Generative AI

What is Generative AI?

Unlike traditional AI, which typically focuses on analysing or recognising data, generative AI goes a step further by generating entirely new outputs that mimic human-like creativity.

Generative AI has the potential to transform everyday business operations by automating tasks, enhancing creativity, and improving customer experiences.

Where Generative AI Can Be Used

Content Creation & Marketing
Generative AI models can generate blog posts, social media captions, ad copy, and product descriptions, as well as creating personalised experiences
Product Design and Customisation
Personalise products by analysing individual preferences and generating custom designs or recommendations
Sales and Lead Generation
Analyse market data and customer profiles to generate targeted leads. By looking at historical sales data, generative AI can also predict future trends and forecasts
Finance and Accounting
Generate financial reports, income statements, and balance sheets, AI tools can also create invoices based on predefined templates and customer data
Product and Service Optimisation
Generate insights from customer feedback, reviews, and surveys, assist with running and analysing A/B tests and suggest optimisations for process automation
Legal and Compliance
Automatically generate and analyse legal contracts, non-disclosure agreements (NDAs), and other business documents based on standardised templates, as well as detecting potential issues or risks
Supply Chain and Logistics
Optimise inventory levels by predicting demand and creating restocking recommendations, reducing overstocking and understocking issues

How Generative AI Works

Generative AI typically uses advanced techniques from deep learning, particularly:

Generative Adversarial Networks (GANs): Consists of two models — a "generator" that creates data and a "discriminator" that evaluates it. The generator tries to produce realistic content, while the discriminator tries to distinguish between real and generated content. Through this process, the generator gets better over time.
Transformers: Used in models like GPT, transformers are deep learning architectures that excel at processing sequential data. They can understand context and generate content that follows logical patterns and coherence.
Variational Autoencoders (VAEs): VAEs are used in generative tasks to create new variations of data, such as images, while ensuring the generated data maintains a realistic structure.

Types of Generative AI

Text Generation: Models like GPT (Generative Pre-trained Transformer) can generate human-like text, answer questions, write stories, or even simulate conversations.
Tokenisation: The process of breaking text into smaller units, like words or phrases, called "tokens."
Image Generation: Models like DALL·E or MidJourney can create images from text descriptions, producing visuals that never existed before.
Video Generation: Emerging models can generate or manipulate video content, creating new scenes, animations, or deepfake-like material based on text or existing videos.

Benefits of Generative AI for Businesses:

Increased Efficiency: Automate repetitive tasks, such as content generation or customer support to save time and reduce operational costs.
Scalability: Generative AI tools can scale quickly, allowing businesses to handle large volumes of data, customer interactions, or content without needing to add significant human resources.
Cost Savings: By automating tasks traditionally done manually, businesses can save money on labour costs and reduce the risk of human error.
Enhanced Creativity: AI can serve as a powerful tool for brainstorming and generating new ideas, whether for marketing campaigns, product designs, or customer solutions.
Personalisation at Scale: Businesses can offer a personalised experience for each customer, even as their customer base grows, without needing to manually tailor each interaction.