April 22, 2025 Does AI Help in Energy Efficiency?

How a twin (AI+data) transformation significantly lowers your energy bills.

When you ask ChatGPT this question, keep in mind that the response itself requires around ten times the electricity of a Google search. It is important we first acknowledge the energy demands of AI. The necessary computing power to drive AI models puts pressure not so much on the end-user companies but on the infrastructure behemoths that possess the data centers.

Microsoft has increased its CO2 emissions to nearly 30% since 2020 and Google has almost 50% more emissions in 2023 than in 2019. But this increase is likely to be offset in the coming years primarily due to specialized hardware and low-power 3D chips. Nvidia claims its new SuperChip has performance improvements of about 30% while consuming less energy by 25%.

Globally reports suggest that AI is on track to offset 5-10% of global greenhouse gas emissions by 2030.

How Can Manufacturing Offset Its Energy Consumption?

The industrial sector accounts for a quarter of the total energy consumed in the US. It represents the second largest source of CO2 emissions in the country second only to transportation. But the striking fact is that about 50% of the energy the industries consume is wasted. To put this in perspective, about $65 billion is wasted every year. That’s a huge figure and AI has a tremendous potential to dramatically cut this waste, benefit manufacturers by bringing down their sizable spending on energy, and more importantly contribute to climate change.

6 Major Sources of Energy Waste in Manufacturing

A study published in Elsevier undertaken by the universities of Cambridge and Tokyo, identifies 13 types of energy waste, six of them being major contributors of waste in manufacturing. These are:

1. Idle Devices and Frequent Cold Boots: Energy consumption on devices left on during non-production times.
2. Lack of Maintenance: Energy losses from air, heat, or motor-related inefficiencies.
3. Overproduction: Energy wasted on unsold or stored products.
4. Inefficient Operation of Equipment: Poor machine operation due to lack of skilled labor.
5. Downtime: Energy consumption during production stoppages.
6. Over-Processing and Defective Products: Energy wasted through unnecessary or faulty production steps.

We can effectively classify the above six waste types into two categories.

a. Production-related waste
b. Equipment-related waste

How can AI help?

Tackling Production-related Waste

A critical analysis would tell you that production-related waste arises from a poor optimization of man, materials, and machines. An artificial intelligence solution in this regard comes in the form of an AI-based production scheduler and resource manager that helps you dramatically cut down production-related waste through a dynamic allocation of resources in real-time.

How Does an AI-based Production Scheduler and Resource Manager Work?

Upon receiving a customer order, the AI analyzes key order details like product specifications, quantities, and deadlines. It then scans historical data on production times, material availability, and machine performance, while also considering real-time factors such as worker shifts and current workloads. Using this information, it creates an optimized production schedule. Once the plan is in motion, the AI continuously monitors for real-time disruptions like labor shortages, machine breakdowns, or material delays, making dynamic adjustments to keep the schedule on track and resource usage efficient.

In essence,

a. The AI schedules machines only when needed thus minimizing idle time and cold starts.
b. Through an accurate forecast, it ensures that the production aligns with the real-time customer needs thus preventing excess inventory and overproduction.
c. It identifies optimal performance parameters for each machine and ensures the machines run at peak efficiency, preventing sub-optimal or inefficient operation.
d. The AI can detect early signs of performance decline in machines, allowing for preventive maintenance, while dynamically scheduling around maintenance needs, ensuring minimal disruption.

Tackling Equipment-related Waste

An AI-powered predictive maintenance solution aims to improve asset productivity by anticipating machine breakdowns. In an industry that is highly dependent on machinery, the faster an operator identifies the issues and repairs the machine, the more they can minimize efficiency loss and ultimately energy wastage.

How does AI-powered Predictive maintenance Work?

Set up an AI-driven predictive maintenance solution by installing sensors in machines, setting up data networks, and integrating a predictive analytics system.

The solution consists of three functions:

a. Anomaly detection: Identifying irregular or unidentified behaviors in machine data
b. Diagnostics and prognostics: Determining the root cause of anomalies and predicting outcomes if left unaddressed.
c. Prescription and optimization: Recommends actions and maintenance schedules to optimize performance and prevent future failures.

An artificial intelligence solution can also perform quality testing (using computer vision), and thereby prevent the production of faulty products (the sixth major contributor of energy waste).

This brings us to the second part of our discussion: All of the above AI solutions can’t function on their own. They are made possible primarily by one thing: Data!

No Data, No AI?

The above popular phrase needs to be fine-tuned because it is the quality of data rather than its quantity that takes precedence. The data volume is rarely the issue given the amount of organizational data that have ballooned over the last decade. On the other hand, poor data quality is the primary showstopper for AI projects getting into production.

a. Companies that have shown higher data maturity in the form of centralized repositories are still not able to power their AI systems given the presence of inaccurate or outdated data.
b. Most manufacturers tend to have data scattered across silos due to fragmented automation. Apart from accuracy, data completeness and accessibility is a common problem.
c. Manufacturers who still rely on paper-based systems face data visibility issues apart from the aforementioned data issues.

Whatever stages of data maturity the manufacturer is in, they must realize that the road to AI must be sealed with five qualities of data.

5 Data Qualities that Define Your AI Output

To achieve data quality means the ability to make the below transition from circle 1 to circle 2. From data that is ‘lost’, ‘hidden’, ‘inaccurate’, ‘incomplete’, and ‘inaccessible’ to data that is “visible, secure, accurate, complete and accessible.”

But the pressing question now is how to accelerate this data quality and speed up the AI transformation.

How to Embark on a Twin Transformation of Both Data and AI

Is it possible even for manufacturers in their early stages of digitization to parallely embark on AI modernization?

The answer lies in unifying data and AI workloads. Technology partners such as Trigent with deep expertise in data and AI modernization, combined with extensive industry knowledge, can significantly accelerate your data and AI transformation. By adopting a modern data architecture, such as the Lakehouse architecture, organizations can unify AI and data workflows more efficiently through key components:

Centralized Data Management: Ensures all your structured, semi-structured, and unstructured data is consolidated into a single unified platform, enabling seamless access and eliminating silos.

Optimized AI Workflows: The AI models are fed with real-time data without delays or bottlenecks. The system as a whole is designed to handle large data volumes to accommodate the growing needs of the business. The AI model is not only accurate but designed to be efficient, reducing computational costs and energy use. The tasks within the AI lifecycle are automated, ensuring minimized human intervention and smooth processes.

Secure, Scalable, and Collaborative Infrastructure: The unified platform fosters collaboration between data engineers, scientists, and business users, accelerating AI project development. The infrastructure is fully scalable and secured with robust governance, ensuring real-time decision-making through tools like Databricks and Delta Lake for adaptive insights.

The future of manufacturing belongs to those companies who seamlessly unify data and AI workloads to enhance operational efficiency and remain competitive. Manufacturers who embark on this twin data + AI transformation not only significantly reduce energy waste but accelerate their digital maturity and continue to evolve towards industry 4.0 and beyond.

About the Author:
As the Associate Vice President – Program Management at Trigent Software, Andy Pandia wears many hats as he works closely with teams to help them streamline processes and execute solutions efficiently to scale faster. He believes in achieving growth and transformation through innovation and focuses on building new capabilities to offer a more enriching client experience. He aims to create value by harnessing the collective power of people, technology, and analytics.

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