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AI has the power to help organizations achieve high-impact results by transforming industrial processes so they can reduce waste and drive sustainable growth.

But while AI continues to reshape the way business is done across industries, many companies in the manufacturing, construction, and oil and gas sectors are under-deploying AI technologies in industrial operations.

As a result, they continue to waste raw materials, overuse energy and water resources, experience too much downtime, and suffer manufacturing defects that negatively affect their margins.

Fortunately, possibilities are virtually endless for organizations that decide to harness AI.

Here are the top five AI use cases that organizations can implement to unlock new value with the right AI strategy for their business:

Leverage energy optimization to cut costs

Manufacturers around the globe are grappling with the challenge of cutting energy use, which continues to be among the industry’s highest operating costs and most significant climate change contributors.

The average manufacturing facility uses 95.1 kWh of electricity and 536,500 Btu of natural gas per square foot each year, according to energy consulting company E Source.

Plus, industrial energy use is responsible for almost 30 per cent of all U.S. greenhouse gas emissions, which contribute to global climate change, according to the Energy Star Program, part of the U.S. Environmental Protection Agency and U.S. Department of Energy.

Enter AI. When a manufacturing organization embeds AI predictive tools into its operations, it can forecast – with near-absolute precision – the optimal fuel and energy expenditure of every asset in its facility.
As a result, manufacturers are able to:
  • Reduce energy spend

  • Achieve better overall equipment effectiveness

  • Optimize asset utilization

  • Cut greenhouse emissions

Reduce raw material waste by streamlining processes

Reducing raw material loss is crucial for food manufacturers aiming to boost their bottom line.
In industrialized countries, 30 per cent of food waste is produced during processing or manufacturing, according to fungi-based alternative protein Mycorena maker.

With superior AI tools, food manufacturers can spot defects within their manufacturing processes and fix them to cut excess by-products.

When this happens, organizations

  • Minimize production costs

  • Cut waste

  • Increase margins

  • Boost output volume

Improve sustainability by minimizing water use

Today, it’s not enough for manufacturers to invest in green initiatives – they’re expected to demonstrate that they’re reducing their environmental footprint and meeting ambitious net-zero carbon targets.
But lowering carbon emissions and water consumption, while maintaining the desired product output, is an ongoing challenge.
According to The U.S. Census Bureau, the U.S. manufacturing industry requires 18 billion gallons per day of water for use in production operations, which is almost 25 per cent of global freshwater withdrawals.

However, by optimizing water level settings with the latest AI tools, manufacturers can make more accurate estimations to adjust water levels to fit their needs.

  • Reduce carbon footprint

  • Advance sustainability efforts

  • Cut operational spend

  • Decrease waste and rework costs

  • Boost output

Cut unplanned downtime with AI-based predictive maintenance

Unanticipated downtime, whether from rotating equipment used in oil and gas or forklifts and bulldozers in construction, can result in negative financial impacts for organizations.
According to a 2016 Kimberlite study, only a 1 per cent downtime rate, equivalent to 3.65 days, ends up costing oil and gas companies more than $5 million.
To put this into perspective, offshore organizations average more than 27 days of unplanned downtime a year – or $38 million in losses annually.

In construction, the cost of the down unit, including all its associated resources, equals $350 per hour for a total downtime cost of $2,800 over eight hours.

When companies adopt an AI-based platform as part of their predictive maintenance program, they can proactively forecast failures and optimize maintenance.

Most importantly, they can:

  • Boost operational efficiency

  • Minimize production losses

  • Reduce operation and maintenance spend

Advance product conformity to boost batch reliability

Product variability can rack up enormous costs for manufacturing facilities and affect output quality.

While each manufacturer has a different per cent of accepted product variability, it drives waste and production costs up because customers can’t use defective products for intended purposes.

This is why more and more manufacturers rely on sophisticated AI tools to optimize processes that can improve yield.

AI solutions can forecast the quality of output based on prior yield and predicted yields levels, empowering manufacturers to:

  • Raise production volume

  • Improve first-pass yield

  • Enhance asset utilization

Are you harnessing AI in your business?

The right AI platform can help your organization extract new, laser-sharp insights using your own data.

Your operating systems, industrial processes, sensors and machines store millions of data points that you can extract for a full picture of patterns and interrelationships affecting your operations.

By harnessing these models, you’re helping your operation teams make smarter decisions based on accurate data, rather than intuition or previous achievements.

Moreover, these models will adapt as your business needs evolve over time and help you harness disruptive moments to your advantage.

EAIGLE’s Analytics and Reporting strategize business operation techniques using your company’s data analytics and reports. To learn more, send us an email at or visit