Industry: IT and Telecommunication
Published Date: December-2024
Format: PPT*, PDF, EXCEL
Delivery Timelines: Contact Sales
Number of Pages: 189
Report ID: PMRREP34995
The global natural language understanding (NLU) market is predicted to reach a value of US$ 13.7 Bn by 2024. It is anticipated to experience a healthy CAGR of 22.5% during the forecast period to reach a size of US$ 56.7 Bn by 2031. The assessment period is projected to have an emphasis on multimodal AI that combines text, image, and voice data. Over 60% of enterprises are estimated to use generative NLU models for content management and automation by 2031.
Growth will likely be driven by an increase in adoption of IoT devices and 5G connectivity. Edge-based NLU deployments are likely to power 70% of conversational AI systems by 2031. Emphasis on low-resource and regional language support will cater to diverse linguistic markets, thereby fostering expansion.
Multilingual systems are likely to dominate 80% of global AI communication solutions by 2030. Text analysis applications in the market is estimated to be 27.8% through the forecast period as businesses require text analysis to extract actionable insights from data to make decisions.
-market-outlook_01.jpg)
Key Highlights of the Industry
|
Market Attributes |
Key Insights |
|
Natural Language Understanding (NLU) Market Size (2024E) |
US$ 13.7 Bn |
|
Projected Market Value (2031F) |
US$ 56.7 Bn |
|
Global Market Growth Rate (CAGR 2024 to 2031) |
22.5% |
|
Historical Market Growth Rate (CAGR 2019 to 2023) |
9.9% |
Natural language understanding (NLU) market in North America is predicted to hold a market share of 43.2% in 2024. The region is home to tech giants like Amazon, Microsoft, Google, and IBM. These companies are leading the development of advanced NLU solutions. The U.S. is at the forefront of AI research, including NLU advancements like GPT-3, BERT, and other transformer models.
Widespread adoption of cloud computing and AI-as-a-Service platforms is bolstering the deployment of NLU applications in industries like finance, healthcare, and customer service. Large enterprises across various sectors in the region are leveraging NLU for automation, data analysis, customer support, and conversational AI.
Asia Pacific natural language understanding (NLU) market is predicted to witness a CAGR of 27.9% through 2031. The region is witnessing rapid advancements in technology along with digital transformation, particularly in leading countries like India, China, and Japan.
Asia Pacific has a robust demand for NLU systems that can understand local dialects and regional languages, thereby making the market more unique. The rapid rise of e-commerce platforms, especially in China and India are driving the demand for AI and NLU in customer service, sales support, and personalization.
The region is witnessing high penetration of smartphones and smart devices, specifically in India and China, thereby rising the demand for NLU-based voice assistants and automate systems.
Businesses are in need of fully integrated NLU systems that can perform a wide range of tasks. These tasks require working together seamlessly to deliver value to end users. Companies are therefore looking for NLU solutions that can be customized to specific industries and use cases.
Solutions are becoming increasingly cloud-based or hybrid, thereby offering scalability and ease of integration across different environments. Rise of AI-as-a-Service (AIaaS) and subscription models further bolster demand owing to their cost-efficiency and ease of integration.
AS companies expand their operations globally, they will require NLU solutions that can understand and process multiple languages and regional dialects. This particular requirement is making complete solutions with multilingual capabilities more appealing to consumers.
Hybrid NLU models enable organizations to choose where specific data is stored and processed. This gives businesses the flexibility to scale their NLU operations without compromising on security and performance. Cloud portion of hybrid NLU systems offer virtually unlimited computational power that is crucial for training and deploying large-scale AI models.
Approximately 60% of enterprises in regulated sectors prefer hybrid cloud models for processing NLU data to maintain control over sensitive information while benefiting from the cloud’s scalability. Hybrid models enable organizations to optimize costs by balancing the requirements for on-premises and cloud infrastructure. They enable enterprises to achieve cost savings of up to 30% to 40% compared to fully on-premise or fully cloud-based solutions.
Potential growth in the global natural language understanding (NLU) market is estimated to be fueled by advancements in generative AI, real-time application, and edge computing. Models like GPT-4 and beyond are predicted to further improve applications in content creation, knowledge management, and summarization. NLU systems are expected to incorporate sentiment and emotion analysis for personalized and user experiences.
The forecast period is projected to witness decrease latency and increased adoption of edge-based NLU systems, especially in IoT devices and real-time applications. There is anticipated to be a focus on energy-efficient AI models to address environmental growth. Increase in internet penetration along with a demand for multilingual NLU is estimated to foster expansion in emerging markets like Africa and Latin America.
-market-insights-and-key-trends.jpg)
The natural language understanding (NLU) market growth witnessed a CAGR of 9.9% during the historical period. Growth during this period was mainly driven by early adoption in consumer service, sentiment analysis, and chatbots. It witnessed a rise of voice assistants, like Alexa, Google Assistant, and Siri) along with advancements in deep learning models like GPT-2 and BERT.
NLU adoption witnessed rapid growth during the period in customer service with chatbots and voice assistants handling 85% of customer interactions. The COVID-19 pandemic accelerated digital transformation, thereby boosting the demand for chatbots and virtual assistants for customer support.
The end of the historical period witnessed growth in multimodal NLU systems that combined text, voice, and image understanding. There was also an increase in the adoption of low-resource language models along with real-time processing capabilities. Voice search and recommendation systems powered by NLU became mainstream, contributing to a 25% increase in e-commerce revenue during the period.
Advances in Generative AI Remains a Key Driver
Generative AI models like OpenAI’s GPT-4 or Google’s LaMDA excel in maintaining multi-turn conversation context, providing superior consumer service experiences. Businesses using AI chatbots reportedly saved up to 30% in customer service operations.
Generative AI uses NLU to produce articles, social media content, and marketing materials tailored to specific audiences. For instance, Jasper.ai that integrates NLU ad NLG is being use by 100,000 businesses for marketing content generation. NLU-driven generative models are being used in financial and legal industries to summarize lengthy documents, thereby saving up to 60% of time on manual processing.
Generative AI uses NLU to craft customized product descriptions, offers, and emails for consumers. Platforms like Netflix and Amazon have witnessed 20% to 30% rise in consumer engagement through personalized recommendations.
Adoption by SMEs to Foster Demand
Small and medium-sized enterprises are adopting NLU technologies to improve operations, enhance customer engagement, and streamline processes. Cloud platforms like Microsoft Azure, Google Cloud, and AWS offer pay-as-you-go NLU services, thereby allowing SMEs to avoid the upfront costs associated with on-premises solutions.
Use of NLU-powered chatbots an virtual assistants allow SMEs to offer 24/7 customer support, enhance customer satisfaction, and handle inquiries efficiently. Nearly, 62% of consumers prefer interacting with businesses through chatbots for basic queries.
SMEs that use chatbots report up to 60% cost savings in consumer service operations. They progressively leverage NLU for automating routine tasks can save up to 30% in operational costs for SMEs, thereby freeing up resources for strategic activities.
Performance Limitations in Real-time Applications
Real-time NLU systems like chatbots and voice assistants need instantaneous processing to maintain user engagement. Large language models require substantial computational resources. Delays of more than 2 to 3 seconds can disrupt conversational flow, resulting in suer frustration.
Survey showcased 70% of users abandon slow or unresponsive AI systems. NLU applications usually rely on large transformer-based models that demand substantial processing power for interference. Running inference on GPT-3 requires 350ms to 500ms per query on state-of-the-art hardware that can scale in multi-user environments.
Real-time NLU applications are often required to handle multiple simultaneous interactions like consumer service platforms. Companies reported a 30% decrease in consumer satisfaction when NLU systems fail to scale during peak usage. Current models struggle to continuously interpret evolving conversational context in real-time applications. For instance,
Customer support bots may fail to recognize user frustration that was expressed earlier in the conversation. Training large AI models like GPT-3 has a carbon footprint equivalent to 700,000 kilometres driven by a car. Real-time inference can multiply energy use based on active users.
Open Source and API Economy to Augment Expansion
Open-source platforms like PyTorch, Hugging Face Transformers, Rasa, and TensorFlow provide pre-built NLU models and tools for developers, thereby decreasing development time and costs. There are around 80% of AI developers leverage open-source tools with NLU being a key application.
NLU APIs provided by companies like AWS Comprehend, OpenAI App, IBM Watson NLU, and Google Cloud Natural Language API enable businesses to integrate sophisticated NLU capabilities without requiring in-depth AI expertise. By 2025, 70% of enterprises are predicted to adopt at least one open-source AI or ML technology, with NLU being a primary focus.
APIs and open-source tools enable developers to quickly prototype and deploy NLU-based applications like chatbots, document summarization, and sentiment analysis. Businesses using NLU APIs can decrease development time by 30% to 50% compared to building solutions from ranch.
Focus on Emotional Intelligence to Enhance Opportunities
Integration of Emotional Intelligence (EI) in natural language understanding NLU systems is a transformative trend that aims to make AI interactions human-like by interpreting and responding to emotions embedded in text or speech. NLU systems analyse text to detect sentiment in order to better understand user emotions.
Advanced NLU models can classify text or speech in emotions like anger, fear, joy, and sadness. They are used in virtual assistants, employee well-being tools, and mental health diagnostics. EI in NLU focuses on contextual analysis to understand subtle cues like humour, frustration, or sarcasm to ensure appropriate responses.
Emotionally intelligent NLU-powdered chatbots can detect frustration or dissatisfaction while escalating issues to human agents. Businesses using emotion-aware chatbots have reported 20% increase in consumer satisfaction with a 15% decrease in churn rates.
Companies in the natural language understanding (NLU) market are continuously investing in advanced AI and machine learning algorithms to enhance the accuracy and efficiency of NLU. They are incorporating transformer models for sophisticated comprehension of the language. Companies are also exploring multilingual capabilities along with cross-domain adaptability to expand their market reach.
Manufacturers are offering tailored solutions for specific industries like finance, education, healthcare, and retail. They are offering customizable APIs and platforms that enable businesses to integrate NLU in their unique workflows.
Companies in the NLU market are partnering with cloud service providers like AWS and Azure to leverage scalable infrastructure. They are also collaborating with academic institutions and AI research labs for cutting-edge developments. Businesses are building alliances with vertical-specific technology vendors to improve domain expertise.
Recent Industry Developments
|
Attributes |
Details |
|
Forecast Period |
2024 to 2031 |
|
Historical Data Available for |
2019 to 2023 |
|
Market Analysis |
US$ Billion for Value |
|
Key Regions Covered |
|
|
Key Market Segments Covered |
|
|
Key Companies Profiled in the Report |
|
|
Report Coverage |
|
|
Customization and Pricing |
Available upon request |
By Offering
By Type
By Application
By End Use
By Region:
To know more about delivery timeline for this report Contact Sales
The market is anticipated to reach a size of US$ 56.7 Bn by 2031.
NLU is at the core of several AI-driven applications as it enables machines to interact with human in a natural way.
NLU transforms unstructured text in insights.
Google LLC, Microsoft Corporation, International Business Machines Corporation, and Amazon.com, Inc. are the prominent companies in the industry.
The market is anticipated to witness a CAGR of 22.5% through the assessment period.
