Bill Scudder, AspenTech
Bill Scudder, General Manager, AIoT Solutions at Aspen Technology.
There has been much investment in AI technologies in recent years among capital intensive industries as they look to improve efficiencies and enhance processes.
In the current difficult and uncertain economic climate, we are seeing an ever-greater number of organisations across these sectors start to look at how they can rapidly adapt and accelerate their digital transformation strategy. With autonomous production, operational agility and remote collaboration becoming increasingly critical to business continuity, the potential impact of Industrial AI has never been more evident.
AI is a transformational technology. In recent years, we have seen considerable investment in democratising access to it through various AI/machine learning (ML) platforms, frameworks, and toolkits. This has indeed accelerated the enablement of AI-based use cases, but it has not necessarily translated to significant business value, especially in the industrial sector. It is worth remembering that AI is a complex, multi-faceted and rapidly evolving area of technology. According to the AI: Built to Scale study by Accenture from December 2019, 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives, yet 76% report they struggle with how to scale.
From theory to practical application
Across the capital intensive industries, this disconnect between an appreciation of the benefits AI can bring, and a lack of understanding of how to extend those benefits across an entire organisation has been especially acute. There is often a divergence between the well-understood theoretical benefits of AI and the challenge of putting these into practice across a whole organisation.
Over 2021, however, we would expect to see this beginning to change. Through the course of this year, it is likely that we will see industrial organisations begin to move to a more business-first mindset. We expect to see them increasingly applying AI technology to domain-specific industrial challenges with a focus on business outcomes. Yet, even though exploring and identifying industrial AI-enabled use cases may be a source of fascination to some, the starting point of any organisational strategy should never be the technology. Instead, it has to begin with identifying the business' problems, corporate objectives, and strategic goals.
Making it happen
Increased adoption of AI and automation across these industries is today especially being driven by workforce shifts and a resulting loss of domain expertise.
Taken together, these factors are resulting in a growing need to automate knowledge-sharing across the process industries. This is creating a greater requirement for more intelligence-rich applications – but, ironically, a lack of in-house data science skills continues to be one of the top barriers to AI adoption. In line with this, in 2021, we will likely see more industrial businesses growing their investment in lowering the barriers to the adoption of AI by implementing targeted, embedded Industrial AI applications that bring together AI, data science, purpose-built software and domain expertise. This will be a vital factor in overcoming a shortage of skills and significantly reducing the need for organisations to recruit large numbers of data scientists.
Reaping the rewards
Embedded AI applications will enable users to carry out their domain-specific operations with increased accuracy, quality, reliability, and sustainability throughout the industrial asset lifecycle. So, as we move through 2021, it is becoming increasingly incumbent on ambitious organisations across the capital-intensive industries to look at how they might better leverage industrial AI and asset optimisation platforms.
A key part of this is that there needs to be enhanced emphasis on democratising the application of AI to domain-specific industrial challenges with a focus on business outcomes. To thrive in today’s volatile marketplace, organisations must be able to simultaneously optimise their assets and processes across real-world business objectives like economics, margins, sustainability and more.
In 2021, we would expect to see a significant increase in productivity as the biggest benefit of industrial AI across capital-intensive process industries. Through the adoption of industrial AI, next-generation asset optimisation solutions can be implemented without data science experts, enabling industrial organisations to start opening the door to new levels of safety and productivity across their operations.
Beyond this, there is a lot to be excited about in terms of the likely future development of AI and automation. 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.
- The author, Bill Scudder, is General Manager of AIoT Solutions at Aspen Technology.
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