Artificial intelligence helps businesses make smart decisions
Artificial intelligence (AI) can support intelligent functionality for manufacturers by helping the system sense, understand, perform and learn.
While the term artificial intelligence (AI) is well-recognized, it can mean different things in different situations – and as such, it can be tricky to define. While most people think of AI as a technology in its own right, in reality it is more of a general term used to refer to a number of different technologies that enable systems to act intelligently.
When it comes to business applications, AI can support intelligent functionality by helping the system sense, understand, perform and learn. By using machine learning or deep learning to train a system, the system can assess how to act in each situation by analyzing data, rather than relying on prescriptive, hard-coded actions. The resulting agility and responsiveness mean that quality, accuracy and overall performance can be improved – and this is what makes the system truly intelligent.
In the current climate and with uncertain times ahead, several enterprises are looking at how they can adapt and accelerate their digital transformation strategy. With remote collaboration, operational agility and autonomous production becoming ever-more critical to business continuity – the importance of AI is on top-of-mind of many executives today.
What sets AI apart from other automation technologies is its ability to learn and adapt. In an industrial environment AI systems can have a significant impact on business performance by reducing manual labor: quickly identifying patterns in large amounts of data and analyzing and extracting features from both structured and unstructured datasets. Most importantly, it can learn from these tasks and improve over time.
Machine learning can be executed in a number of ways – supervised learning, unsupervised learning and reinforcement learning. Supervised learning uses pre-organized training data and feedback from humans to learn the relationship of given inputs to a given output. This method is useful if the input data and predicted behavior type is already classified, but the algorithm needs to be applied to multiple different datasets.
Unsupervised learning does not require any pre-defined labels in the data – no output variables need to be pre-identified, and the algorithm can analyze input data to find patterns and make classifications. And reinforcement learning allows the system to learn to perform a task by trial and error. In essence, this method is based on rewards and punishments, with the overall aim of maximizing rewards and minimizing punishments in the feedback received for its actions. This approach is particularly useful when there isn’t a lot of training data to use, it’s difficult to identify the desired outcome and this is the only real way to interact with and learn from the data.
Why, what and how?
In an increasingly digital world some organizations are looking to AI to revolutionize more than just their technology: they want it to redefine business processes as a whole. From pioneering innovation to everyday customer service, AI is transforming the business landscape, and defining this paradigm shift is the key to understanding enterprise AI.
Enterprise AI can be viewed across three levels. The first level identifies the ‘why’ and the ‘what’ – the business applications that use data to provide greater value to its stakeholders. The second level identifies the suite of AI capabilities that can be leveraged to power the business application. And the third level looks at the ‘how’ – which machine learning methods can deliver the pre-identified AI capability.
Using this framework, the complexities of AI-based business applications can be simplified and fully assessed to allow enterprises to build an all-inclusive AI program, analyze and define the business value for each AI initiative, and determine the basic requirements that would drive a successful AI program and justify investment.
While there is clear business value in adopting enterprise AI, asset-intensive, process-based industries are significantly behind other sectors when it comes to implementation.
This is largely due to the need for new skills and a lack of quality data. According to Gartner, 56% of enterprise leaders feel they need updated skills to accomplish AI-enabled tasks, and 34% say that poor data quality is a key concern. 42% of Gartner respondents also said they don’t fully understand the benefits of AI or the implied return on investment (ROI) due to the challenge of quantifying the benefits of AI.
By 2024, ROI will be measured by quantifying AI investments and linking them to specific key performance indicators (KPIs) – giving the future of enterprise AI a clear direction of travel in terms of measurement and real-world statistics. And by establishing a common understanding of AI’s enterprise value and setting out clear guidance for business application, organizations can capitalize on the simple constellation of AI framework to implement successful AI projects, now and in the future.
This article originally appeared on Control Engineering Europe’s website.