Low code no code AI platforms function much like other LCNC platforms, allowing non-technical users to leverage AI.  It employs a drag-and-drop interface that enables customers to personalize AI solutions with pre-built models and data connectors. No code platforms go a step further— users can create and deploy artificial intelligence models and applications without any code. Its immense potential lies in the fact that even organizations with limited programming knowledge can unleash the power of AI.


Market Outlook for No Code Low Code AI

Low-code development technologies will reach $26.9 billion in 2023, up 19.6% from 2022, according to Gartner. By 2025, 70% of new workplace apps will use LCNC technology, a big jump from less than 25% in 2020. The spurt results from companies using complex legacy technologies and techniques having to formulate resilient responses to changing market requirements. This has increased the demand for quick and cost-effective applications, fueling the advent of low-code and no-code (LCNC) development platforms.


The growing popularity of LCNCs also reflects the growing requirement to develop applications and digitize processes more quickly. Despite the AI programmer scarcity, LCNC AI helps firms innovate and go to market more quickly.


Use Cases of Low Code No Code AI
  • Predictive modeling: Low-code and no-code AI platforms can be used to build predictive models, such as those used for customer segmentation, fraud detection, and demand forecasting.
  • Data analysis: These platforms can analyze large amounts of data and extract insights, such as those used for customer profiling, market research, and social media analytics.
  • Natural Language Processing (NLP): Low-code and no-code AI platforms can build NLP models, such as sentiment analysis, language translation, and text summarization.
  • Computer vision: These platforms can build image and video recognition models, such as object detection, facial recognition, and optical character recognition (OCR).
  • Chatbot and virtual assistance: Low-code and no-code AI platforms can build chatbots and virtual assistants to automate customer service, technical support, and sales tasks.
  • Business process automation: These platforms can automate repetitive tasks, such as data entry, invoicing, and compliance checks.
  • Internet of Things (IoT) and edge computing: Low-code and no-code AI platforms can build and deploy AI models to IoT devices and edge computing systems to improve device performance and reduce data transmission costs.


Industries with High Usage of LCNC AI Applications 

Currently, many industries and organizations use low-code and no-code AI, including:

  • Healthcare: Low-code and no-code AI platforms are used to analyze medical images and patient data, predict patient outcomes and automate administrative tasks
  • Finance: These platforms detect fraud, predict credit risk, and automate compliance tasks.
  • Retail and e-commerce: Low-code and no-code AI platforms are used to personalize product recommendations, predict demand, and optimize pricing.
  • Manufacturing:These platforms optimize production processes, predict equipment failures, and improve quality control.
  • Transportation and logistics: Low-code and no-code AI platforms optimize routes, predict demand, and automate inventory management.
  • Government and public sector Low-code and no-code AI platforms predict crime, optimize traffic flow, and automate benefits processing.
  • Media and advertising: These platforms target and personalize advertising, predict viewer engagement, and automate content creation.
  • Energy: Low-code and no-code AI platforms optimize energy production, predict equipment failures, and automate compliance tasks.

As AI technologies evolve, many industries and business areas will adopt low-code and no-code platforms.


The Future of Low Code, No Code AI

We can expect low-code and no-code AI platforms to become increasingly automated and self-service oriented. As a result, users can design and deploy AI models and applications with minimal support. As LCNC AI platforms combine with cloud, IoT, and edge computing, they can provide more powerful and adaptable AI solutions.


Moreover, with greater democratization of AI, organizations can leverage AI for strategic decision-making rather than for executive tasks. For instance, LCNC systems will focus more on providing transparent and interpretable models that consumers can trust. Natural language processing (NLP) will enable LCNC systems to handle complex tasks, including language translation, sentiment analysis, and text summarization.


Wrapping Up

As companies embrace AI, low-code and no-code AI markets are projected to increase. On the other hand, a major disadvantage could be an increasing dependence on a single provider, leading to compromised security. However, the benefits far outweigh the risks.  The frameworks are based on coding languages such as PHP, Python, and Java. Users work in graphically rich simulation environments where they can drag and drop program components, link them, and observe what happens. LCNC platforms are also minimal in maintenance and extremely scalable, allowing operations to achieve enterprise-grade performance, have great readability, expedite debugging and code updates, and allow faster iteration. 


As a result, the LCNC wave could be a major driving force toward becoming what Gartner has termed a “Composable Enterprise.”  

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