< Key Hightlight >

 The global Natural Language Processing (NLP) market size to grow from USD  11.6 billion in 2020 to USD 35.1 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 20.3% during the forecast period. Growing demand for cloud-based NLP solutions to reduce overall costs and better scalability and increasing usage of smart devices to facilitate smart enviroments are expected to drive the NLP market growth. The rise in the adoption of NLP-based applications across verticals to enhance customer experience and increase in investments in the healthcare vertical is expected to offer opportunities for NLP vendors. 

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COVID-19 Impact on Global NLP Market

The NLP market is expected to witness a slowdown in 2020 due to the global lockdown. The COVID-19 pandemic has increased the churn rate and shuddered almost every vertical. The lockdown is impacting global manufacturing, and supply chain and logistics as the continuity of operations for various verticals is getting badly impacted. The verticals facing the greatest drawbacks are manufacturing, transportation and logistics, and retail and consumer goods. The availability of essential items is impacted due to the lack of manpower to work on production lines, supply chains, and transportation, although the essential items are exempted from the lockdown. The condition is expected to come under control by early 2021, while the demand for NLP solutions and services is expected to increase due to the increased demand for enhancing customer experiences and building personalized relationships with prospects. Several verticals are already planning to deploy a diverse array of NLP solutions and services for enabling digital transformation initiatives, which address mission-critical processes, improve operations, and differentiate customer viewing experiences. The reduction in operational costs, better customer experiences, and improved customer churn rate, enhanced visibility into processes and operations, and improved real-time decision-making are key business and operational priorities expected to drive the adoption of NLP.

Market Dynamics

Driver: Increasing usage of smart devices to facilitate smart environments

With the advancement in IoT and communication technologies, it has become possible to set up communication between various devices. Such a concept has grown to facilitate a smart home environment and connected vehicles. Technological advancement and digital transformation have changed the way industries perform their operations and communicate with their customers. NLP has proven extremely beneficial in facilitating interactions between users and systems or machines. The smart device can be a mobile phone, an industrial device installed in a plant, or a device operating home/building environment. The rising demand for voice-based solutions interfaces with NLP-based applications offers users enhanced functionalities, such as the verbal command capability with instant query management. . The smart home consists of numerous smart devices, such as thermostats, lights, security and monitoring devices, and climate control devices. Consumers prefer voice mode to give commands to such smart devices.

Restraint: Complexities due to the usage of code-mixed language while implementing NLP solutions

Code-switching or Code-Mixed (CM) language is the alternation of languages within a conversation or utterance and is a common communicative phenomenon that occurs in multilingual communities across the world. Traditionally, CM language has been associated with informal or casual speech. There is evidence that in several societies, such as urban India and Mexico, CM language has become the default code of communication. It has also pervaded written text, especially in computer-mediated communication and social media. NLP tasks, such as normalization, language identification, language modeling, part-of-speech tagging, and dependency parsing, machine translation, and Automatic Speech Recognition (ASR), face issues while working on non-canonical multilingual data in which two or more languages are mixed. The characteristics of mixed data affect tasks in different ways, sometimes by changing the definition (for example, in language identification, the shift from document-level to word-level), and sometimes by creating new lexical and syntactic structures (for example, mixed words that consist of morphemes from two different languages).

Opportunity: Increase in investments in the healthcare vertical

The healthcare industry is generating a huge amount of data nowadays, with an increased pace of digitalization among hospitals and other healthcare premises. Healthcare organizations are betting high on such enormous useful data with the analytics-driven approach. However, such organizations are finding it difficult without sophisticated systems as it is challenging to analyze the text data. However, NLP has appeared as an important technology to extract meaningful insights from such a large volume of data. The consequent demand for effective data management and advanced data analytics has also seen a significant rise in the healthcare industry over the last ten years. Moreover, it is expected that there will be a huge scope of opportunities for NLP technologies in the healthcare industry in the next five years. Personal Health Records (PHRs) are becoming widely accepted, and new initiatives have been taken to make it easier to download and share medical records with different medical and insurance providers. It is expected that this market will further flourish with the growing trend of superior data management and analytics mobile apps. NLP has enabled predictive analytics and helped minimize population health concerns. Moreover, the adoption of NLP in healthcare is rising because of its recognized potential to search, analyze, and interpret massive amounts of patient datasets. By means of advanced medical algorithms, ML, along with NLP technology, has the potential to harness relevant insights from data that was previously hidden in text form. In the current COVID-19 pandemic situation, organizations are using NLP to access the landscape of scientific papers relevant to the coronavirus pandemic.

Challenge: Interoperability and reliability issues while deploying NLP algorithms

AI is implemented through ML using a computer to run specific software that can be trained. ML can help systems process data with the help of algorithms and identify certain features from that dataset. NLP enables computers to understand human language; however, it works on the algorithms designed for specific tasks. There are a lot of system connectivity issues, such as interoperability, inaccessibility, and non-contextual answers. . In July 2017, researchers at the Facebook AI Research (FAIR) lab found that chatbots had deviated from the script and were communicating in a language created by themselves that humans could not understand. Moreover, there are few tasks that can be only performed by humans and not machines and vice versa. For instance, the proliferation of smartphones and social media has introduced a new level of derived languages, which can either be used as jargon or shorten a sentence. NLP techniques would not be able to identify these texts that are grammatically incorrect. Hence, this kind of gap between humans and machines can be considered a challenge in the market. To apply NLP techniques appropriately, a computer system must be able to understand the input in various languages, and similarly, the machine must be able to analyze and assimilate the extended communication.

Among verticals, the healthcare and life sciences segment to grow at the highest CAGR during the forecast period

The NLP market is segmented into the various verticals, particularly BFSI, IT and telecom, retail and ecommerce, healthcare and life sciences, transportation and logistics , government and public sector, energy and utilities, manufacturing, others (education, travel and hospitality, and media and entertainment). The healthcare and life sciences vertical is expected to grow at the highest CAGR during the forecast period. The vertical’s high growth rate can be attributed to the increasing healthcare complexities and growing need for advanced NLP-driven EHRs to extract meaningful insights from unstructured clinical data. To address the COVID-19 impact on the BFSI vertical, the adoption of digital technologies such as video banking facilities, AI-supported tools, and conversational platforms has become essential.

North America to hold the largest market size during the forecast period

The NLP market has been segmented into five regions: North America, Europe, APAC, MEA, and Latin America. Among these regions, North America is projected to hold the largest market size during the forecast period. Improvements in cloud computing platforms, which are now more efficient, affordable, and capable of processing complex information, have led to the growth of inexpensive software development tools and plentiful datasets, which play a vital role in the development of AI technology in the US market. APAC is expected to grow at the highest CAGR during the forecast period on account of the rising awareness and increasing AI investments.

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Key Market Players

The NLP vendors have implemented various types of organic as well as inorganic growth strategies, such as new product launches, product upgradations, partnerships and agreements, business expansions, and mergers and acquisitions to strengthen their offerings in the market. The major vendors in the global NLP market include IBM (US), Microsoft (US), Google (US), AWS (US), Facebook (US), Apple (US), 3M (US), Intel (US), SAS Institute (US), Baidu (China), Inbenta (US), Veritone (US), Dolbey (US), Narrative Science (US), Bitext (Spain), Health Fidelity (US), Linguamatics (UK), Conversica (US), SparkCognition (US), Automated Insights (US), Gnani.ai (India), Niki (India), Mihup (India), Observe.AI (US), Hyro (US), Just AI (England), RaGaVeRa (India). The study includes an in-depth competitive analysis of these key players in the NLP market with their company profiles, recent developments, and key market strategies.

Scope of the Report

This research report categorizes the NLP market based on component, type, application, deployment mode, organization size, vertical, and region.

By component:

• Solutions
  • Platform
  • Software tools
• Services
  • Professional Services
    • Consulting
    • System Integration and  Implementation
    • Support and Maintenance
  • Managed Services

By deployment mode:

• On-premises
• Cloud
  • Public Cloud
  • Private Cloud
  • Hybrid Cloud

By organization size:

• Small and Medium-sized Enterprises (SMEs)
• Large Enterprises

By type:

• Rule-based
• Statistical
• Hybrid

By application:

• Sentiment Analysis
• Data Extraction
• Risk and Threat Detection
• Automatic Summarization
• Content Management
• Language Scoring
• Others (Portfolio Monitoring, HR and Recruiting, and Branding and Advertising).

By vertical:

• BFSI, IT and Telecom,
• Retail and Ecommerce
• Healthcare and Life Sciences
• Transportation and Logistics
• Government and Public Sector
• Energy and Utilities
• Manufacturing
• Others ( Education, Travel and Hospitality, and Media and Entertainment).

By region:

• North America
  • US
  • Canada
• Europe
  • UK
  • Germany
  • France
  • Rest of Europe 
• APAC
  • China
  • India
  • Japan
  • Rest of APAC
• MEA
  • KSA 
  • UAE
  • South Africa
  • Rest of MEA
• Latin America
  • Brazil
  • Mexico
  • Rest of Latin America

Recent Developments:

• In June 2020,IBM launched Watson Works to address the challenges of returning to workplaces. Watson Works would address the challenges, such as managing facilities and optimizing space allocation by using real-time data, prioritizing employee health by enabling employers to make evidence-based decisions, and maximizing the effectiveness of contact tracing by providing organizations with care agents and contact tracers.
• In June 2020, Microsoft partnered with Adaptive Biotechnologies to launch the ImmuneCODE database to share a population-wide immune response of the COVID-19 virus.
• In April 2020, AWS expanded its business in Italy. With this expansion, AWS now spans 76 Availability Zones in 24 geographic regions around the world and announced plans for nine more Availability Zones and three more AWS Regions in Indonesia, Japan, and Spain.
• In June 2019, Intel and Baidu collaborated on Neural Network Processor for training and optimizing the hardware based on 2nd generation Intel Xeon Scalable processors to speed up performance for workloads incorporating NLP, visual applications, speech synthesis, and other scenarios.
• In April 2019, AWS enhanced AI services for NLP, speech-to-text transcription, and image detection. AWS added new features to its AI services, such as Amazon Comprehend. The new features allow users to process texts in the encrypted format as well.   
• In February 2019, Microsoft announced the general availability of the Microsoft Healthcare Bot in the Azure Marketplace. The Microsoft Healthcare Bot is a cloud service that powers conversational AI for healthcare-related processes.
• In October 2018, Inbenta collaborated with Alphanumeric Systems to provide flexibility and adaptability to the customer in any environment by integrating and building upon its AI software into the products and services provided by the consultancy.