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Leveraging Artificial Intelligence to Accelerate Digital Transformation Strategy amidst the Current Economic Conditions
August 12, 2020 Blog


By Adi Pendyala, Senior Director at Aspen Technology

As the world’s economies grapple with the current fallout, every organization, whether small or large, public or private, is finding new ways to operate effectively and meet the needs of their customers as lockdowns, international border closures and quarantine measures disrupt the economic value chain from manufacturing to logistics.

According to The World Bank’s Global Economic Prospects Report released in June 2020, COVID-19 led to steep recessions across many countries. The baseline forecast in the report envisioned a 5.2 percent contraction in global GDP in 2020—the deepest global recession in decades.

Artificial intelligence (AI) plays an important role in providing the tools to augment decision making, generate faster and more insightful analytics and enable real-time supply chain management. Machine learning technology enables computers to mimic human intelligence and ingest large volumes of data to quickly identify patterns and insights.

Artificial intelligence (AI) is a well-recognised and used buzzword. However, it means different things in different situations. Whilst most people think of AI as a technology in its own right, it is 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 drastically 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 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.


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 largely 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 does not 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.

Finally, 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 are limited training data to use, or when it is 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 of Enterprise AI

In the fast-paced digital world, organisations are turning to AI to revolutionise more than just their technology. Instead they are looking 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. 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.

The framework also enables the complexities of AI-based business applications to 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.


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 market research firm Gartner, 56% of enterprise leaders feel they need updated skills to accomplish AI-enabled tasks, and 34% said that poor data quality is a key concern. 42% of Gartner respondents also said they do not 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.