Today, artificial intelligence (AI), machine learning (ML), robots, which novelists and screenwriters dreamed of a century ago, have already gone beyond the imagination. They are even embodied in feasible business scenarios, becoming a profitable investment. Accounting, detection of fraudulent schemes, evaluation of various factors related to potential customers, resource planning and reporting are already entrusted to algorithms in a different sectors.
According to the study “Machine Learning: Science, Not Fiction,” conducted by the International Association of Specialists in Finance, Accounting and Auditing (ACCA), 58% of respondents agree to implement AI in the next three years. Market leaders are already working with AI technology today, while others are at least seriously thinking about it, assessing potential risks. There is nothing to blame them for, the introduction of such technologies entails many inconveniences. However, the potential benefits that they provide also promise huge dividends. In this article, we will try to shed light on the implementation of AI in business and finally explain how it works.
This article is only the first in a series regarding artificial intelligence. Read other interesting materials about machine learning, deep learning and other issues related to AI in our blog.
AI in business, you say? What is this all about?
In a narrow sense, AI is a software that simulates the functioning of the human brain. This includes all sorts of Sofia-like robots and, to some extent, neural networks, which can tell you whether the cat is shown in the picture. But we'll talk about them whenever.
Talking about AI in a business context, “artificial intelligence” is a general term used to refer to a number of technologies, namely:
- Machine learning. Machine learning is based on the use of statistics tools. International shipping companies such as Mitsui OSK Lines (MOL) are already using machine learning to maximize profits. These companies install thousands of components on their cargo ships, long-haul trucks and smaller equipment. This helps managers identify accident patterns and create preventative maintenance schedules that keep their ships and trucks moving.
Retailers such as Amazon also play a leading role in machine learning. The online trading giant uses machine learning to increase the effectiveness of its delivery network and predict customer needs. For example, Amazon has created an “expected delivery” protocol that allows you to predict the number and geographical spread of orders for specific products before they happen. As a result, the company now ships popular products, such as phone accessories and household items, to local distribution centers in anticipation of future purchases.
In addition, according to McKinsey’s forecast, machine learning will help manufacturing enterprises reduce material delivery time by 30% and achieve fuel savings of 12% by streamlining their processes. According to company estimates, companies can increase gross income by 13% if they fully integrate artificial intelligence-based technologies in their business.
- Smart robotics. Today's smart machines are artificial intelligence-based systems that self-learn by analyzing information from the outside world. They are already used in a number of industries and perform a wide variety of tasks, from medical diagnostics to autopilots in cars. A rather promising direction, given that the US Department of Energy has long included the development of autonomous driving technology in the state budget, which could mean more than $1 billion available to large and small companies developing similar technologies. As the industry reaches a projected value of $ 42 billion by 2025, new opportunities will appear in it.
- Virtual assisting. A virtual assistant is a software product that provides customers with round-the-clock assistance in using sites or finding the right information. Products like IBM Watson Studio - this is just from this opera. Building a custom assistant based on artificial intelligence can provide the best service, which can positively affect the flow of new customers.
- Automated decision management. The work of such services is based on the ability of regulated systems to make decisions regarding recurring issues without human intervention. AI-based decision management systems are already being used in logistics and human resource management. According to a Gartner press release, it is AI-augmented analytics and decision making based on it that will be the next big trend to change the way we receive, share and process content.
- Speech Recognition. This is about the ability of computer programs to hear and understand human speech. Voice-activated personal assistants, such as Google Assistant and Nuance Intelligent Virtual Assistant, are already helping executives and other professionals increase their efficiency and grow their business. AI personal assistants can perform many of the same tasks as administrative assistants. This includes appointments arrangement, adding events to your calendar, booking airline tickets and hotels, and more. By the way, they work 24 hours a day and 365 days a year.
- Natural language data processing. This technology is aimed at processing data and transforming it into text that is understandable to humans. Thus, it is convenient to operate with analytical data. According to Gartner, in 2020, 50% of analytic queries are generated through data-driven searches using natural language, voice, or automatically. The need to analyze complex data combinations and make analytics accessible to everyone in the organization will facilitate wider implementation, allowing analytics tools to be as simple as a search interface or chat with a virtual assistant.
I am the owner of the company, not a technical specialist. Now show me what you’ve got there!
Right away, sir. So, in order to introduce AI into your business, first of all, you need to identify the real need for it. You should never implement technology “for the sake of technology” or “because the supplier has made a good offer.” Artificial intelligence should solve problems that will allow businesses to gain a competitive advantage in the long term, and it should be identified by a top manager with a couple of good AI specialists. Moreover, it was on his shoulders to decide on the feasibility of introducing AI. Perhaps, after a series of simple calculations, it turns out that doing everything the old fashioned way is more profitable.
For many companies, such a “pain point” will be demand forecasting: artificial intelligence systems are already doing an excellent job in this area by analyzing data arrays. A case in point is the H&M apparel sales network, which also managed to significantly increase demand by predicting trends. The company has developed a self-learning system that, by analyzing sales and the latest trends, predicts what items are worth selling in each of its 4288 stores. Previously, store managers observed an increase in demand for some product. Then they ordered additional batches. While they arrived, demand could already decrease (or it could be a fake signal). Now the system compares the current data with one of its patterns in annual orders, so that two to three weeks earlier it is ordered to “stock up” with certain things. Algorithms have already passed the test in Sweden, by some miracle allowing to remove 40% of the goods from boutiques, while not reducing sales. But the company has much more far-reaching plans for it.
A bit of technical specifics
So how do you incorporate AI into the business so that it works? Well, if you are interested in the technical part only, then you need the AI ââitself, which you will use to optimize a particular task, and then you need to train it. Speaking about training, the sequence of actions is the following:
- Gather data. In order for AI to be able to extract valuable information from a large data array and predict the future, it is necessary to “feed” it an array of information.
- Label the data. The collected data must be marked on some basis: for example, to determine whether the consumer is satisfied. To do this, the label for each user whether "satisfied" or "unsatisfied" must be put manually.
- Teach your AI. After labeling, the data must be shown to artificial intelligence: it will learn to find patterns in it and in the future will be able to independently put this marker on other users.
- Make the labeling a standard. In order for AI to work on the entire enterprise, you need to create a specific standard for data labeling and make it corporate-wide.
Next everything comes to repeat of the second and third steps. However, keep in mind that for a really high-quality implementation it is necessary to have competent AI specialists in staff or in outsourcing.
At the same time, AI for business can learn even when the company doesn’t have enough data. There are several methods for this:
- Lean and Augmented Learning. It allows you to create your own data based on a small number of samples;
- Transfer Learning. It transfers the solution from one task for which there is enough data to another;
- Probability-based modeling. It is used to create “synthetic” data for AI training.
Of course, everything above is rather generalized, but it all works something like this.
AI has the potential to provoke the same grandiose breakthrough in technology that the Internet once did. The question is only when and who manage to quickly implement it and receive the maximum benefit. Well, we in our turn are always ready to help. Yes, we can do that too! Just contact our experts and we will discuss everything.