Artificial Intelligence, or AI for short, is not uniformly defined. Since artificial intelligence is an interdisciplinary research field of computer science, cognitive science, and engineering, definitions of AI vary, depending on the field. For practical application, the following general definition has proven useful.
Artificial intelligence refers to computers, robots, or other machines that mimic human intelligence and exhibit intelligent behaviors.
IT systems are no longer just able to process inputs according to instructions but are increasingly taking on tasks that require judgment, learning, and logic. They can solve problems independently, make decisions and improve them based on their experience.
Although the description of artificial intelligence evokes associations with super-intelligent robots, today’s applications are far from this scenario away.
This is because there are two types of artificial intelligence:
The term narrow artificial intelligence is somewhat misleading as what this type of AI does is enormous. In this case, narrow only means that it is limited to a specific task area. A narrow AI for process optimization in logistics cannot be used in pricing. However, in its field, it can easily save companies hundreds of thousands of euros. The performance of narrow AI has made huge progress in recent years. It is now integrated into more and more business applications.
Typical use cases of AI in commerce:
Supporting management decisions, predictive maintenance of machines, chat bots in customer support, optimization of delivery routes, price optimization.
Powerful Artificial Intelligence is so far mainly a philosophical thought experiment. It means super-intelligent machines that are superior to humans in every area. But these will probably remain science fiction for many centuries to come.
In the context of artificial intelligence, the buzzwords deep learning and machine learning or machine learning also frequently come up. They are often used synonymously with AI, although this is not entirely correct in terms of content.
AI expert Andrew Ng explains the connection clearly:
Artificial intelligence describes all technologies that allow systems to act intelligently. Simple methods such as if-then rules or decision trees can be used, but also more complex methods such as machine learning algorithms or deep learning.
In machine learning, IT systems are programmed with the help of large amounts of sample data or empirical values so that they can carry out a certain process better and better because they optimize their decision criteria.
Deep learning, in turn, is a form of machine learning in which computers learn independently from their experience with the help of artificial neural networks.
Figure 1: Artificial Intelligence, Machine learning und Deep learning
Machine learning is considered a key technology for artificial intelligence. This is because learning is a prerequisite for developing and improving intelligent behavior.
Machine learning involves developing algorithms to solve specific problems and feeding them with huge data sets. The larger the database, the better the systems recognize patterns and the more precise results they can deliver or the more accurate recommendations and predictions they can make.
Typically, the systems need to be trained by humans to learn when they have mismatched/evaluated data and to improve their efficiency. In some cases, the systems also learn from the results they produce and adapt their algorithm themselves. Typical use cases include image recognition or automated stock buying for portfolio optimization.
AI-supported analysis of large amounts of data can facilitate work in many business areas. In particular, machine learning systems can do simple, repetitive tasks and those that require high accuracy better than humans.
In deep learning, computers learn via neural networks. The systems do not need to be trained by humans, or they need to be trained less intensively. Deep learning systems can also process larger amounts of data.
Artificial neural networks are modeled after the human brain, in which nerve cells (neurons) are interconnected via neural pathways. In artificial neural networks, an input is passed through and processed by a multilayer network of artificial neurons. The more multilayered the neural network, the more complex the information processing can be. The system learns more about the information with each layer, recognizing whether its assumptions from the data were correct and correcting its actions on its own.
Unlike traditional machine learning, deep learning systems can process unstructured data, i.e., speech and non-categorized images, text and video. The barrier to entry for deep learning is thus lower, and the time required to maintain the system is much less. At the same time, the quality of predictions and recommendations are much more precise and accurate than traditional ML systems.
While machine learning delivers good results in pattern recognition, deep learning plays to its strengths in predictions and recommendations for action. This is also a focus of AI research. After all, smart applications that prevent people from making wrong decisions and support them in making better strategic decisions offer greater added value than simple AI data evaluation.
Figure 2: A deep learning neural network
AI can be used to add value to any business process in retail. With smart tools, processes can be carried out more efficiently, customer needs can be identified more accurately, and business can be scaled more easily.
From customer acquisition to product design and logistics to pricing and customer management, AI will be everywhere as a natural part of the business in the future, supporting human employees in their decisions and tasks.
Text recognition, automation, sensor technology – artificial intelligence is currently used primarily to improve processes and increase efficiency. For good reason: the savings potential is enormous.
According to a Capgemini survey, retailers can reduce their expenses in this way by up to $340 per year.
Automation, AI and chatbots can significantly improve the customer experience.
In a study by Liveperson and Forrester Consulting, more than half of the companies surveyed said customer satisfaction had improved over the past 12 months. Three-quarters of the companies also noted higher revenue growth, which they attributed to the use of AI applications.
Companies that invest in the latest digital technologies in their supply chain benefit from revenue increases.
According to a PwC study, companies can expect increases of up to 7.7 percent per year and cost reductions of 6.8 percent per year.
Smart applications can identify which goods need to be reordered and when, and report information about inventory levels back to production. And today, companies can already use AI applications to optimize delivery routes, shorten delivery times and reduce costs. In the future autonomously driving vehicles will run on even more efficient processes – without any human intervention at all.
Despite rising online sales, the majority of consumers continue to shop locally. Artificial intelligence can help companies connect their brick-and-mortar retail with their online store in a way that drives more sales. Through apps, email marketing and customer accounts, insights into customer needs and behavior can be gained and actions derived to continue offline shopping experiences online in a personalized way.
According to Harvard Business Review, companies that use AI in sales can increase leads by more than 50 percent and reduce phone time by up to 60-70 percent thanks to intelligent online customer engagement.
IT security is one of the most important application areas of AI and is being used by more and more companies. According to a Capgemini report, two-thirds of companies are convinced that they will only be able to identify cyber threats with the help of AI in the future.
These smart systems detect unusual data movements earlier than human administrators and can provide appropriate indications or eliminate threats on their own. In addition to known threats, deep-learning systems can also identify new forms of malware and render them harmless.
One of the simplest use cases of artificial intelligence is in the automation of repetitive and low-demand tasks. According to a ZEW study commissioned by the German Federal Ministry of Economics, companies that use AI can generate around 25 percent more profit – and not just because of cost savings, but because they can invest the freed-up resources in innovation development. As a result, the number of companies producing world market firsts has risen by 4 percent.
Pricing is a key growth lever for companies. With AI-powered dynamic pricing, companies can have optimal prices calculated automatically.
The smart systems analyze supply and demand, determine which prices customers are still willing to pay, and update prices in the online store fully automatically. The result: sales increases of up to 10 percent.
Figure 3: AI has a multitude of benefits in retail
If online retailers want to achieve high visibility on Google and Amazon and maximize their sales, they can no longer avoid dynamic price optimization today. Demand and competitor prices change too quickly to be reflected by a manual pricing strategy.
Until now, online retailers have used traditional systems for dynamic price optimization: if the competitor’s prices fell, the tool reduced its own prices within a predefined framework. However, these applications react too slowly and work too unspecifically.
According to a McKinsey study, companies fail to determine the best price in 30% of cases.
To take advantage of the opportunities presented by rapidly changing market conditions, more and more online retailers are therefore turning to AI-supported price optimization. These smart applications ensure that companies charge the best prices for them at all times. They balance the fine line between “too cheap” and “too expensive” and (semi-)automatically find the optimal price point with which suppliers optimize their profits.
The latest machine learning is used to calculate these optimal prices. The algorithms evaluate internal and external databases and take into account many more influencing factors than traditional systems, for example:
Prices are no longer cost-driven, but are based on companies’ goals and business strategy.
With just a few clicks, users can specify the sales, revenue and profit they want to achieve and which factors the smart tools should include in their dynamic price optimization. From this, the software determines the best prices within a few minutes. Thanks to AI, the applications can provide valid forecasts of price developments. Unlike traditional systems, companies can now identify trends early on and adjust their prices.
In the past, dynamic pricing could usually only be done for the entire range or a product category. Here, too, AI opens up new possibilities: Thanks to smart tools, companies can now adjust their prices quickly and automatically down to the product level. This also allows the sales potential of short-term market changes to be exploited. Sales increases of up to 10 percent speak for themselves and have led to AI establishing itself as the standard in online retail pricing.
According to a PwC study, only one in five companies calculated their prices automatically in 2019, but the proportion is now likely to be significantly higher. One thing is certain: those who do without smart support in the future will have a hard time offline and online.
7Learnings offers companies a simple and smart way to optimize their prices. Using the latest deep learning technology, our application predicts demand, sales, and price elasticity for any product. And with just one click, our customers can generate market-driven prices that maximize profits while taking into account limiting factors such as current inventory levels.
Price optimization can be configured based on individual pricing strategy and pre-determined KPIs, enabling companies to reach their target revenue faster, expand their market position and strengthen their corporate brand. Smart price optimization saves our customers time and frees them up to drive growth.
Leading European online retailers rely on 7Learnings. The success is reflected in the bottom line. Read some case studies here.
With every IT investment – including AI applications – the question arises: should you develop in-house or buy externally? There can be no blanket recommendation. However, many companies think they can save costs for supposedly expensive external tools by developing in-house. But this logic falls short, especially for applications with artificial intelligence.
Developing and training machine learning algorithms requires a great deal of specialist knowledge that IT employees rarely have and that they would first have to acquire successively. In addition, the projects usually take more time than planned and failures must be taken into account during implementation before the AI delivers satisfactory results.
In many cases, it is more cost-effective and beneficial for the quality of the application to rely on external AI tools. This allows IT staff to focus on other strategically important tasks and ensures that the application is ready for use in a manageable time, at a fixed cost and with reliable quality.
But how big is the advantage when the competition also uses such applications? Customized algorithms may achieve the same effectiveness as purchased applications, but if your competitors are not yet using AI, you as a company are also creating a competitive advantage with supposed standard solutions. And if smart price optimization is already common in your industry, powerful tools still give you the best chance to get on par with your competition. There is only one scenario in which you are sure to lose: If you continue to postpone the investment in artificial intelligence.