Senior Managing Director , FTI Consulting
Artificial intelligence (AI) and machine learning (ML) may be among the most over-hyped buzzwords of the last decade. Still, behind the hype, these technologies have tangible and meaningful applications that can help companies gain insights into large volumes of unstructured data, support predictive decision-making, improve customer experience and detect vulnerabilities, among other things. Given the accelerating pace of change within organizations and across technology advancements, it can be difficult for business leaders to stay abreast of the benefits and risks associated with AI and ML as well as the key differences between these evolving technologies.
So, what are the key differences? Simply put, AI is a field of data science in which intelligent capabilities, such as perception, reasoning and decision-making, are programmed (via algorithm) into computers. ML is rather a technological capability that refers to the autonomous recognition of patterns and regularities.
There are four types of AI:
ML on the other hand, is a subset of AI, in which the machine recognises patterns and regularities. There are three types of ML:
An important subset of machine learning is deep learning, in which the machine emulates human neural networks. The learning process takes place without any previously processed data and is done by independently processing large amounts of unstructured data. While many machine learning algorithms have only two layers (input and output), deep learning is based on multiple hidden layers. For example, via deep learning, a machine can learn natural language processing.
AI and ML have numerous use cases in a wide range of industries. They are often used to increase efficiency or improve IT and information security processes.
For example, conversational AI ensures that self-service processes are optimized to improve customer satisfaction. The AI learns which outputs should be made depending on customer input via ML. One example is customer service via a chatbot. Customers quickly get an answer to their questions without the need for customer service staff to take every call.
AI can also help extract and analyse information from important documents. This feature can be useful in analysing medical records, grouping similar issues from a large volume of complaint letters, finding key evidence in an investigation and more. Another possible application is in learning and monitoring manufacturing processes. AI can predict bottlenecks, improve manufacturing quality, and evaluate the safety of operations.
In an information security scenario, AI and ML can help detect, prevent and respond to incidents and vulnerabilities. The technology can perform a risk analysis, recommend actions or take action independently, which allows the IT and information security team to improve processes, efficiency and accuracy, even when dealing with a large volume of inputs.
However, the use of AI carries risks as well and these technologies can be exploited. The BSI (German Federal Office for Information Security) has published standards to assess the security of AI-based systems operating in the cloud. These standards provide an important framework for evaluating solutions and ensuring they are adequately protected.
Ultimately, AI and ML are more than hype. They can improve processes, unlock new data and make IT more effective. The key to unlocking the benefits is to understand the capabilities, set clear objectives for how they will be utilised and address potential risks at the outset of implementation.
The views expressed herein are those of the author(s) and not necessarily the views of FTI Consulting, its management, its subsidiaries, its affiliates, or its other professionals.