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市场调查报告书
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1377266

无程式码人工智慧平台市场 - 全球产业规模、份额、趋势、机会和预测（按组件、组织规模、技术、产业、地区、竞争预测和机会细分，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个工作天内

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简介目录

2022 年，全球无程式码人工智慧平台市场估值为 42.1 亿美元，预测期内CAGR为 27.89%。在日益数位化的世界中企业不断变化的需求以及人工智慧（AI）技术的不断进步的推动下，全球无程式码人工智慧平台市场目前正在经历显着的成长和转型。无程式码人工智慧平台在重塑组织开发和部署人工智慧解决方案的方式方面发挥关键作用，提供了一种用户友好的方法，使非技术用户能够利用人工智慧的力量。随着企业努力保持竞争力并满足当今数据驱动环境不断变化的需求，对无程式码人工智慧平台的需求正在上升，从而培育了一个充满活力和竞争的市场，并带来了充满希望的机会。

无程式码人工智慧平台市场成长的主要驱动力之一是人工智慧的民主化。传统的人工智慧开发通常需要高度专业的技能和对复杂演算法的深刻理解。然而，借助无程式码人工智慧平台，组织可以弥合技能差距，并使领域专家、业务分析师和公民开发人员能够在无需大量编码或资料科学专业知识的情况下创建人工智慧应用程式。人工智慧的民主化使创新民主化，并加速了人工智慧在各行业的采用。

数据驱动决策的兴起进一步推动了对无程式码人工智慧平台的需求。企业意识到资料是一种宝贵的资产，人工智慧可以从这些资料中释放出可操作的见解。无程式码人工智慧平台为资料准备、建模和部署提供直觉的介面，使组织能够利用人工智慧的力量来改善决策、自动化流程并获得竞争优势。

市场概况
预测期 2024-2028
2022 年市场规模 42.1亿美元
2028 年市场规模 185.9亿美元
2023-2028 年CAGR 27.89%
成长最快的细分市场 大型企业
最大的市场 北美洲

此外，无程式码人工智慧平台正在推动企业提高成本效率和生产力。传统的人工智慧开发可能是资源密集且耗时的。无程式码平台简化了开发流程，减少了建置和部署人工智慧解决方案所需的时间和资源。这使组织能够更快地将产品推向市场并更快地实现投资回报。

数据驱动的决策：

数据驱动的决策是全球无程式码人工智慧平台市场蓬勃发展的关键驱动力。在日益以数据为中心的世界中，组织认识到利用资料做出明智决策并获得竞争优势的价值。无代码人工智慧平台使各行业的用户能够利用资料，而无需广泛的编码或资料科学专业知识。在本文中，我们将探讨对数据驱动决策的重视如何推动无程式码人工智慧平台市场的成长。

资料在当代商业营运中日益增长的重要性怎么强调也不为过。组织从各种来源收集大量资料，包括客户互动、操作流程和物联网设备。如果正确分析这些资料，可以提供有价值的见解、为策略提供资讯并推动效率和有效性的提高。然而，释放资料的全部潜力历来是一项复杂且资源密集的任务。

这就是无程式码人工智慧平台的意义。这些平台使人工智慧和资料分析工具的存取民主化，允许更广泛的用户（包括业务分析师和领域专家）处理资料并建立人工智慧驱动的解决方案。无程式码平台的使用者友善介面使具有特定领域知识的个人能够探索资料、创建预测模型并获得可操作的见解，而无需广泛的程式设计技能。

无程式码人工智慧平台市场的主要驱动力之一是对即时决策的渴望。在当今快节奏的商业环境中，快速做出数据驱动决策的能力是一种竞争优势。无程式码人工智慧平台使组织能够快速开发人工智慧模型和数据驱动的应用程序，确保决策者能够获得最新的见解。例如，在电子商务中，这些平台可用于根据客户的浏览和购买历史记录即时为客户提供个人化产品推荐。

此外，自动化需求推动了无程式码人工智慧平台的全球市场。随着组织寻求简化营运并减少人工干预，人工智慧驱动的自动化变得越来越重要。无程式码平台允许用户透过创建人工智慧驱动的机器人和应用程式来自动化流程和工作流程，这些机器人和应用程式可以执行资料输入、客户支援和内容生成等任务。这种自动化不仅提高了效率，还释放了人力资源，用于更具策略性的活动。

无程式码人工智慧平台的可扩展性和多功能性也有助于其成长。这些平台可用于各种行业和功能，从行销和销售到金融和医疗保健。组织可以轻鬆地调整它们来应对特定挑战并抓住机会。此外，随着资料量的不断增长，无程式码人工智慧平台提供了可扩展的解决方案，用于处理大型资料集并从中提取见解。

另一个重要的驱动因素是组织内部人工智慧开发民主化的需求。资料科学家和人工智慧专家的需求量很大，但这些领域的熟练专业人员却很短缺。无程式码人工智慧平台透过允许业务用户和领域专家积极参与人工智慧模型的开发来弥补这一技能差距。技术和非技术利益相关者之间的这种合作增强了创新，并确保人工智慧解决方案与业务目标保持一致。

总之，数据驱动的决策是推动全球无程式码人工智慧平台市场的强大力量。这些平台使组织能够利用资料进行即时决策、自动化和可扩展性，而无需广泛的技术专业知识。随着数据驱动范式的不断发展，对促进数据驱动洞察和应用的可访问人工智慧工具的需求只会增长。无程式码人工智慧平台将在帮助组织充分利用资料潜力并做出更明智、敏捷和有竞争力的决策方面发挥关键作用。

成本效益与生产力：

成本效率和生产力提升是推动全球无程式码人工智慧平台市场快速成长的关键驱动力。这些平台为组织提供了强大的工具包，可以简化流程、降低开发成本并提高生产力，而无需广泛的编码或资料科学专业知识。在本文中，我们将探讨对成本效率和生产力的追求如何推动无程式码人工智慧平台市场的扩张。

采用无程式码人工智慧平台的主要驱动力之一是可以显着节省成本。传统的人工智慧开发通常需要对熟练的资料科学家、开发人员和基础设施进行大量投资。这些成本对许多组织来说可能令人望而却步，尤其是小型企业和新创公司。无程式码人工智慧平台使人工智慧开发民主化，使更广泛的用户能够以极低的成本创建人工智慧应用程式。这种成本效率使各种规模的组织都可以使用人工智慧，从而在各个行业中实现其优势的民主化。

无程式码人工智慧平台提供的简化开发流程可以节省时间，进而提高生产力。传统的人工智慧开发週期可能漫长且资源密集，涉及资料预处理、模型训练和微调。无程式码平台提供预先建置范本、拖放介面和自动化工作流程，大大减少了开发人工智慧应用程式所需的时间。开发的加速加快了人工智慧解决方案的上市时间，使组织能够快速回应不断变化的市场动态和客户需求。

此外，无程式码人工智慧平台使非技术专业人员能够积极参与人工智慧开发，从而有助于提高生产力。业务分析师、领域专家和公民资料科学家可以利用这些平台来创建适合其特定需求的人工智慧模型和应用程式。技术和非技术团队之间的这种协作促进了创新，并使组织能够利用了解其行业和业务流程细微差别的员工的专业知识。

自动化是无程式码人工智慧平台市场生产力提高的另一个驱动力。这些平台使组织能够自动执行重复性和劳动密集型任务，从而释放人力资源用于更具策略性和增值性的活动。例如，在客户支援方面，使用无程式码平台建立的人工智慧聊天机器人可以处理日常查询，让人工代理专注于复杂的客户互动。这不仅提高了效率，也提高了客户满意度。

无程式码人工智慧平台的可扩展性也是其提高生产力的关键因素。随着组织的发展和收集更多资料，对可扩展人工智慧解决方案的需求变得至关重要。无程式码平台提供了扩展人工智慧应用程式的灵活性，以适应不断增长的资料负载和用户需求。这种可扩展性确保人工智慧解决方案能够随着组织的扩张而继续创造价值。

此外，市场的全球性有助于提高生产力。无程式码人工智慧平台是多功能工具，可应用于各行业和职能，包括行销、金融和医疗保健。组织可以调整这些平台来应对特定挑战并抓住各自领域的机会。这种多功能性消除了为每个用例客製化解决方案的需要，进一步减少了开发时间和成本。

总之，成本效率和生产力是全球无程式码人工智慧平台市场的核心驱动力。这些平台为组织提供了一种经济有效且高效的方式来开发人工智慧应用程序，从而实现人工智慧优势的民主化。透过减少开发时间和成本，使非技术用户能够参与人工智慧开发，并促进自动化和可扩展性，无程式码人工智慧平台使组织能够利用人工智慧的变革潜力，并在日益数据驱动的世界中保持竞争力。随着对人工智慧驱动解决方案的需求不断上升，这些平台将在重塑组织创新和营运方式方面发挥关键作用。

主要市场挑战

现实世界数据的复杂性：

现实世界资料的复杂性为全球无程式码人工智慧平台市场带来了巨大挑战。虽然这些平台因其承诺简化人工智慧开发并使更广泛的受众能够使用而受到欢迎，但处理现实世界资料的复杂性带来了不可低估的障碍。

主要挑战之一源自于现实世界资料固有的可变性和混乱性。与学术和受控环境中经常使用的原始、结构良好的资料集不同，现实世界的资料充满了不一致、缺失值、错误和杂讯。这种复杂性源自多种来源，包括资料输入错误、感测器不准确、不同的资料格式以及医疗保健、金融和物联网等领域产生的资料的动态性质。

无程式码人工智慧平台依靠自动化和预先建构演算法来创建人工智慧模型，在面对如此复杂的资料时它们可能会陷入困境。例如，在医疗保健领域，病患记录可能包含手写笔记、不一致的格式或遗失的资讯。这使得无程式码平台难以提取有意义的见解或创建准确的预测模型。使用者经常发现自己在资料预处理上花费了大量的时间和精力，这可能会抵消无程式码平台所承诺的一些节省时间的好处。

此外，现实世界的资料可能是高度非结构化的，这带来了另一层复杂性。自然语言文字、图像、音讯和非结构化资料格式在社群媒体分析或内容处理等领域很常见。无程式码人工智慧平台主要擅长处理结构化资料，但在处理非结构化或半结构化资料时可能面临限制。这些限制可能会阻碍用户在其应用程式中充分发挥人工智慧潜力的能力。

此外，现实世界的资料通常涉及处理来自多个来源的资料，这可能会使资料整合过程更加复杂。整合挑战可能包括资料清理、将不同来源的资料与不同的模式对齐，以及确保资料的一致性和品质。无程式码人工智慧平台的使用者可能会发现自己需要应对这些复杂性，从而导致潜在的挫折感和比最初预期更陡峭的学习曲线。

解决处理复杂的真实资料的挑战对于无程式码人工智慧平台兑现其承诺并为不同行业提供有价值的人工智慧解决方案至关重要。为了应对这些挑战，平台开发人员需要投资增强资料预处理能力，包括资料清理、转换和标准化。这可以减轻使用者的负担，提高整体使用者体验。

此外，开发更好地支援非结构化和半结构化资料分析的工具和功能至关重要。无程式码平台应该扩展其功能，以满足处理文字、图像和其他形式的非结构化资料不断增长的需求。这可以使用户能够挖掘隐藏在非结构化资料来源中的有价值的见解。

此外，提供无缝资料整合功能和流行资料来源的连接器可以简化处理来自多个来源的资料的过程。这将使用户能够更有效地存取和分析资料，最终增强无程式码人工智慧平台的可用性和有效性。

总之，现实世界资料的复杂性对全球无程式码人工智慧平台市场构成了重大挑战。为了充分释放这些平台的潜力，让人工智慧更容易被使用，开发者和提供者必须专注于提高资料处理能力，特别是在处理杂乱、非结构化和多来源资料。克服这些挑战将有助于确保无程式码人工智慧平台能够兑现人工智慧开发民主化的承诺，并使广泛的行业和用户受益。

数据驱动决策

虽然全球无程式码人工智慧平台市场正在经历显着的成长和转型，但在此背景下也存在与数据驱动决策相关的挑战。数据驱动的决策是人工智慧的一个基本面，其挑战影响无程式码人工智慧平台的有效性和采用。在这里，我们探讨了全球无程式码人工智慧平台市场中与数据驱动决策相关的一些关键挑战：

数据品质和可访问性：

无程式码人工智慧平台市场中数据驱动决策的主要挑战之一是确保资料的品质和可存取性。为了让人工智慧模型提供准确可靠的见解，它们需要高品质、结构良好且相关的资料。然而，组织经常面临与资料清洁度、完整性和准确性相关的问题。资料品质不足可能会导致有缺陷的预测和不可靠的决策支援。

此外，资料可存取性可能是一个挑战，因为相关资料可能分散在不同的系统、部门甚至外部来源中。整合和协调不同的资料来源可能是一个复杂且耗时的过程，可能会延迟人工智慧模型在无程式码平台上的部署。

资料隐私和合规性：

资料隐私和合规性是资料驱动决策中的关键考虑因素，特别是在监管严格的行业（例如欧洲的医疗保健、金融和 GDPR 合规性）。无代码人工智慧平台在处理敏感资讯时必须遵守资料保护和隐私法。确保资料匿名、加密并符合相关法规是一项复杂的任务。公司必须实施强大的资料治理策略和安全措施来保护客户和组织资料。

遵守不断变化的资料隐私法规可能具有挑战性，因为法规可能会随着时间的推移而发生变化，需要对人工智慧模型和资料实践进行持续监控和调整。在资料实用性与隐私和合规性之间取得平衡仍然是全球无程式码人工智慧平台市场的一个挑战。

偏见与公平：

在无程式码平台上开发的人工智慧模型可能会继承训练资料中存在的偏差，这可能导致不公平或歧视性的决策。解决人工智慧演算法中的偏见并确保公平性是一项复杂的挑战。它需要持续的监控、审计和缓解工作，以识别和纠正模型训练和部署期间可能出现的偏差。

无程式码人工智慧平台必须提供工具和功能，以允许使用者评估和减轻其人工智慧模型中的偏见。此外，解决公平性挑战需要提高用户意识并进行教育，以了解资料和演算法中可能存在的潜在偏差，并采取积极措施将其最小化。

可解释性和透明度：

当决策者能够理解并信任人工智慧模型的输出时，数据驱动的决策是最有效的。然而，人工智慧模型，尤其是深度学习模型，由于其复杂性通常被认为是「黑盒子」。无程式码人工智慧平檯面临的挑战是提供可解释性和透明度工具，使用户能够了解人工智慧模型如何做出决策。

确保透明度和可解释性对于监管合规性、道德考虑和用户信任至关重要。应对这项挑战需要开发模型可解释性技术，并从复杂的人工智慧模型中产生人类可理解的见解。

数据集成和可扩展性：

随着组织的成长和发展，其资料生态系统变得更加复杂。无程式码人工智慧平台必须能够与各种资料来源无缝集成，包括遗留系统、云端资料库和即时资料流。可扩展性也很重要，因为随着业务的扩展，组织可能需要处理和分析大量资料集。

挑战在于提供强大的资料整合功能，同时保持效能和可扩展性。组织应考虑无程式码人工智慧平台的长期可扩展性和灵活性，以确保它们能够适应不断增长的资料量和不断变化的业务需求。

总之，虽然全球无程式码人工智慧平台市场在人工智慧开发民主化方面具有显着优势，但数据驱动的决策提出了与资料品质、隐私和合规性、偏见和公平性、可解释性和资料集成相关的挑战。应对这些挑战需要整体方法，结合技术解决方案、资料治理实践和用户教育，以确保人工智慧驱动的决策准确、公平且值得信赖。

主要市场趋势

与低程式码开发整合：

无程式码和低程式码的融合：全球无程式码人工智慧平台市场的一大趋势是无程式码和低程式码开发平台的融合。无程式码平台专注于让具有最少编码经验的使用者创建人工智慧解决方案，而低程式码平台则迎合具有一定编码知识的使用者。这两种方法的合併产生了一个全面的解决方案，可以容纳更广泛的用户，从公民开发人员到专业开发人员。

混合开发环境：无程式码人工智慧平台越来越多地提供混合开发环境，让使用者在无程式码和低程式码模式之间无缝切换。这种灵活性使用户能够从无程式码方法开始，并在需要时逐渐合併自订程式码，从而提供更通用和可扩展的开发体验。

加速解决方案交付：低程式码功能与无程式码人工智慧平台的整合加速了解决方案交付。使用者可以利用预先建置的元件和人工智慧模型，同时保留透过低程式码脚本自订和扩充功能的灵活性。这一趋势有助于加快人工智慧解决方案的开发和部署，缩短组织的上市时间。

人工智慧驱动的自动化：

人工智慧驱动的流程自动化：无程式码人工智慧平台越来越多地用于自动化各行业的重复性和基于规则的流程。透过整合人工智慧和机器学习功能，这一趋势超越了传统的机器人流程自动化 (RPA)。组织正在利用无程式码平台来建立人工智慧驱动的机器人和工作流程，可以自动分析资料、做出决策和执行任务。

智慧型文檔处理 (IDP)：使用人工智慧驱动的自动化文件处理是一种日益增长的趋势。无程式码 AI 平台配备了 IDP 功能，使组织能够从发票、合约和电子邮件等文件中提取结构化和非结构化资料。这种趋势对于提高资料输入、合规性和文件管理的效率特别有利。

人工智慧增强的客户服务：无程式码人工智慧平台使组织能够透过聊天机器人和虚拟助理实现客户互动自动化，从而增强客户服务营运。这些人工智慧驱动的解决方案可以即时回应客户查询、个人化互动并简化支援流程。因此，企业可以提高客户满意度并降低支援成本。

产业特定解决方案：

无程式码人工智慧的垂直化：无程式码人工智慧平台越来越关注垂直化，针对特定产业或用例客製化解决方案。透过提供行业特定的模板、预先建构的模型和工作流程，这些平台使组织能够应对其行业内独特的挑战和机会。

医疗保健应用：医疗保健产业正在见证无程式码人工智慧平台在医学影像分析、病患资料处理和远距医疗支援等应用中的采用激增。无代码解决方案使医疗保健专业人员能够更轻鬆地实施人工智慧驱动的工具并改善患者护理。

金融服务：在金融领域，无程式码人工智慧平台被用于诈欺侦测、风险评估和演算法交易。这些平台提供适合金融业特定监管要求的合规解决方案。

製造和物联网：无程式码人工智慧正在製造业和物联网 (IoT) 中找到应用。组织可以使用无程式码平台来开发预测维护模型、品质控制系统和生产最佳化解决方案，而无需丰富的编码专业知识。

细分市场洞察

产品类型见解

机上连接 (IFC) 领域在全球无程式码人工智慧平台 (IFEC) 市场中占据主导地位。

IFC 是指为飞机上的乘客提供网路连线。这使得乘客能够与工作、家人和朋友保持联繫，并在旅行时存取他们最喜欢的线上内容和服务。

由于多种因素，国际金融公司市场正在快速成长，其中包括：

乘客对高速网路存取的需求不断增加

串流影音和音讯服务的采用率不断提高

越来越多地使用行动装置进行工作和娱乐

扩大航空公司和服务提供者提供的 IFC 解决方案的可用性。

区域洞察

由于多种因素，北美成为全球人工智慧（AI）感测器市场的主导地区，包括：

主要人工智慧感测器公司实力强劲：北美是一些全球领先的人工智慧感测器公司的所在地，例如英特尔、高通和 ADI 公司。这些公司处于人工智慧感测器创新和开发的前沿。

各产业对人工智慧感测器的高需求：人工智慧感测器在北美广泛应用于消费性电子、汽车、医疗保健和製造等产业。这些产业对人工智慧感测器的需求很高，预计未来几年将会成长。

人工智慧感测器的早期采用：北美企业和组织是人工智慧感测器的早期采用者。这让他们在人工智慧感测器市场拥有先发优势。

完善的AI感测器研发基础设施：北美拥有完善的AI感测器研发基础设施。这包括资金、合格研究人员和测试设施的可用性。

预计未来几年北美仍将是全球人工智慧感测器市场的主导地区。然而，由于该地区企业和组织对人工智慧感测器的需求不断增加以及该地区人工智慧感测器公司数量的不断增加，预计亚太地区将以最快的速度成长。

以下是北美如何使用人工智慧感测器的一些范例：

消费性电子：AI感测器应用于北美的各种消费性电子设备，如智慧型手机、智慧电视、智慧音箱等。例如，人工智慧感测器在智慧型手机中用于脸部辨识、手势辨识和扩增实境。智慧电视中使用人工智慧感测器进行语音控制和内容推荐。人工智慧感测器用于智慧扬声器中，用于语音控制和音乐串流。

汽车：人工智慧感测器在北美被用于各种汽车应用，例如先进驾驶辅助系统（ADAS）和自动驾驶汽车。例如，人工智慧感测器

目录

第 1 章：产品概述

  • 市场定义
  • 市场范围
    • 涵盖的市场
    • 考虑学习的年份
    • 主要市场区隔

第 2 章：研究方法

  • 研究目的
  • 基线方法
  • 主要产业伙伴
  • 主要协会和二手资料来源
  • 预测方法
  • 数据三角测量与验证
  • 假设和限制

第 3 章：执行摘要

第 4 章：客户之声

第 5 章：全球无程式码人工智慧平台市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按组件（无代码 AI 平台、服务）
    • 依组织规模（大型企业、中小企业）
    • 按技术（资料准备与整合工具、预测分析、自动机器学习 (AutoML)、自然语言处理、电脑视觉、其他）
    • 按行业（BFSI、IT 与电信、能源与公用事业、零售与电子商务、医​​疗保健、製造业、政府、教育、其他）
    • 按地区
  • 按公司划分 (2022)
  • 市场地图

第 6 章：北美无程式码人工智慧平台市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按组件
    • 按组织规模
    • 依技术
    • 按行业分类
    • 按国家/地区
  • 北美：国家分析
    • 美国
    • 墨西哥

第 7 章：亚太地区无程式码人工智慧平台市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按组件
    • 按组织规模
    • 依技术
    • 按行业分类
    • 按国家/地区
  • 亚太地区：国家分析
    • 中国
    • 印度
    • 日本
    • 韩国
    • 澳洲

第 8 章：欧洲无程式码人工智慧平台市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按组件
    • 按组织规模
    • 依技术
    • 按行业分类
    • 按国家/地区
  • 欧洲：国家分析
    • 德国
    • 英国
    • 法国
    • 义大利
    • 西班牙

第 9 章：南美洲无程式码人工智慧平台市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按组件
    • 按组织规模
    • 依技术
    • 按行业分类
    • 按国家/地区
  • 南美洲：国家分析
    • 巴西
    • 阿根廷
    • 哥伦比亚

第 10 章：中东和非洲无程式码人工智慧平台市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按组件
    • 按组织规模
    • 依技术
    • 按行业分类
    • 按国家/地区
  • 中东和非洲：国家分析
    • 沙乌地阿拉伯
    • 南非
    • 阿联酋

第 11 章：市场动态

  • 司机
  • 挑战

第 12 章：市场趋势与发展

第 13 章：公司简介

  • 微软公司
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 谷歌有限责任公司
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 国际商业机器公司
    • 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
  • 亚马逊网路服务公司
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 亚庇公司
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 外部系统
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 门迪克斯有限公司
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 派加系统公司
    • 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：策略建议

关于我们及免责声明

简介目录
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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