Artificial Intelligence (AI) is one of the key drivers of business development. In terms of how information technology guides a company’s fortunes, AI is ushering in a new era. But what does artificial intelligence actually mean? And as an executive manager, what steps should you be taking?
The term “Artificial Intelligence” (AI) is a buzzword that describes self-learning computer systems designed to assist – and ultimately replace – human decision-making by using specific algorithms to analyse very large quantities of data. There are many conventional computer programs which, while capable of triggering complex operations, could not be described as artificial intelligence. As a rule, they always follow the same pre-programmed steps: input – processing – output.
By contrast, artificial intelligence performs four steps, as follows:
A closer look at these four steps reveals the enormous potential of AI applications. Artificial intelligence is capable of processing data from an extraordinary variety of sources (1. Sense). These include image data, voice input, and indeed more or less any kind of digital data, with no need to spend time programming an interface for interconnecting different computer programs. This facility means that a very wide variety of types and quantities of data can be processed, subject to very few limitations.
The way data is processed is also superior to conventional systems. Here, in addition to standard processing, comprehension-enhancing AI methods are also used (2. Comprehend) such as language understanding and deep learning. Whereas conventional computer programs can only process clearly defined data such as correctly completed forms, AI applications are also capable of understanding and processing, for example, unstructured e-mails from customers.
Artificial intelligence also offers a broader range of options for outputting the processed data (3. Act), for example by means of intelligent process or device management.
But the fourth and final step (4. Learn) is where the true potential of artificial intelligence lies. AI systems are capable of learning from their mistakes or from actual feedback – not only during the training phase, but also in general day-to-day operation. This means they are also capable of continuous self-development with the aim of delivering increasingly accurate results.
Overall, however, even the explanation provided above does not fully describe the nature of artificial intelligence. This is because, in reality, there is no such thing as artificial intelligence! The term “artificial intelligence” is really a collective term embracing a broad range of very varied applications. Kris Hammond, for example, describes 28 “functional components” of artificial intelligence in his widely quoted overview (2016).
Almost all AI systems consist of multiple building blocks. Thus a chatbot that automatically answers customer queries, for example, generally uses the following components: generation of natural-language texts, language understanding, text extraction, and communication. An “intelligent” system is essentially the result of ingenious combinations of several different functional components.
The possible applications of AI in finance are expected to make significant progress over the next few years. This is why responsible managers should start to familiarise themselves with the various aspects of artificial intelligence as soon as possible – and then identify useful, company-specific areas of application and implement preliminary pilot projects. They will need to become especially familiar with two areas in particular: first, the opportunities offered by artificial intelligence. And second, the conditions necessary for implementing it, such as an AI-compatible IT infrastructure, AI-friendly data management, and development of the necessary skills by the IT specialists and managers concerned.
Artificial intelligence will give executive managers in finance much greater freedom by liberating them from the need to spend time collecting and processing data. AI will also enable them to generate much broader, deeper analyses of business-related data. Executives who swiftly familiarise themselves with the issues involved will be in an excellent position to help their companies gain clear competitive advantages.