We believe that the greatest value of artificial intelligence is that it helps a person do more work, improve consumer experience and interaction within their communities https://www.techdailytimes.com/ruslan-timofeev-breakthrough-in-deve.... And success here requires a people-first approach. Artificial intelligence holds great promise for business transformation by creating intelligent processes in the value chain, as well as intelligent products and services in the market. Therefore, the leaders of companies should act as initiators of increasing the level of AIQ and building an intelligent business. Only they are able to bring about organizational and cultural changes, as well as adjust business models for the implementation of AI solutions. To do this, top management should place artificial intelligence at the center of its organizational strategy and stimulate AIQ growth, creating all the necessary conditions for itself.

TECHNOLOGY “Over the past two years, Orange has been actively investing in AI. And, despite the fact that it is not currently our main business, everything may change in the near future. " Harness the Power of Combinatorial Innovation Artificial intelligence is not one technology, but a collection of many technologies, the effect of which can only be fully felt when they are used together. Combinatorial innovations like these offer great opportunities for transforming the business model of an enterprise. For example, Amazon Go (8) is a new kind of offline store that has no cash registers. It uses the same technologies as in self-driving cars: machine vision, collection and synthesis of data from various sensors, and deep learning algorithms. Amazon was able to do this by actively investing in AI R&D and obtaining a patent for the underlying technology in 2014. (9) Internal Opportunities: Companies that see these opportunities for change build their own AI labs or invest in and even buy AI startups. For example, the State Grid Corporation of China has more than 200 laboratories, at least two of which specialize in artificial intelligence solutions - the Sichuan Provincial AI Lab and the Electricity Robot Technology Lab. External cooperation: Not all companies are willing to spend significant funds on internal development of AI technologies, and here cooperation with other organizations can take center stage. Companies need to understand what is an area of transformational change and what is a growth point. For example, a credit scoring simulator, which allows making decisions on potential customers quickly, can become a competitive advantage for a bank, and can also help to make money on intellectual property for the basic algorithm. On the other hand, a chatbot for interacting with bank clients can be created based on the technology of an external partner, while it will “grow smarter” due to the company's own data on the client's actions.
DATA Realize the importance of data convergence The true value of AI is not in some algorithm or neural network per se, but rather in how data analytics can change the business itself. But business leaders should always keep the following key points in mind when it comes to data and artificial intelligence. Access to data: Effective use of AI technologies requires access to a large amount of diverse data, and not just to a certain part of the array. Data can divide the market into two camps, where many companies are trying to oppose large sites or technology companies that own or have at their disposal large amounts of data. An example is Intel's investment in self-driving technology, including the $ 15 billion purchase of Mobileye, which the company's CEO describes as “our strategy to make Intel the driving force behind the data revolution across all technologies and industries.” (10) Data Convergence : Another problem is that most of the data is either "raw" (unstructured, not available for immediate use) or scattered (difficult to combine). To provide access to master data, companies will need to invest in existing IT systems. They will need to create opportunities to converge and make sense of data from sources such as IoT sensors or social media. The higher the density and variability of the data, the greater the chances of discovering "unknown unknowns" - relationships that no one knew existed or were simply not searched for. Data Monetization: Sustainable market differentiation requires machines that can handle not only streams of data in real time, but also cycles of ever-changing data items. Understanding customer actions enables data transformation into money. Therefore, companies must develop the ability to create feed-back data streams for learning. They should also take advantage of the network effect of the data — the more users a model collects data, the more difficult it is for competitors to collect comparable amounts of data to train their own models. And what to do with all this data collated? Make AI the hub where data flows to create intelligent products and services. For example, Verizon is offering a big data AI platform to its service provider partners through its Exponent project. This solution allows telecom operators to monetize their data, including unstructured data, through the use of machine learning technologies and deep analytics. (11) Data Sharing: It is very important to organize the sharing of data in a secure environment. For example, a consortium of German automakers has acquired HERE, a provider of map data and related services that are an important part of self-driving technology. (12) Automakers have organized the sharing of data from each vehicle to develop the market as a whole. The issues of information security are of great importance here, so that the consortium members can fearlessly share information on an ongoing basis. (13) This is a very important example: companies must act on what they do best and take advantage of opportunities to benefit from joint projects while respecting information protection and data privacy.