In the past two years, the adoption of industrial AI has increased from 19 percent to over 30 percent. Wide adoption can be particularly felt in the processing and energy industries, such as chemicals, gas and oil. Large operational data volumes and high-value assets depend on a myriad of parameters. As a consequence, AI applications have the capacity to help via predictive maintenance, machine vision technology, predictive quality control, AI-enabled inventory management, and other tech stacks worth mentioning in the lines below.
The boom in software tools speeds the adoption of industrial AIoT
AIoT is a subdivision of industrial artificial intelligence mainly connected to AI being integrated into IoT data sources in industrial settings. Globally, there are over 400 vendors that provide AIoT software solutions, and many of them are developing software as AI-based platforms for factories. Several examples are Genix Industrial Analytics and AI Suite from ABB, Autonomous Production Advisor from Schneider Electric, and FactoryTalk from Rockwell Automation.
As far as usability is concerned, Genix for instance provides various operational performance management services and advanced analytics to ensure supply chain efficiency, sustainability, and asset integrity. The availability of AI tools is also ruled by the development of hyperscalers, such as Microsoft Azure, AWS, and Google Cloud. Amazon SageMaker from AWS, provides customizable, pre-integrated solutions that don’t take more than a few minutes to be deployed.
Simplified maintenance and development of AI solutions
AutoML, or automated machine learning, is now a standard offering. As ML-related tasks increase in complexity, the wide adoption of ML applications has triggered a high demand for off-the-shelf methods that don’t require any technical knowledge. In simple terms, AutoML targets progressive automation, and numerous companies use the technology to increase the added value of their current AI offerings.
For example, SKF recently upgraded its AutoML offering by combining temperature data with machine process data to reduce costs. Developing an ML model from scratch demands extensive resources and domain expertise. Automated ML has the capacity to eliminate the experimentation factor, thereby saving a lot of time on deployment for data scientists and business experts.
ML Ops, or machine learning operations, eases both maintenance and model management. It’s a new discipline that streamlines AI processes in manufacturing settings. As the overall performance of AI models becomes outdated over time, leaders are joining forces with startups such as Seldon, DataRobot, and Grid.AI to enable users to track changes in input data distribution.
AI merges into existing use cases and applications
New AI features are being integrated into legacy software systems. Apart from the top AI tools currently available in the market – AWS SageMaker, MC Azure ML, and Google Cloud – there seems to be a rising trend in improving manufacturing execution systems, legacy software suites, and enterprise resource planning. All three can benefit from significant improvements via new AI capabilities.
Epicor Software, a well-known ERP provider, currently focuses its efforts on infusing AI into its Epicor Virtual Assistant (EVA). By using AIoT, numerous industrial use cases are on the verge of being upgraded. Altering existing infrastructures in terms of hardware and software capabilities streamlines quality control applications. Machine vision provider Cognex, for example, recently launched a deep learning tool that integrates with outdated vision systems. In this way, users can mix and match traditional tools with deep learning tools and add them to complex electronics and medical contexts.
As a pioneering technology, artificial intelligence (AI) moves at full speed in factory settings. Via new applications, enterprises at large can roll out reporting and use cases with strong potential to reach mass market adoption. Also, the increase in popularity of IoT platforms, cloud computing, and powerful AI chips pave the way for the materialization of a new generation of IoT smart solutions in software development.