Global natural language processing market is estimated to witness high growth, owing to upsurge in digitalization and rising demand for smart devices
Global Natural Language Processing Market is estimated to be valued at US$ 26.71 Bn in 2024, exhibiting a CAGR of 25.8% over the forecast period 2024-2031. The market growth is driven by advancements in digital transformation along with growing demand for smart devices and applications. Furthermore, increasing investment in AI and machine learning by technology giants as well as startups can drive the market growth.
Market Dynamics:
Growing digitalization and reliance on technology - Modern businesses are increasingly relying on digital technologies for various operations like marketing, sales, customer service, and others. This growing digital transformation boosts demand for advanced technologies like NLP that can help machines understand human languages. NLP tools are widely being adopted across industries for applications like chatbots, voice assistants, content classification and sentiment analysis.
Rising demand for smart devices and AI assistants - Proliferation of smart devices and AI assistants powered by technologies like Alexa, Siri, and others has also boosted need for highly advanced NLP capabilities. Major tech companies are investing heavily in R&D to develop more human-like and contextual conversational abilities in devices and applications. This increasing focus on enhancing man-machine interaction can drive the market growth.
Market Drivers:
Increased Adoption of Conversational AI and Chatbots
One of the major drivers of natural language processing market is increased adoption of conversational AI and chatbots across various industries. Chatbots and virtual assistants powered by NLP are being used widely by companies to automate customer support, sales and marketing functions. These offer 24/7 assistance to customers and can handle basic queries much more efficiently than traditional call centers. With advances in NLP technology, chatbots are also gaining human-like conversation abilities, and this boosts their adoption. Many businesses are implementing chatbots on their websites and mobile apps to enhance customer experience and drive sales.
Rising Demand for Sentiment and Text Analysis Tools
Growing demand for sentiment and text analysis tools from businesses can drive the market growth. These tools leverage NLP to understand customer sentiments from product or service reviews, social media posts and survey responses. These provide valuable insights into customer opinions, complaints, feedback and brand perceptions. Many organizations are using sentiment analysis to optimize their marketing strategies, product development and customer service processes based on real-time feedback. Text classification and categorization tools are also gaining traction as these can automatically process huge volumes of text data and extract useful information. Growing need for business intelligence from unstructured text data can drive the natural language processing market growth.
Market Restraints:
Lack of Training Data
One of the key challenges faced by NLP developers is the lack of sufficient high-quality labeled training data. Most NLP models rely on large annotated corpora to learn complex language patterns and relationships. However, it is difficult and expensive to manually label huge amounts of text data for diverse domains and languages. The performance of NLP systems degrades significantly without proper training. While there are efforts to develop techniques for weakly supervised or unsupervised learning, data scarcity remains a major restraint, especially for niche domains and languages other than English.
High Development Costs
Developing robust and accurate NLP systems involves high capital investments. It requires skilled data scientists, linguistics experts and machine learning engineers to architect, train and deploy these solutions. The computing power needed for model training on large datasets also incurs heavy infrastructure costs. For small organizations, it may not be financially feasible to build advanced in-house NLP capabilities. Even outsourcing development adds to the expense. The high costs associated with NLP hamper its adoption for non-core use cases by companies with limited budgets.
Market Opportunities:
Integration with Conversational Platforms
There is significant opportunity for NLP vendors to integrate their technologies with popular conversational platforms like Alexa, Google Assistant, Siri, and others. As voice interfaces gained popularity, integrating NLP skills for tasks like entity extraction, translation, summarization, and others can enhance the functionalities of these assistants. Partnerships with platform providers will help NLP companies to gain visibility and expand their presence across mobile and IoT devices. Monetization also becomes easier by offering NLP as a service to platform developers.
Emerging Applications in Healthcare
Healthcare is another promising market for NLP with several emerging applications like clinical documentation, medical coding, decision support, telehealth and pharmaceutical research. NLP can automate medical transcription, code clinical notes for reimbursement and flag important details in reports. Technologies like question answering and text summarization also have roles in virtual care delivery and continuing education. With growing health data volumes and focus on value-based care, NLP can deliver impactful solutions. The unmet needs in this sector offer lucrative opportunities for NLP vendors to focus on.
Link - https://www.coherentmarketinsights.com/market-insight/natural-language-processing-market-1090
Key Developments
- On June 7, 2024, Upstage, a prominent AI company, optimized its flagship large language model (LLM), Solar Mini, to run efficiently on Intel Core Ultra processors. Upstage will also integrate its WriteUp app, a writing assistant powered by Solar Mini, to operate advanced generative AI tools offline on Windows PCs with Intel processors. This integration aims to provide users with a seamless AI experience, accessible anytime and anywhere.
- On June 3, 2024, Emerson’s Test and Measurement business, formerly known as NI, reiterated its commitment to comprehensive testing approaches. At the NI Connect 2024 conference, the company announced significant updates to core software products like NI LabVIEW and shared its vision for intelligent test strategies. Emerson showcased the latest advancements in NI hardware and software, designed to enhance engineers' efficiency, allowing them to focus more on root cause analysis and collaboration within their organizations.
- On February 2, 2024, a leading data lake platform provider announced its acquisition of Einblick, a startup founded by researchers from MIT and Brown University. Einblick specializes in AI-driven technology that translates natural language questions into code, charts, and models. This acquisition demonstrates the data lake platform giant's commitment to expandits AI capabilities and offerings in data analysis and visualization.
Key Market Players:
Key companies covered as a part of this study include Adobe Inc., Amazon.com, Inc., Baidu Inc., Google, LLC., Hewlett Packard Enterprise Development LP, IBM Corporation, Inbenta Holdings Inc., Intel Corporation, JUST AI LIMITED, Linguamatics, Microsoft Corporation, NetBase Quid, Inc., NVIDIA Corporation, Qualcomm Incorporated, SAP SE, SAS Institute Inc., SoundHound AI, Inc., Texas Instrument, Verint System Inc.
