No-Code AI Platforms Market by Technology (NLP, Predictive Analytics, Deep Learning) Automation Platforms (Conversational AI, Business Process Automation), Application (Workflow Automation, Text Translation, Chatbots) - Global Forecast to 2029
[352 Pages Report] The global no-code AI platforms market is expected to grow from USD 4.9 billion in 2024 to USD 24.8 billion in 2029, at a CAGR of 38.2% during the forecast period. No-code AI technology enables users to develop artificial intelligence models effortlessly, even without programming expertise. It operates through a user-friendly interface where users input data and specify the desired AI model type, automating the model creation process. This innovation democratizes AI, making it accessible beyond technical specialists. By automating repetitive tasks, conducting data analysis, and facilitating swift decision-making, No-Code AI enhances business efficiency and fosters growth. It empowers businesses to harness AI capabilities without relying on extensive teams of data scientists or programmers, thereby conserving time and resources. Furthermore, it fosters innovation and enhances competitiveness, enabling businesses to adapt swiftly in dynamic markets.
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Market Dynamics
Driver: Empowering rapid prototyping and collaboration with no-code AI platforms
The surge in demand for rapid prototyping and experimentation is propelling the adoption of no-code AI platforms, which enable individuals across diverse fields to create AI applications without extensive coding skills. These platforms offer several advantages, including swift testing and refinement of ideas into functional prototypes using visual tools and immediate iterations. They also drive cost and time efficiency by accelerating early-stage validation, reducing development expenses, and expediting product launches. Moreover, it fosters enhanced collaboration among designers, developers, and stakeholders, enabling non-technical team members to actively contribute to the design process and improve decision-making and project outcomes. Additionally, no-code AI platforms democratize AI application development by empowering designers to validate ideas without technical constraints, focusing on user experience and aesthetics, and making innovation more inclusive and approachable for a wider audience. This trend reflects a fundamental shift in how AI technology is accessed and utilized, opening new avenues for creativity, collaboration, and advancement across industries.
Restraint: Balancing customization and simplicity in no-code AI platforms to impede market growth
No-Code AI platforms have democratized AI development by making it accessible to a broader audience. However, their limited customization options can impede market growth. These platforms typically rely on pre-built models and a structured framework, restricting users from making substantial adjustments to meet their specific needs. This inflexibility can be particularly problematic for organizations with unique requirements or complex use cases requiring advanced customization. Additionally, the simplistic design of No-Code AI platforms may not be suitable for creating sophisticated and nuanced AI models. Users aiming to build highly specialized applications or requiring deeper control over the underlying algorithms may find these platforms inadequate. This limitation can lead to a mismatch between the platform's capabilities and user requirements, potentially hindering the adoption of No-Code AI in certain industries or use cases. To address this challenge, No-Code AI platforms need to balance simplicity and customization, offering advanced options for users needing greater control over their AI models. Enhancing customization while maintaining user-friendly interfaces can cater to a broader spectrum of users and drive market growth.
Opportunity: Rising demand for streamlining operations drives business efficiency
The increasing demand for streamlining operations and maximizing efficiency through automation is driving the market for no-code AI platforms. As businesses aim to leverage artificial intelligence (AI) and machine learning (ML) to automate tasks and extract real-time insights, there is a growing requirement for user-friendly tools that can bridge the gap between technical complexities and operational needs. No-code AI platforms play a critical role in democratizing AI development, empowering non-technical users to effortlessly create and interact with AI models. This democratization not only accelerates AI adoption but also enables organizations to harness the transformative potential of AI without relying solely on specialized technical skills. By simplifying the process of AI model deployment, these platforms facilitate improved operational efficiency and informed decision-making. Consequently, the relentless pursuit of automation to enhance productivity and drive innovation is fueling the rapid expansion of the global market for no-code AI platforms, reshaping the dynamics of contemporary business operations.
Challenge: Navigating vendor lock-in in the no-code AI platform market
The issue of vendor lock-in within the no-code AI platform sphere presents a significant business challenge. These platforms often utilize proprietary coding languages, complicating the process of migrating to alternative vendors. Consequently, businesses are confronted with substantial costs and operational risks during transitions, necessitating extensive retooling and retraining efforts. Furthermore, the proprietary nature of these platforms restricts seamless integration with other systems, amplifying reliance on the original vendor. Addressing this challenge entails conducting thorough evaluations of vendors' long-term roadmaps, licensing models, and flexibility. By strategically selecting a no-code AI platform, businesses can enhance their ability to manage data and applications effectively while mitigating the risks associated with vendor lock-in.
No-code AI platforms Market Ecosystem
The no-code AI platforms ecosystem thrives on a diverse array of stakeholders. Solutions providers such as Bubble, Levity, and others offer intuitive tools for creating AI-driven applications without coding. Services providers such as AWS and Google offer scalable infrastructure and managed services, catering to varying needs. End users, from startups to enterprises, leverage these platforms for streamlined automation and predictive analytics. Regulatory bodies ensure compliance and ethical use, shaping the framework for responsible AI deployment. Together, these entities foster innovation while addressing concerns of accessibility, transparency, and ethical implications in the rapidly evolving landscape of no-code AI solutions.
By Vertical, BFSI segment accounts for the largest market size during the forecast period.
The BFSI sector is increasingly embracing no-code AI platforms due to their ability to democratize AI adoption and accelerate digital transformation. These platforms empower financial institutions to create and deploy AI solutions without extensive programming skills, reducing development cycles and costs significantly. By leveraging no-code AI tools, banks and insurance companies can enhance operational efficiency, improve customer experiences with personalized services, and strengthen risk management through advanced analytics. Moreover, these platforms enable quick prototyping and iteration of AI applications, fostering innovation and agility within the sector. As regulatory pressures mount, no-code AI platforms also offer built-in compliance features, ensuring adherence to industry standards and enhancing trust in AI-driven decision-making processes. Overall, the growing adoption of no-code AI platforms in BFSI underscores their pivotal role in driving competitive advantage and operational excellence in the financial services landscape.
By Data Modality, text segment accounts for the largest market size during the forecast period.
Text data modality is rapidly gaining significant market share within the evolving landscape of no-code AI platforms. As businesses increasingly recognize the value of unstructured text data, these platforms enable seamless integration and analysis without complex coding requirements. From sentiment analysis to chatbot development and content categorization, the versatility of text data fuels innovation across industries. This growing adoption is driven by the accessibility and scalability offered by no-code platforms, democratizing AI capabilities for a broader range of users. As a result, organizations can extract actionable insights from textual sources with greater efficiency and accuracy, driving decision-making and enhancing customer experiences. The expanding market signals a shift towards more intuitive, user-friendly AI solutions tailored to harness the power of textual information effectively.
North America to account for the largest market size during the forecast period.
The no-code AI platform market in North America is witnessing significant growth, driven by the increasing adoption of AI technologies across various industries. The region's robust technological infrastructure, strong research and development capabilities, and presence of key market players have contributed to its dominance. These platforms empower businesses and individuals with varying technical backgrounds to create sophisticated AI solutions without extensive coding knowledge. From startups to established enterprises, industries such as finance, healthcare, and retail are leveraging these tools to streamline operations, enhance customer experiences, and drive innovation. With user-friendly interfaces and pre-built modules for tasks like data analysis, natural language processing, and predictive modeling, no-code AI platforms democratize access to advanced technologies.
Key Market Players
The major No-code AI platform solutions and service providers include IBM (US) , Google (US) , Microsoft (US) , AWS (US), Salesforce (US), C3 AI (US), H2O. ai (US), Qlik (US), Clarifai (US), DataRobot (US), Dataiku (US), SymphonyAI (US), Altair (US), Levity (Germany), Akkio (US), Aito (Finland), Obviously AI (US), Pecan AI (Israel), Kore.ai (US), Konverse AI (US), Yellow.ai (US), MokeyLearn (US), Roboflow (US), NanoNets (US), Noogata (Israel), Rasa (US), Builder.ai (UK), Appy Pie (US), Accern (US), RunwayML (US), and Bubble (US). These companies have used both organic and inorganic growth strategies such as product launches, acquisitions, and partnerships to strengthen their position in the no-code AI platforms market.
Scope of the Report
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Report Metrics |
Details |
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Market size available for years |
2019–2029 |
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Base year considered |
2023 |
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Forecast period |
2024–2029 |
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Forecast units |
USD Billion |
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Segments Covered |
Offering (solutions [by type { reporting & visualization tools, AutoML platforms, and automation platforms (flow builder tools, conversational AI building tools, business process automation tools, and others), and other solution types}, by deployment mode [cloud, and on-premises]}, and services), Technology (predictive analytics, deep learning, natural language processing, computer vision), Data Modality (text, image, video, audio & speech, and multimodal), Application ( workflow automation, text translation & generation, platform building, chatbot & virtual assistants, predictive lead scoring, digital workflow design, visual recognition & object detection, predictive customer churn, and other applications), Vertical (BFSI, retail & ecommerce, automotive, transportation & logistics, government & defense, healthcare & life sciences, telecommunications, energy & utilities, manufacturing, agriculture, IT/ITeS, media & entertainment, and other verticals). |
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Geographies covered |
North America, Europe, Asia Pacific, Middle East & Africa, Latin America |
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Companies covered |
IBM (US), Microsoft (US), Google (US), AWS (US), Salesforce (US), C3 AI (US), H2O. ai (US), Qlik (US), Clarifai (US), DataRobot (US), Dataiku (US), SymphonyAI (US), Altair (US), Levity (Germany), Akkio (US), Aito (Finland), Obviously AI (US), Pecan AI (Israel), Kore.ai (US), Konverse AI (US), Yellow.ai (US), MokeyLearn (US), Roboflow (US), NanoNets (US), Noogata (Israel), Rasa (US), Builder.ai (UK), Appy Pie (US), Accern (US), RunwayML (US), and Bubble (US). |
This research report categorizes the No-code AI platforms market based on offering (solutions [By type, deployment mode] & services), technology, data modality, application, vertical, and region.
Offering:
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Solutions
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By Type
- Reporting & Visualization Tools
- AutoML Platforms
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Automation Platforms
- Flow Builder Tools
- Conversational AI Building Tool
- Business Process Automation Tools
- Others
- Others
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By Deployment Mode
- Cloud
- On-Premises
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By Type
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Services
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Professional Services
- Consulting Services
- Deployment & Integration Services
- Support & Maintenance Services
- Managed Services
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Professional Services
By Technology:
- Predictive Analytics
- Natural Language Processing
- Deep Learning
- Computer Vision
By Data Modality:
- Text
- Image
- Video
- Audio & Speech
- Multimodal
By Application:
- Workflow Automation
- Text Translation & Generation
- Platform Building
- Chatbot & Virtual Assistants
- Predictive Lead Scoring
- Digital Workflow Design
- Visual Recognition & Object Detection
- Predictive Customer Churn
- Other Applications
By Vertical:
- BFSI
- Retail & eCommerce
- Automotive, Transportation & Logistics
- Government & Defense
- Healthcare & Life Sciences
- Telecommunications
- Energy & Utilities
- Manufacturing
- Agriculture
- IT/ITeS
- Media & Entertainment
- Other Verticals
By Region:
- North America
- Europe
- Asia Pacific
- Middle East & Africa
- Latin America
Recent Developments:
- In May 2024, IBM and Salesforce expand partnership to advance open, trusted AI and data ecosystems, combining IBM Watsonx with Salesforce Einstein for greater customer choice, responsible AI development, and industry-specific solutions to enhance customer experiences while safeguarding data and accelerating AI adoption through IBM Consulting and diverse partner ecosystem.
- In April 2024, C3 AI and Google Cloud have partnered to democratize generative AI with no-code tools on the Google Cloud Marketplace, making it more accessible to enterprises. This enables more companies to benefit from the technology and improve access to data, boost productivity, and deliver better personalized experiences.
- In April 2024, Google unveiled Vertex AI Agent Builder, a no-code tool designed to streamline the creation of AI agents. This tool simplifies the process of building and deploying conversational agents, empowering users to efficiently train and guide them for enhanced performance. With Vertex AI Agent Builder, users can easily develop and deploy production-ready conversational agents, enabling them to improve the accuracy and quality of responses from AI models.
- In October 2023, Salesforce announced the acquisition of Airkit.ai, which bolsters its Service Cloud division, integrating AI-powered customer service solutions. This addition complements existing capabilities, enriching the ecosystem for more advanced AI-driven customer experiences. This strategic move solidifies Salesforce's leadership in AI-driven customer engagement, leveraging its platform and ecosystem to maintain a competitive edge and stay at the forefront of innovation in the industry.
- In September 2022, Altair a global leader in computational science and AI, has signed a definitive agreement to acquire RapidMiner, a leading provider of advanced data analytics and machine learning (ML) software.
- In March 2022, JADBio, a leading automated machine learning no-code platform has teamed up with AWS to unveil the inclusion of its Automated Machine Learning platform on the renowned AWS Marketplace. It streamlines the process of discovering, testing, purchasing, and deploying software designed to operate seamlessly on AWS.
- In September 2021, Qlik acquired Big Squid, a leader in no-code automated machine learning (AutoML). This acquisition enhances Qlik’s offerings with advanced augmented analytics capabilities, including key driver analysis, predictive analytics, and what-if scenario planning.
Frequently Asked Questions (FAQ):
What is No-code AI platforms?
No-code AI platforms aim to democratize artificial intelligence by enabling users to deploy AI and machine learning models through a visual, code-free interface, often with drag-and-drop features. This category includes dedicated no-code AI tools and some automation tools, such as RPA software, that integrate AI capabilities within a no-code interface.
Which region is expected to hold the highest share in the no-code AI platforms market?
North America dominates the no-code AI platforms market, spearheading innovation and adoption. With a robust tech ecosystem and extensive investment in AI, the region's businesses leverage user-friendly tools to streamline operations and enhance productivity. From startups to enterprises, the allure of accessible AI solutions fuels rapid growth, solidifying North America's position in this burgeoning industry.
Which are key verticals adopting no-code AI platform solutions and services?
No-code AI platform offering is adopted by various verticals including BFSI, retail & ecommerce, automotive, transportation & logistics, government & defense, healthcare & life sciences, telecommunications, energy & utilities, manufacturing, agriculture, IT/ITeS, media & entertainment, and other verticals.
Which are the key drivers supporting the market growth for no-code AI platforms market?
The key drivers that propel the no-code AI platforms market are the growing need to reduce dependency on extensive coding expertise, and rising need to empowering rapid prototyping and collaboration with no-code AI platforms
Who are the key vendors in the market for no-code AI platforms market?
The key vendors in the global No-code AI platforms market include IBM (US), Microsoft (US), Google (US), AWS (US), Salesforce (US), C3 AI (US), H2O. ai (US), Qlik (US), Clarifai (US), DataRobot (US), Dataiku (US), SymphonyAI (US), Altair (US), Levity (Germany), Akkio (US), Aito (Finland), Obviously AI (US), Pecan AI (Israel), Kore.ai (US), Konverse AI (US), Yellow.ai (US), MokeyLearn (US), Roboflow (US), NanoNets (US), Noogata (Israel), Rasa (US), Builder.ai (UK), Appy Pie (US), Accern (US), RunwayML (US), and Bubble (US). .
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The research study for the no-code AI platforms market involved extensive secondary sources, directories, journals, and paid databases. Primary sources were mainly industry experts from the core and related industries, preferred No-Code AI platform providers, third-party service providers, consulting service providers, end users, and other commercial enterprises. In-depth interviews were conducted with various primary respondents, including key industry participants and subject matter experts, to obtain and verify critical qualitative and quantitative information and assess the market’s prospects.
Secondary Research
The market size of companies offering No-Code AI platforms solutions, and services was determined based on secondary data available through paid and unpaid sources. It was also arrived at by analyzing the product portfolios of major companies and rating the companies based on their performance and quality.
In the secondary research process, various sources were referred to identify and collect information for this study. Secondary sources included annual reports, press releases, and investor presentations of companies; white papers, journals, and certified publications; and articles from recognized authors, directories, and databases. The data was also collected from other secondary sources, such as journals, government websites, blogs, and vendors' websites. Additionally, no-code AI platform spending in various countries was extracted from the respective sources. Secondary research was mainly used to obtain key information related to the industry’s value chain and supply chain to identify key players based on solutions, services, market classification, and segmentation according to offerings of major players, industry trends related to offering, technology, data modality, application, and regions, and key developments from both market- and technology-oriented perspectives.
Primary Research
In the primary research process, various primary sources from both the supply and demand sides were interviewed to obtain qualitative and quantitative information on the market. The primary sources from the supply side included various industry experts, including Chief Experience Officers (CXOs); Vice Presidents (VPs); directors from business development, marketing, and No-Code AI platforms expertise; related key executives from No-Code AI platforms solution vendors, System Integrators (SIs), professional service providers, and industry associations; and key opinion leaders.
Primary interviews were conducted to gather insights, such as market statistics, revenue data collected from solutions, and services, market breakups, market size estimations, market forecasts, and data triangulation. Primary research also helps understand various trends related to technologies, applications, deployments, and regions. Stakeholders from the demand side, such as Chief Information Officers (CIOs), Chief Technology Officers (CTOs), Chief Strategy Officers (CSOs), and end users using No-Code AI platforms solutions, and services, were interviewed to understand the buyer’s perspective on suppliers, products, service providers, and their current usage of No-Code AI platforms solutions, and services, which would impact the overall No-Code AI platforms market.
The following is the breakup of primary profiles:
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Market Size Estimation
Multiple approaches were adopted for estimating and forecasting the no-code AI platforms market. The first approach involves estimating the market size by summation of companies’ revenue generated through the sale of solutions and services.
Market Size Estimation Methodology-Top-down approach
In the top-down approach, an exhaustive list of all the vendors offering solutions, and services in the No-Code AI platforms market was prepared. The revenue contribution of the market vendors was estimated through annual reports, press releases, funding, investor presentations, paid databases, and primary interviews. Each vendor's offerings were evaluated based on the breadth of offerings, technology, data modality, applications, and vertical. The aggregate of all the companies’ revenue was extrapolated to reach the overall market size. Each subsegment was studied and analyzed for its global market size and regional penetration. The markets were triangulated through both primary and secondary research. The primary procedure included extensive interviews for key insights from industry leaders, such as CIOs, CEOs, VPs, directors, and marketing executives. The market numbers were further triangulated with the existing MarketsandMarkets’ repository for validation.
Market Size Estimation Methodology-Bottom-up approach
In the bottom-up approach, the adoption rate of No-Code AI platforms solutions, and services among different end users in key countries with respect to their regions contributing the most to the market share was identified. For cross-validation, the adoption of No-Code AI platforms solutions, and services among industries, along with different use cases with respect to their regions, was identified and extrapolated. Weightage was given to use cases identified in different regions for the market size calculation.
Based on the market numbers, the regional split was determined by primary and secondary sources. The procedure included the analysis of the No-Code AI platforms market’s regional penetration. Based on secondary research, the regional spending on Information and Communications Technology (ICT), socio-economic analysis of each country, strategic vendor analysis of major No-Code AI platforms providers, and organic and inorganic business development activities of regional and global players were estimated. With the data triangulation procedure and data validation through primary values, the exact values of the overall No-Code AI platforms market size and segments’ size were determined and confirmed using the study.
Top-down and Bottom-up approaches
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Data Triangulation
After arriving at the overall market size using the market size estimation processes as explained above, the market was split into several segments and subsegments. To complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment, data triangulation and market breakup procedures were employed, wherever applicable. The overall market size was then used in the top-down procedure to estimate the size of other individual markets via percentage splits of the market segmentation.
Market Definition
No-code AI platforms aim to democratize artificial intelligence by enabling users to deploy AI and machine learning models through a visual, code-free interface, often with drag-and-drop features. This category includes dedicated no-code AI tools and some automation tools, such as RPA software, that integrate AI capabilities within a no-code interface.
According to C3 AI, no-code is an approach that allows users to create application functionality without writing traditional code. Users design applications and workflows by linking building blocks in a graphical user interface and selecting implementation details via a menu-driven interface. Built on a model-driven architecture, no-code platforms use a declarative approach for writing simplified functions and expressions. These environments target business users or analysts with limited programming experience but substantial domain knowledge to create or modify workflows. They also enable experienced programmers to make quick changes without altering code.
Stakeholders
- No-Code AI Platforms Solution Providers
- AI Technology Providers
- Professional and Managed Service Providers
- Industry Associations
- Research Institutions
- System Integrators
- Technology Consultants
- Independent Software Vendors (ISVs)
- Consulting Firms
- Value-Added Resellers (VARs)
- Government Agencies
Report Objectives
- To define, describe, and predict the No-Code AI platforms market by offering (solutions, and services), technology, data modality, application, and vertical.
- To provide detailed information related to major factors (drivers, restraints, opportunities, and industry-specific challenges) influencing the market growth.
- To forecast the market size of segments with respect to five main regions: North America, Europe, Asia Pacific, Middle East & Africa, and Latin America.
- To strategically analyze micromarkets with respect to individual growth trends, prospects, and contributions to the overall market.
- To analyze opportunities in the market and provide details of the competitive landscape for stakeholders and market leaders.
- To analyze competitive developments, such as partnerships, mergers and acquisitions, and product developments, in the market.
- To analyze the impact of the recession across all the regions in the No-Code AI platforms market.
Available Customizations
With the given market data, MarketsandMarkets offers customizations as per the company’s specific needs. The following customization options are available for the report:
Product Analysis
- The product matrix provides a detailed comparison of the product portfolio of each company.
Geographic Analysis as per Feasibility
- Further breakup of the North American No-code AI platforms Market
- Further breakup of the European Market
- Further breakup of the Asia Pacific Market
- Further breakup of the Middle East & Africa Market
- Further breakup of the Latin American No-code AI platforms Market
Company Information
- Detailed analysis and profiling of additional market players (up to five)
Growth opportunities and latent adjacency in No-Code AI Platforms Market