The technology industry continues to be a hotbed of innovation, with activity driven by the amalgamation of technological progress, heightened connectivity, and the urgency for businesses to enhance efficiency and competitiveness in an ever-changing marketplace, as well as growing importance of technologies such as machine learning, natural language processing, data analytics, and predictive analytics. These technologies enable network administrators to optimize network performance, automate routine tasks, improve security, and enhance overall network management efficiency. In the last three years alone, there have been over 3.6 million patents filed and granted in the technology industry, according to GlobalData’s report on Innovation in Artificial Intelligence: AI-assisted network management. Buy the report here.
However, not all innovations are equal and nor do they follow a constant upward trend. Instead, their evolution takes the form of an S-shaped curve that reflects their typical lifecycle from early emergence to accelerating adoption, before finally stabilising and reaching maturity.
Identifying where a particular innovation is on this journey, especially those that are in the emerging and accelerating stages, is essential for understanding their current level of adoption and the likely future trajectory and impact they will have.
300+ innovations will shape the technology industry
According to GlobalData’s Technology Foresights, which plots the S-curve for the technology industry using innovation intensity models built on over 2.5 million patents, there are 300+ innovation areas that will shape the future of the industry.
Within the emerging innovation stage, finite element simulation, ML-enabled blockchain networks, and generative adversarial network (GAN) are disruptive technologies that are in the early stages of application and should be tracked closely. Demand forecasting applications, intelligent embedded systems, and deep reinforcement learning are some of the accelerating innovation areas, where adoption has been steadily increasing. Among maturing innovation areas, wearable physiological monitors, smart lighting, and smart climate control systems are now well established in the industry.
Innovation S-curve for artificial intelligence in the technology industry
AI-assisted network management is a key innovation area in artificial intelligence
AI-assisted network management refers to leveraging artificial intelligence (AI) algorithms and technologies to automate and enhance network operations and management processes. This encompasses the analysis of network data, prediction of network performance, and automation of tasks such as troubleshooting, configuration, and policy enforcement. By incorporating AI, organisations can streamline network management, lower operational costs, and enhance overall performance.
GlobalData’s analysis also uncovers the companies at the forefront of each innovation area and assesses the potential reach and impact of their patenting activity across different applications and geographies. According to GlobalData, there are 240+ companies, spanning technology vendors, established technology companies, and up-and-coming start-ups engaged in the development and application of AI-assisted network management.
Key players in AI-assisted network management – a disruptive innovation in the technology industry
‘Application diversity’ measures the number of different applications identified for each relevant patent and broadly splits companies into either ‘niche’ or ‘diversified’ innovators.
‘Geographic reach’ refers to the number of different countries each relevant patent is registered in and reflects the breadth of geographic application intended, ranging from ‘global’ to ‘local’.
Patent volumes related to AI-assisted network management
Source: GlobalData Patent Analytics
Cisco is one of the leading patent filers in AI-assisted network management. The company’s patents are aimed at application dependency mapping that can be automated in a network. The network can capture traffic data for flows passing through the network using a sensor network that provides multiple perspectives for the traffic. The network can analyse the traffic data to identify endpoints of the network. It can also identify particular network configurations from the traffic data, such as a load balancing schema or a subnetting schema.
The network can partition the endpoints based on the network configurations and perform similarity measurements of endpoints in each partition to determine clusters of each partition. The clusters can make up nodes of an application dependency map, and relationships between and among the clusters to make up edges of the application dependency map.
Other prominent patent filers in the AI-assisted network management space include Huawei and International Business Machines (IBM).
In terms of geographic reach, Ipanema Technologies leads the pack, followed by Adaptive Spectrum and Signal Alignment (ASSIA) and Permutive. In terms of application diversity, Ayyeka Technologies holds the top position, followed by AO Kaspersky Lab and Amazon.
AI-assisted network management delivers automated processes, improve network performance, enhance security, optimise resource allocation, and enable data-driven decision making. By leveraging AI algorithms and technologies, organisations can transform their network operations, increase productivity, reduce costs, and stay ahead in the rapidly evolving digital landscape.
To further understand how artificial intelligence is disrupting the technology industry, access GlobalData’s latest thematic research report on Artificial Intelligence (AI) – Thematic Intelligence.