What is the current market size and future outlook for the natural language understanding market?
The natural language understanding market size has grown exponentially in recent years. It will grow from $21.52 $ billion in 2024 to $28.42 $ billion in 2025 at a compound annual growth rate (CAGR) of 32.1%. The growth in the historic period can be attributed to demand for real-time insights, healthcare diagnostics, and monitoring, growth in semantic search, integration with business intelligence tools, and increasing data availability.
The natural language understanding market size is expected to see exponential growth in the next few years. It will grow to $85.64 $ billion in 2029 at a compound annual growth rate (CAGR) of 31.8%. The growth in the forecast period can be attributed to the rapid adoption of chatbots, expanding digital transformation initiatives, demand for multimodal NLU, and growth of conversational AI. Major trends in the forecast period include integration with virtual reality (VR) and augmented reality (AR), advancements in sentiment analysis, advancements in neural network architectures, and IoT integration.
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How has the natural language understanding market evolved, and what factors have shaped its growth?
The growing adoption of artificial intelligence (AI) is expected to propel the growth of natural language understanding going forward. Artificial intelligence (AI) encompasses a broad set of technologies and algorithms that enable machines to simulate human intelligence, including tasks such as problem-solving, learning from data, and making decisions. The expanding adoption of artificial intelligence (AI) can be attributed to several factors: advances in computing power and cloud technology, the availability of extensive data for training algorithms, enhancements in algorithm development and model optimization, and increased investment and funding in AI research and development. Natural language understanding in AI enables systems to interpret and respond to human language, enhancing capabilities in tasks such as sentiment analysis, chatbots, and automated customer service. For instance, in May 2024, according to Eurostat, a Belgium-based governing body, in 2023, large enterprises using AI technologies increased to 30.4%, up from 28.4% in 2021, indicating a notable growth in the adoption of AI solutions among these organizations. Therefore, the growing adoption of artificial intelligence (AI) is driving the growth of the natural language understanding market.
What are the major segments of the natural language understanding market?
The natural language understandingmarket covered in this report is segmented –
1) By Type: Rule-Based, Statistical, Hybrid
2) By Offering: Solutions, Services
3) By Deployment Type: Cloud-Based, On-Premise
4) By Application: Chatbots And Virtual Assistants, Customer Experience Management, Text Analysis, Sentiment Analysis, Interactive Voice Response (IVR) And Message Routing, Data Capture, Machine Translation, Other Applications
5) By Vertical: Banking, Business & Finance And Insurance, Healthcare And Life Science, Retail And E-commerce, IT & Communication Technology And IT & Communication Technology Enabled Services (IT And ITeS), Transport & Logisticsation And Logistics, Government And Public Sector, Media And Entertainment, Manufacturing, Telecommunication, Other Verticals
Subsegments:
1) By Rule-Based: Syntax-based Parsing, Semantic-based Parsing, Grammar Rules, Expert Systems
2) By Statistical: Machine Learning, Probabilistic Models, Deep Learning Algorithms, Statistical Parsing
3) By Hybrid: Rule-Based and Machine Learning Integration, Statistical and Deep Learning Combination, Hybrid Parsing Techniques, Ensemble Methods
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Which companies dominate the natural language understanding market?
Major companies operating in the natural language understanding market are Amazon Web Services Inc., Apple Inc., Google LLC, Microsoft Corporation, Meta Platforms Inc., International Business Machines Corporation, Oracle Corporation, Baidu Inc., SAS Institute Inc., OpenAI, DataBricks Inc, Teradata Corporation, Qualtrics, C3.ai Inc, H2O.ai, SoundHound Inc., Iodine Software LLC, Inbenta, Crayon Data, Haptik Inc.
How will evolving trends contribute to the growth of the natural language understanding market?
Major companies operating in the natural language understanding market are focusing on developing data platforms with large language model capabilities to gain a competitive edge. A large language model is an advanced AI system trained on vast amounts of text data to understand and generate human-like language, useful in diverse applications such as translation and content creation. For instance, in May 2024, International Business Machines Corporation, a US-based provider of hybrid cloud and AI partnered with Salesforce Inc., a US-based provider of enterprise cloud computing solutions, to bring together IBM Watsonx AI and data platform capabilities with the Salesforce Einstein 1 Platform for greater customer choice and flexibility in AI and data deployment. The Einstein 1 Platform allows companies to safely connect any data to build AI-powered apps with low code to deliver customer relationship management (CRM) experiences. This will empower teams to make data-driven decisions and take actions directly in their flow of work.
What are the key regional dynamics of the natural language understanding market, and which region leads in market share?
North America was the largest region in the natural language understanding market in 2024. The regions covered in the natural language understanding market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
What Does The Natural Language Understanding Market Report 2025 Offer?
The natural language understanding market research report from The Business Research Company offers global market size, growth rate, regional shares, competitor analysis, detailed segments, trends, and opportunities.
Natural language understanding (NLU) refers to the branch of artificial intelligence (AI) that focuses on the ability of computers to comprehend and interpret human language in a way that is meaningful and useful. It involves the development of algorithms and models that enable machines to understand the nuances of language, including syntax, semantics, context, and intent.
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