Cognitive Automation Market, By Component (Solutions and Services), By Technology (Machine Learning, Natural Language Processing, Computer Vision, Robotic Process Automation, and Others), By End User (Banking, Financial Services, and Insurance (BFSI), IT and Telecommunications, Manufacturing, Healthcare, Retail and E-commerce, and Others), By Geography (North America, Latin America, Europe, Asia Pacific, Middle East & Africa)

Published In : Aug 2024

Code : CMI7324

Pages :154

Formats :

Excel and PDF

Industry : Information and Communication Technology

Market Challenges: High implementation costs of cognitive automation solutions

Global cognitive automation market growth can be hampered due to high implementation costs associated with cognitive automation solutions. Developing advanced cognitive capabilities requires massive investments in research and development. Companies offering cognitive automation products and services need to employ teams of skilled AI engineers, data scientists, and other technical experts to build and maintain their cognitive platforms. These R&D costs are then passed on to customers. Integrating cognitive solutions with existing IT infrastructure of organizations also involves significant customization and support expenses. For many small and medium enterprises, the total cost of ownership of cognitive technologies acts as a barrier for adoption. While the long term savings potential of cognitive automation is huge, its upfront capital expenditure requirements are prohibitive for cost-sensitive businesses. This high variable cost structure poses a notable challenge for widespread commercialization of the cognitive automation in the near future.

Market Opportunities: Integration of Cognitive Automation with Emerging Technologies like IoT, robotics and blockchain

Integration of these solutions with other adjacent emerging technologies like IoT, robotics, and blockchain can offer market growth opportunities. As AI capabilities enhance to take inputs from a variety of connected devices and sensors, cognitive systems can leverage real-time data streams from IoT networks to automate decision making for industrial operations. Cognitive functionalities like computer vision, speech recognition and predictive analytics can empower robotics with more intelligent autonomy. The convergence of cognitive computing and blockchain holds promise for self-learning, transparent applications spanning finance, supply chain and healthcare. Seamless collaboration of cognitive platforms with IoT, robotics and distributed ledgers can spawn innovative automation use cases and significantly expand the scope of the market. With technology giants investing heavily in these combinational solutions, their market proliferation can drive major revenues for cognitive automation vendors.