Will Digital Assistants Be Able To Replace Humans Completely?
Zyfra improves the efficiency and safety of the mining, oil & gas, chemical and engineering industries. About Zyfra. getty  Digital assistants have rapidly conquered many areas of human life. This trend has also affected industrial enterprises, where AI technologies are developing exponentially. According to Research and Markets forecasts, in 2019, the global market for virtual assistants amounted to a little bit more than $3.6 billion, and by 2030, this sum will increase up to $73.2 billion. Hence, the interest of investors, businesspeople and enterprises in this market will likely increase. Digital Advisers: Key Points In Brief  Introducing digital assistants into industrial enterprises represents the chance to create a uniform system for information collection, transmission and analysis. Digital assistants can be united into a single interconnected product that can become an efficient tool designed to fulfill production tasks and get data about raw materials and finished products. From my perspective, virtual bots are most effective where automation of routine processes is required. Besides, they can help staff, including inexperienced and experienced employees, in a more efficient manner. For example, such an approach is justified when producing goods in accordance with strict quality standards. The introduction of digital assistants into industrial enterprises still fails to keep pace with that of bank departments and chain stores. The reason is that the production process is costly. Any inaccurate changes can increase unjustified expenses and the number of faulty products, as well as create a potential danger for the personnel. For example, my organization created a digital adviser for a manufacturing company of seamless pipes that connected with an automated production process management system in a real-time mode. It accumulates historical data and generates settings for the production of pipes with a defined geometrical arrangement. Errors in the manual collection of these parameters can cause huge losses, and the cost of one lost hour of a pipe-rolling machine is several thousands of dollars. Besides, there are a few more reasons why manufacturing companies dont rush to introduce bot assistants:   In some companies, the production process is divided by departments that actually work separately from each other.  The innovation process in the heavy equipment industry takes so much time because long-term investments in production require huge sums of money.  Working out efficient behavior scenarios for machine intelligence requires hiring highly qualified specialists who are always scarce in the labor market.  In cases of personnel conservatism, employees often see innovations as a potential threat leading to a reduction in job openings at the enterprise, so some manual labor workers are wary of the appearance of digital technologies. As for now, the introduction of assistants occurs where it is required to solve a problem of focused specialization. Its challenging to optimize entire areas. This kind of work cant be delegated to outside parties since creating a system requires taking the existing business processes into account, often those that function without any pattern. The Problem Of Introducing Assistants In Enterprises  Companies from Western Europe and the United States are the primary market leaders of AI introduction in IIoT. For example, the globally known company Siemens has developed MindSphere, an open cloud OS for IIoT. In developing countries, IIoT solutions are often on the level of academic work in research institutions. Development of the industry is also slowed down by some institutional problems. Nowadays, theres a chance of introducing digital advisers at all phases of manufacturing. However, its important that the company's management clearly defines the end goal of implementing the technology. This goal may be to increase the quantity and quality of products, to ensure continuous monitoring of compliance with the production process or to optimize the company's budget. Trends should not be at the forefront, but rather the needs of the company. The correct approach to modernization is the key to seeing quick results from a project and an effective solution to the tasks at hand. Development of digital assistants is hindered because of a lack of universal ways for problem-solving. Each production facility is a complicated system with unique tasks. Moreover, introduction of IIoT technologies requires deep knowledge of the industry the enterprise operates in. A serious problem is differences between companies and enterprises in terms of the material base level. Implementation of a unified information space is not possible without coordinating the activities of different departments. For example, warehouse logistics of finished products can be performed by different people who work separately. Elimination of such disadvantages is the most critical task. Successful implementation of AI also depends on the flexibility of its retraining in casework parameters of production lines that need to be changed. The machine brain can be adjusted only in the course of work, when AI learns to solve problems based on real-life experience. This process should be managed by specialists who are well acquainted with the specifics of certain enterprise operations. The introduction of machine intelligence into production processes is slowed down because of many problems. Nevertheless, more and more large companies and enterprises express their wish to modernize manufacturing with the help of IIoT technologies, particularly when it is a matter of replacing unskilled manual labor. These decisions allow the company to be more competitive in the market. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives.