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市場調查報告書

商品編碼

1845804

全球對話式人工智慧平台軟體市場規模（按組件、部署、技術、區域覆蓋和預測）

Global Conversational AI platform Software Market Size By Component, By Deployment, By Technology (Natural Language Processing, Machine Learning, Text-to-Speech ), By Geographic Scope And Forecast

出版日期: 2025年09月25日 | 出版商:

Verified Market Research | 英文 202 Pages | 商品交期: 2-3個工作天內

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簡介目錄

對話式人工智慧平台軟體市場規模及預測

對話式人工智慧平台軟體市場規模預計在 2024 年達到 2.3482 億美元，到 2031 年將達到 5.8976 億美元，2024 年至 2031 年的複合年成長率為 12.2%。

對話式人工智慧平台軟體是一個使機器和使用者能夠以自然、類似人類的方式進行連接的系統。它利用人工智慧 (AI)、自然語言處理 (NLP) 和機器學習 (ML) 來理解、處理和回應語音和文字等對話形式的使用者輸入。這些平台用於創建聊天機器人、虛擬助理和其他人工智慧代理，這些代理可以自動化客戶服務、提供建議並協助完成更高級的任務。透過複製人類交互，它們可以改善用戶體驗、加快交互速度並提高業務效率。

對話式人工智慧平台和軟體的未來令人興奮不已。隨著人工智慧技術的進步，這些平台預計將變得更加直覺和情境感知，從而實現更豐富、更有意義的對話。它們將嵌入醫療、教育、銀行和零售等各行各業，提供客製化服務、預測分析和自動化決策。此外，隨著多模態人工智慧（結合語音、文字和視覺輸入）的出現，對話式人工智慧將改變我們與科技的互動方式，使虛擬助理更加人性化，並可能在個人和職業環境中都變得不可或缺。

全球對話式人工智慧平台軟體市場動態

影響全球對話式人工智慧平台軟體市場的關鍵市場動態是：

關鍵市場促進因素

人工智慧客戶服務解決方案的採用率不斷提高：對有效客戶服務的需求不斷成長，推動了對話式人工智慧技術的採用。預計 70% 的客戶接觸點將包括機器學習應用程式、聊天機器人和行動傳訊等新興技術，高於 2018 年的 15%。這一顯著成長反映了人工智慧對話系統在客戶支援領域的快速應用。

個人化使用者體驗需求日益成長：對話式人工智慧解決方案使企業能夠與客戶進行更具針對性的互動。 91% 的消費者更傾向於選擇能夠識別他們、記住他們並提供相關優惠和推薦的品牌。這項數據凸顯了對話式人工智慧技術能夠成功實現個人化的價值。

降低成本，提升營運效率：對話式人工智慧平台的實施將大幅降低營運成本。光是聊天機器人一項，預計到2022年每年就能節省超過80億美元，而2017年僅需2,000萬美元。如此龐大的成本節約正鼓勵企業採用對話式人工智慧技術，因為它有可能實現日常客戶互動的自動化，最大限度地減少對人工客服的依賴，並提高處理大量詢問的效率。這些平台能夠實現更快的反應速度和全天候服務，在提升客戶體驗的同時降低成本。

主要挑戰

資料隱私與安全：對話式人工智慧平台管理著大量敏感的用戶數據，包括個人資訊和財務資訊。隨著《一般資料保護規範》（GDPR）和《加州消費者隱私法案》（CCPA）等法規日益嚴格，確保合規至關重要。為了避免資料洩露，人工智慧系統需要強大的加密和安全措施。即使是微小的安全漏洞也可能損害公司品牌，削弱用戶信任，並因法律後果而限制其採用。安全問題往往會在擁有強大法律體制的行業（例如醫療保健和金融）引發摩擦。

響應準確性和自然度：為了創造價值，人工智慧系統必須產生準確且自然的響應。訓練不足的模型可能導致誤解、錯誤答案和尷尬的交互作用。挑戰在於使用多樣化、高品質的資料來訓練人工智慧模型，以捕捉人類語言的細微差別。管理多種語言和方言尤其具有挑戰性。不一致的回應會造成糟糕的使用者體驗，阻礙參與度，削弱對平台的信任，並阻礙其在客戶服務、電子商務和其他行業的應用。

與舊有系統整合：許多公司仍在使用未考慮人工智慧設計的舊有系統。將對話式人工智慧平台整合到如此過時的基礎設施中，會帶來巨大的技術挑戰，包括相容性、資料存取和工作流程。公司必須投資系統現代化或創建API來彌合新舊技術之間的差距。此類整合挑戰可能會延遲人工智慧的採用、增加成本並限制其潛在優勢，所有這些都會影響市場成長。

主要趨勢：

自然語言處理 (NLP) 的應用日益廣泛：自然語言處理 (NLP) 的進步正在推動對話式人工智慧平台的日臻完善。 NLP 幫助人工智慧理解人類對話的脈絡、意圖和細微差別，從而實現更準確、更自然的對話。這一趨勢正推動市場向前發展，因為它使人工智慧更易於使用，最大限度地減少溝通誤傳，並提升消費者體驗。通用預測 (GPT) 等深度學習模型的興起進一步增強了這種能力，使人工智慧能夠產生更接近人類的反應，從而實現更廣泛的應用。

多模態人工智慧介面的成長：多模態人工智慧融合了語音、文字和視覺互動，正日益普及。用戶可透過多種管道與人工智慧互動，包括語音助理、聊天機器人和影像識別，從而提升用戶體驗。這一趨勢源於對跨平台和設備、適應性更強的人工智慧解決方案的需求。多模態人工智慧透過提供更具吸引力和互動性的體驗來提升用戶幸福感，從而推動其應用範圍的擴大，尤其是在零售、醫療保健和客戶服務等領域。

語音商務與客戶支援：在 Alexa 和 Google Assistant 等智慧助理的推動下，語音商務的興起正在推動對話式人工智慧平台的需求。企業正在採用人工智慧語音技術來改善客戶服務，並提供更流暢的購買體驗。這一趨勢正推動企業投資對話式人工智慧，以保持競爭力。語音介面支援免持即時互動，這在電子商務中至關重要，它使消費者的互動更加便捷，並帶來更高的轉換率和客戶維繫。

Conversational AI Platform Software is a system that allows machines and users to connect in a natural, human-like way. It uses artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) to comprehend, process, and reply to user inputs in conversational formats like voice or text. These platforms are used to create chatbots, virtual assistants, and other AI-powered agents that can automate customer service, provide recommendations, and even help with more sophisticated tasks. By replicating human dialogue, they improve user experiences, expedite interactions, and increase corporate operational efficiency.

The future prospects of conversational AI platform software is enormous and intriguing. As AI technology progresses, these platforms are projected to become more intuitive and context-aware, allowing for richer and more meaningful conversations. They will be incorporated into a variety of industries, including healthcare, education, banking, and retail, to automate tailored services, predictive analytics, and decision-making. Furthermore, with the emergence of multimodal AI (which combines voice, text, and visual inputs), conversational AI may alter how we engage with technology, making virtual assistants more human-like and necessary in both personal and professional settings.

Global Conversational AI Platform Software Market Dynamics

The key market dynamics that are shaping the global conversational AI platform software market include:

Key Market Drivers:

Increasing Adoption of AI-Powered Customer Service Solutions: The increasing demand for effective customer service is driving the deployment of conversational AI technologies. 70% of customer contacts were predicted to include emerging technologies such as machine learning apps, chatbots, and mobile messaging, up from 15% in 2018. This huge increase reflects the fast adoption of AI-powered conversational systems in customer support.

Rising Demand for Personalized User Experiences: Conversational AI solutions allow organizations to create more tailored interactions with their customers. 91% of consumers prefer to purchase with brands that identify, remember, and make relevant offers and recommendations. This figure emphasizes the value of personalization, which conversational AI technologies can provide successfully.

Cost Reduction and Operational Efficiency: Implementing conversational AI platforms drastically decrease operating expenses, with chatbots alone expected to save enterprises over $8 billion per year by 2022, compared to only $20 million in 2017. This enormous rise in cost savings is prompting organizations to adopt conversational AI technologies due to their potential to automate routine customer interactions, minimize reliance on human agents, and improve efficiency in dealing with high-volume inquiries. These platforms provide faster reaction times and 24/7 service availability, improving customer experience while reducing costs.

Key Challenges:

Data Privacy and Security: Conversational AI platforms manage enormous amounts of sensitive user data, such as personal and financial information. As rules like GDPR and CCPA get more stringent, ensuring compliance is important. To avoid data breaches, AI systems must have strong encryption and security measures. Even modest security flaws can harm a company's brand, erode user trust, and lead to legal ramifications that limit adoption. Security problems frequently cause friction in industries such as healthcare and finance, where strong legal frameworks are in place.

Accuracy and Naturalness of Responses: To deliver value, AI systems must produce accurate and natural-sounding responses. Poorly trained models might result in misinterpretation, erroneous responses, and awkward dialogue. The difficulty is to train AI models with diverse, high-quality data that captures the nuances of human language. It is especially challenging to manage many languages and dialects. Inconsistent responses degrade user experience, impede engagement, and erode trust in the platform, hurting adoption in customer service, e-commerce, and other industries.

Integration with Legacy Systems: Many firms use legacy systems that were not designed with AI in mind. Integrating conversational AI platforms into these older infrastructures presents substantial technological problems, such as compatibility, data access, and workflows. Companies must invest in modernizing their systems or creating APIs to bridge the gap between old and new technologies. These integration issues can cause deployment delays, increase costs, and limit the potential benefits of AI, all of which have an impact on market growth.

Key Trends:

Increased Use of Natural Language Processing (NLP): Advances in Natural Language Processing (NLP) are increasing the sophistication of conversational AI platforms. NLP helps AI to grasp the context, intent, and nuances of human speech, resulting in more accurate and natural conversations. This trend is propelling the market forward by making AI more user-friendly, minimizing miscommunication, and increasing consumer experiences. The rise of deep learning models such as GPT has taken this capability even further, allowing AI to generate more human-like responses, resulting in widespread use.

Growth in Multimodal AI Interfaces: Multimodal AI, which combines speech, text, and visual interactions, is gaining traction. Users can interact with AI through a variety of channels, including voice assistants, chatbots, and image recognition, which improves the user experience. This trend is being driven by the demand for more adaptable and adaptive AI solutions that can work across several platforms and devices. Multimodal AI increases user happiness by providing more engaging and interactive experiences, which leads to better adoption rates, especially in areas such as retail, healthcare, and customer service.

Voice Commerce and Customer Support: The advent of voice commerce, led by smart assistants such as Alexa and Google Assistant, is increasing demand for conversational AI platforms. Businesses are adopting AI-powered voice technologies to improve customer service and deliver more seamless purchasing experiences. This trend is driving corporations to invest in conversational AI in order to remain competitive. Voice interfaces make interactions more convenient for consumers by enabling hands-free and real-time involvement, which is critical in e-commerce, resulting in increased conversion rates and customer retention.

Global Conversational AI Platform Software Market Regional Analysis

Here is a more detailed regional analysis of the global conversational AI platform software market:

North America:

North America continues to dominate the global AI Platform Software market, owing to its strong technological infrastructure, significant expenditures, and widespread usage across numerous sectors. This leadership is supported by major federal funding for AI research, such as the National Science Foundation's $1.9 billion allocation in fiscal year 2023, up from $1.5 billion in 2021. AI adoption is growing, with McKinsey & Company projecting that 56% of North American enterprises would have integrated AI into at least one function by 2021, and the FDA has authorized over 300 AI-enabled medical devices.

North America region benefits from a strong startup ecosystem, as seen by AI startups in the United States obtaining $50 billion in venture capital funding in 2021, up 55% from the previous year. This flood of finance fuels innovation and the creation of new AI applications.

The U.S. Bureau of Labor Statistics predicts that employment in AI-related positions will increase by 15% between 2021 and 2031, highlighting the sector's growing economic importance. In the financial sector, 75% of large US institutions have already implemented AI strategies, highlighting the importance of AI in improving fraud detection, risk management, and tailored services. These factors contribute to North America's sustained leadership and growth in the AI Platform Software market.

Asia-Pacific:

The Asia Pacific region is experiencing the growth in the AI Platform Software market, owing to rapid economic expansion, increased digitalization, and significant government assistance. According to International Data Corporation (IDC), the region's AI industry, excluding Japan, is predicted to develop at a compound annual growth rate (CAGR) of 50.6% between 2020 and 2024, reaching $29.3 billion in 2024. China, India, and Japan are important contributors, with China seeking to grow its AI core industry to more than 1 trillion yuan by 2030 and India expected to achieve a $7.8 billion AI market by 2025. The growing adoption of AI across a variety of areas, including healthcare and fintech, is fueling this expansion.

The Asia Pacific region benefits from significant government programs such as China's New Generation Artificial Intelligence Development Plan and India's National Strategy for Artificial Intelligence, both of which give financial and strategic assistance. The availability of competent talent-China produces 50,000 AI grads every year, while India produces over 2.6 million STEM graduates each year-ensures a strong workforce for AI development. Significant expenditures in AI by both governments and the commercial sector, combined with a high rate of AI adoption in industries such as finance and healthcare, demonstrate the region's growing importance in the global AI scene.

Global Conversational AI Platform Software Market: Segmentation Analysis

The Global Conversational AI platform Software Market is Segmented on the basis of Component, Deployment, Technology, And Geography.

Conversational AI Platform Software Market, By Component

Solutions

Services

Based on Component, the market is bifurcated into Solutions and Services. In the Conversational AI platform software market, the Solutions segment is currently dominant and steadily advancing. This section comprises AI-powered chatbots, virtual assistants, and natural language processing tools that improve consumer engagement and automate procedures. The growing use of these technologies by enterprises looking to increase customer service efficiency and personalization fuels their dominance. The Services section, which includes consultancy, integration, and support services, is rapidly growing as a result of the increased demand for specialized skills and ongoing maintenance to maximize AI deployment and performance.

Conversational AI Platform Software Market, By Deployment

Cloud-Based

On-Premises

Based on Deployment, the market is segmented into Cloud-Based and On-Premises. The Cloud-Based deployment sector is dominant and rapidly expanding. This is largely due to the flexibility, scalability, and cost-effectiveness of cloud solutions, which enable organizations to simply scale their AI capabilities and link them with other cloud services. Cloud-based platforms also benefit from frequent updates and improvements, allowing customers to access the most recent features without making large infrastructure investments. The On-Premises deployment category is increasing at a more gradual pace, owing to higher upfront costs and more complex maintenance requirements. It is still relevant for enterprises with severe data security and compliance requirements who wish to keep their AI systems within their IT infrastructure.

Conversational AI Platform Software Market, By Technology

Natural Language Processing (NLP)

Machine Learning (ML)

Text-to-Speech (TTS)

Based on Technology, the market is divided into Natural Language Processing (NLP), Machine Learning (ML), and Text-to-Speech (TTS). Machine Learning (ML) is a major and constantly expanding segment. ML's widespread use in industries such as finance, healthcare, and e-commerce fuels its domination, allowing firms to automate decision-making, increase predictive analytics, and improve operational efficiency. The segment's growth is being driven by algorithm breakthroughs, increased data availability, and significant expenditures in machine learning technology. Natural Language Processing (NLP) is the fastest-expanding segment. NLP's capacity to allow robots to understand, interpret, and synthesize human language is increasingly being used in applications such as chatbots, virtual assistants, and sentiment analysis. The rapid development of NLP capabilities, fueled by advances in deep learning and huge language models, is hastening its adoption and expansion across a wide range of industries.

Conversational AI Platform Software Market, By Geography

North America

Europe

Asia Pacific

Rest of the world

On the basis of geographical analysis, the Global Conversational AI platform Software Market is classified into North America, Europe, Asia Pacific, and Rest of the world. North America is currently leading the AI platform software market, led by tech behemoths like Google, Microsoft, and IBM. However, Asia Pacific is emerging as the fastest-growing area, thanks to high economic expansion, a big population, and increased digitalization. Countries such as China and India are leading the way, with huge investments in AI research and development.

Key Players

The "Global Conversational AI Platform Software Market" study report will provide valuable insight with an emphasis on the global market. The major players in the market are Microsoft (Azure Bot Service), Google (Dialogflow), IBM (Watson Assistant), Amazon Web Services (AWS), Oracle (Digital Assistant), SAP (Conversational AI), Nuance Communications, Rasa, Kore.ai, Haptik, Avaamo, SoundHound AI, Invoca and Boost.ai. The competitive landscape section also includes key development strategies, market share, and market ranking analysis of the above-mentioned players globally.

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with product benchmarking and SWOT analysis. The competitive landscape section also includes key development strategies, market share, and market ranking analysis of the above-mentioned players globally.

1 INTRODUCTION OF GLOBAL CONVERSATIONAL AI PLATFORM SOFTWARE MARKET

1.1 Introduction of the Market
1.2 Scope of Report
1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

3.1 Data Mining
3.2 Validation
3.3 Primary Interviews
3.4 List of Data Sources

4 GLOBAL CONVERSATIONAL AI PLATFORM SOFTWARE MARKET OUTLOOK

4.1 Overview
4.2 Market Dynamics
- 4.2.1 Drivers
- 4.2.2 Restraints
- 4.2.3 Opportunities
4.3 Porters Five Force Model
4.4 Value Chain Analysis

5 GLOBAL CONVERSATIONAL AI PLATFORM SOFTWARE MARKET, BY TYPE

5.1 Overview
5.2 Cloud-based
5.3 On-Premises

6 GLOBAL CONVERSATIONAL AI PLATFORM SOFTWARE MARKET, BY APPLICATION

6.1 Overview
6.2 Small And Medium Enterprises
6.3 Large Enterprises

7 GLOBAL CONVERSATIONAL AI PLATFORM SOFTWARE MARKET, BY GEOGRAPHY

7.1 Overview
7.2 North America
- 7.2.1 U.S.
- 7.2.2 Canada
- 7.2.3 Mexico
7.3 Europe
- 7.3.1 Germany
- 7.3.2 U.K.
- 7.3.3 France
- 7.3.4 Rest of Europe
7.4 Asia Pacific
- 7.4.1 China
- 7.4.2 Japan
- 7.4.3 India
- 7.4.4 Rest of Asia Pacific
7.5 Rest of the World
- 7.5.1 Latin America
- 7.5.2 Middle East

8 GLOBAL CONVERSATIONAL AI PLATFORM SOFTWARE MARKET COMPETITIVE LANDSCAPE

8.1 Overview
8.2 Company Market Ranking
8.3 Key Development Strategies

9 COMPANY PROFILES

9.1 Acobot
- 9.1.1 Overview
- 9.1.2 Financial Performance
- 9.1.3 Product Outlook
- 9.1.4 Key Developments
9.2 ExecVision
- 9.2.1 Overview
- 9.2.2 Financial Performance
- 9.2.3 Product Outlook
- 9.2.4 Key Developments
9.3 FunnelDash
- 9.3.1 Overview
- 9.3.2 Financial Performance
- 9.3.3 Product Outlook
- 9.3.4 Key Developments
9.4 Gong.io
- 9.4.1 Overview
- 9.4.2 Financial Performance
- 9.4.3 Product Outlook
- 9.4.4 Key Developments
9.5 Activechat
- 9.5.1 Overview
- 9.5.2 Financial Performance
- 9.5.3 Product Outlook
- 9.5.4 Key Developments
9.6 LivePerson
- 9.6.1 Overview
- 9.6.2 Financial Performance
- 9.6.3 Product Outlook
- 9.6.4 Key Developments
9.7 Marchex
- 9.7.1 Overview
- 9.7.2 Financial Performance
- 9.7.3 Product Outlook
- 9.7.4 Key Developments
9.8 LiveChat
- 9.8.1 Overview
- 9.8.2 Financial Performance
- 9.8.3 Product Outlook
- 9.8.4 Key Developments
9.9 Brazen
- 9.9.1 Overview
- 9.9.2 Financial Performance
- 9.9.3 Product Outlook
- 9.9.4 Key Developments
9.10 Continually
- 9.10.1 Overview
- 9.10.2 Financial Performance
- 9.10.3 Product Outlook
- 9.10.4 Key Developments