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市場調查報告書
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1441566

超級自動化：市場佔有率分析、行業趨勢和統計數據、成長預測（2024-2029）

Hyperautomation - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2024 - 2029)
出版日期: | 出版商: Mordor Intelligence | 英文 120 Pages | 商品交期: 2-3個工作天內

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簡介目錄

超自動化市場規模預計到 2024 年為 129.5 億美元，預計到 2029 年將達到 319.5 億美元，預測期內（2024-2029 年）年複合成長率為 19.80%。

超級自動化 - 市場

推動全球超級自動化市場成長的主要原因是全球數位化的不斷提高。由於業務自動化需求不斷成長，需要有效後端處理管理的公司最常使用數位流程自動化解決方案。因此，更多的公司正在成立，包括 BFSI 商業、工業和線上零售商。隨著公司更快採用自動化，市場正在進一步成長。透過將傳統上由人類執行的所有重複性手動任務自動化，組織可以顯著加快業務，同時減少錯誤。

主要亮點

  • 製造業擴大採用自動化預計將推動超級自動化市場的發展。此外，思科預測，到 2022 年，全球 285 億台連網裝置中的一半以上將透過機器對機器 (M2M) 連線進行連線。下一代機器人和自動化技術為製造業提供了革命性的機會，可以提高生產力、品質、安全性和成本指標。世界各地的製造商都認知到這一點。此外，多年來機器人自動化支出的增加主要擴大了研究市場的關注範圍。
  • 此外，RPA和人工智慧的應用提高了國家和地區層面的安全標準。公司利用超級自動化來發現安全缺陷並避免災難性事故。許多已開發國家的國防工業目前正在探索超級自動化，以實現安全通訊協定和程序的現代化。將 RPA 和人工智慧涵蓋通訊協定中受益最大的產業是航太。市場參與企業之間策略聯盟的成長趨勢可能會進一步促進全球市場的成長。公司擴大全球影響力並與其他組織合作以協助開發全球市場的情況很常見。
  • 透過超級自動化擴大自動化將重新構想患者照護並改善健康結果。超級自動化透過為現有業務添加功能來增加人類專業知識和資料的價值。透過語音辨識和複雜的演算法，語音生物辨識技術分析患者的語音以確認其身份，並將其與登記表上輸入的資訊進行比較。添加身份驗證層以防止在接觸過程中冒充患者。
  • 此外，語音生物識別的一些應用在疫情期間引起了人們的關注。研究正在探索使用語音生物識別來檢測受感染疾病 -19 影響的患者。例如，2021 年 9 月，人工智慧公司 Biometric Vox 與穆爾西亞 Cruces 醫院和 Virgen de la Arixaca 醫院心臟科主任 Domingo Pascual Figal 合作進行的一項研究表明，語音辨識。 COVID-19感染疾病的成功率為 80%。
  • 相反，由於超級自動化相對較新，因此更多機構需要提供有關此類先進技術的優質培訓。對技能型專業人員的需求和實際供給需要更加平衡。這可能會對全球產業的成長產生重大影響，因為專家需要更多時間才能實際有效地實施超級自動化。全球市場需要教育領域的更多投資者來創造學習機會，以培養超級自動化產業的合格技術人才。

超自動化市場趨勢

機器學習領域預計將推動市場成長

  • 機器學習 (ML) 是人工智慧 (AI) 的一個子領域，它使訓練演算法能夠透過統計方法進行分類和預測，從而揭示資料探勘計劃中的重要見解。這些見解推動應用程式和業務內的決策，並在理想情況下影響關鍵成長指標。這圍繞著演算法、模型和計算複雜性，需要熟練的專業人員來開發這些解決方案。資料科學和人工智慧的進步提高了自動化機器學習的性能。隨著企業意識到這項技術的潛力，其採用率在預測期內可能會增加。公司以訂閱方式銷售自動化機器學習解決方案，讓客戶可以輕鬆使用該技術。此外，它還提供付費使用制的彈性。
  • 人工智慧允許機器獨立於人類進行推理並得出結論。人工智慧的主要目標是創建能夠像人類一樣思考的電腦程式或機器人。被稱為機器學習 (ML) 的人工智慧領域使用學習演算法從過去學習並讓我們進步。汽車可以被編程為遵守交通號誌燈，但它們也可以學習其他汽車和自己的駕駛經驗，以減少道路上發生事故的頻率。因此，作為超自動化的一部分，它成為幫助設備在根據工作流程執行之前學習和思考所需的最重要的技術之一。
  • 據精算師協會 (SOA) 稱，近三分之二的高階主管預計預測分析工具到 2023 年可將組織成本降低 15% 或更多。未來的分析將開啟超級自動化在該產業的應用。
  • 本公司開發新的解決方案或將新功能融入現有產品中，以滿足不同客戶的廣泛需求並增加市場佔有率。例如，2021 年 3 月，Oracle Machine Learning AutoML 使用者介面使初學者和經驗豐富的資料科學家可以更輕鬆地設計和部署機器學習模型。 OML AutoML UI 是 Oracle 自治資料庫上的 Oracle 機器學習的新元件，提供基於瀏覽器的無程式碼介面，只需點擊幾下即可自動執行機器學習建模並減少配置。 OML AutoML UI 是 Oracle 實驗室開發的先進專有技術，並使用 Oracle Machine Learning 的先進資料庫庫內演算法。
  • 此外，企業中機器學習使用案例的增加為超自動化市場的成長創造了機會。例如，根據 Algorithmia 的數據，57% 的受訪者表示，2021 年人工智慧和機器學習的主要用例是改善客戶體驗。人工智慧和機器學習可用於改善多種業務運作。

預計北美將佔據主要市場佔有率

  • 北美是全球超自動化市場的重要地區之一，因為許多重要的市場相關人員都駐紮在那裡。根據 MAPI 的數據，2018 年至 2021 年美國製造業產量預計將成長 2.8%，進一步增加超級自動化和控制技術在該國的採用。此外，最近的關稅上調可能會迫使美國製造商透過自動化以更低的成本生產產品。投資關稅前超級自動化的汽車公司處於領先地位，並已成為其他公司削減成本的藍圖。
  • 汽車經銷商或許能夠透過超級自動化來預測客戶的需求，並且擁有運作良好的供應鏈。借助預測分析，人們可以預測車輛需求的意外變化，系統將立即做出回應。提供減少錯誤和開銷的建議。此系統分析銷售歷史記錄並使用正確的標準來預測需求趨勢並使您的倉庫保持最新狀態。
  • 此外，汽車產量的增加預計將推動該地區所研究的市場。例如，根據 OICA 的數據，2021 年北美生產了超過 1,343 萬輛汽車。北美經濟嚴重依賴汽車生產。更重要的是，2021年，美國汽車產業生產了約917萬輛汽車。
  • 在美國，過去兩年，UPMC 健康計畫負責人發現，透過新實施的 Astrata NLP 輔助工具，他們的工作速度可以提高約 38 倍。作為一個擁有 40 家醫院的醫療系統，我們為三個州的數百萬患者提供服務。 2021 年 2 月，UPMC Enterprises 宣布推出Astrata，這是 UPMC Enterprises 內成立的最新公司。此外，Astrata 的目標是在明年將員工數量增加 30%，並於最近加入了 UPMC。新公司的資料科學家將使用雲端基礎的NLP 來建立工具，使付款人能夠更好地理解非結構化 EHR資料，並提高醫療保健有效性資料和資訊集的品質和人口健康衡量標準，為更準確的評估鋪平道路。
  • 此外，北美生物識別發展的興起預計將在預測期內推動所研究的市場。例如，2021年4月，微軟公司和Nuance Communications Inc.宣布，雙方已就微軟收購Nuance達成最終協議。 Nuance 是一家總部位於麻薩諸塞州伯靈頓的跨國電腦軟體技術公司，銷售語音辨識和人工智慧軟體。 Nuance 為世界各地的企業提供人工智慧專業知識和消費者參與解決方案。解決方案包括互動式語音應答 (IVR)、虛擬助理以及數位和生物識別解決方案。為了創建下一代客戶參與和安全解決方案，該公司將這些知識與 Azure、Teams 和 Dynamics 365 等 Microsoft 雲端的廣度和深度相結合。

超自動化產業概述

全球超級自動化市場適度分散，並且擁有許多公司。兩家公司繼續投資於策略合作夥伴關係和產品開發，以擴大市場佔有率。目前市場上發生的一些事件包括：

2022 年 6 月 - 低程式碼自動化和整合播放器 Tray.io 宣布推出旨在加速企業超級自動化工作的新功能。 Tray.io 使用 Connector Builder 為所有使用者類型提供端對端連接，讓低程式碼開發人員快速、有效率、直覺地按需建立可重複使用連接器。此外，為開發人員提供的全新 Connectivity API 體驗讓他們更輕鬆地將數百個底層端點整合到三個 API 呼叫中。

2022 年 5 月 - 全球數位付款領域的 Visa 與品牌語言最佳化領域的 Phrasee 宣布簽署歐洲獨家協議。這份為期三年的協議是 Visa 對包括歐洲頂級 B2B 金融服務公司在內的客戶進行策略性投資的一部分。 Phrasee 透過其經銷商計劃向 Visa 客戶提供先進的機器學習和自然語言生成技術。

其他福利

  • Excel 格式的市場預測 (ME) 表
  • 3 個月分析師支持

目錄

第1章 簡介

  • 研究假設和市場定義
  • 調查範圍

第2章調查方法

第3章執行摘要

第4章市場洞察

  • 市場概況
  • 產業吸引力－波特五力分析
    • 供應商的議價能力
    • 買方議價能力
    • 新進入者的威脅
    • 替代產品的威脅
    • 競爭公司之間的敵意強度
  • 價值鏈分析
  • 技術簡介
  • COVID-19 對市場的影響

第5章市場動態

  • 市場促進因素
    • 製造領域自動化趨勢不斷發展
    • 數位化不斷滲透，對提高效率和降低營運成本的需求不斷增加。
    • 擴大RPA和AI的應用
  • 市場限制因素
    • 初始實施成本很高
    • 缺乏熟練人才

第6章市場區隔

  • 依技術類型
    • 生物識別
    • 情境感知計算
    • 自然學習生成
    • 聊天機器人
    • 機器人流程自動化
    • 機器學習
  • 按最終用戶產業
    • BFSI
    • 零售
    • 資訊科技與電信
    • 教育
    • 車
    • 製造業
    • 醫療保健和生命科學
  • 按地區
    • 北美洲
    • 亞太地區
    • 歐洲
    • 拉丁美洲
    • 中東和非洲

第7章 競爭形勢

  • 公司簡介
    • Alteryx
    • Automation Anywhere
    • SolveXia
    • Mitsubishi Electric Corporation
    • Catalytic Inc
    • OneGlobe LLC
    • Automate.io
    • UiPath
    • akaBot
    • Rocketbot
    • Simple Fractal

第8章投資分析

第9章市場的未來

簡介目錄
Product Code: 93171

The Hyperautomation Market size is estimated at USD 12.95 billion in 2024, and is expected to reach USD 31.95 billion by 2029, growing at a CAGR of 19.80% during the forecast period (2024-2029).

Hyperautomation - Market

The key reason fueling the growth of the global hyperautomation market is the increase in digitalization worldwide. A digital process automation solution is most frequently used in firms that need effective back-end processing administration due to the rising demand for business automation. As a result, more enterprises are being founded, including BFSI businesses, industrial industries, and online retailers. The market is growing more due to companies adopting automation faster. An organization can significantly speed up operations while lowering errors by automating all of the repetitive manual tasks previously carried out by humans.

Key Highlights

  • The increasing implementation of automation in the manufacturing sector is expected to drive the hyperautomation market. Further, Cisco has predicted that by 2022, over half of the 28.5 billion connected devices on the planet will get connected via machine-to-machine (M2M) connections. The next generation of robotics and automation technologies represents a revolutionary opportunity for manufacturing to improve productivity, quality, safety, and cost metrics. It is something that manufacturers all over the world are aware of. Additionally, rising spending on robotic automation year over year is primarily broadening the study market's focus.
  • Moreover, applications of RPA and AI have enabled the rise of security standards at the national and regional levels. Companies use hyperautomation to spot safety lapses and avert catastrophic mishaps. Many industrialized economies' defense industries are now exploring hyperautomation to modernize their security protocols and procedures. The industry that stands to gain the most from incorporating RPA and AI in its protocols is aerospace. Due to the rising trend in strategic alliances among market participants, there is more potential for increased worldwide market growth. It is common to see businesses partnering with other organizations to expand their global reach and assist the development of the worldwide market.
  • Scaling automation through hyperautomation is rethinking patient care and improving health outcomes. Hyperautomation increases the value of human expertise and data by adding capabilities to existing operations. Through speech recognition and clever algorithms, voice biometrics technology analyzes patients' voices to verify their identities and compare them to the information they gave on their registration forms. It adds a layer of authentication to prevent patient impersonation during any contact.
  • Furthermore, several applications were noted for voice biometrics during the pandemic. Research studies have explored the use of voice biometrics in detecting COVID-19-affected patients. For instance, in September 2021, research undertaken by artificial intelligence company Biometric Vox, in collaboration with the Cruces hospital and Domingo Pascual-Figal, head of Cardiology at Murcia's Virgen de la Arrixaca hospital, outlined the use of voice recognition to help in the detection of COVID-19 cases with 80% success rate.
  • On the Flipside, since hyperautomation is relatively new, more institutions need to provide high-quality training in such advanced technology. The demand and the actual supply of skilled professionals need to be more balanced. Because it will take more time to train professionals before they can practically and effectively execute hyperautomation, this could significantly impact the growth of the global industry. For creating learning opportunities to develop qualified and skilled people in the hyperautomation industry, the global market needs additional investors in the educational sector.

Hyper Automation Market Trends

The Machine Learning Segment is Expected to Drive the Market's Growth

  • Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that enables training algorithms to make classifications or predictions through statistical methods, uncovering critical insights within data mining projects. These insights drive decision-making within applications and businesses, ideally impacting key growth metrics. Since it revolves around algorithms, models, and computational complexity, skilled professionals must develop these solutions. The performance of automated machine learning has advanced due to data science and artificial intelligence improvements. Companies recognize the potential of this technology, and hence its adoption rate is likely to rise over the forecast period. Companies are selling automated machine learning solutions on a subscription basis, making it easier for customers to use this technology. Furthermore, it offers flexibility on a pay-as-you-go basis.
  • AI enables machines to reason independently of humans and comes to their conclusions. The main goal of AI is to create computer programs or robots capable of thinking similarly to humans. A branch of AI known as machine learning, or ML, uses learning algorithms to enable it to learn from the past and advance. People can program a car to follow traffic lights, but it can also learn from other vehicles and from its own driving experience to reduce the frequency of accidents on the road. So, as a part of hyper-automation, it will be one of the most crucial technologies required to help devices learn and think before performing according to the workflow.
  • According to the Society of Actuaries (SOA), nearly two-thirds of executives anticipate that predictive analytic tools will cut organizational costs by 15% or more by 2023. Future analytics will open up hyperautomation applications in this industry.
  • The companies are developing new solutions or incorporating new features in their existing products to cater to a wide range of needs of different customers and to expand their market share. For instance, in March 2021, The Oracle Machine Learning AutoML User Interface made it simple for novice and experienced data scientists to design and deploy machine learning models. OML AutoML UI, a new component of Oracle Machine Learning on Oracle Autonomous Database, provides a no-code browser-based interface that automates machine learning modeling and reduces deployment to a few clicks. OML AutoML UI is an advanced, proprietary technology developed by Oracle Labs that uses Oracle Machine Learning's sophisticated in-database algorithms.
  • Further, the rise in the use case of machine learning for companies will create an opportunity for the hyperautomation market to grow. For instance, according to Algorithmia, the top use cases for artificial intelligence and machine learning in 2021, as per 57% of respondents, are for increasing customer experience. Using AI and ML can improve several business operations.

North America is Expected to Hold a Major Market Share

  • North America is one of the prominent regions for the global hyperautomation market because many essential market players are situated there. According to MAPI, US manufacturing production will increase by 2.8% from 2018 to 2021, further increasing the adoption of hyperautomation and control technologies in the country. Also, the recent increase in tariffs will likely force manufacturers in the US to produce goods at a lower cost, achieved through automation. Auto companies that invested in hyperautomation pre-tariffs are ahead of the game and are the cost-saving blueprint for other companies.
  • Auto dealers may anticipate what client wants with hyper-automation and outfit themselves with a well-functioning supply chain. With the aid of predictive analytics, people can expect any unforeseen changes in vehicle demands, and the system responds promptly. It offers advice on reducing errors and overhead expenditures. The system analyses the sales history and forecasts demand behavior using pertinent criteria to keep the warehouse current.
  • Further, the rise in automotive production is expected to drive the studied market in the region. For instance, according to OICA, In 2021, over 13.43 million automobiles were made in North America. The North American economy is heavily dependent on the production of vehicles. The further point is that in 2021, the US car sector produced about 9.17 million vehicles.
  • In the US, over the last two years, the UPMC Health Plan abstractors found they can work around 38 times faster with the new implementation of Astrata's NLP-assisted tools. As a 40-hospital health system, it serves millions of patients across three states. In February 2021, UPMC Enterprises announced the launch of Astrata, the newest company incubated in UPMC Enterprises. Further, Astrata aims to increase its workforce by 30% over the coming year, and it joined a UPMC in recent years. Data scientists at the new company use cloud-based NLP to build tools enabling the payers to understand unstructured EHR data better, paving the way toward more accurate assessments of quality and population health measurements against Healthcare Effectiveness Data and Information Set.
  • Moreover, the rise of the developments towards biometrics in North America will drive the studied market over the forecasted period. For instance, in April 2021, Microsoft Corp. and Nuance Communications Inc. announced they reached a definitive deal for Microsoft to buy Nuance. Nuance is a Burlington, Massachusetts-based multinational computer software technology firm that sells speech recognition and AI software. Nuance provides AI expertise and consumer engagement solutions to enterprises worldwide. The solutions include Interactive Voice Response (IVR), virtual assistants, and digital and biometric solutions. To create next-generation customer engagement and security solutions, companies will combine this knowledge with the breadth and depth of Microsoft's cloud, including Azure, Teams, and Dynamics 365.

Hyper Automation Industry Overview

The global hyperautomation market is moderately fragmented, with the presence of many companies. The companies continuously invest in strategic partnerships and product developments to gain more market share. Some of the current events in the market are:

June 2022 - Tray.io, a low-code automation and integration player, announced new capabilities designed to accelerate enterprise hyper-automation initiatives. With Connector Builder, Tray.io provides end-to-end connectivity for all user types, enabling low-code developers to create reusable connectors on-demand fast, efficiently, and visually. Additionally, the integration of hundreds of underlying endpoints into only three API calls is made simpler with a new Connectivity API experience for developers.

May 2022 - A global player in digital payments, Visa, and Phrasee, a player in brand language optimization, announced the establishment of an exclusive agreement for Europe. The three-year contract is a component of Visa's strategic investment in its customers, including the top B2B financial services companies in Europe. Phrasee will make its advanced machine learning and natural language generation technologies available to Visa customers through its reseller program.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET INSIGHTS

  • 4.1 Market Overview
  • 4.2 Industry Attractiveness - Porter's Five Forces Analysis
    • 4.2.1 Bargaining Power of Suppliers
    • 4.2.2 Bargaining Power of Buyers
    • 4.2.3 Threat of New Entrants
    • 4.2.4 Threat of Substitute Products
    • 4.2.5 Intensity of Competitive Rivalry
  • 4.3 Value Chain Analysis
  • 4.4 Technology Snapshot
  • 4.5 Impact of COVID-19 on the Market

5 MARKET DYNAMICS

  • 5.1 Market Drivers
    • 5.1.1 Increasing Automation Trends in the Manufacturing Sector
    • 5.1.2 Increased penetration of digitalization, coupled with growing demand for improved efficiency and reduced operating costs
    • 5.1.3 Growing applications of RPA and AI
  • 5.2 Market Restraints
    • 5.2.1 High initial cost of adoption
    • 5.2.2 Lack of skilled personnel

6 MARKET SEGMENTATION

  • 6.1 By Technology Type
    • 6.1.1 Biometrics
    • 6.1.2 Context-Aware Computing
    • 6.1.3 Natural Learning Generation
    • 6.1.4 Chatbots
    • 6.1.5 Robotic Process Automation
    • 6.1.6 Machine Learning
  • 6.2 By End-User Industry
    • 6.2.1 BFSI
    • 6.2.2 Retail
    • 6.2.3 IT & Telecom
    • 6.2.4 Education
    • 6.2.5 Automotive
    • 6.2.6 Manufacturing
    • 6.2.7 Healthcare & Life Science
  • 6.3 By Geography
    • 6.3.1 North America
    • 6.3.2 Asia-Pacific
    • 6.3.3 Europe
    • 6.3.4 Latin America
    • 6.3.5 Middle East and Africa

7 COMPETITIVE LANDSCAPE

  • 7.1 Company Profiles
    • 7.1.1 Alteryx
    • 7.1.2 Automation Anywhere
    • 7.1.3 SolveXia
    • 7.1.4 Mitsubishi Electric Corporation
    • 7.1.5 Catalytic Inc
    • 7.1.6 OneGlobe LLC
    • 7.1.7 Automate.io
    • 7.1.8 UiPath
    • 7.1.9 akaBot
    • 7.1.10 Rocketbot
    • 7.1.11 Simple Fractal

8 INVESTMENT ANALYSIS

9 FUTURE OF THE MARKET