“Autonomous Operations” uses AI to manage processes and execute actions, responding in real time to changes in demand and other challenges
A recent Gartner study found that nearly 70% of executives at large global enterprises believe their company’s operating model is not capable of continuously adapting—and 40% say their business models will not be economically viable within a decade if their operating model does not change. To help organizations adapt to new business challenges, NTT DATA, a global leader in information technology and consulting services, introduces Autonomous Operations.
Autonomous operations are intelligent, self-adaptive and self-managed processes that use cutting-edge technologies, with artificial intelligence as the protagonist, to make data-driven business decisions. In practice, these are processes that have the ability to manage and execute actions independently – without human action – and adapt, in real time, their actions in response to variations in demand and other signals perceived in the real world.
Bruno Magalhães
“Autonomous Operations are here to replace business models that are still based on fragmented operations management, which results in a lack of coordination and delays. The result? Inefficient processes and high costs, an unsustainable situation in a competitive business environment like the one we have today on a global level,” says Bruno Magalhães, Operations Director at NTT DATA.
The application of autonomous operations generates efficiency gains with process optimization, waste reduction and better resource allocation; cost reduction, with failure prevention and optimization of the use of resources such as water, energy and inputs; faster and more accurate decision-making based on the analysis of large volumes of data in real time, identification of patterns and trends, process optimization based on information updated in real time.
The rapid evolution of AI is one of the factors driving the growth of autonomous operations. Research from Hostinger Tutorials, published this year, shows that the AI market is expected to grow by 37% each year until 2030, with a combined annual investment of US$$300 billion by companies.
Evandro Armelin
“Technology is the engine that drives autonomous operations. Through advanced algorithms and real-time data analysis, we can optimize every step of the production chain, from inventory management to logistics. The key to success lies in cross-functional collaboration, combining strategic business knowledge with technological expertise to create solutions that drive competitiveness and operational excellence, or enable business scaling in talent shortage scenarios,” says Evandro Armelin, NTT DATA’s Lead Partner for Digital Technology.
Use cases
Autonomous operations can be applied in a variety of situations. For example, health insurance reimbursement. In many insurance companies, this process is carried out in an unstructured manner, and the health insurance customer can see their process completed in several days. With the autonomous operations approach, the response can be given practically in real time.
NTT DATA recently implemented autonomous operations at a steel mill that wanted to automate the process of transferring materials between production plants. By using technologies such as AI, the process became more efficient. The results: a 15% reduction in inventory and a 23% increase in the efficiency of material transfer speed between plants.
Another case study was conducted in the customer service department of a call center operator. The company faced challenges such as high staff turnover, increased service costs, and a lack of standardized processes. To address this, customer service began using AI and machine learning algorithms, which are capable of interpreting customer feelings and intentions during calls and suggesting solutions to customer problems. This resulted in fewer errors in customer service, improved customer experience, and reduced costs.
“Autonomy in operations is not just a trend, it is the next industrial revolution. By integrating artificial intelligence and machine learning into our processes, we are shaping a future where operational efficiency reaches unprecedented levels. This transformation allows us to focus on strategic decisions, driving innovation and sustainable growth,” says Bruno Magalhães.
