Artificial intelligence is already helping owners of many businesses today. It helps focus on relevant problems by facilitating the automation of repetitive tasks in the enterprise. It supports and accelerates the work of analysts by classifying, collating and visualizing collected data. But is artificial intelligence also capable of helping in providing data-driven business decisions?
The role of AI in business decision-making – table of contents:
- Decision-making – what is the problem?
- Decision-making methods
- Decision-making areas supported by AI
Many business owners dream of the following situation: analytical tools based on artificial intelligence collect real-time data on various aspects of the company’s operations. They are connected to a data warehouse, giving AI a holistic view of the company’s situation against its competitors and the overall market situation. Using all this data, AI makes an accurate analysis of the current state of the company, as well as its near and distant future. We wrote about the capabilities of artificial intelligence in business data analysis (BDA, BDI) in a previous article.
However, what would happen if AI not only outlined possible paths for the development of a company but suggested decisions on what to do for the company to grow optimally and make the best possible profits? Or even, if it delivered the right business decisions?
Decision-making – what is the problem?
The basis for making accurate decisions of any type is knowledge of the relationship between events and processes. Both humans and artificial intelligence keep making mistakes while aiming at predicting the future chance of success by collecting and analyzing data on the past. Statistically, the chances of making a more accurate decision rise in a so-called closed system, that is a situation that is not subject to external influences. The chances of success also increase when accompanied by an extensive data set describing similar past relationships in different ways.
Artificial intelligence has an advantage over humans because it can flawlessly analyze much larger volumes of data and see patterns in it that are invisible to the human eye. It can, for example, spot cyclical changes in a company’s location-dependent demand for services in the blink of an eye, or pick out from a visually unattractive resume the candidate’s optimal combination of skills for the company.
However, the issue of decision-making by artificial intelligence is very complex. After all, it is another matter to visualize a set of collected data, and another to indicate the optimal course of action. This is because it concerns decisions in risky situations, based on incomplete data. It also involves the influence of completely unpredictable factors that have serious consequences, called black swans.
Humans have an advantage over artificial intelligence because in making decisions they can take into account external factors whose impact on the company’s situation may not be obvious or direct. These include, for instance, political events that affect the price and availability of raw materials, or the character traits of a candidate for a particular position that compensates for slightly less experience. A person can also plan a framework that determines the factors taken into account during decision-making, that is, look at the process as a whole.
Companies take on various methods to make the process easier and more orderly to cope with the risks, uncertainties, and responsibilities associated with making business decisions. These include:
- Eisenhower matrix – which organizes decisions on axes of urgency and importance to help make decisions on the order in which tasks should be performed
- SPADE – a multi-faceted framework that emphasizes single-person accountability for decisions based on sharing the experience of the entire team
- Agile Inception – which provides the framework for the first conceptual and decision-making phase of the agile team’s work
- Integrated Thinking – a method that focuses on the exploration of possibilities and rapid prototyping of solutions
How can artificial intelligence assist in their application? At the current stage of development, AI can primarily help prepare optimal solutions for specific phases of decision-making. This is because it is applied point-by-point. In other words, today’s AI can relieve employees from performing the tedious tasks of finding and processing information, for example, choosing the optimal price for a product. It’s up to the decision-makers, however, to determine how artificial intelligence should search for answers. In other words, they’ll have to indicate its competitive products, retail locations as well as target customer group, to name a few.
Decision-making areas supported by AI
Artificial intelligence excels at supporting or even making narrow decisions. We use its capabilities daily using, for example, prompts when writing emails. Based on our language, writing style as well as an ever-growing base of connections between words and phrases, artificial intelligence is increasingly accurate in suggesting the next term, phrase or punctuation mark. One would like to say that it catches our intentions on the fly – an as-yet unsaid sentence or thought.
Analysis and decision-making based on incomplete data work on a similar principle. By analyzing previous information, AI can fill in the missing fields, that is, it somehow “guesses” what should be in an empty cell of a table or point of a chart.
Therefore, artificial intelligence today supports diverse but specialized decision-making areas. It finds application in, among other things:
- entering documents into databases – even in situations where they are delivered to the company in paper form or contain incomplete or poorly structured data, AI can accurately organize the information and decide which collection the document belongs to,
- answering questions asked in natural language – decision-making makes artificial intelligence capable of responding accurately to questions asked, and taking the initiative by asking follow-up questions, as we wrote about when discussing chatbots, voicebots and virtual assistants,
- business process management – in a situation of incomplete data, AI can decide to move on to one of the cliques of alternative next steps included in the process map
- process automation – the action of artificial intelligence enables the automation of workflows between different programs that support the company
The decision-making areas supported by artificial intelligence today are narrow in scope. The vision of the future outlined at the beginning is a mere guess, the days of AI leading the companies are possibly unlikely at all.
However, expanding the scope of AI through collaborative modules to analyze and manage different processes opens up unpredictable possibilities. We will try to look into the future of artificial intelligence in supporting business decisions and processes in our following article.
AI in business:
- Artificial intelligence in business - Introduction
- Threats and opportunities of AI in business (part 1)
- Threats and opportunities of AI in business (part 2)
- AI applications in business - overview
- What is NLP, or natural language processing in business
- Automatic document processing
- AI and social media – what do they say about us?
- Automatic translator. Intelligent localization of digital products
- AI-assisted text chatbots
- The operation and business applications of voicebots
- Virtual assistant technology, or how to talk to AI?
- Business NLP today and tomorrow
- How can artificial intelligence help with BPM?
- Will artificial intelligence replace business analysts?
- The role of AI in business decision-making
- What is Business Intelligence?
- Scheduling social media posts. How can AI help?
- Automated social media posts