The machine translation market is projected to exhibit exponential growth by 2027 owing to escalating demand for the process across varied applications. Moreover, spiraling amounts of user-generated content are also expected to support the demand for machine translation through the forecast period.

Additionally, industry players are implementing expansion strategies to enhance their consumer reach as well as market standing across key regions, which has been favorable for industry outlook across the globe. For instance, in June 2021, Zoom, a leading video communications provider, announced the acquisition of Karlsruhe Information Technology Solutions or Kites, a start-up focused on real-time machine translation (MT).

Zoom added that the acquisition would help eliminate gaps in language between users. Notably, the project would entail the development of an AI-operated virtual assistant to offer live translation capabilities in video conferences. As a result, improving technological landscape has created massive growth pockets for product penetration, thereby facilitating business expansion.

For extensive assessment, the machine translation market has been divided on the basis of deployment model, technology, application, and region. With respect to technology, the market has been bifurcated into neural machine translation, statistical machine translation (SMT), hybrid machine translation, rule-based machine translation (RBMT), and example-based machine translation.

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The statistical machine translation (SMT) sub-segment is expected to see substantial growth by 2027, progressing at a notable CAGR through the assessment timeframe. Emerging need to translate large volumes of content on demand is likely to drive SMT technology adoption in the market in the coming years.

In terms of deployment model, the market has been segregated into on-premise and cloud. The machine translation market from the cloud sub-segment is estimated to amass a sizable revenue by the end of the forecast period, growing at a considerable pace over 2021-2027. The flexibility and cost-effectiveness of a cloud-based machine translation model are anticipated to bolster segmental demand through the following years.

From the application perspective, the market has been categorized into BFSI, electronics, automotive, e-commerce, healthcare, military & defense, and IT & telecommunications. The machine translation market from the e-commerce application segment is calculated to expand at a steady pace through the projected timeline to reach a notable valuation by the end of 2027. Surging demand for translation services in banks that have branches in various countries is set to fuel segmental expansion in the future.

The IT & telecom sub-segment is speculated to witness lucrative growth by 2027, registering a robust CAGR through the analysis time period. Rapid development of the telecom infrastructure across the globe would propel the demand for structured cabling to improve connectivity and speed in facilities, which is anticipated to impel segmental demand in the coming years.

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In the regional landscape, the Middle East & Africa machine translation market is slated to showcase stable expansion through the estimated timeline to garner a respectable valuation by 2027. The growing tourism sector in the UAE is projected to expedite the development of public infrastructure facilities, which is anticipated to foster regional market outlook.

Chapter 1 Methodology and Scope

• 1.1 Scope & definitions
• 1.2 Methodology and forecast parameters
• 1.3 Impact of COVID-19
  • 1.3.1 North America
  • 1.3.2 Europe
  • 1.3.3 Asia Pacific
  • 1.3.4 Latin America
  • 1.3.5 MEA
• 1.4 Data sources
  • 1.4.1 Secondary
  • 1.4.2 Primary

Chapter 2 Executive Summary

• 2.1 Machine translation industry 360 degree synopsis, 2017 - 2027
  • 2.1.1 Business trends
  • 2.1.2 Regional trends
  • 2.1.3 Technology trends
  • 2.1.4 Deployment model trends
  • 2.1.5 Application trends

Chapter 3 Machine Translation Industry Insights

• 3.1 Industry segmentation
• 3.2 Impact of coronavirus (COVID-19) pandemic
  • 3.2.1 Global outlook
  • 3.2.2 Regional outlook
    • 3.2.2.1 North America
    • 3.2.2.2 Europe
    • 3.2.2.3 Asia Pacific
    • 3.2.2.4 Latin America
    • 3.2.2.5 MEA
  • 3.2.3 Industry value chain
    • 3.2.3.1 Software developer
    • 3.2.3.2 Cloud service providers
    • 3.2.3.3 Marketing & distribution channel
  • 3.2.4 Competitive landscape
    • 3.2.4.1 Strategy
    • 3.2.4.2 Business growth
• 3.3 Industry ecosystem analysis
  • 3.3.1 Vendor matrix
• 3.4 Technology & innovation landscape
  • 3.4.1 Automated translation
  • 3.4.2 Raw machine translation
  • 3.4.3 AI & machine learning
  • 3.4.4 Cloud computing
• 3.5 Regulatory landscape
• 3.6 Industry impact forces
  • 3.6.1 Growth drivers
    • 3.6.1.1 Rapid demand for localization of marketing strategies & content among businesses
    • 3.6.1.2 Growing need for machine translation services to facilitate communication between trading organizations
    • 3.6.1.3 Increasing need for cost efficient and high-speed translation
    • 3.6.1.4 Investment in AI in North America and Europe
    • 3.6.1.5 Adoption of cloud-based services
    • 3.6.1.6 Rapid adoption of smart devices globally
    • 3.6.1.7 Rising demand to improve customer experience in Asia Pacific and Latin America
  • 3.6.2 Pitfalls and challenges
    • 3.6.2.1 Lack of quality and accuracy
    • 3.6.2.2 Accessibility of open source translation software
• 3.7 Growth potential analysis
• 3.8 Porter's analysis
  • 3.8.1 Supplier power
  • 3.8.2 Buyer power
  • 3.8.3 Threat of new entrants
  • 3.8.4 Threat of substitutes
• 3.9 PESTEL analysis

Chapter 4 Competitive Landscape,

• 4.1 Introduction
• 4.2 Company market share
• 4.3 Competitive analysis of major market players
  • 4.3.1 Google LLC
  • 4.3.2 IBM Corporation
  • 4.3.3 Lionbridge Technologies, Inc.
  • 4.3.4 Microsoft Corporation
  • 4.3.5 AWS (Amazon.com, Inc.)
• 4.4 Competitive analysis of other prominent market players
  • 4.4.1 Moravia IT s.r.o. (RWS Holdings)
  • 4.4.2 Raytheon BBN Technologies Corp. (Raytheon Technologies)
  • 4.4.3 Welocalize, Inc.
  • 4.4.4 Yandex N.V.
  • 4.4.5 Baidu, Inc.
• 4.5 Vendor adoption matrix