SoloPoint Insights

Product development is a complex and highly regulated process that can be time-consuming, expensive, and often requires extensive testing. With the integration of artificial intelligence (AI) in product development, engineering components are being developed faster, more efficiently, and more accurately.

Here’s how different industries are integrating AI into their product development processes:

• Aerospace:

• • AI is being used in aerospace product development to speed up design cycles and reduce risk and cost. AI’s ability to effectively learn from design systems such as CAD and CAE can produce “real-time” solutions while meeting data safety concerns.
  • Example: With AI, Airbus has reduced the time it takes to predict the pressure field on the external body of airplanes from one hour with the CAE approach down to 30 milliseconds. This has allowed engineers to increase simulation and computation tasks by 10,000 fold.

• Automation:
  • Automation equipment used in industrial facilities is unable to assess and react to changing conditions. However, with the integration of AI, companies can develop smart manufacturing processes that are more adaptable and responsive with the help of machine learning algorithms and sensors.
  • Example: An AI-powered tractor company in Fremont, Monarch, has designed and developed tractors that use a full camera and sensor suite to map crops and provide real-time alerts, leading to more efficient equipment performance, predictive maintenance, and improved safety measures.

• Biotech:
  • Traditional methods for developing biotechnology products rely on slow and laborious trial-and-error-based systems. With the use of AI and ML, companies can shorten operational cycle times, increase quality, and reduce overall costs and raw material consumption.
  • Example: Lonza, a contract development and manufacturing organization based in Switzerland, uses AI in several ways, including research, CAD design, protein profile assessment, and the prediction of side effects for novel therapy forms.
    
  • Example 2: A company in the Bay Area, TeselaGen, is using AI to accelerate the design-build-test-learn engineering cycle, as well as to search and organize large amounts of data captured by the equipment with third-party lab technicians and bioinformaticians.

• Medical Device:

• • In the medical device industry, AI is being used in patient data management and storage, remote surgical equipment, clinical trials, and more. AI’s ability to automate data collection, product inspection, and quality control for regulatory purposes can increase efficiency and eliminate the possibility of human error. Since 2022, the FDA has been granting accelerated approvals of medical devices with AI, and this is expected to continue through 2023.

• • Example: EMVision, an Australian start-up company, used AI-based electromagnetic imaging technology to create a lightweight brain-scanning device that can diagnose a stroke within minutes.
    
  • Example 2: A US-based start-up, Butterfly Network, uses AI along with semiconductors and cloud technology to create a cheaper and more efficient handheld medical imaging device to replace traditional transducers, allowing for whole-body imaging from a single probe.
    

While product development is a complex and regulated process, AI has shown immense potential in transforming different industries and creating innovative solutions across various sectors. With AI, engineering components can now be developed with greater efficiency, speed, and accuracy, reducing the time and cost required for testing and development.

For businesses to capitalize on this AI movement, highly skilled engineers need to be brought in to help implement this process. To start your candidate hunt for this specialized skillset, call the staffing experts at Solopoint Solutions: