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無程式碼人工智慧平台市場 - 全球產業規模、佔有率、趨勢、機會和預測（按組件、組織規模、技術、產業、地區、競爭預測和機會細分，2018-2028 年）

No-Code AI platform Market - Global Industry Size, Share, Trends, Opportunity, and Forecast Segmented By Component, By Organization Size, By Technology, By Industry, By Region, By Competition Forecast & Opportunities, 2018-2028
出版日期: | 出版商: TechSci Research | 英文 182 Pages | 商品交期: 2-3個工作天內

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簡介目錄

2022 年，全球無程式碼人工智慧平台市場估值為 42.1 億美元，預測期內CAGR為 27.89%。在日益數位化的世界中企業不斷變化的需求以及人工智慧（AI）技術的不斷進步的推動下，全球無程式碼人工智慧平台市場目前正在經歷顯著的成長和轉型。無程式碼人工智慧平台在重塑組織開發和部署人工智慧解決方案的方式方面發揮關鍵作用，提供了一種方便用戶使用的方法，使非技術用戶能夠利用人工智慧的力量。隨著企業努力保持競爭力並滿足當今數據驅動環境不斷變化的需求，對無程式碼人工智慧平台的需求正在上升，從而培育了一個充滿活力和競爭的市場，並帶來了充滿希望的機會。

無程式碼人工智慧平台市場成長的主要驅動力之一是人工智慧的民主化。傳統的人工智慧開發通常需要高度專業的技能和對複雜演算法的深刻理解。然而，借助無程式碼人工智慧平台，組織可以彌合技能差距，並使領域專家、業務分析師和公民開發人員能夠在無需大量編碼或資料科學專業知識的情況下創建人工智慧應用程式。人工智慧的民主化使創新民主化，並加速了人工智慧在各行業的採用。

數據驅動決策的興起進一步推動了對無程式碼人工智慧平台的需求。企業意識到資料是一種寶貴的資產，人工智慧可以從這些資料中釋放出可操作的見解。無程式碼人工智慧平台為資料準備、建模和部署提供直覺的介面，使組織能夠利用人工智慧的力量來改善決策、自動化流程並獲得競爭優勢。

市場概況
預測期 2024-2028
2022 年市場規模 42.1億美元
2028 年市場規模 185.9億美元
2023-2028 年CAGR 27.89%
成長最快的細分市場 大型企業
最大的市場 北美洲

此外，無程式碼人工智慧平台正在推動企業提高成本效率和生產力。傳統的人工智慧開發可能是資源密集且耗時的。無程式碼平台簡化了開發流程，減少了建置和部署人工智慧解決方案所需的時間和資源。這使組織能夠更快地將產品推向市場並更快地實現投資回報。

數據驅動的決策：

數據驅動的決策是全球無程式碼人工智慧平台市場蓬勃發展的關鍵驅動力。在日益以數據為中心的世界中，組織認知到利用資料做出明智決策並獲得競爭優勢的價值。無代碼人工智慧平台使各行業的用戶能夠利用資料，而無需廣泛的編碼或資料科學專業知識。在本文中，我們將探討對數據驅動決策的重視如何推動無程式碼人工智慧平台市場的成長。

資料在當代商業營運中日益成長的重要性怎麼強調也不為過。組織從各種來源收集大量資料，包括客戶互動、操作流程和物聯網設備。如果正確分析這些資料，可以提供有價值的見解、為策略提供資訊並推動效率和有效性的提高。然而，釋放資料的全部潛力歷來是一項複雜且資源密集的任務。

這就是無程式碼人工智慧平台的意義。這些平台使人工智慧和資料分析工具的存取民主化，允許更廣泛的用戶（包括業務分析師和領域專家）處理資料並建立人工智慧驅動的解決方案。無程式碼平台的使用者友善介面使具有特定領域知識的個人能夠探索資料、創建預測模型並獲得可操作的見解，而無需廣泛的程式設計技能。

無程式碼人工智慧平台市場的主要驅動力之一是對即時決策的渴望。在當今快節奏的商業環境中，快速做出數據驅動決策的能力是一種競爭優勢。無程式碼人工智慧平台使組織能夠快速開發人工智慧模型和數據驅動的應用程式，確保決策者能夠獲得最新的見解。例如，在電子商務中，這些平台可用於根據客戶的瀏覽和購買歷史記錄即時為客戶提供個人化產品推薦。

此外，自動化需求推動了無程式碼人工智慧平台的全球市場。隨著組織尋求簡化營運並減少人工干預，人工智慧驅動的自動化變得越來越重要。無程式碼平台允許用戶透過創建人工智慧驅動的機器人和應用程式來自動化流程和工作流程，這些機器人和應用程式可以執行資料輸入、客戶支援和內容生成等任務。這種自動化不僅提高了效率，還釋放了人力資源，用於更具策略性的活動。

無程式碼人工智慧平台的可擴展性和多功能性也有助於其成長。這些平台可用於各種行業和功能，從行銷和銷售到金融和醫療保健。組織可以輕鬆地調整它們來應對特定挑戰並抓住機會。此外，隨著資料量的不斷成長，無程式碼人工智慧平台提供了可擴展的解決方案，用於處理大型資料集並從中提取見解。

另一個重要的促進因素是組織內部人工智慧開發民主化的需求。資料科學家和人工智慧專家的需求量很大，但這些領域的熟練專業人員卻很短缺。無程式碼人工智慧平台透過允許業務用戶和領域專家積極參與人工智慧模型的開發來彌補這一技能差距。技術和非技術利益相關者之間的這種合作增強了創新，並確保人工智慧解決方案與業務目標保持一致。

總之，數據驅動的決策是推動全球無程式碼人工智慧平台市場的強大力量。這些平台使組織能夠利用資料進行即時決策、自動化和可擴展性，而無需廣泛的技術專業知識。隨著數據驅動範式的不斷發展，對促進數據驅動洞察和應用的可訪問人工智慧工具的需求只會成長。無程式碼人工智慧平台將在幫助組織充分利用資料潛力並做出更明智、敏捷和有競爭力的決策方面發揮關鍵作用。

成本效益與生產力：

成本效率和生產力提升是推動全球無程式碼人工智慧平台市場快速成長的關鍵驅動力。這些平台為組織提供了強大的工具包，可以簡化流程、降低開發成本並提高生產力，而無需廣泛的編碼或資料科學專業知識。在本文中，我們將探討對成本效率和生產力的追求如何推動無程式碼人工智慧平台市場的擴張。

採用無程式碼人工智慧平台的主要驅動力之一是可以顯著節省成本。傳統的人工智慧開發通常需要對熟練的資料科學家、開發人員和基礎設施進行大量投資。這些成本對許多組織來說可能令人望而卻步，尤其是小型企業和新創公司。無程式碼人工智慧平台使人工智慧開發民主化，使更廣泛的用戶能夠以極低的成本創建人工智慧應用程式。這種成本效率使各種規模的組織都可以使用人工智慧，從而在各個行業中實現其優勢的民主化。

無程式碼人工智慧平台提供的簡化開發流程可以節省時間，進而提高生產力。傳統的人工智慧開發週期可能漫長且資源密集，涉及資料預處理、模型訓練和微調。無程式碼平台提供預先建置範本、拖放介面和自動化工作流程，大大減少了開發人工智慧應用程式所需的時間。開發的加速加快了人工智慧解決方案的上市時間，使組織能夠快速回應不斷變化的市場動態和客戶需求。

此外，無程式碼人工智慧平台使非技術專業人員能夠積極參與人工智慧開發，從而有助於提高生產力。業務分析師、領域專家和公民資料科學家可以利用這些平台來創建適合其特定需求的人工智慧模型和應用程式。技術和非技術團隊之間的這種協作促進了創新，並使組織能夠利用了解其行業和業務流程細微差別的員工的專業知識。

自動化是無程式碼人工智慧平台市場生產力提高的另一個驅動力。這些平台使組織能夠自動執行重複性和勞動密集型任務，從而釋放人力資源用於更具策略性和增值性的活動。例如，在客戶支援方面，使用無程式碼平台建立的人工智慧聊天機器人可以處理日常查詢，讓人工代理專注於複雜的客戶互動。這不僅提高了效率，也提高了客戶滿意度。

無程式碼人工智慧平台的可擴展性也是其提高生產力的關鍵因素。隨著組織的發展和收集更多資料，對可擴展人工智慧解決方案的需求變得至關重要。無程式碼平台提供了擴展人工智慧應用程式的靈活性，以適應不斷成長的資料負載和用戶需求。這種可擴展性確保人工智慧解決方案能夠隨著組織的擴張而繼續創造價值。

此外，市場的全球性有助於提高生產力。無程式碼人工智慧平台是多功能工具，可應用於各行業和職能，包括行銷、金融和醫療保健。組織可以調整這些平台來應對特定挑戰並抓住各自領域的機會。這種多功能性消除了為每個用例客製化解決方案的需要，進一步減少了開發時間和成本。

總之，成本效率和生產力是全球無程式碼人工智慧平台市場的核心驅動力。這些平台為組織提供了一種經濟有效且高效的方式來開發人工智慧應用程式，從而實現人工智慧優勢的民主化。透過減少開發時間和成本，使非技術用戶能夠參與人工智慧開發，並促進自動化和可擴展性，無程式碼人工智慧平台使組織能夠利用人工智慧的變革潛力，並在日益數據驅動的世界中保持競爭力。隨著對人工智慧驅動解決方案的需求不斷上升，這些平台將在重塑組織創新和營運方式方面發揮關鍵作用。

主要市場挑戰

現實世界數據的複雜性：

現實世界資料的複雜性為全球無程式碼人工智慧平台市場帶來了巨大挑戰。雖然這些平台因其承諾簡化人工智慧開發並使更廣泛的受眾能夠使用而受到歡迎，但處理現實世界資料的複雜性帶來了不可低估的障礙。

主要挑戰之一源自於現實世界資料固有的可變性和混亂性。與學術和受控環境中經常使用的原始、結構良好的資料集不同，現實世界的資料充滿了不一致、缺失值、錯誤和雜訊。這種複雜性源自多種來源，包括資料輸入錯誤、感測器不準確、不同的資料格式以及醫療保健、金融和物聯網等領域產生的資料的動態性質。

無程式碼人工智慧平台依靠自動化和預先建構演算法來創建人工智慧模型，在面對如此複雜的資料時它們可能會陷入困境。例如，在醫療保健領域，病患記錄可能包含手寫筆記、不一致的格式或遺失的資訊。這使得無程式碼平台難以提取有意義的見解或創建準確的預測模型。使用者經常發現自己在資料預處理上花費了大量的時間和精力，這可能會抵消無程式碼平台所承諾的一些節省時間的好處。

此外，現實世界的資料可能是高度非結構化的，這帶來了另一層複雜性。自然語言文字、圖像、音訊和非結構化資料格式在社群媒體分析或內容處理等領域很常見。無程式碼人工智慧平台主要擅長處理結構化資料，但在處理非結構化或半結構化資料時可能面臨限制。這些限制可能會阻礙用戶在其應用程式中充分發揮人工智慧潛力的能力。

此外，現實世界的資料通常涉及處理來自多個來源的資料，這可能會使資料整合過程更加複雜。整合挑戰可能包括資料清理、將不同來源的資料與不同的模式對齊，以及確保資料的一致性和品質。無程式碼人工智慧平台的使用者可能會發現自己需要應對這些複雜性，從而導致潛在的挫折感和比最初預期更陡峭的學習曲線。

解決處理複雜的真實資料的挑戰對於無程式碼人工智慧平台兌現其承諾並為不同行業提供有價值的人工智慧解決方案至關重要。為了應對這些挑戰，平台開發人員需要投資增強資料預處理能力，包括資料清理、轉換和標準化。這可以減輕使用者的負擔，提高整體使用者體驗。

此外，開發更好地支援非結構化和半結構化資料分析的工具和功能至關重要。無程式碼平台應該擴展其功能，以滿足處理文字、圖像和其他形式的非結構化資料不斷成長的需求。這可以使用戶能夠挖掘隱藏在非結構化資料來源中的有價值的見解。

此外，提供無縫資料整合功能和流行資料來源的連接器可以簡化處理來自多個來源的資料的過程。這將使用戶能夠更有效地存取和分析資料，最終增強無程式碼人工智慧平台的可用性和有效性。

總之，現實世界資料的複雜性對全球無程式碼人工智慧平台市場構成了重大挑戰。為了充分釋放這些平台的潛力，讓人工智慧更容易被使用，開發者和提供者必須專注於提高資料處理能力，特別是在處理雜亂、非結構化和多來源資料。克服這些挑戰將有助於確保無程式碼人工智慧平台能夠兌現人工智慧開發民主化的承諾，並使廣泛的行業和用戶受益。

數據驅動決策

雖然全球無程式碼人工智慧平台市場正在經歷顯著的成長和轉型，但在此背景下也存在與數據驅動決策相關的挑戰。數據驅動的決策是人工智慧的一個基本面，其挑戰影響無程式碼人工智慧平台的有效性和採用。在這裡，我們探討了全球無程式碼人工智慧平台市場中與數據驅動決策相關的一些關鍵挑戰：

數據品質和可訪問性：

無程式碼人工智慧平台市場中數據驅動決策的主要挑戰之一是確保資料的品質和可存取性。為了讓人工智慧模型提供準確可靠的見解，它們需要高品質、結構良好且相關的資料。然而，組織經常面臨與資料清潔度、完整性和準確性相關的問題。資料品質不足可能會導致有缺陷的預測和不可靠的決策支援。

此外，資料可存取性可能是一個挑戰，因為相關資料可能分散在不同的系統、部門甚至外部來源中。整合和協調不同的資料來源可能是一個複雜且耗時的過程，可能會延遲人工智慧模型在無程式碼平台上的部署。

資料隱私和合規性：

資料隱私和合規性是資料驅動決策中的關鍵考慮因素，特別是在監管嚴格的行業（例如歐洲的醫療保健、金融和 GDPR 合規性）。無代碼人工智慧平台在處理敏感資訊時必須遵守資料保護和隱私法。確保資料匿名、加密並符合相關法規是一項複雜的任務。公司必須實施強大的資料治理策略和安全措施來保護客戶和組織資料。

遵守不斷變化的資料隱私法規可能具有挑戰性，因為法規可能會隨著時間的推移而發生變化，需要對人工智慧模型和資料實踐進行持續監控和調整。在資料實用性與隱私和合規性之間取得平衡仍然是全球無程式碼人工智慧平台市場的一個挑戰。

偏見與公平：

在無程式碼平台上開發的人工智慧模型可能會繼承訓練資料中存在的偏差，這可能導致不公平或歧視性的決策。解決人工智慧演算法中的偏見並確保公平性是一項複雜的挑戰。它需要持續的監控、審計和緩解工作，以識別和糾正模型訓練和部署期間可能出現的偏差。

無程式碼人工智慧平台必須提供工具和功能，以允許使用者評估和減輕其人工智慧模型中的偏見。此外，解決公平性挑戰需要提高用戶意識並進行教育，以了解資料和演算法中可能存在的潛在偏差，並採取積極措施將其最小化。

可解釋性和透明度：

當決策者能夠理解並信任人工智慧模型的輸出時，數據驅動的決策是最有效的。然而，人工智慧模型，尤其是深度學習模型，由於其複雜性通常被認為是「黑盒子」。無程式碼人工智慧平檯面臨的挑戰是提供可解釋性和透明度工具，使用戶能夠了解人工智慧模型如何做出決策。

確保透明度和可解釋性對於監管合規性、道德考慮和用戶信任至關重要。應對這項挑戰需要開發模型可解釋性技術，並從複雜的人工智慧模型中產生人類可理解的見解。

資料整合和可擴展性：

隨著組織的成長和發展，其資料生態系統變得更加複雜。無程式碼人工智慧平台必須能夠與各種資料來源無縫整合，包括遺留系統、雲端資料庫和即時資料流。可擴展性也很重要，因為隨著業務的擴展，組織可能需要處理和分析大量資料集。

挑戰在於提供強大的資料整合功能，同時保持效能和可擴展性。組織應考慮無程式碼人工智慧平台的長期可擴展性和靈活性，以確保它們能夠適應不斷成長的資料量和不斷變化的業務需求。

總之，雖然全球無程式碼人工智慧平台市場在人工智慧開發民主化方面具有顯著優勢，但數據驅動的決策提出了與資料品質、隱私和合規性、偏見和公平性、可解釋性和資料整合相關的挑戰。應對這些挑戰需要整體方法，結合技術解決方案、資料治理實踐和用戶教育，以確保人工智慧驅動的決策準確、公平且值得信賴。

主要市場趨勢

與低程式碼開發整合：

無程式碼和低程式碼的融合：全球無程式碼人工智慧平台市場的一大趨勢是無程式碼和低程式碼開發平台的融合。無程式碼平台專注於讓具有最少編碼經驗的使用者創建人工智慧解決方案，而低程式碼平台則迎合具有一定編碼知識的使用者。這兩種方法的合併產生了一個全面的解決方案，可以容納更廣泛的用戶，從公民開發人員到專業開發人員。

混合開發環境：無程式碼人工智慧平台擴大提供混合開發環境，讓使用者在無程式碼和低程式碼模式之間無縫切換。這種靈活性使用戶能夠從無程式碼方法開始，並在需要時逐漸合併自訂程式碼，從而提供更通用和可擴展的開發體驗。

加速解決方案交付：低程式碼功能與無程式碼人工智慧平台的整合加速了解決方案交付。使用者可以利用預先建置的元件和人工智慧模型，同時保留透過低程式碼腳本自訂和擴充功能的靈活性。這一趨勢有助於加快人工智慧解決方案的開發和部署，縮短組織的上市時間。

人工智慧驅動的自動化：

人工智慧驅動的流程自動化：無程式碼人工智慧平台擴大用於自動化各行業的重複性和基於規則的流程。透過整合人工智慧和機器學習功能，這一趨勢超越了傳統的機器人流程自動化 (RPA)。組織正在利用無程式碼平台來建立人工智慧驅動的機器人和工作流程，可以自動分析資料、做出決策和執行任務。

智慧型文檔處理 (IDP)：使用人工智慧驅動的自動化文件處理是一種日益成長的趨勢。無程式碼 AI 平台配備了 IDP 功能，使組織能夠從發票、合約和電子郵件等文件中提取結構化和非結構化資料。這種趨勢對於提高資料輸入、合規性和文件管理的效率特別有利。

人工智慧增強的客戶服務：無程式碼人工智慧平台使組織能夠透過聊天機器人和虛擬助理實現客戶互動自動化，從而增強客戶服務營運。這些人工智慧驅動的解決方案可以即時回應客戶查詢、個人化互動並簡化支援流程。因此，企業可以提高客戶滿意度並降低支援成本。

產業特定解決方案：

無程式碼人工智慧的垂直化：無程式碼人工智慧平台越來越關注垂直化，針對特定產業或用例客製化解決方案。透過提供行業特定的模板、預先建構的模型和工作流程，這些平台使組織能夠應對其行業內獨特的挑戰和機會。

醫療保健應用：醫療保健產業正在見證無程式碼人工智慧平台在醫學影像分析、病患資料處理和遠距醫療支援等應用中的採用激增。無代碼解決方案使醫療保健專業人員能夠更輕鬆地實施人工智慧驅動的工具並改善患者護理。

金融服務：在金融領域，無程式碼人工智慧平台被用於詐欺偵測、風險評估和演算法交易。這些平台提供適合金融業特定監管要求的合規解決方案。

製造和物聯網：無程式碼人工智慧正在製造業和物聯網 (IoT) 中找到應用。組織可以使用無程式碼平台來開發預測維護模型、品質控制系統和生產最佳化解決方案，而無需豐富的編碼專業知識。

細分市場洞察

產品類型見解

機上連接 (IFC) 領域在全球無程式碼人工智慧平台 (IFEC) 市場中佔據主導地位。

IFC 是指為飛機上的乘客提供網路連線。這使得乘客能夠與工作、家人和朋友保持聯繫，並在旅行時存取他們最喜歡的線上內容和服務。

由於多種因素，國際金融公司市場正在快速成長，其中包括：

乘客對高速網路存取的需求不斷增加

串流影音和音訊服務的採用率不斷提高

擴大使用行動裝置進行工作和娛樂

擴大航空公司和服務提供者提供的 IFC 解決方案的可用性。

區域洞察

由於多種因素，北美成為全球人工智慧（AI）感測器市場的主導地區，包括：

主要人工智慧感測器公司實力強勁：北美是一些全球領先的人工智慧感測器公司的所在地，例如英特爾、高通和 ADI 公司。這些公司處於人工智慧感測器創新和開發的前沿。

各產業對人工智慧感測器的高需求：人工智慧感測器在北美廣泛應用於消費性電子、汽車、醫療保健和製造等產業。這些產業對人工智慧感測器的需求很高，預計未來幾年將會成長。

人工智慧感測器的早期採用：北美企業和組織是人工智慧感測器的早期採用者。這讓他們在人工智慧感測器市場擁有先發優勢。

完善的AI感測器研發基礎設施：北美擁有完善的AI感測器研發基礎設施。這包括資金、合格研究人員和測試設施的可用性。

預計未來幾年北美仍將是全球人工智慧感測器市場的主導地區。然而，由於該地區企業和組織對人工智慧感測器的需求不斷增加以及該地區人工智慧感測器公司數量的不斷增加，預計亞太地區將以最快的速度成長。

以下是北美如何使用人工智慧感測器的一些範例：

消費性電子：AI感測器應用於北美的各種消費性電子設備，如智慧型手機、智慧電視、智慧音箱等。例如，人工智慧感測器在智慧型手機中用於臉部辨識、手勢辨識和擴增實境。智慧電視中使用人工智慧感測器進行語音控制和內容推薦。人工智慧感測器用於智慧揚聲器中，用於語音控制和音樂串流。

汽車：人工智慧感測器在北美被用於各種汽車應用，例如先進駕駛輔助系統（ADAS）和自動駕駛汽車。例如，人工智慧感測器

目錄

第 1 章：產品概述

  • 市場定義
  • 市場範圍
    • 涵蓋的市場
    • 研究年份
    • 主要市場區隔

第 2 章：研究方法

  • 研究目的
  • 基線方法
  • 主要產業夥伴
  • 主要協會和二手資料來源
  • 預測方法
  • 數據三角測量與驗證
  • 假設和限制

第 3 章：執行摘要

第 4 章：客戶之聲

第 5 章：全球無程式碼人工智慧平台市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按組件（無代碼 AI 平台、服務）
    • 依組織規模（大型企業、中小企業）
    • 按技術（資料準備與整合工具、預測分析、自動機器學習 (AutoML)、自然語言處理、電腦視覺、其他）
    • 按行業（BFSI、IT 與電信、能源與公用事業、零售與電子商務、醫​​療保健、製造業、政府、教育、其他）
    • 按地區
  • 按公司分類 (2022)
  • 市場地圖

第 6 章：北美無程式碼人工智慧平台市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按組件
    • 按組織規模
    • 依技術
    • 按行業分類
    • 按國家/地區
  • 北美：國家分析
    • 美國
    • 墨西哥

第 7 章：亞太地區無程式碼人工智慧平台市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按組件
    • 按組織規模
    • 依技術
    • 按行業分類
    • 按國家/地區
  • 亞太地區：國家分析
    • 中國
    • 印度
    • 日本
    • 韓國
    • 澳洲

第 8 章：歐洲無程式碼人工智慧平台市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按組件
    • 按組織規模
    • 依技術
    • 按行業分類
    • 按國家/地區
  • 歐洲：國家分析
    • 德國
    • 英國
    • 法國
    • 義大利
    • 西班牙

第 9 章：南美洲無程式碼人工智慧平台市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按組件
    • 按組織規模
    • 依技術
    • 按行業分類
    • 按國家/地區
  • 南美洲：國家分析
    • 巴西
    • 阿根廷
    • 哥倫比亞

第 10 章：中東和非洲無程式碼人工智慧平台市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按組件
    • 按組織規模
    • 依技術
    • 按行業分類
    • 按國家/地區
  • 中東和非洲：國家分析
    • 沙烏地阿拉伯
    • 南非
    • 阿拉伯聯合大公國

第 11 章：市場動態

  • 促進要素
  • 挑戰

第 12 章：市場趨勢與發展

第 13 章：公司簡介

  • 微軟公司
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 谷歌有限責任公司
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 國際商業機器公司
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • Salesforce.com 公司
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 亞馬遜網路服務公司
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 亞庇公司
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 外部系統
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 門迪克斯有限公司
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 派加系統公司
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 快速基地有限公司
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services

第 14 章：策略建議

關於我們及免責聲明

簡介目錄
Product Code: 16790

The Global No-Code AI platform Market was valued at USD 4.21 Billion in 2022 and is growing at a CAGR of 27.89% during the forecast period. The Global No-Code AI Platform Market is currently experiencing a significant surge and transformation, driven by the evolving demands of businesses in an increasingly digital world and the continuous advancements in artificial intelligence (AI) technology. No-Code AI platforms are playing a pivotal role in reshaping how organizations develop and deploy AI-powered solutions, offering a user-friendly approach that empowers non-technical users to harness the power of AI. As businesses strive to stay competitive and meet the evolving needs of today's data-driven landscape, the demand for No-Code AI platforms is on the rise, fostering a dynamic and competitive market with promising opportunities.

One of the primary drivers behind the growth of the No-Code AI Platform Market is the democratization of AI. Traditional AI development often required highly specialized skills and a deep understanding of complex algorithms. However, with No-Code AI platforms, organizations can bridge the skills gap and empower domain experts, business analysts, and citizen developers to create AI applications without extensive coding or data science expertise. This democratization of AI democratizes innovation and accelerates AI adoption across industries.

The rise of data-driven decision-making is further fueling the demand for No-Code AI platforms. Businesses recognize that data is a valuable asset, and AI can unlock actionable insights from this data. No-Code AI platforms provide intuitive interfaces for data preparation, modeling, and deployment, enabling organizations to harness the power of AI to improve decision-making, automate processes, and gain a competitive advantage.

Market Overview
Forecast Period2024-2028
Market Size 2022USD 4.21 Billion
Market Size 2028USD 18.59 billion
CAGR 2023-202827.89%
Fastest Growing SegmentLarge Enterprises
Largest MarketNorth America

Additionally, No-Code AI platforms are driving cost-efficiency and productivity gains for businesses. Traditional AI development can be resource-intensive and time-consuming. No-Code platforms streamline the development process, reducing the time and resources required to build and deploy AI solutions. This enables organizations to achieve faster time-to-market and realize a return on investment more quickly.

No-Code AI platforms are also promoting innovation by fostering a culture of experimentation and rapid prototyping. Businesses can quickly iterate and test AI models and applications, allowing for the exploration of new use cases and the adaptation of AI to evolving business needs.

Moreover, the No-Code AI Platform Market is witnessing the integration of AI into various business functions, from customer service and marketing to finance and supply chain management. No-Code AI platforms offer a wide range of AI capabilities, such as natural language processing, computer vision, and predictive analytics, making AI accessible for diverse business applications.

Security and compliance considerations are also shaping the No-Code AI Platform Market. Organizations must ensure that their AI solutions built on No-Code platforms adhere to data privacy regulations and cybersecurity best practices. No-Code AI platforms are responding to these concerns by incorporating robust security features and compliance tools.

Continuous innovation in No-Code AI technology is driving market competition. Established industry players and startups are investing in research and development to deliver user-friendly, feature-rich platforms that cater to a wide range of industries and use cases. Partnerships with data providers, cloud providers, and industry-specific experts are common strategies to expand the capabilities of No-Code AI platforms and offer organizations a powerful and customizable AI toolkit.

In conclusion, the Global No-Code AI Platform Market is flourishing due to the democratization of AI, data-driven decision-making, cost-efficiency gains, innovation promotion, security and compliance considerations, and ongoing technological advancements. No-Code AI platforms are at the forefront of accelerating AI adoption and helping organizations harness the full potential of AI without the need for extensive coding or data science expertise. As businesses continue to invest in No-Code AI platforms to drive innovation and achieve competitive advantages, the market is poised for sustained growth and evolution..

Key Market Drivers

Democratization of AI

The democratization of AI is a powerful force driving the global market for No-Code AI platforms. This transformational trend represents the widening access to artificial intelligence capabilities, enabling individuals and organizations with varying levels of technical expertise to harness the potential of AI without the need for extensive coding or programming skills. In this article, we will explore the significance of AI democratization and its impact on the burgeoning No-Code AI platform market.

Traditionally, AI development required specialized knowledge in machine learning, data science, and programming languages such as Python or R. This high barrier to entry limited the adoption of AI technologies to a select group of experts and well-funded organizations. However, the democratization of AI has changed this landscape dramatically. No-Code AI platforms empower a broader audience, including business analysts, domain experts, and citizen developers, to create and deploy AI solutions with relative ease.

One of the primary drivers of the No-Code AI platform market is the growing demand for AI-powered solutions across various industries. Businesses recognize the competitive advantages that AI can offer in terms of automation, predictive analytics, and enhanced decision-making. No-Code AI platforms bridge the skills gap, allowing organizations to quickly develop AI applications tailored to their specific needs. For example, in healthcare, medical professionals can use No-Code AI platforms to create diagnostic tools or predictive models without extensive coding expertise.

Moreover, the democratization of AI contributes to innovation and creativity. It fosters a culture of experimentation and exploration, enabling individuals and teams to ideate and prototype AI solutions rapidly. By removing the technical complexities associated with AI development, No-Code platforms empower users to focus on problem-solving and innovation, rather than getting bogged down in coding details.

The global market for No-Code AI platforms is further fueled by the rise of citizen data scientists. These are individuals within organizations who have domain expertise but lack formal data science training. No-Code AI platforms empower citizen data scientists to leverage their industry knowledge and craft AI solutions to address specific challenges. This trend enhances collaboration between technical and non-technical stakeholders within organizations, leading to more holistic and effective AI implementations.

The scalability and cost-effectiveness of No-Code AI platforms also contribute to their rapid adoption. Traditional AI development often requires substantial investments in infrastructure, skilled personnel, and time-consuming development cycles. No-Code platforms streamline the AI development process, reducing costs and time-to-market significantly. Small and medium-sized enterprises (SMEs), in particular, benefit from these platforms, as they can compete on a level playing field with larger enterprises in terms of AI adoption.

Additionally, the democratization of AI through No-Code platforms aligns with the broader movement toward responsible AI. By making AI development more accessible, these platforms enable a wider range of stakeholders to participate in the ethical and fair deployment of AI technologies. This inclusivity helps ensure that AI solutions are developed with diverse perspectives and that biases and ethical concerns are more likely to be identified and addressed.

In conclusion, the democratization of AI is a driving force behind the global market for No-Code AI platforms. These platforms empower a diverse range of users to create and deploy AI solutions, fostering innovation, scalability, and cost-effectiveness. As AI continues to permeate various industries, the democratization trend will play a pivotal role in shaping the future of AI adoption, making it more accessible, ethical, and beneficial to society at large. The No-Code AI platform market is poised for substantial growth as organizations seek to unlock the transformative potential of AI without the need for extensive technical expertise..

Data-Driven Decision-Making:

Data-driven decision-making is a key driver behind the burgeoning global market for No-Code AI platforms. In an increasingly data-centric world, organizations recognize the value of harnessing data to make informed decisions and gain a competitive edge. No-Code AI platforms empower users across various industries to leverage data without the need for extensive coding or data science expertise. In this article, we will explore how the emphasis on data-driven decision-making is fueling the growth of the No-Code AI platform market.

The growing importance of data in contemporary business operations cannot be overstated. Organizations collect vast amounts of data from various sources, including customer interactions, operational processes, and IoT devices. This data, when properly analyzed, can provide valuable insights, inform strategies, and drive improvements in efficiency and effectiveness. However, unlocking the full potential of data has historically been a complex and resource-intensive task.

Herein lies the significance of No-Code AI platforms. These platforms democratize access to AI and data analytics tools, allowing a broader range of users, including business analysts and domain experts, to work with data and build AI-powered solutions. The user-friendly interface of No-Code platforms empowers individuals with domain-specific knowledge to explore data, create predictive models, and derive actionable insights without the need for extensive programming skills.

One of the primary drivers of the No-Code AI platform market is the desire for real-time decision-making. In today's fast-paced business environment, the ability to make quick, data-driven decisions is a competitive advantage. No-Code AI platforms enable organizations to develop AI models and data-driven applications rapidly, ensuring that decision-makers have access to up-to-date insights. For example, in e-commerce, these platforms can be used to personalize product recommendations for customers in real-time based on their browsing and purchase history.

Furthermore, the global market for No-Code AI platforms is fueled by the demand for automation. As organizations seek to streamline operations and reduce manual intervention, AI-driven automation is becoming increasingly important. No-Code platforms allow users to automate processes and workflows by creating AI-driven bots and applications that can perform tasks such as data entry, customer support, and content generation. This automation not only improves efficiency but also frees up human resources for more strategic activities.

The scalability and versatility of No-Code AI platforms also contribute to their growth. These platforms can be used in various industries and functions, from marketing and sales to finance and healthcare. Organizations can easily adapt them to address specific challenges and seize opportunities. Additionally, as the volume of data continues to grow, No-Code AI platforms provide a scalable solution for handling and extracting insights from large datasets.

Another significant driver is the need for democratizing AI development within organizations. Data scientists and AI experts are in high demand, but there is a shortage of skilled professionals in these fields. No-Code AI platforms bridge this skills gap by allowing business users and domain experts to actively participate in the development of AI models. This collaboration between technical and non-technical stakeholders enhances innovation and ensures that AI solutions are aligned with business objectives.

In conclusion, data-driven decision-making is a powerful force driving the global market for No-Code AI platforms. These platforms empower organizations to leverage data for real-time decision-making, automation, and scalability without the need for extensive technical expertise. As the data-driven paradigm continues to evolve, the demand for accessible AI tools that facilitate data-driven insights and applications will only grow. No-Code AI platforms are poised to play a pivotal role in enabling organizations to harness the full potential of their data and make more informed, agile, and competitive decisions.

Cost-Efficiency and Productivity:

Cost-efficiency and productivity gains are pivotal drivers fueling the rapid growth of the global No-Code AI platform market. These platforms offer organizations a powerful toolkit to streamline processes, reduce development costs, and boost productivity without the need for extensive coding or data science expertise. In this article, we'll explore how the pursuit of cost-efficiency and productivity is propelling the expansion of the No-Code AI platform market.

One of the primary drivers behind the adoption of No-Code AI platforms is the potential for significant cost savings. Traditional AI development often demands substantial investments in skilled data scientists, developers, and infrastructure. These costs can be prohibitive for many organizations, particularly smaller businesses and startups. No-Code AI platforms democratize AI development, enabling a broader range of users to create AI applications at a fraction of the cost. This cost efficiency makes AI accessible to organizations of all sizes, democratizing its benefits across industries.

The streamlined development process offered by No-Code AI platforms translates into time savings, driving productivity gains. Traditional AI development cycles can be lengthy and resource-intensive, involving data preprocessing, model training, and fine-tuning. No-Code platforms provide pre-built templates, drag-and-drop interfaces, and automated workflows, dramatically reducing the time required to develop AI applications. This acceleration in development leads to faster time-to-market for AI solutions, enabling organizations to respond swiftly to changing market dynamics and customer needs.

Moreover, No-Code AI platforms contribute to increased productivity by empowering non-technical professionals to participate actively in AI development. Business analysts, domain experts, and citizen data scientists can leverage these platforms to create AI models and applications tailored to their specific needs. This collaboration between technical and non-technical teams fosters innovation and enables organizations to tap into the expertise of employees who understand the nuances of their industries and business processes.

Automation is another driver of productivity gains in the No-Code AI platform market. These platforms allow organizations to automate repetitive and labor-intensive tasks, freeing up human resources for more strategic and value-added activities. For instance, in customer support, AI-powered chatbots built using No-Code platforms can handle routine inquiries, leaving human agents to focus on complex customer interactions. This not only enhances efficiency but also improves customer satisfaction.

The scalability of No-Code AI platforms is also a critical factor in their ability to drive productivity. As organizations grow and collect larger volumes of data, the need for scalable AI solutions becomes paramount. No-Code platforms provide the flexibility to scale AI applications to accommodate increasing data loads and user demands. This scalability ensures that AI solutions can continue to deliver value as organizations expand.

Furthermore, the global nature of the market contributes to productivity improvements. No-Code AI platforms are versatile tools that can be applied across various industries and functions, including marketing, finance, and healthcare. Organizations can adapt these platforms to address specific challenges and seize opportunities in their respective domains. This versatility eliminates the need for custom-built solutions for each use case, further reducing development time and costs.

In conclusion, cost-efficiency and productivity are central drivers of the global No-Code AI platform market. These platforms offer organizations a cost-effective and efficient way to develop AI applications, democratizing access to AI benefits. By reducing development time and costs, enabling non-technical users to participate in AI development, and facilitating automation and scalability, No-Code AI platforms empower organizations to harness the transformative potential of AI and stay competitive in an increasingly data-driven world. As the demand for AI-driven solutions continues to rise, these platforms are poised to play a pivotal role in reshaping how organizations innovate and operate..

Key Market Challenges

Complexity of Real-World Data:

The complexity of real-world data poses a substantial challenge in the Global No-Code AI Platform Market. While these platforms have gained popularity for their promise of simplifying AI development and making it accessible to a wider audience, the intricacies of dealing with real-world data present hurdles that cannot be underestimated.

One of the primary challenges stems from the inherent variability and messiness of real-world data. Unlike the pristine, well-structured datasets often used in academic and controlled environments, real-world data is riddled with inconsistencies, missing values, errors, and noise. This complexity arises from a multitude of sources, including data entry errors, sensor inaccuracies, varying data formats, and the dynamic nature of data generated in fields like healthcare, finance, and IoT.

No-Code AI platforms rely on automation and pre-built algorithms to create AI models, and they may struggle when confronted with such data complexities. For instance, in the healthcare sector, patient records can contain handwritten notes, inconsistent formatting, or missing information. This makes it challenging for No-Code platforms to extract meaningful insights or create accurate predictive models. Users often find themselves spending a significant amount of time and effort in data preprocessing, which can negate some of the promised time-saving benefits of No-Code platforms.

Furthermore, real-world data can be highly unstructured, which poses another layer of complexity. Natural language text, images, audio, and unstructured data formats are common in fields like social media analysis or content processing. No-Code AI platforms primarily excel at handling structured data but may face limitations when working with unstructured or semi-structured data. These limitations can hinder users' ability to harness the full potential of AI in their applications.

Additionally, real-world data often involves dealing with data from multiple sources, which can further complicate the data integration process. Integration challenges may include data cleaning, aligning data from different sources with varying schemas, and ensuring data consistency and quality. Users of No-Code AI platforms may find themselves needing to navigate these complexities, leading to potential frustrations and a steeper learning curve than initially anticipated.

Addressing the challenge of handling complex, real-world data is crucial for No-Code AI platforms to deliver on their promise and provide valuable AI solutions across diverse industries. To mitigate these challenges, platform developers need to invest in enhancing data preprocessing capabilities, including data cleaning, transformation, and normalization. This can reduce the burden on users and improve the overall user experience.

Moreover, developing tools and features that better support the analysis of unstructured and semi-structured data is essential. No-Code platforms should expand their capabilities to accommodate the growing demand for working with text, images, and other forms of unstructured data. This can empower users to tap into the valuable insights hidden within unstructured data sources.

Furthermore, providing seamless data integration capabilities and connectors to popular data sources can simplify the process of working with data from multiple origins. This would enable users to access and analyze data more efficiently, ultimately enhancing the usability and effectiveness of No-Code AI platforms.

In conclusion, the complexity of real-world data represents a significant challenge in the Global No-Code AI Platform Market. To fully unlock the potential of these platforms and make AI more accessible, developers and providers must focus on improving data handling capabilities, particularly in dealing with messy, unstructured, and multi-source data. Overcoming these challenges will be instrumental in ensuring that No-Code AI platforms can deliver on their promise of democratizing AI development and benefiting a broad range of industries and users..

Data-Driven Decision-Making

While the Global No-Code AI Platform Market is experiencing significant growth and transformation, there are also challenges associated with data-driven decision-making in this context. Data-driven decision-making is a fundamental aspect of AI, and its challenges impact the effectiveness and adoption of No-Code AI platforms. Here, we explore some of the key challenges related to data-driven decision-making in the Global No-Code AI Platform Market:

Data Quality and Accessibility:

One of the primary challenges in data-driven decision-making within the No-Code AI Platform Market is ensuring the quality and accessibility of data. For AI models to provide accurate and reliable insights, they require high-quality, well-structured, and relevant data. However, organizations often face issues related to data cleanliness, completeness, and accuracy. Inadequate data quality can lead to flawed predictions and unreliable decision support.

Additionally, data accessibility can be a challenge, as relevant data may be dispersed across different systems, departments, or even external sources. Integrating and harmonizing disparate data sources can be a complex and time-consuming process, potentially delaying the deployment of AI models on No-Code platforms.

Data Privacy and Compliance:

Data privacy and compliance are critical considerations in data-driven decision-making, especially in industries with strict regulations (e.g., healthcare, finance, and GDPR compliance in Europe). No-Code AI platforms must adhere to data protection and privacy laws while handling sensitive information. Ensuring that data is anonymized, encrypted, and compliant with relevant regulations is a complex task. Companies must implement robust data governance policies and security measures to protect customer and organizational data.

Complying with evolving data privacy regulations can be challenging, as regulations may change over time, requiring ongoing monitoring and adjustments to AI models and data practices. Balancing data utility with privacy and compliance remains a challenge in the Global No-Code AI Platform Market.

Bias and Fairness:

AI models developed on No-Code platforms may inherit biases present in the training data, which can lead to unfair or discriminatory decisions. Addressing bias and ensuring fairness in AI algorithms is a complex challenge. It requires continuous monitoring, auditing, and mitigation efforts to identify and rectify biases that may emerge during model training and deployment.

No-Code AI platforms must provide tools and functionalities to allow users to assess and mitigate bias in their AI models. Furthermore, addressing the fairness challenge requires awareness and education among users to understand the potential biases that can exist in data and algorithms and to take proactive steps to minimize them.

Interpretability and Transparency:

Data-driven decision-making is most effective when the decision-makers can understand and trust the AI models' output. However, AI models, especially deep learning models, are often considered "black boxes" due to their complexity. No-Code AI platforms face the challenge of providing interpretability and transparency tools that allow users to understand how AI models arrive at their decisions.

Ensuring transparency and interpretability is crucial for regulatory compliance, ethical considerations, and user trust. Addressing this challenge involves developing techniques for model explainability and generating human-understandable insights from complex AI models.

Data Integration and Scalability:

As organizations grow and evolve, their data ecosystems become more complex. No-Code AI platforms must be capable of seamlessly integrating with various data sources, including legacy systems, cloud databases, and real-time data streams. Scalability is also essential, as organizations may need to process and analyze massive datasets as their operations expand.

The challenge lies in providing robust data integration capabilities while maintaining performance and scalability. Organizations should consider the long-term scalability and flexibility of No-Code AI platforms to ensure they can accommodate growing data volumes and evolving business needs.

In conclusion, while the Global No-Code AI Platform Market offers significant advantages in democratizing AI development, data-driven decision-making poses challenges related to data quality, privacy and compliance, bias and fairness, interpretability, and data integration. Addressing these challenges requires a holistic approach, combining technology solutions, data governance practices, and user education to ensure that AI-driven decisions are accurate, fair, and trustworthy.

Key Market Trends

Integration with Low-Code Development:

The Convergence of No-Code and Low-Code: One significant trend in the Global No-Code AI Platform Market is the convergence of No-Code and low-code development platforms. While No-Code platforms focus on enabling users with minimal coding experience to create AI solutions, low-code platforms cater to users with some coding knowledge. The merging of these two approaches results in a comprehensive solution that accommodates a broader range of users, from citizen developers to professional developers.

Hybrid Development Environments: No-Code AI platforms are increasingly offering hybrid development environments that allow users to switch between No-Code and low-code modes seamlessly. This flexibility empowers users to start with a No-Code approach and gradually incorporate custom code when needed, providing a more versatile and scalable development experience.

Accelerated Solution Delivery: The integration of low-code capabilities with No-Code AI platforms accelerates solution delivery. Users can leverage pre-built components and AI models while retaining the flexibility to customize and extend functionality through low-code scripting. This trend facilitates faster AI solution development and deployment, reducing time-to-market for organizations.

AI-Powered Automation:

AI-Driven Process Automation: No-Code AI platforms are increasingly being used to automate repetitive and rule-based processes across various industries. This trend goes beyond traditional robotic process automation (RPA) by integrating AI and machine learning capabilities. Organizations are leveraging No-Code platforms to build AI-powered bots and workflows that can analyze data, make decisions, and execute tasks autonomously.

Intelligent Document Processing (IDP): The use of AI-powered automation for document processing is a growing trend. No-Code AI platforms are equipped with IDP capabilities that enable organizations to extract structured and unstructured data from documents, such as invoices, contracts, and emails. This trend is particularly beneficial for improving efficiency in data entry, compliance, and document management.

AI-Enhanced Customer Service: No-Code AI platforms are empowering organizations to enhance their customer service operations by automating customer interactions through chatbots and virtual assistants. These AI-driven solutions can provide real-time responses to customer queries, personalize interactions, and streamline support processes. As a result, businesses can improve customer satisfaction and reduce support costs.

Industry-Specific Solutions:

Verticalization of No-Code AI: No-Code AI platforms are increasingly focusing on verticalization, tailoring their solutions to specific industries or use cases. By providing industry-specific templates, pre-built models, and workflows, these platforms enable organizations to address unique challenges and opportunities within their sectors.

Healthcare Applications: The healthcare industry is witnessing a surge in the adoption of No-Code AI platforms for applications such as medical image analysis, patient data processing, and telemedicine support. No-Code solutions are making it easier for healthcare professionals to implement AI-driven tools and improve patient care.

Financial Services: In the financial sector, No-Code AI platforms are being used for fraud detection, risk assessment, and algorithmic trading. These platforms offer compliance-ready solutions tailored to the specific regulatory requirements of the financial industry.

Manufacturing and IoT: No-Code AI is finding applications in manufacturing and the Internet of Things (IoT). Organizations can use No-Code platforms to develop predictive maintenance models, quality control systems, and production optimization solutions, all without extensive coding expertise.

Segmental Insights

Offering Type Insights

The In-Flight Connectivity (IFC) segment is dominating the global No-Code AI platform (IFEC) market.

IFC refers to the provision of internet connectivity to passengers on board aircraft. This allows passengers to stay connected with their work, family, and friends, and to access their favorite online content and services while traveling.

The IFC market is growing rapidly due to a number of factors, including:

Increasing demand for high-speed internet access from passengers

Growing adoption of streaming video and audio services

Increasing use of mobile devices for work and entertainment

Expanding availability of IFC solutions from airlines and service providers.

Regional Insights

North America is the dominating region in the global Artificial Intelligence (AI) sensor market due to a number of factors, including:

Strong presence of major AI sensor companies: North America is home to some of the world's leading AI sensor companies, such as Intel, Qualcomm, and Analog Devices. These companies are at the forefront of AI sensor innovation and development.

High demand for AI sensors from a variety of industries: AI sensors are used in a wide range of industries in North America, including consumer electronics, automotive, healthcare, and manufacturing. The demand for AI sensors from these industries is high and is expected to grow in the coming years.

Early adoption of AI sensors: North American businesses and organizations have been early adopters of AI sensors. This has given them a first-mover advantage in the AI sensor market.

Well-developed infrastructure for AI sensor research and development: North America has a well-developed infrastructure for AI sensor research and development. This includes the availability of funding, qualified researchers, and testing facilities.

North America is expected to remain the dominant region in the global AI sensor market in the coming years. However, the Asia Pacific region is expected to grow at the fastest rate, due to the increasing demand for AI sensors from businesses and organizations in the region and the growing number of AI sensor companies in the region.

Here are some examples of how AI sensors are being used in North America:

Consumer electronics: AI sensors are used in a variety of consumer electronics devices in North America, such as smartphones, smart TVs, and smart speakers. For example, AI sensors are used in smartphones for facial recognition, gesture recognition, and augmented reality. AI sensors are used in smart TVs for voice control and content recommendation. And AI sensors are used in smart speakers for voice control and music streaming.

Automotive: AI sensors are used in a variety of automotive applications in North America, such as advanced driver assistance systems (ADAS) and self-driving cars. For example, AI sensors

Table of Contents

1. Product Overview

  • 1.1. Market Definition
  • 1.2. Scope of the Market
    • 1.2.1. Markets Covered
    • 1.2.2. Years Considered for Study
    • 1.2.3. Key Market Segmentations

2. Research Methodology

  • 2.1. Objective of the Study
  • 2.2. Baseline Methodology
  • 2.3. Key Industry Partners
  • 2.4. Major Association and Secondary Sources
  • 2.5. Forecasting Methodology
  • 2.6. Data Triangulation & Validation
  • 2.7. Assumptions and Limitations

3. Executive Summary

4. Voice of Customers

5. Global No-Code AI platform Market Outlook

  • 5.1. Market Size & Forecast
    • 5.1.1. By Value
  • 5.2. Market Share & Forecast
    • 5.2.1. By Component (No-Code AI Platforms, Services)
    • 5.2.2. By Organization Size (Large Enterprises, Small and Medium Enterprises)
    • 5.2.3. By Technology (Data Preparation and Integration Tools, Predictive Analytics, Automated Machine Learning (AutoML), Natural Language Processing, Computer Vision, Others)
    • 5.2.4. By Industry (BFSI, IT & Telecom, Energy & Utilities, Retail & E-Commerce, Healthcare, Manufacturing, Government, Education, Others)
    • 5.2.5. By Region
  • 5.3. By Company (2022)
  • 5.4. Market Map

6. North America No-Code AI platform Market Outlook

  • 6.1. Market Size & Forecast
    • 6.1.1. By Value
  • 6.2. Market Share & Forecast
    • 6.2.1. By Component
    • 6.2.2. By Organization Size
    • 6.2.3. By Technology
    • 6.2.4. By Industry
    • 6.2.5. By Country
  • 6.3. North America: Country Analysis
    • 6.3.1. United States No-Code AI platform Market Outlook
      • 6.3.1.1. Market Size & Forecast
        • 6.3.1.1.1. By Value
      • 6.3.1.2. Market Share & Forecast
        • 6.3.1.2.1. By Component
        • 6.3.1.2.2. By Organization Size
        • 6.3.1.2.3. By Technology
        • 6.3.1.2.4. By Industry
      • 6.3.1.3. Canada No-Code AI platform Market Outlook
      • 6.3.1.4. Market Size & Forecast
        • 6.3.1.4.1. By Value
      • 6.3.1.5. Market Share & Forecast
        • 6.3.1.5.1. By Component
        • 6.3.1.5.2. By Organization Size
        • 6.3.1.5.3. By Technology
        • 6.3.1.5.4. By Industry
    • 6.3.2. Mexico No-Code AI platform Market Outlook
      • 6.3.2.1. Market Size & Forecast
        • 6.3.2.1.1. By Value
      • 6.3.2.2. Market Share & Forecast
        • 6.3.2.2.1. By Component
        • 6.3.2.2.2. By Organization Size
        • 6.3.2.2.3. By Technology
        • 6.3.2.2.4. By Industry

7. Asia-Pacific No-Code AI platform Market Outlook

  • 7.1. Market Size & Forecast
    • 7.1.1. By Value
  • 7.2. Market Share & Forecast
    • 7.2.1. By Component
    • 7.2.2. By Organization Size
    • 7.2.3. By Technology
    • 7.2.4. By Industry
    • 7.2.5. By Country
  • 7.3. Asia-Pacific: Country Analysis
    • 7.3.1. China No-Code AI platform Market Outlook
      • 7.3.1.1. Market Size & Forecast
        • 7.3.1.1.1. By Value
      • 7.3.1.2. Market Share & Forecast
        • 7.3.1.2.1. By Component
        • 7.3.1.2.2. By Organization Size
        • 7.3.1.2.3. By Technology
        • 7.3.1.2.4. By Industry
    • 7.3.2. India No-Code AI platform Market Outlook
      • 7.3.2.1. Market Size & Forecast
        • 7.3.2.1.1. By Value
      • 7.3.2.2. Market Share & Forecast
        • 7.3.2.2.1. By Component
        • 7.3.2.2.2. By Organization Size
        • 7.3.2.2.3. By Technology
        • 7.3.2.2.4. By Industry
    • 7.3.3. Japan No-Code AI platform Market Outlook
      • 7.3.3.1. Market Size & Forecast
        • 7.3.3.1.1. By Value
      • 7.3.3.2. Market Share & Forecast
        • 7.3.3.2.1. By Component
        • 7.3.3.2.2. By Organization Size
        • 7.3.3.2.3. By Technology
        • 7.3.3.2.4. By Industry
    • 7.3.4. South Korea No-Code AI platform Market Outlook
      • 7.3.4.1. Market Size & Forecast
        • 7.3.4.1.1. By Value
      • 7.3.4.2. Market Share & Forecast
        • 7.3.4.2.1. By Component
        • 7.3.4.2.2. By Organization Size
        • 7.3.4.2.3. By Technology
        • 7.3.4.2.4. By Industry
    • 7.3.5. Australia No-Code AI platform Market Outlook
      • 7.3.5.1. Market Size & Forecast
        • 7.3.5.1.1. By Value
      • 7.3.5.2. Market Share & Forecast
        • 7.3.5.2.1. By Component
        • 7.3.5.2.2. By Organization Size
        • 7.3.5.2.3. By Technology
        • 7.3.5.2.4. By Industry

8. Europe No-Code AI platform Market Outlook

  • 8.1. Market Size & Forecast
    • 8.1.1. By Value
  • 8.2. Market Share & Forecast
    • 8.2.1. By Component
    • 8.2.2. By Organization Size
    • 8.2.3. By Technology
    • 8.2.4. By Industry
    • 8.2.5. By Country
  • 8.3. Europe: Country Analysis
    • 8.3.1. Germany No-Code AI platform Market Outlook
      • 8.3.1.1. Market Size & Forecast
        • 8.3.1.1.1. By Value
      • 8.3.1.2. Market Share & Forecast
        • 8.3.1.2.1. By Component
        • 8.3.1.2.2. By Organization Size
        • 8.3.1.2.3. By Technology
        • 8.3.1.2.4. By Industry
    • 8.3.2. United Kingdom No-Code AI platform Market Outlook
      • 8.3.2.1. Market Size & Forecast
        • 8.3.2.1.1. By Value
      • 8.3.2.2. Market Share & Forecast
        • 8.3.2.2.1. By Component
        • 8.3.2.2.2. By Organization Size
        • 8.3.2.2.3. By Technology
        • 8.3.2.2.4. By Industry
    • 8.3.3. France No-Code AI platform Market Outlook
      • 8.3.3.1. Market Size & Forecast
        • 8.3.3.1.1. By Value
      • 8.3.3.2. Market Share & Forecast
        • 8.3.3.2.1. By Component
        • 8.3.3.2.2. By Organization Size
        • 8.3.3.2.3. By Technology
        • 8.3.3.2.4. By Industry
    • 8.3.4. Italy No-Code AI platform Market Outlook
      • 8.3.4.1. Market Size & Forecast
        • 8.3.4.1.1. By Value
      • 8.3.4.2. Market Share & Forecast
        • 8.3.4.2.1. By Component
        • 8.3.4.2.2. By Organization Size
        • 8.3.4.2.3. By Technology
        • 8.3.4.2.4. By Industry
    • 8.3.5. Spain No-Code AI platform Market Outlook
      • 8.3.5.1. Market Size & Forecast
        • 8.3.5.1.1. By Value
      • 8.3.5.2. Market Share & Forecast
        • 8.3.5.2.1. By Component
        • 8.3.5.2.2. By Organization Size
        • 8.3.5.2.3. By Technology
        • 8.3.5.2.4. By Industry

9. South America No-Code AI platform Market Outlook

  • 9.1. Market Size & Forecast
    • 9.1.1. By Value
  • 9.2. Market Share & Forecast
    • 9.2.1. By Component
    • 9.2.2. By Organization Size
    • 9.2.3. By Technology
    • 9.2.4. By Industry
    • 9.2.5. By Country
  • 9.3. South America: Country Analysis
    • 9.3.1. Brazil No-Code AI platform Market Outlook
      • 9.3.1.1. Market Size & Forecast
        • 9.3.1.1.1. By Value
      • 9.3.1.2. Market Share & Forecast
        • 9.3.1.2.1. By Component
        • 9.3.1.2.2. By Organization Size
        • 9.3.1.2.3. By Technology
        • 9.3.1.2.4. By Industry
    • 9.3.2. Argentina No-Code AI platform Market Outlook
      • 9.3.2.1. Market Size & Forecast
        • 9.3.2.1.1. By Value
      • 9.3.2.2. Market Share & Forecast
        • 9.3.2.2.1. By Component
        • 9.3.2.2.2. By Organization Size
        • 9.3.2.2.3. By Technology
        • 9.3.2.2.4. By Industry
    • 9.3.3. Colombia No-Code AI platform Market Outlook
      • 9.3.3.1. Market Size & Forecast
        • 9.3.3.1.1. By Value
      • 9.3.3.2. Market Share & Forecast
        • 9.3.3.2.1. By Component
        • 9.3.3.2.2. By Organization Size
        • 9.3.3.2.3. By Technology
        • 9.3.3.2.4. By Industry

10. Middle East & Africa No-Code AI platform Market Outlook

  • 10.1. Market Size & Forecast
    • 10.1.1. By Value
  • 10.2. Market Share & Forecast
    • 10.2.1. By Component
    • 10.2.2. By Organization Size
    • 10.2.3. By Technology
    • 10.2.4. By Industry
    • 10.2.5. By Country
  • 10.3. Middle East & Africa: Country Analysis
    • 10.3.1. Saudi Arabia No-Code AI platform Market Outlook
      • 10.3.1.1. Market Size & Forecast
        • 10.3.1.1.1. By Value
      • 10.3.1.2. Market Share & Forecast
        • 10.3.1.2.1. By Component
        • 10.3.1.2.2. By Organization Size
        • 10.3.1.2.3. By Technology
        • 10.3.1.2.4. By Industry
    • 10.3.2. South Africa No-Code AI platform Market Outlook
      • 10.3.2.1. Market Size & Forecast
        • 10.3.2.1.1. By Value
      • 10.3.2.2. Market Share & Forecast
        • 10.3.2.2.1. By Component
        • 10.3.2.2.2. By Organization Size
        • 10.3.2.2.3. By Technology
        • 10.3.2.2.4. By Industry
    • 10.3.3. UAE No-Code AI platform Market Outlook
      • 10.3.3.1. Market Size & Forecast
        • 10.3.3.1.1. By Value
      • 10.3.3.2. Market Share & Forecast
        • 10.3.3.2.1. By Component
        • 10.3.3.2.2. By Organization Size
        • 10.3.3.2.3. By Technology
        • 10.3.3.2.4. By Industry

11. Market Dynamics

  • 11.1. Drivers
  • 11.2. Challenge

12. Market Trends & Developments

13. Company Profiles

  • 13.1. Microsoft Corporation
    • 13.1.1. Business Overview
    • 13.1.2. Key Revenue and Financials
    • 13.1.3. Recent Developments
    • 13.1.4. Key Personnel
    • 13.1.5. Key Product/Services
  • 13.2. GOOGLE LLC
    • 13.2.1. Business Overview
    • 13.2.2. Key Revenue and Financials
    • 13.2.3. Recent Developments
    • 13.2.4. Key Personnel
    • 13.2.5. Key Product/Services
  • 13.3. International Business Machines Corporation
    • 13.3.1. Business Overview
    • 13.3.2. Key Revenue and Financials
    • 13.3.3. Recent Developments
    • 13.3.4. Key Personnel
    • 13.3.5. Key Product/Services
  • 13.4. Salesforce.com, Inc.
    • 13.4.1. Business Overview
    • 13.4.2. Key Revenue and Financials
    • 13.4.3. Recent Developments
    • 13.4.4. Key Personnel
    • 13.4.5. Key Product/Services
  • 13.5. Amazon Web Services, Inc.
    • 13.5.1. Business Overview
    • 13.5.2. Key Revenue and Financials
    • 13.5.3. Recent Developments
    • 13.5.4. Key Personnel
    • 13.5.5. Key Product/Services
  • 13.6. APPIAN CORPORATION
    • 13.6.1. Business Overview
    • 13.6.2. Key Revenue and Financials
    • 13.6.3. Recent Developments
    • 13.6.4. Key Personnel
    • 13.6.5. Key Product/Services
  • 13.7. OutSystems
    • 13.7.1. Business Overview
    • 13.7.2. Key Revenue and Financials
    • 13.7.3. Recent Developments
    • 13.7.4. Key Personnel
    • 13.7.5. Key Product/Services
  • 13.8. Mendix B.V.
    • 13.8.1. Business Overview
    • 13.8.2. Key Revenue and Financials
    • 13.8.3. Recent Developments
    • 13.8.4. Key Personnel
    • 13.8.5. Key Product/Services
  • 13.9. PEGASYSTEMS INC.
    • 13.9.1. Business Overview
    • 13.9.2. Key Revenue and Financials
    • 13.9.3. Recent Developments
    • 13.9.4. Key Personnel
    • 13.9.5. Key Product/Services
  • 13.10. Quick Base, Inc.
    • 13.10.1. Business Overview
    • 13.10.2. Key Revenue and Financials
    • 13.10.3. Recent Developments
    • 13.10.4. Key Personnel
    • 13.10.5. Key Product/Services

14. Strategic Recommendations

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