Taha Douaji
3 mins
.
August 29, 2024
Source: freekpik.com
According to Forbes, around 90% of digital data is unstructured. These data do not have any predefined format or organisation but carry immense potential to transform a business. Examples include documents, multimedia files, emails, reviews, social media posts, etc.
Since the rise of generative AI, a wide range of industries are investing in the value of unstructured data.
As datasets become more complex and diverse, it becomes more difficult to collect and organise them. While previous AI initiatives could only generate predictions from structured data, generative AI unearths hidden insights from massive heterogeneous datasets and creates original outputs.
Thus, generative AI can enhance creativity, streamline processes, and unlock new revenue streams for businesses. In this article, we will explore what generative AI can do for your business. We will focus on its core applications and future prospects.
Here’s what we will discuss:
Generative AI is a subset of artificial intelligence that can create original content, such as texts, images, videos, designs, etc. With higher computing power, it is unlike traditional AI which relies on structured data for predictable outcomes. Generative AI can be a game changer for businesses seeking innovative breakthroughs.
It consists of algorithms often trained on vast amounts of unlabelled data to create new and seemingly original content. Their task is to identify the underlying pattern from a highly unstructured dataset. In the process, these algorithms understand the nuances and variations within that data and create new instances that are similar to but not exact replicas of the training data.
At its core, Generative AI models rely on probability and deep learning and are designed to predict the likelihood of a particular output. Furthermore, they can iteratively improve their predictions to create more accurate and realistic content. This process is often referred to as "sampling". Examples of generative AI models include ChatGPT, Dall-E, GitHub Copilot, etc.
Following are the 3 primary types of Generative AI:
Generative Adversarial Networks (GANs):
Widely used in generating realistic images, and video synthesis, GANs are among the most powerful generative models. They consist of two neural networks, the generator and the discriminator. The generator creates new data instances (e.g., images, text, sound) from random noise, while the discriminator distinguishes between real and fake data.
Variational Autoencoders (VAEs):
Variational Autoencoders (VAEs) are a type of generative AI model that learn to compress data into a simpler form, known as a "latent space," and then reconstruct it back into the original or a new version. This process enables them to generate new content that resembles the original data. VAEs are particularly useful for creating new images, detecting unusual patterns, and reducing the size of large datasets while retaining essential information. For example, VAEs can generate new, unique images of faces by learning from a large dataset of existing faces.
Transformer Models:
Transformer-based models, such as GPT (generative pre-trained transformer), are revolutionising text generation. They use attention mechanisms to process sequences of data and generate coherent and contextually relevant text. They are widely used in applications like chatbots, content creation, and language translation.
Generative AI is transforming businesses by enabling innovative applications across various domains. Here’s a detailed look at how generative AI is reshaping various aspects of business operations.
Customer support is an area where generative AI can play an important role. AI-driven chatbots powered by ChatGPT can provide quick, accurate responses to customer inquiries and enhance overall customer experience. They can offer personalised recommendations by analyzing customer interactions.
A retailer could use generative AI to build a chatbot that helps customers find products, answer their questions, and process orders with minimal human intervention.
Similarly, in the healthcare industry, AI-driven chatbots can assist patients with appointment scheduling and answer health-related questions.
Leveraging generative AI for customer support can significantly enhance customer experience and improve operational efficiency.
Generative AI is making strides in business process automation by optimising workflows. One such instance is Intelligent Document Processing (IDP). It is a powerful use case where AI automates the handling of large document volumes. These include contracts, invoices, and compliance forms. IDP platforms can classify, extract, and validate data from these documents, reducing processing times and errors.
For example, construction companies are streamlining financial document management, including invoices, delivery notes, and bank cheques, using an IDP called Pedant. It leverages artificial intelligence (AI) to automate data extraction, classification, and verification from such documents.
Generative AI is transforming product development through faster, more efficient design processes. AI tools can analyse vast amounts of data to generate optimised product designs based on cost, durability, and aesthetics. This accelerates the design cycle and reduces costs associated with traditional methods.
In manufacturing, AI-driven design tools can analyse constraints like weight, strength, and material usage to generate optimised designs for products such as car parts or airplane components. Additionally, generative AI can help create personalised product designs based on individual customer preferences, ensuring products meet the unique needs of customers.
By integrating AI into product development, businesses can bring innovative products to market more efficiently and stay ahead of the competition.
Generative AI offers substantial advantages in data analytics by uncovering hidden patterns and trends. AI models can process and synthesise large amounts of data to provide actionable insights, helping businesses optimise operations, identify growth opportunities, and enhance customer satisfaction.
For example, AI can perform sentiment analysis on social media data to gauge customer perceptions about a brand, product, or service. By understanding customer sentiment, businesses can refine marketing strategies, enhance customer experiences, and proactively address potential issues. AI can also predict customer churn by analysing feedback and behavioural patterns, allowing companies to implement personalised retention strategies.
Generative AI is useful in coding and software development. It helps developers create efficient, error-free code more quickly. AI-powered tools can analyse existing codebases to suggest improvements, generate new code snippets, and even assist in bug detection and resolution. They significantly reduce development time and enhance code quality.
For example, AI tools like Codex (used in GitHub Copilot) can assist developers by generating code based on natural language descriptions of functionality. They automate repetitive coding tasks and provide recommendations for code completion. This allows developers to concentrate more on complex logic and design.
As technology evolves, we can expect generative AI to become more sophisticated and capable of generating increasingly complex outputs. It will likely play a critical role in enhancing creativity, automating complex tasks, and optimising decision-making processes.
However, alongside its advancements, generative AI must be developed responsibly, with a focus on addressing ethical concerns and mitigating potential biases. Organisations adopting generative AI will need to prioritise ethical guidelines and strict governance to harness its benefits completely.
In conclusion, the future of generative AI lies in its ability to innovate and transform industries while being aligned with ethical standards. Companies that embrace this technology thoughtfully and strategically will be well-positioned to lead in the next era of digital transformation.
