Two Large Barriers to Widescale AI Adoption in Chemical Manufacturing

Use Cases & Projects, Scaling AI Lynn Heidmann

A report by Accenture postulated $550 billion could be unlocked for the chemical industry in the next decade by digitalization. Though chemical companies are eager to embrace opportunities that will enhance efficiency and boost revenue and profitability, few have fully harnessed the available tools.

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Why? There are a variety of barriers to AI implementation in the chemical industry that are common across all industries - issues like data quality, data access, and the ability to operationalize machine learning models are taxing for all.

But there are also a few specific challenges for chemical manufacturers, including:

Lack of Technical Expertise

Hiring for data and analytics talent is actually an issue for most companies. More often than not, companies go about hiring a data scientist before they even have a project or goals in mind. This doesn’t make sense for several reasons, not least of which because there isn’t just one kind of data scientist, and so hiring the right one with the right skills depends heavily on use case.

The answer to this challenge lies in data democratization. By providing the right self-service data tools for existing staff (even those that are non-technical), the business can start becoming data-driven from the ground up. For example, GE Aviation was able to mobilize its staff to start using data in their everyday work with a self-service data program.

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Organizational Change Management.

Obviously, transforming into a data-driven organization that embraces AI processes isn’t easy, so one of the challenges that can’t be ignored is an underlying resistance to change, especially addressing the fear of automation and employee hostility toward changing roles.

The fact is that there is a very real fear, especially in the manufacturing sector, that jobs will be automated away and that they will be fired. But that doesn’t mean that manufacturers need to shy away from introducing new tools and automation that are critical to protecting the organization against larger risk, not the last of which is human error.

Instead, it means accepting and facing this challenge through education. Not only education about why automation is important, but how humans fit into the process and what their role will be going forward.

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