CXOToday has engaged in an exclusive interview with Johanna Pingel – Product Marketing Manager, MathWorks and Prashant Rao, Head of Application Engineering – India, MathWorks.
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How is AI currently transforming productivity and innovation across sectors?
It’s impossible to ignore the changes that AI is bringing to businesses and consumers. It’s truly an exciting time as many companies are beginning to incorporate AI’s potential into their organizational workflows.
AI has the potential to revolutionize productivity across all sectors by automating routine tasks, optimizing complex processes, and providing insights through data analysis. It could transform everything from manufacturing to healthcare through multiple applications, such as predictive maintenance, visual inspection, and fleet analytics.
However, many organizations have not fully integrated and leveraged AI capabilities into their existing applications for many reasons. Organizations still need to bridge the gap between AI’s potential productivity benefits and promises and the current organizational hurdles.
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What are some of the key challenges organisations face when integrating AI into their operations, and how can they overcome these hurdles?
In theory, the promises of AI are exciting, but companies still face many obstacles when dealing with AI in the real world. Three common challenges companies face:
- Legacy systems: Engineering organizations often depend on legacy systems that are not designed to integrate with modern AI technologies. Upgrading these systems is costly and time-consuming.
- Skillset gaps: There is a shortage of AI talent. Engineering teams may lack the necessary skills to develop, implement, and maintain AI solutions, leading to a reliance on external consultants or lengthy training periods.
- Data quality: Effective AI relies on vast amounts of high-quality data. Many engineering firms struggle with data silos, inconsistent data formats, and incomplete datasets, which hinder AI implementation.
The good news: organizations that are aware of these issues can begin to empower their existing workforce to create an environment that fosters AI innovation.
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How do you see AI shaping the future of industries, and what are the emerging trends that organisations should be aware of?
Today, it’s hard to find an area where AI is NOT transforming industries and individuals’ daily lives. Let’s talk about two newer AI trends that are helping transform businesses:
- The first is AI integration in newer areas. The smart factory trend is revolutionizing the manufacturing sector by integrating advanced technologies like IoT and AI into production processes. These factories use real-time data analytics and machine learning to optimize operations, enhance efficiency, and reduce downtime. This includes applications like Predictive Maintenance, Quality Control, and Robotics.
- The second is increasing engineer productivity using large language models (LLMs). LLMs like GPT-4 are transforming workplaces by enabling more intelligent and intuitive interactions with data and systems. An exciting example of increased productivity is using code-assist and data analysis as AI-driven tools to help engineers and scientists focus on the most challenging problems efficiently.
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In what ways can AI drive competitive advantage for businesses, and what are some notable examples of successful AI adoption?
Incorporating AI into existing engineered systems can drive competitive advantage by enhancing efficiency, accuracy, and decision-making processes. Two examples of incorporating AI into applications:
Using AI to design and deploy embedded algorithms
A traditional approach to algorithm development involves writing a program that processes input to produce a desired output. However, sometimes, the equations are too complex or too computationally intensive to be deployed. And sometimes, an AI algorithm is simply more accurate. A specific example of this is predicting a battery’s state of charge. Rather than using a traditional method such as an extended Kalman filter, one alternative is to create a virtual sensor using AI, which is able to better generalize and provide accurate results with the right training data.
This example provides a result that could be both faster and more accurate than a first principles-based model, which can provide engineers with an advantage over traditional methods.
Predictive Maintenance and Operational Efficiency
In sectors like manufacturing and infrastructure, AI-powered predictive maintenance systems analyze data from sensors and machinery to predict equipment failures before they occur. For example, in a smart factory setting, AI algorithms can monitor the health of critical components and schedule maintenance only when necessary, reducing unplanned downtime and extending the lifespan of machinery. This not only cuts maintenance costs but also ensures continuous, efficient operation. Businesses implementing predictive maintenance can achieve higher operational efficiency and reliability, setting themselves apart from competitors relying on traditional maintenance approaches.
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How is MathWorks’ suite of tools, including MATLAB and Simulink, enabling organisations to effectively develop and deploy AI solutions?
MATLAB® and Simulink® provide a comprehensive environment for modeling, simulation, and analysis, which significantly enhances the efficiency and effectiveness of the engineering process. Our customers regularly incorporate AI into the design and development of engineered systems. Here are two examples of engineering systems incorporating AI into their applications:
- In the wireless sector, Qoherent was able to test and validate a MATLAB synthetic data set and compare different AI models created using MATLAB and wireless toolboxes. See more details: Qoherent Uses MATLAB to Accelerate Research on Next-Generation AI for Wireless
- In Renewable Energy, SEGULA Technologies uses MATLAB and Simulink to create models incorporating AI to reduce production time and costs and provide the transportation sector with a clean alternative to fossil fuels. See more details: This Clean Power Source Is Helping Fuel the Future of Transportation
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Can you provide examples of recent projects or case studies where MathWorks’ tools have significantly advanced AI applications in specific sectors such as automotive or aerospace?
Aerospace and Automotive industries have many applications in which AI can help optimize their processes. By leveraging MathWorks AI tools, customers can develop innovative solutions without having to start from scratch. The following example highlights customers developing engineering solutions using MathWorks AI tools: Developing Sensor Fusion and Perception Algorithms for Autonomous Landing of Unmanned Aircraft in Urban Environments
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How does MathWorks support the integration of AI into engineering processes and what unique advantages do your tools offer compared to other solutions in the market?
MathWorks has been in the engineering business for over 40 years, and engineers know and trust MATLAB and Simulink to help them do their best engineering work. With the incorporation of AI tools, such as Deep Learning Toolbox™, engineers can seamlessly incorporate AI algorithms into their existing designs, simulating all complex scenarios before deploying algorithms to complete engineering systems.
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