Source: businessworld.in
Artificial intelligence (AI) is a well-recognised and used buzzword. However, it means different things in different situations – and as such, it can be tricky to define. Whilst most people think of AI as a technology in its own right, it’s actually 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 analysing data, rather than relying on prescriptive, hard-coded actions. The resulting agility and responsiveness mean that quality, accuracy and overall performance are dramatically improved as a result – 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 rapidly adapt and accelerate their digital transformation strategy. With remote collaboration, operational agility and autonomous production becoming ever more critical to their business continuity – the importance of AI is on top-of-mind of many executives.
The importance of machine learning
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 dramatically reducing manual labour: quickly identifying patterns in large amounts of data and analysing 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-organised 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 behaviour type is already classified, but the algorithm needs to be applied to multiple different datasets. Unsupervised learning doesn’t require any pre-defined labels in the data – no output variables need to be pre-identified, and the algorithm can analyse 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 maximising rewards and minimising 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.
The why, what and how of enterprise AI
In an increasingly digital world, organisations are looking to AI to revolutionise more than just their technology: it’s redefining 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. The “Constellation of AI,” a paradigm introduced in the book Human + Machine: Reimagining Work in the Age of AI by Paul R. Daugherty and H. James Wilson, is one such framework that exists to try and explain the application of AI on an enterprise level.
Using this framework, 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, analyse and define the business value for each AI initiative, and determine the basic requirements that would drive a successful AI program and justify investment.
The future of AI adoption
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.
However, by 2024, ROI will be measured by quantifying AI investments and linking them to specific 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, organisations can capitalise on the simple Constellation of AI framework to implement successful AI projects, now and in the future.
