Crystal Gaze: AiThority Predictions from Bill Scudder, GM of AIoT Solutions, Aspen Technology

Hi Bill. Tell us about the recent innovations in AI-based applications and how they impact enterprise and industrial productivity?

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. But while this has accelerated the enablement of AI-based use cases, it 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 organizations 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 organizational strategy is never the technology. It will begin with identifying the business problems, corporate objectives, and strategic goals.

What would drive the further adoption of AI and automation in the industry?

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

In 2021, we’ll 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 overcome a lack of skills and drastically reduce the need for many data scientists.

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.

In 2021, how can companies better leverage Industrial AI and Asset optimization platforms?

To thrive in today’s volatile market, companies must simultaneously optimize their assets and processes across business objectives such as margins, economics, sustainability and more.

In 2021, we will 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 optimization solutions can be implemented without data science experts, implying industrial organizations can open the door to new levels of safety and productivity in their operations.

In 2021, which segments or domains in AI automation / AutoML would be most excited about?

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.

Thank you, Bill! That was fun and we hope to see you back on AiThority.com soon.