Automation of business processes is spreading widely across big businesses (80%), and the automation level will continue to grow. However, machine learning technologies are only used by a third of enterprises (33%).
Moscow, November 26, 2019
Machine learning is already in use in public management, retail and sales, industry and power engineering, while medical and pharmaceutical companies are only about to start embracing it. The NAFI analytical center and Mains Group, a company specializing in education and information technologies, are presenting results of a study into the demand for machine learning technologies among big Russian businesses.
Process automation has become widespread among big businesses
There is awareness of machine learning technologies, nevertheless, they are not used at the majority of big businesses
CIOs expect further growth in business process automation, in particular, thanks to large-scale implementation of machine learning
As of today, machine learning is most frequently used in sales
The most promising areas for future machine learning implementation are operational excellence, analytics, and research
Machine learning introduction is primarily driven by cost reduction, while productivity gains and optimization of interaction among units are also among key drivers
The most common hurdles are the need for process reengineering and unpreparedness of company personnel
95% decision-makers in IT at big businesses see machine learning as a useful tool that will become more in demand in the future
For the purposes of this study, process automation means putting routine procedures or standard tasks under control of a digital information system. Examples of process automation are customer relations management (CRM) systems, automatic management systems for warehouses, supplies, manufacture, accounting, HR, etc.
Machine learning means a process in which a system (AI) processes a big number of examples (cases), detects patterns, and uses them to analyze and forecast new data specifications. For instance, a system can analyze complaints filed with a company, and make complaint forecasts.
Top managers of the majority of big business are generally aware or have heard something of machine learning.
This creates a kind of hurdle for entry and active use of the technology. People are simply not ready to commit to a technology they do not know.
Most of the big businesses (80%) whose representatives were surveyed use automation in business processes, and every fifth business (20%) boasts automation of all business processes. The number of automated business processes at enterprises will continue to grow.
Even though business process automation is used by the majority of companies that participated in the study, only a third of them use machine learning (33%).
Public administration;The organizations surveyed for the study mostly use machine learning for HR management and recruiting (50%), but they plan to introduce it in analytics and research, too (43%). The main hurdles in machine learning introduction in the surveyed public sector companies are cyber security issues, and the main drivers are productivity gains (72% each);
Retail and trade;As expected, sales have the highest rate of machine learning introduction (86%). Representatives of surveyed companies also plan to introduce it in logistics (42%), operational excellence, and marketing (37% each). What primarily keeps them away from machine learning introduction is the need for process reengineering (42%), and cost cutting is what primarily drives machine learning introduction forward (58%);
Industry and energy;According to representatives of surveyed companies, machine learning is most frequently integrated in operational excellence (68%) and logistics (50%). The surveyed industry and power engineering companies that do not yet use machine learning in operational excellence are planning to start doing so (48%). Their hurdles are personnel unpreparedness and lack of available data on machine learning (37% each), and their drivers are reduction of data analysis timeframes and cost cutting (42% each);
Healthcare and pharma;Representatives of surveyed companies are most willing to introduce machine learning in analytics and research (71%), as well as in marketing (57%) and operational excellence (43%). They are primarily stopped by lack of available data on machine learning (71%), and driven forward by potential image gains (57%), reduction of decision-making timeframes, and higher development efficiency (43% each);
Financial sector;More than 50% of banks and insurers admitted that they saw the need for process reengineering in order to introduce machine learning and were ready to get down to it in the next 2-3 years. Companies of the finance sector expect these initiatives to contribute to service quality improvement, cost cutting, and reduction of decision-making timeframes. They see solutions based on machine learning technologies as optimal for these purposes.;
Responses may make up more than 100% in total, as survey participants could choose several possible answers.
A major reason behind slow technology introduction is its delayed effect. Taking into account that AI based models need time to learn, and then a relatively long period is required for introduction and alignment, it may take a while until any economic effect is generated. In companies with a short planning horizons, the management just cannot afford waiting. That is why decisions are often made in favor of less efficient, but quick solutions, to the extent of hiring a big number of low-skilled personnel.
Russian companies are becoming more prepared for digital economy. This is exemplified by our own products for big business automation based on machine learning technology. At the same time, the gap in up-to-date technology penetration in big and medium businesses may be massive. The study established that public management, finance, industry, retail, and healthcare were the leaders in the use of machine learning technology. Other sectors are lagging behind partly due to the nature of digitalization hurdles. Key problems we are facing in cooperation with the big business include processing of unstructured data, insufficient budgets, and shortage of qualified personnel. Based on the obtained data, we assume that we will enter a period of active transition to machine learning, data classification, and experience gaining in the next 5-7 years
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