Requirement for learning to operate in newer work models
Learning is a constant process of proacting or reacting to a situation. The pandemic is just another event that triggered the need for learning to operate in newer work models.
IT professionals are required to develop products in a distributed environment. They must focus on the soft aspects of frequency matching with fellow teammates, which is a natural phenomenon in an onsite environment. In a consulting business, continuously adapting to newer situations for effective solutions and communication is important. In the last two years, we have invested heavily in enhancing human-to-human interaction by developing and using efficient collaborative tools. We, however, knew that the entire team must operate at 100 per cent efficiency to adapt to the situation. Working in the metaverse is requiring our teams to experience, learn and improve hybrid work culture so that it can be translated into the meta world.
Future of AI
Only five percent of the most important areas is experiencing AI adoption. Today, AI is mostly focused on understanding human behaviour for commercial purposes. The next decade will see significant advances in green businesses like food processing, natural resource utilisation and sustainable environment creation. AI adoption will be the differentiating factor and catalyst for green businesses. The metaverse will be the next big commercial wave with AI and VR technologies expanding ingaming, education, healthcare, entertainment, retail and logistics industries.
Upcoming opportunities in RPA
Individuals and enterprises can adopt RPA and set up automation to process higher workloads and drive consistent quality. RPA helps individuals archive files. Enterprises like banks can use RPA to identify potential fraud. RPA and AI have combined to produce intelligent automated solutions. The concept of bots handling initial customer services is widely being adopted by enterprises, and it is a huge testimonial to what RPA + AI can achieve. They have much-untapped potential, which will be unleashed in this decade in the fields of supply chain and logistics, banking and financial services, healthcare, and retail, and especially in customer service, data entry and processing for operations.
Edge Computing: Reducing the latency for End-users
Within this decade, enterprises may be running tens of thousands of edge devices. With the number of device-business-operations, like vehicles, cameras, and IoT devices increasing, the need to manage them effectively is getting crucial. Edge computing methods distribute and manage computing capacities closer to the data generators and leverage the computing capacities on these edge devices. With the 5G network, computing tasks are moving to the devices themselves, on data warehouses, retail floors, factories, and similar systems.
Although this mechanism presents a great deal of security risk, it can reduce latency for the user for processes that need to be performed quickly. For example, monitoring pipeline pressure in petroleum plants needs a quick mechanism to react if the sensor data crosses the safety threshold. In such cases, the delay in sending the data back to centralised computing where it is analysed and acted upon can lead to fatal outcomes. Edge computing where the server is set up at the edge device can be configured to shut the valve to react quickly on threshold crossings.
IT professionals are required to develop products in a distributed environment. They must focus on the soft aspects of frequency matching with fellow teammates, which is a natural phenomenon in an onsite environment. In a consulting business, continuously adapting to newer situations for effective solutions and communication is important. In the last two years, we have invested heavily in enhancing human-to-human interaction by developing and using efficient collaborative tools. We, however, knew that the entire team must operate at 100 per cent efficiency to adapt to the situation. Working in the metaverse is requiring our teams to experience, learn and improve hybrid work culture so that it can be translated into the meta world.
Future of AI
Only five percent of the most important areas is experiencing AI adoption. Today, AI is mostly focused on understanding human behaviour for commercial purposes. The next decade will see significant advances in green businesses like food processing, natural resource utilisation and sustainable environment creation. AI adoption will be the differentiating factor and catalyst for green businesses. The metaverse will be the next big commercial wave with AI and VR technologies expanding ingaming, education, healthcare, entertainment, retail and logistics industries.
Upcoming opportunities in RPA
Individuals and enterprises can adopt RPA and set up automation to process higher workloads and drive consistent quality. RPA helps individuals archive files. Enterprises like banks can use RPA to identify potential fraud. RPA and AI have combined to produce intelligent automated solutions. The concept of bots handling initial customer services is widely being adopted by enterprises, and it is a huge testimonial to what RPA + AI can achieve. They have much-untapped potential, which will be unleashed in this decade in the fields of supply chain and logistics, banking and financial services, healthcare, and retail, and especially in customer service, data entry and processing for operations.
Edge Computing: Reducing the latency for End-users
Within this decade, enterprises may be running tens of thousands of edge devices. With the number of device-business-operations, like vehicles, cameras, and IoT devices increasing, the need to manage them effectively is getting crucial. Edge computing methods distribute and manage computing capacities closer to the data generators and leverage the computing capacities on these edge devices. With the 5G network, computing tasks are moving to the devices themselves, on data warehouses, retail floors, factories, and similar systems.
Although this mechanism presents a great deal of security risk, it can reduce latency for the user for processes that need to be performed quickly. For example, monitoring pipeline pressure in petroleum plants needs a quick mechanism to react if the sensor data crosses the safety threshold. In such cases, the delay in sending the data back to centralised computing where it is analysed and acted upon can lead to fatal outcomes. Edge computing where the server is set up at the edge device can be configured to shut the valve to react quickly on threshold crossings.
