Natural Language Generation Market Size & Outlook, 2025-2033

Natural Language Generation Market Size

The global natural language generation market size was valued at USD 1.55 billion in 2024 and is projected to grow from USD 2.01 billion in 2025 to reach USD 15.8 billion by 2033, growing at a CAGR of 29.4% during the forecast period (2025-2033).

Technology developments resulted in the emergence of artificial intelligence (AI), enabling organizations to better understand their customers' activities and behavior. This is a crucial reason for increasing the Natural Language Generation market share.

Natural Language Generation (NLG) is a method in which machines generate human-readable language or text from data inputs. NLG systems examine structured data and transform it into coherent, contextually relevant narratives. Technological advancements resulted in the advent of artificial intelligence (AI), allowing firms to understand better their consumers' activities and behavior, which is a crucial element in boosting market growth. Furthermore, introducing big data and other associated technologies has accelerated the development of the Natural Language Generation Market. Again, the increased demand for fraud detection and anti-money laundering, customer experience management, and predictive maintenance applications will drive market expansion for the NLG.

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Natural Language Generation Market Growth Factor

Increasing Demand for Data-Driven Insights

NLG is valued for converting enormous amounts of data into human-readable narratives. Businesses and industries will place a higher value on the ability to see and analyze data as it is generated in 2024 to assure data system performance. Banks and investment organizations manage massive amounts of financial data, such as market trends, consumer transactions, and economic statistics. NLG technology can evaluate this data and produce meaningful narratives such as reports, summaries, and investment insights. For example, a financial institution can use NLG to provide individualized investment reports for clients that summarize portfolio performance, market trends, and data-driven recommendations.

In contrast, NLG may evaluate patient data, treatment outcomes, and medical research in healthcare to produce thorough patient reports, treatment summaries, and research results. For example, NLG systems can assist clinicians with interpreting medical test findings and providing patient-friendly explanations.

Furthermore, Gartner expects that by 2025, 80% of data and analytics insights will be given in natural language, highlighting the trend toward more intuitive and accessible data-driven insights. This estimate emphasizes the critical role that NLG is predicted to play in providing meaningful narratives from data to a broader audience within enterprises. Companies that use NLG for data-driven insights see increased decision-making efficiency because complicated datasets are transformed into actionable information that experts across departments can understand. As a result, the data-driven world will influence the Natural Language Generation market trend.

Market Restraint

Limited Understanding of the Context and the Ambiguity

One challenge encountered by Natural Language Generation (NLG) systems is the intricate comprehension of context and the management of language that can have several interpretations. The complexity of human language arises from its delicate contextual clues and the presence of many meanings, which poses a challenge for NLG algorithms to comprehend and synthesize text in various contexts accurately. Research published in the Journal of Artificial Intelligence Research revealed that NLG systems frequently have difficulties effectively learning and producing text in situations with several viable interpretations. The study documented cases in which NLG systems generated text that needed more transparency or could have conveyed the intended significance accurately due to contextual ambiguity.

Moreover, industry studies on the deployment of NLG technology highlight the continuous endeavors of researchers and developers to improve the contextual comprehension abilities of NLG systems. Although machine learning and natural language processing have made significant progress, dealing with the subtleties of human language continues to be a challenging and ever-changing subject of study in the field of natural language generation (NLG).

Market Opportunity

Expansion in Customer Experience and Personalization

The emergence of Natural Language Generation (NLG) offers organizations a substantial chance to transform client engagement by delivering tailored and contextually appropriate content. Natural Language Generation (NLG) empowers enterprises to dynamically produce customized messages, suggestions, and product descriptions that adapt to individual clients' distinct preferences and requirements. This customized strategy improves consumer satisfaction, promotes brand loyalty, and boosts conversion rates.

Amazon and Netflix, among other e-commerce sites, utilize Natural Language Generation (NLG) to provide users with exceptionally tailored experiences. When customers explore Amazon products, NLG algorithms examine their browsing history, purchasing patterns, and demographic data to produce customized product suggestions. These suggestions, conveyed in plain language, align with the customer's interests and preferences, resulting in greater involvement and improved conversion rates. McKinsey & Company's analysis reveals that personalization has the potential to provide a return on investment (ROI) that is five to eight times higher than marketing spending while also increasing sales by 10% or more.

Moreover, a survey conducted by Accenture unveiled that 91% of consumers are more inclined to engage in transactions with firms that demonstrate the ability to acknowledge and retain information about and furnish pertinent offers and suggestions. Natural Language Generation (NLG) enables organizations to take advantage of this potential by providing customized content that connects with individual customers, ultimately improving the customer experience and cultivating enduring brand loyalty.

Regional Analysis

North America is the most significant global natural language generation market shareholder and is estimated to grow at a CAGR of 29.6% over the forecast period. The North American region dominated the market in 2023, owing to the region's significant adoption of advanced technologies and increased investment in NLG firms to capitalize on the market's vast potential. For example, In July 2019, NextStage AM, a management firm, announced an investment in Yseop, a provider of NLG solutions. This investment would help the latter expand its international market and accelerate the deployment of NLG solutions tailored to various verticals in the United States. Predictive maintenance is more likely to grow in developed regions such as North America, which aids in expanding the natural language generation (NLG) market. 

Furthermore, there is an increasing adoption of smartphones in the region. Smartphone penetration in the United States has steadily increased in recent years, and it is expected to reach around 92% by 2023. As a result, a large amount of data is generated, increasing demand for advanced NLG analytical solutions and automatically contributing to the growth of the region's natural language generation (NLG) market. 

Asia-Pacific Market Trends

Asia-Pacific is anticipated to exhibit a CAGR of 29.9% over the forecast period. Asia Pacific is expected to have the highest CAGR over the forecast period. Countries such as China, India, Japan, South Korea, and Australia are embracing cloud technology. A growing number of natural language generation startups in India, including vPhrase, Stride.ai, and others, offer promising growth opportunities for the regional market. The increased use of artificial intelligence technologies in banking, financial services, and insurance is expected to drive regional market growth. 

Additionally, government initiatives to promote AI adoption in countries such as Singapore and South Korea are helping to drive the growth of NLG technologies in the region. In June 2023, the Monetary Authority of Singapore (MAS) launched an open-source toolkit to promote responsible AI usage in the financial industry. The Veritas Toolkit version 2.0 enables financial institutions to conduct assessments based on the Fairness, Ethics, Accountability, and Transparency (FEAT) principles. These principles guide financial firms in responsibly incorporating AI and data analytics into their products and services.

Europe Market Trends

The Europe Natural Language Generation (NLG) market is expanding and being adopted across various industries, owing to rising demand for data-driven insights, personalized communication, and content generation process automation. The EU's digital decade goals include bringing all vital public services fully online for businesses and citizens by 2030. In 2023, the EU will invest 4.5 billion in digital transformation, including core digital technology development and critical infrastructure support. The European Union's emphasis on data-driven innovation and digital transformation initiatives hastens the spread of NLG technologies among member states.

In MEA, the adoption of advanced technology and rapid digitization in countries such as the UAE and Saudi Arabia has fueled the growth of the natural language generation (NLG) market. 

Solution Insights

The BFSI sector dominated the market, encompassing banking, financial services, and insurance. Natural Language Generation (NLG) is employed in the BFSI sector to automate financial reporting, create customized customer interactions, and generate investment assessments. The role of Natural Language Generation (NLG) in financial services involves generating narratives that resemble human language from intricate data collections. NLG also assists in compliance reporting and improves client engagement by providing tailored financial insights. Natural Language Generation (NLG) assists financial institutions in automating the process of generating reports, guaranteeing adherence to regulatory obligations. Moreover, using NLG to create customized communication enhances client interactions by delivering comprehensible and enlightening information.

In the Government and Defense sector, Natural Language Generation (NLG) can aid in generating concise and understandable reports, automating documentation procedures, and enhancing communication between organizations. For example, Natural Language Generation (NLG) can create automated summaries of security threats, enhancing the promptness and effectiveness of decision-making in defense and security situations. Natural Language Generation (NLG) streamlines communication between government and defense agencies by automating the production of reports and documents. This results in enhanced information distribution and expedites prompt reactions to security and policy-related issues.

Application Insights

Natural Language Generation (NLG) can produce comprehensive reports and narratives summarizing suspicious behaviors, aiding financial institutions and regulatory bodies in comprehending and addressing prospective hazards. Natural Language Generation (NLG) aids in converting intricate data into understandable insights, enabling expeditious decision-making in fraud prevention and compliance endeavors. Natural Language Generation (NLG) improves the effectiveness of fraud detection and anti-money laundering procedures by automatically producing reports emphasizing irregularities, trends, and possible hazards. These aid financial institutions in proactively addressing fraudulent actions and fulfilling regulatory obligations.

Natural Language Generation (NLG) is crucial in Customer Experience Management since it automates the creation of customer feedback summaries, sentiment analysis reports, and personalized communication for customer interactions. Natural Language Generation (NLG) assists enterprises in comprehending and enhancing the customer experience by providing practical and implementable insights. Natural Language Generation (NLG) improves client Experience Management by automating the process of analyzing and conveying client feedback. Concise and unambiguous reports empower organizations to make well-informed decisions, tackle consumer complaints, and improve overall satisfaction.

Enterprise Insights

Large companies can use NLG to extend communication, automate complex reporting, and enable data-driven decision-making across departments. NLG systems can handle massive volumes of data, generate detailed reports, and run large-scale processes. For example, a multinational organization can use NLG to generate financial reports automatically, maintaining uniformity and accuracy across multiple business units. NLG provides a platform for large companies to automate communication and reporting procedures at scale. Large organizations can increase overall organizational effectiveness by automating report generation. This ensures a standardized and efficient approach to data analysis and communication.

Small and Medium Enterprises (SMEs) can use Natural Language Generation (NLG) to improve communication, automate reporting, and gain actionable insights from data. NLG systems are especially useful for SMEs with limited resources since they allow for creating clear and concise reports without requiring substantial manual work. 

Deployment Mode Insights

On-premise deployment entails installing and operating NLG software on the organization's local servers and infrastructure. This deployment strategy offers enterprises enhanced authority over the NLG system, data protection, and customization. Organizations with particular security or compliance needs often select it to ensure that NLG processes remain within their infrastructure. The on-premise implementation gives enterprises direct control over their Natural Language Generation (NLG) environment. This makes it a great option for firms with strict security and regulatory requirements. This feature enables customization according to specific organizational needs and offers direct control over system resources.

Cloud deployment hosts Natural Language Generation (NLG) solutions on cloud infrastructure. This allows users to access and employ NLG capabilities remotely via the internet. Cloud deployment provides businesses with flexibility, scalability, and accessibility, making it an ideal choice for those who emphasize the ease of implementing and maintaining their systems. Organizations that choose cloud deployment can utilize Natural Language Generation (NLG) services without requiring a large on-site infrastructure. Utilizing cloud deployment for natural language generation (NLG) enables enterprises to quickly establish and scale NLG capabilities without substantial initial investments in hardware or technical proficiency. It provides the capability to access from a distance, work together with others, and smoothly combine with other services based on the Cloud.

List of key players in Natural Language Generation Market

1. Arria NLG
2. Amazon Web Services Inc.
3. IBM Corporation
4. Narrative Science
5. Automated Insights
6. Artificial Solutions
7. YSEOP
8. Conversica Inc.
9. NarrativeWave Inc.
10. Phrase Analytics Solutions Pvt. Ltd
11. Linguastat Inc.

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Recent Developments

• November 2023- IBM and AWS partnered to introduce a novel cloud database service that allows customers to enhance data management for AI workloads.

• June 2023- Yseop, the world's leading AI software business and pioneer in Generative AI, announced the release of Yseop Copilot, the world's most secure content automation solution for regulated sectors and the next iteration of the Augmented Analyst platform.