Harnessing the power of AI in manufacturing

In recent years, there has been considerable investment in democratizing access to AI technologies within capital-intensive industries through various AI/machine learning (ML) platforms, frameworks, and toolkits. The AI Market Report 2020-2025 from IoT Analytics estimates the global Industrial AI market size will reach $72.5 bn by 2025. In the Middle East, PwC estimates the potential economic impact of the technology to amount to $320 billion by 2030, listing the manufacturing sector along with the financial, education, public services and pharmaceutical sectors to have the biggest opportunity for AI in the region.

Focusing on Business Outcomes

While the enablement of AI-based use cases has accelerated, this has not necessarily translated to significant business value, especially within the industrial sector. According to the AI: Built to Scale study by Accenture, nearly 69% of executives in industrial organisations acknowledge they know how to pilot a program, but they struggle to scale their industrial AI strategy across the enterprise.

In 2021, we’ll see industrial organizations pivot to a business-first mindset with an increased emphasis on applying AI technology to domain-specific industrial challenges with a focus on business outcomes. While exploring and identifying industrial AI-enabled use cases may be intriguing, the starting point of any organisational strategy is never the technology. It will begin with identifying business problems, corporate objectives, and strategic goals.

Focus on Automation

Workforce shifts and the resulting loss of domain expertise are driving the need to automate knowledge-sharing across the process industries. This is creating a greater need for more intelligence-rich applications – but ironically, a lack of in-house data science skills is one of the top barriers to AI adoption.

The new year will see more industrial organizations increase investment in lowering the barriers to AI adoption by deploying targeted embedded Industrial AI applications that combine data science and AI with purpose-built software and domain expertise. This will be the key to overcoming a lack of skills and will drastically reduce the dependency on data scientists, who are in short supply as it is. These embedded AI applications will allow users to efficiently and successfully perform their domain-specific operations with increased accuracy, quality, reliability and sustainability throughout the industrial asset lifecycle.

Focus on Asset Optimisation

To thrive in today’s volatile market, companies must simultaneously optimise their assets and processes across business objectives such as margins, economics, sustainability and more. Through the adoption of industrial AI in 2021, next-generation asset optimisation solutions can be implemented without data science experts, implying industrial organisations can open the door to new levels of safety and productivity in their operations.

Across industrial plants, semi-autonomous and autonomous processes will be created over time, as live data is collected, aggregated, conditioned and fed into intelligence-rich applications to evaluate scenarios, gain insight and drive continuous operational improvements. Furthermore, cognitive guidance systems, powered by AI and machine learning, will empower personnel across critical operations, extending their capabilities so they can make faster and more accurate decisions. To summarise, 2021 will see a significant increase in productivity as the biggest benefit of industrial AI across capital-intensive, process industries.