IDENTIFY REVENUE SHIFTS, MARKET ADJACENCIES & UNKNOWNS THAT IMPACT
AWS IN CLOUD COMPUTING
Revenue shifts of your clients: The telecom and media and entertainment sector has been undergoing digital transformation with the adoption of blockchain technology. Blockchain technology allows the telecom operators to enable new use cases such as OSS/BSS process management, identity management, smart contracts, connectivity provisioning, and payments. With technology advancement in blockchain, crypto assets and government initiatives, telecom operators can address their business process optimization and enable secure payments for their clients. Unknowns: Several industries such as media and entertainment, banking, insurance, and government sectors possess high growth opportunity areas in the blockchain industry with the increasing venture capital funding and investments in blockchain technology in these sectors. In the coming years, the telecom operator’s reliance on blockchain technology for smart contracts and payments will generate a new revenue stream for telecom operators and software vendors in the blockchain market. Interconnection: Crypto asset management market, which is valued >USD 1 billion and growing with a CAGR of ~20% could also be a game changer for telecom operators and blockchain solution vendors with increasing adoption of the crypto asset management solutions for managing and trading cryptocurrency and crypto assets.
Revenue Shifts of Clients/Client’s Clients / Partners/ Vendors: The manufacturing industry is witnessing a new wave of technological revolution, which is boosting the idea for the implementation of AI in factories/plants. AI-based solutions are adopted in manufacturing facilities to improve productivity by maximizing asset utilization, minimizing downtime, and improving machine efficiency. Moreover, AI in manufacturing is expected to enhance productivity through quality control by detecting defects and help in the predictive maintenance of factory machinery. Increasing data volume derived from the manufacturing value chain has led to the involvement of AI in IoT throughout the production process. A properly developed AI-based algorithm is used to plan machine maintenance adaptively, rather than on a fixed schedule. The main application is predictive maintenance and process automation & supply chain management thereby helps to decrease the cost of equipment failure and associated production losses. AI in IoT has created new opportunities for the transportation and mobility vertical. The rapid growth in the world population and urbanization will lead to the growth of smart mobility and transport across the globe. AI-based services with the help of predictive analytics mainly focus on recognizing the pattern and estimating resource requirements and probable congestion patterns in route planning. Energy & Utilities is one of the largest industries serving a huge customer base. The adoption of AI in IoT solutions is rapidly changing the operational and performance model of this industry. The smart grid backed with AI and machine learning capabilities have been trending in this vertical lately. AI in IoT in energy and utilities helps in many cases such as finding new sources of energy, analyzing minerals in the ground, forecasting refinery sensor malfunction, and restructuring oil delivery to gain cost-effectiveness and efficiency. Interconnections Impacting Your Clients & Clients’ Clients: A strongly coupled digitized system in the industry improves the overall quality and reduces the cost by improving defect tracking and forecasting abilities. The data obtained from traditional enterprise and supply chain via sensors play a critical role in the successful operation. The Industrial Internet of Things (IIoT) plays a key role in the adoption of AI-based technology. Industrial IoT makes industrial processes efficient, productive, and innovative by enabling an architecture that provides real-time information about operational and business systems. IoT generates huge data through various cameras, connected devices, sensors, etc. To handle and for processing AI algorithms, a chipset is among the critical parameters. The faster chipset can process data required to create an AI system. Currently, AI chipsets are mostly deployed in data centers/high-end servers as end-computers are currently incapable of handling such huge workloads and do not have enough power and time frame. Unknowns Identified: Many companies are coming up with new solutions for AI in IoT. Recently Google launched "Coral", a platform for building IoT hardware with on-device AI. Coral is powered by Edge TPU and would work with Google Cloud IoT for connected device management. Advanced connectivity technologies play a central role in facilitating the manufacturing industry’s efforts to connect millions of assets and devices. Technologies such as 5G and ultra-broadband can ensure that vertical, horizontal, and cross-geographic integration across the value chain is seamless. To perform any task for AI, there is a need for NLP and ML-based software to gain mastery while solving individual processes. This software would be able to provide improved performance and productivity to enterprises over time, instead of providing a one-time boost.