As the world moves towards a more tech-centred mode of operations, businesses embrace artificial intelligence (AI) and machine learning. AI is becoming a significant part of production and manufacturing today and is used in processes that require automation and increased productivity. AI mimics human intelligence and completes projects faster and with more efficiency. It also operates 24/7.
One of the essential components of AI is data labelling. Data labelling identifies raw data and adds meaningful labels for machine learning models to learn from. This can be done through a data labelling platform which can help automate the processes to save time and ensure accuracy. When developing reliable products, the data labelling platform will be the backbone of your reliable products.
Here are ways AI can help you do that:
1. Business process automation through data labelling
Business process automation is one of the first steps when creating reliable software. Automation of repetitive tasks in software, websites, or applications will help reduce the need for human intervention. It promotes self-help, aids in 24/7 customer support, and operates with fewer errors. Machines can learn from data collected to develop models that will automate repetitive tasks. (1)
You can use tools such as a data labelling platform for manual or semi-automated data tagging to save time and ensure dataset labels’ accuracy. Once the data are correctly labelled, the machine learning models will learn what to do with unseen data, be they audio files, images, videos, or text files. It will then turn the data into meaningful information. For example, CRM (customer relationship management) software can learn to identify emails, documents, and other communication, and take the required actions. (1)
2. Successful product deployment
Before the launch of any product, businesses should be able to give users the correct date the product will be available on the market. For this to be possible, they should be able to correctly predict the delivery time, and ensure no faults in the product that could delay the launch or lead to recalls. (2)
As a developer or an information technology (IT) person, you know how important it is to ensure your software works well. Reliability is one of the main factors affecting customer experience. Over 56% of businesses believe that reliability is critical to delivering exceptional customer experiences. That’s why deploying reliable software is so important. And AI can help you with that. (2)
3. Rapid prototyping
AI helps product development teams move quickly from concept to prototype. The AI-powered design process can reduce the time spent on a concept while improving quality and reducing costs. The result is a better prototype that can move into production more quickly. (2)
By automating some of the key steps and processes that go into prototyping, AI reduces rework and redundancy and ensures that errors are corrected faster and more efficiently. AI can also spot potential problems in a prototype before it becomes an issue, thus saving time and money by preventing costly product failures or recalls. (2)
4. Predictive maintenance
AI systems can learn from experience and change their behaviour based on new data. This makes them ideal for spotting security threats in real time before they happen. Once a particular threat is detected, AI systems can take immediate action to stop that threat. (3)
Predictive analytics can improve product security by prioritising which vulnerabilities to address first. It can also help identify new threats. These predictive models are typically created using machine learning algorithms, which use data to learn the properties of a system and predict what might happen during its operations. This is a more proactive approach than the traditional cybersecurity practices which are usually reactive. (3)
Prediction models can prioritise vulnerabilities based on their severity and how likely they are to be exploited in an attack. This means product teams with limited resources for patching software or managing security issues can use this information when deciding which ones should take priority over others.
5. Through quality assurance and inspection
With thousands of products moving along the production line each day, companies can’t test each item in detail. Manual inspections only catch obvious issues since human inspectors rely on visual checks and their judgment when evaluating products. (3)
However, manufacturers can automate quality assurance with AI by writing algorithms that check for flaws and defects with greater accuracy. These algorithms may detect issues that wouldn’t be visible to the naked eye. They can turn up problems too subtle for humans to notice. (3)
As the need for more reliable, efficient products increases, manufacturers and developers have turned to AI to help them develop their products. As discussed in the article, AI can help to automate the process, conduct predictive maintenance, and provide quality assurance. Therefore, it has become the backbone of producing reliable products.
- “What Is Data Labelling And What Are Its Applications”, Source: https://thefutureofthings.com/16661-what-is-data-labelling-and-what-are-its-applications/
- “How AI Is Transforming the Product Development Process”, Source: https://www.devteam.space/blog/how-ai-is-transforming-the-product-development-process/
- “AI in Product Development: Role and Benefits”, Source: https://www.analyticssteps.com/blogs/ai-product-development-role-and-benefits