Cognitive Automation Market Size and Trends

Global cognitive automation market is estimated to be valued at USD 11.03 Billion in 2024 and is expected to reach USD 49.86 Billion by 2031, exhibiting a compound annual growth rate (CAGR) of 24% from 2024 to 2031.

Cognitive Automation Market Key Factors

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Global cognitive automation market growth is driven by increasing demand for automation across industries to enhance business process efficiency. Rapid technology advancement such as artificial intelligence, machine learning and natural language processing has enabled the development of intelligent software solutions that can automate routine tasks. Various enterprises are implementing cognitive automation to reduce labor costs and human errors. Moreover, growing need to analyze huge amounts of data efficiently can boost adoption of cognitive automation. Advancements in robotic technology can also integrate cognitive capabilities in robots, thus, driving the market growth in the near future.

Increasing demand for process automation and digital transformation

The pressures of digital transformation compel organizations across the globe to automate respective tasks and processes. Companies are recognizing the benefits that cognitive automation solutions can provide to increase efficiencies, improve customer experiences and fuel innovation. By leveraging technologies like robotic process automation, natural language processing and machine learning, cognitive tools are able to understand structured and unstructured data and use this to automate workflows. This allows previously manual tasks to be performed digitally without human intervention.

The ability of cognitive automation to work alongside humans rather than replace them can boost its adoption. Systems are designed to take over straightforward, rules-based activities and process high volumes of structured data, freeing up employee time and resources. Workers can focus on more complex and strategic responsibilities that require human judgment, problem-solving capabilities and creative thinking. In many sectors such as banking, insurance and telecommunications that rely on heavy transaction processing, cognitive automation is positively impacting productivity, turnaround times and customer satisfaction levels.

Organizations across industry verticals are facing pressures to modernize legacy systems and streamline disjointed processes that have accumulated over time. The flexibility and scalability of cognitive platforms allows companies to digitally transform at their own pace, starting with automating pieces of existing workflows end-to-end before expanding the scope of automation. This staged approach helps minimize disruption while maximizing return on investment. Forward-thinking businesses view cognitive automation not just as a cost-cutting exercise but rather as an opportunity to innovate new digital products, services and experiences for the digital native generation.

For instance, In June 2021, FPT Software, a global technology and IT services provider from Vietnam, announced its partnership with Sitecore, a leading digital experience software supplier in the United States. This collaboration aims to enhance Sitecore's digital marketing services for a wider range of enterprises in Japan by leveraging automated processes, digital transformation, and low-code solutions, ultimately benefiting numerous Japanese businesses.

Growing adoption of artificial intelligence (AI) and machine learning (ML) technologies

Advancements in artificial intelligence, machine learning and related technologies have tremendously accelerated in recent years. This rapid innovation boosts expanding use cases for cognitive automation across industry verticals. AI and ML algorithms that previously required massive training datasets and computational power can now achieve high degrees of accuracy with smaller samples due to improvements in processing speeds, storage capacities and new deep learning techniques.

Vendors are harnessing these AI breakthroughs to develop new categories of cognitive automation software. Advanced natural language processing unlocks automation opportunities from various unstructured data sources like emails, documents and calls. Computer vision enabled by convolutional neural networks empower systems with visual recognition and object detection skills. Integration of knowledge graphs allows cognitive agents to store and understand relationships between entities for contextual responses.

Large technology companies are investing heavily in AI research and its real-world applications through acquisitions and in-house development. These are using their vast financial resources and innovative cultures to remain at the forefront of next generation ML technologies. Software providers seek to partner with or obtain these emerging capabilities to infuse cognitive automation platforms and keep them powered by the latest AI. Governments worldwide are also launching initiatives, programs and regulations to support AI advancement and ensure its safe, fair and responsible use.