Generative AI
Unlocking the True Business Potential
The rapid advancement in technology is transforming business and society. Generative AI has introduced a new epoch of human efficiency and productivity, affecting society and industry in ways unexplored.
The Unimaginable speed and scale of execution stand as a definite promise by Generative AI to enterprises. Automating business processes to deliver exceptional customer and employee experience faster than before is the magic generative AI brings to enterprises.
But the key lies in identifying well-scoped business challenges that create commercial viability for the enterprises. It drives the adoption to a grander scale.
While use cases like automating repetitive tasks or synthesizing insights from unstructured data and documentation is a proven story, measuring the business impact tied to economic benefit, customer service, sustainability outcomes, or business efficiencies is crucial in transforming workplaces.
How Generative AI Evolved?
Generative ai technology is not new to the tech world. It was first introduced in 1960 in chatbots. However, it gained momentum after introducing GANs (Generative Adversarial Networks), a machine learning algorithm that helps generate videos, audio, and images.
The root of Generative AI is in procedural modelling and computer graphics. However, its true capabilities were unlocked with deep learning technology called foundation models. Foundation models made revolutionary changes in generative ai.
Now users can generate complex natural language text, images, codes, and other tasks. Unlike the previous models, foundation models deep learning can process huge volumes of unstructured data.
Artificial intelligence applications trained on these models can execute several tasks. Moreover, Generative Adversarial Networks and Variational Autoencoders revolutionized generative AI modelling making it more effective for users. Advanced architectures such as flow-based models and transformers make generative AI more user-friendly.
One of the most popular generative ai technology applications ChatGPT was released in 2022, Nov. After four months, OpenAI launched GPT-4 with LLM (Large Language Models) with more improved capabilities.
Understanding How ChatGPT Works
There are two major phases of ChatGPT: Pre-training and Inference. The first phase is pre-training, which includes gathering data, and the second phase is inference which focuses on user interactions. The real game is the scalability of the pre-training phase. The non-supervised pre-training method, which is also known as transformer-based language, modelling plays an important role in understanding natural language. It allows users to input as much information as they can to let the AI learn and understand the natural language syntax and semantics.
Training Datasets
There are multiple conversational datasets such as Persona chat, Ubuntu dialogue corpus, and Cornell movie dialogs on which ChatGPT is trained. These datasets enable the users to have more conversational data from ChatGPT.
Dialogue Management
The ability of ChatGPT to engage in multiple conversations and offer personalized answers comes from advanced dialogue management capabilities. It helps in building a more engaging and human-like experience for the users.
Advantages of ChatGPT
Learning
Ability
ChatGPT learns and adapts based on the new interactions and prompts, improving the outcomes.
Human-Like Communication
ChatGPT communicates in a human-like manner.
Contextual
Understanding
ChatGPT provides near to accurate responses customized to user needs based on the prompts.
Open-ended
Responses
One can continue with ChatGPT to get additional information until they find the relevant result.
Use Cases
Enterprises can adopt ChatGPT as virtual agents to interact with customers and handle their queries 24/7. It provides personalized support and can handle a wide range of customer queries. In case of any complex query, it escalates it to human agents. This way, enterprises are improving customer experience and reducing response time.
Research revealed that a company with 5000 customer support agents reduces the time spent handling the issues by 9 percent, increases the issue resolutions by 14 percent, and reduces the agent’s attritions and requests to speak to the manager by 25%.
Source: Accenture
ChatGPT has a massive impact on the marketing and sales category. It provides them the opportunity to develop highly personalized email drafts and social media messages based on the target audience’s interactions and behaviours. There is a list of activities that ChatGPT can help with for marketing and sales professionals, such as producing product descriptions, social media posts, headlines, slogans, and other branding materials.
ChatGPT can help enterprises to troubleshoot some common technical problems. It provides step-by-step instructions for some common software and hardware troubles. Enterprises can embed ChatGPT in IT support departments to reduce response time, minimize downloads and improve IT efficiency.
Software engineers can use ChatGPT in augmented coding and pair programming, which improves their productivity by 20-45 percent because of reducing the time spent on particular activities such as code correction, root-cause analysis, creating the initial code draft, and other activities. One study revealed that software engineers who used Microsoft’s GitHub Copilot finished their tasks faster than engineers who did not.
Source – GitHub
While every organization is excited about the trend of Generative AI, the application is still a less travelled route. To navigate the buzz and realize the true potential, join our workshop, InnovAIte where we’ll dive deep into the cutting-edge possibilities of generative AI and explore how it can revolutionize your business.
