Cognitive Computing Market Size & Share, By Technology (Natural Language Processing, Machine Learning, Automated Reasoning, Information Retrieval); Application; Deployment - Global Supply & Demand Analysis, Growth Forecasts, Statistics Report 2025-2037

Growth Drivers

• Key enablers including IP upgrades and drop in sensor prices: NARA experts estimate that there will be 75.44 billion devices by 2025 connected to the internet worldwide and over 30% of all data replicated or captured will be created in real-time.  Firstly, the revolutionization of Internet protocol (IP) has resulted in forward-thinking perspectives on the integration of cognitive computing across industries. Typically, IP addresses use a 32-bit protocol but the abovementioned estimates have showcased total number of devices eventually would outstrip the protocol’s capacity of providing unique 4.3 billion addresses. The ratification of Internet version protocol 6 (IPv6) is anticipated to suffice the exponential growth of IoT devices.
  
  Manufacturers are retrofitting devices with smart IoT sensors for collecting comprehensive data in real time to further pass it to developing ERP solutions.  As more manufacturers strive to make their legacy systems more intelligent, the sensors and controllers market size has grown substantially and reached USD 6.1 billion in 2020, as per a Microsoft study. A 200% drop in sensor price from 2004, with USD 0.44 per sensor was recorded in 2018. Decreasing costs, coupled with ease of deployment have bolstered Wi-Fi connectivity since 2019. Furthermore, the AWS IoT pricing of MQTT and HTTP messaging is approximately USD 0.7 per million messages.
• Emergence of neuromorphic edge computing: Two primary classes of cognitive computing systems are anticipated to emerge during the forecast timeline. The first can perform tasks without human intervention, including autonomous vehicles and drones. The second enables physicians to diagnose disease and perform surgery. Both classes of evolutionary AI hinge majorly on neuromorphic computing. It maps human functional capabilities using artificial neurons that map characteristics of the human brain.
  
  Current tech leaders are best positioned to benefit from the emergence of neuromorphic computing, given the large capital appetite required to build and maintain complex models. The EU Human Brain Project supported the development of BrainScaleS and SpiNNaker to facilitate neuroscience simulations at scale. The EU-sponsored initiative proposed an online-learning digital spiking neuromorphic (ODIN), an optimized digital neuromorphic processor called has also been proposed for processing complex neuron models. Intel’s Loihi and IBM’s TrueNorth are examples of private advancements in the field, while academic efforts include DYNAPs, IFAT, Neurogrid, and BrainScales-2.
  
  Conventional large-scale neuromorphic computers and chips are silicon-based and developed with metal oxide semiconductor technology. Despite this, there is significant R&D in using new materials such as ferroelectric, phase-change, non-filamentary, topological insulators, and channel-doped biomembranes neuromorphic implementations. Furthermore, rising number of connected devices powered by 5G and IoT has fostered investments in this area. In March 2024, Innatera secured an investment of USD 21.0 million to accelerate its progress on cognitive computing solutions, address the increasing need for sustainable computing solutions, and fuel innovation in the field of AI semiconductors.
• Cognitive computing is becoming more and more necessary for predictive analytics to save operational expenses: The field of cognitive computing has experienced rapid growth due to advancements in artificial intelligence and computer science, and it is expected to continue expanding during the projected time. The primary goal of cognitive computing is to assist organizations in making decisions. Utilizing ML technologies such as predictive analytics, is the ideal approach to executing seamless corporate processes.
  
  Many business users are using these powerful predictive analytics tools to perform routine business activities since they are effective tools for producing exact outcomes. It is projected that 30% of business procedures will be automated by the start of 2030. Predictive analytics solutions also boost revenue by recommending products and services to certain customers based on past data. These strategies are paying off by cutting the administrative expenses of the corporation.

Challenges

• Risks to data security: Concerns about data security remain a major barrier to the cognitive computing market. Providers of cognitive computing solutions are primarily in charge of database administration and supervision. Databases hold financial data such as addresses, picture IDs, debit or credit card information, and a range of sensitive user data. Cybercriminals employ various tactics such as malware, Distributed Denial-of-Service (DDoS), phishing, and SQL injection assaults in an attempt to breach networks and obtain authentic user credentials.
• High initial costs turned out to be an inhibiting factor: The development of cognitive solutions necessitates a substantial initial investment to establish maintenance architecture and infrastructure, resulting in elevated operating expenses. The setup and operation process is far more involved and demands a lot of resources. A significant quantity of electricity, network components, data centers, and other resources are used in this process. Furthermore, it is anticipated that concerns about governmental regulations and ambiguous compliance would restrict the growth of the cognitive computing business. Due to SMEs' lack of understanding, the market is expected to face several difficulties.