Data analytics will fuel innovation. Products will become ever-evolving things, continuously improving functionality and performance. This will boost customer value and promote the transformation of business models.
With the development of globalization and the expansion of digitization more products are becoming commodified. In the meantime, the transition from an industrial to an intellectual society has changed the sources of value from tangible things and assets to the use of intangible information, design and functions. The same product may have different values depending on the user’s sense of value, situation and usage. In some cases, products have emerged from price-cutting wars by adding values ending up with an increased price point. The law of one price, which indicates the value attributes products, is being shifted to multiple prices, which mean value is derived from products by using them.
In the manufacturing industry, servitization that provides products as a solution is becoming popular, an accompanying paradigm shift from the idea that value exists in products themselves to the one that value is generated by using products. For example, customers who wish to shop efficiently and those who wish to enjoy a shopping experience look for different values such as the atmosphere of the store, service of the sales staff, etc. Redefinition of value offered to customers is also happening in the financial, medical, and social welfare fields.
Recent years have seen the emergence of self-driving cars, drones, robots and other machines equipped with AI (collectively called smart machines) that has self-learning functions and moves autonomously. The spread of smart machines is expected to change the roles and functions of both people and machines in society. For example, more factories and other facilities may become unmanned, with robots monitoring machines. Self-driving cars and drones will be handling logistics, and virtual assistants will be serving customers at banks and stores. Robots can already communicate with other robots to share what each has learned on its own. In the future, it is believed that smart machines will be also sharing perceptions, such as collision avoidance, acting in collaboration with one another. While society is human-centered, smart machines are expected to assume a central role in social infrastructure and control, giving rise to a restructured social system and processes.
Although AI now has the capability to beat top-class professionals in Japanese chess (Shogi) and Go through self-learning, this type of AI is specialized in specific skills. There is still no AI that possesses the overall high intelligence of humans. In light of the increasing dependence on decisions made by AI in the future, it may make sense to provide AI with a basic education that lets it make correct decisions similar to human children, allowing for versatility and high intelligence. Unfortunately, AI could also be taught impropriety by malicious developers, allowing it to make incorrect decisions or misjudge between right and wrong. With its increased use, AI will be expected to make decisions outside its expertise such as ethical problems. In short, it will be essential for AI to acquire common sense. Some non-profit organizations have already launched educational institutions which focus on AI. In the future, the education of AI may become critical. Meanwhile, if a smart machine (AI) causes an accident due to its self-learned ability, an issue will occur as to the extent of liability that may fall on the owner, user, manufacturer or software developer. Some have started to consider giving smart machines the status of a legal person, subjecting it to liability.
A study*1 reports that AI and robots will have an impact on the employment of 40% to 50% of a nation’s labor force. In school education and worker training, it may be necessary to have humans learn high cooperativeness and creativity, which are difficult to substitute with AI. However, employment opportunities replaced by AI could exceed those newly created for humans. For this reason, it may be necessary to examine social system first including the social security concept, shorter workdays to increase the number of employees and the implementation of basic incomes.
The development of IT has enabled all kinds of information produced by people and things to be accumulated. Real time analysis of varied and large amounts of information generated real time helps visualize the conditions of customers, markets, society and the environment. Signs of change and correlations that have been elusive can now be linked to: improvements in customer satisfaction; development of new products; diagnosis of illness; and development of medicines. While privacy and other issues must be considered, results may prevent problems based on the correlations of behavior patterns and the environmental conditions with success and failure, illness and accident and criminal action.
Analytical algorithms are the key to deriving value from big data. For example, analysis of customer attributes may generate different results depending on the data and analytical algorithms used. In the consumer finance industry, traditional financial behaviors have included revenues, debt balances and credit histories. In addition to these, an approach that determines the credit risks of individuals based on their overall information including financial behaviors traditionally overlooked and information seemingly unrelated to financial behaviors such as ways of signing a document, majors at colleges and postings on social media, is emerging. While demographic groups with no credit histories can now receive loans, which leads to the correction of some disparities, loans to applicants with high default risks have decreased, allowing some lenders to lower losses from defaults by more than 20%.
Because automobiles, machines, consumer electronics, and other things are connected to the Internet, processes common in the world of IT are expected to be used widely in the physical world. For example, when software updates of computers and digital devices are applied to a wider range of things, users may be able to enjoy the advantages of added features and improved performance without trading in their things. While in the past, things were replaced whenever a new product with enhanced features appeared, it may become common to update only the software while continuing to use the same device (hardware). If that happens, design concepts will change so that software will provide all kinds of features. Modularization, which enables that only parts of a device are replaced depending on software features and agile development methods, which are common in software development might be introduced.
Cars that can be converted to self-driving vehicles by installing software and robots whose functions expand by adding applications have already arrived. Robots that learn tasks and those equipped with AI (artificial intelligence) with self-learning ability also exist. It used to be that the difference of tangibility of goods and services originated other differences. For example, there was time difference between production and consumption of goods, while those of services were simultaneous. The value of goods was determined at the completion of production and deducted through consumption, while that of services was co-created by producers and consumers. Things whose value increases as they are used have made it meaningless to make a distinction between goods and services. This could create an impact on industrial classification and how depreciation is treated in accounting.
It is believed that real-time situational judgment and continual communication among smart machines will help avoid accidents in the future. This could make the quality and reliability of communication functions even more critical. For example, if exploitation of security vulnerability causes a large-scale blackout or traffic jam, the entire society might become dysfunctional. Hacked smart machines might even start assaulting humans.
One way to avoid hacking is to refrain from being constantly connected to the Internet. However, this would make it impossible to take full advantage of existing information. Although strengthening countermeasures against cyber-attacks is required to ensure security, it will also be necessary to build failsafe systems for minimizing the impact of potential failures and for stopping unexpected actions of a smart machine.