Generative AI (GenAI) has the potential to automate a broad range of tasks, which boosts productivity, offers new opportunities, and reduces costs as it does not require technical skills to use generative AI tools or software and is widely available. Tech visionaries believe that GenAI will be accessible to workers worldwide to access information and skills across broader roles and business functions. This makes generative AI one of the most disruptive trends of this decade. According to Gartner, by 2026, more than 80% of companies will have employed generative AI APIs and models and implemented GenAI-enabled apps in production environments, compared to less than 5% in 2023.
In this article, we will explore what democratized generative AI is, how it works, and its current applications.
What is Democratized Generative AI?
Traditionally, artificial intelligence (AI) technologies were limited to technical experts; however, the growing availability of democratized generative AI marks the beginning of a paradigm shift in the technology landscape. The democratized GenAI aims to make AI technology more accessible to a wider range of audiences and focuses on providing user-friendly tools and platforms that allow users to create and interact with AI-powered models.
Democratization enables users from various fields, such as journalism, marketing, the arts, and others, to leverage AI algorithms and models to enhance their tasks and gain valuable insights from the given data.
Why Is Generative AI Democratization so Transformational?
At its core, generative AI democratization revolves around numerous data sources and insights from which numerous businesses and institutes can benefit. From decision-making in business to better public services in government sectors, generative AI reduces business costs by, for example, cutting expenditures and supporting development in working on important tasks.
Here are some specific areas where democratized generative AI can be transformational:
Reducing Entry Barriers
AI democratization reduces the entry barriers to using AI and machine learning (ML) algorithms for businesses and individuals so that they can use open-source datasets to train their AI models across any corner of the world without any financial investment.
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