI meets IIoT
According to Maciej Kranz, a leading technologist, AI, and the Industrial Internet of Things (IIoT), these technologies become transformational when they integrate, interconnect and interwork – with intelligence – to solve complex industry problems. Collectively, they are like the body and brain of industrial digital transformation: IIoT is the body, creating and transmitting data from a variety sources that is sometimes acted upon, while AI is the brain, turning data into intelligence for smarter decisions and enabling the digital future of industrial organizations.
The confluence of these technological forces gives rise to a new digital solution category – the Artificial Intelligence of Things (AIoT) – that centres on unlocking the hidden business value in industrial data. This category describes the combination of AI technologies with the IIoT to enable the next generation of Industrial AI infrastructure, allowing organizations to achieve more efficient IIoT operations, enable seamless human-machine workflows, harmonize industrial data management, and rapidly transform raw data into tangible business outcomes.
According to Accenture, nearly 69% of executives acknowledge they know how to pilot a program but struggle to scale their Industrial AI strategy across the enterprise. Organization strategy needs to start with the identification of business problems, corporate objectives, and strategic goals. Companies need to democratize the application of AI by focusing on business outcomes, making the technology valuable and actionable to create real business value.
Scaling towards an AIoT Strategy
Sharp market volatility means capital-intensive industries have to be more agile in an unprecedented way to survive and thrive. Companies need to capitalize on the rapid convergence of IT and OT. The rise of the digital executive is reshaping the digital transformation strategy of industrial organizations. There is a critical and growing need to access industrial analytics and actionable insights in making business decisions across the enterprise. Organizations need to focus on strategic industrial data management, as well as using AI-enabled technologies to unlock the hidden value in these previously unoptimized and undiscovered sets of industrial data.
Industrial organizations are increasing investment in lowering barriers to AI adoption by deploying fit-for-purpose Industrial AI applications that combine data science and AI with software and domain expertise. This is key to overcoming a lack of in-house skills and drastically reduce the need for an army of data scientists. To scale, many enterprises are adopting new measures to reduce complexity in interoperability, overcome information silos and harmonize towards a cloud-ready infrastructure that bridges legacy systems with next-generation solutions.
Thriving in the New Normal
In light of the tectonic workforce shifts and unprecedented market volatility happening now, industrial organizations will need the capabilities to derive business outcomes from Industrial AI applications to remain relevant in the future. When thoughtfully applied, AI combined with domain expertise will empower organizations to capture and share the knowledge of their experts, accelerate decision-making across the business, drive organizational alignment and leverage advanced operational insights throughout the organization.
