At the very least, you may have heard that students are frenzied about ChatGPT’s application to use it for doing their homework or school projects.

Or you could get to know that applicants are seen using ChatGPT to write cover letters.

It is a very basic level application of ChatGPT, or Generative Pre-trained Transformer, that underpins the ChatGPT, a web chat interface to be used as a QnA tool for query resolution.

But, can you guess, amidst the economic challenges, what else is currently at the epicenter of the discussion points for CEOs?

While every business strategy is to optimize cash flow or working capital right now, CEOs are strategizing different ways in which they can putGenerative AI to work that yields high-level business value for them. And to do this, their eagerness to invest in emerging technologies like Generative AI is quite palpable.

Behind the boardroom meets, Generative AI hovers over the minds of the CEOs.

What the latest report, “What CEOs talked about,” brought to the table is quite surprising.

It is revealed that Generative AI topped the discussion agenda for CEOs in Q2/2023 compared to the other two vulnerable matters (bank troubles and uncertain economy).

While Generative AI and its related use cases and applications continue to increase for boardroom discussions, the keyword itself sees a significant rise of +129% in the last quarter.

Can you assume why?

Why is it that about 55% of CEOs surveyed for Summer 2023 Fortune/Deloitte CEO Survey Insight, confirm to have started experimenting with Generative AI, or 79% of them believe Gen AI will increase efficiencies?

If you want to know why, here’s a bit of a clue for you 一 it is only because Generative AI holds a huge promise for business functions, given the fact it is built on large language modelsmore powerful than any regular AI models best applied to generate new responses or content to reimagine variousindustry-specific use cases.

Let’s dive deep!

If you know what artificial intelligence is, Generative AI seems simple to understand too.

Generative AI stands for Generative Artificial Intelligence,meaning its architecture is also fed with machine learning components similar to AI tools. However, one striking difference between regular AI and Generative AI is their ability to perform functions.

As with AI, you can use data to concentrate on one area. For example, collecting transactions-related data and using it to create a model that identifies anomaly patterns and helps detect fraud.

Similarly, AI models can tap into limited business function data and help improve process efficiency. Overall, traditional AI aims to make predictions and deliver results ahead of time.

However, Generative AI can perform outside of one specific business function. As its name suggests, ‘Generative’; it usually performs as a generator model that can generate anything using a prompt in real time.

A prompt is a question in a Gen AI model interface that processes data to build a relationship with the input and surfaces the most relevant output.

For example, if you ask a Generative AI platform, “What is the difference between a cow and a bull?” it finds out the patterns and features of the animals and provides appropriate answers.

Generative AI does this by using its large language model architecture, which harnesses massive corpora of texts, primarily extracted from wikis, the internet, ebooks, websites, research papers, and lots more, to apply algorithms and find answers using deep learning or neural network-based technologies.

If you want to write code, Generative AI does that. Or if you want Gen AI to find errors in codes, Generative AI will also do that.

The possibilities are immense, more than what you can do with traditional AI tools.

Generative AI accesses massive datasets, underpinning natural language processing (NLP) models such as Large Language Models.

These models have two versions.

• GANs or Generative Adversarial Networks

• Transformer-based models

For a basic-level understanding of how Generative AI works, let’s assume that a prompt is inserted into a Gen AI interface. Gen AI synthesizes or breaks down data,, matches the most relevant answers in the datasets, and provides the output.

The behind-the-scenes natural language processing to surface appropriate responses using Gen AI models is intricate.

Both GANs and Transformer models apply different approaches to generate output.

Generative Adversarial Networks Models

In this model, the primary objective of GANs is to identify a difference between a fake or original sample and provide the correct output, the result of which also redirects to the underlying neural networks to improve their performance.

GANs consist of two parts,

• Generator - a neural network that deliberately generates fake data when fed with random input. Interestingly, what Generator produces becomes training data for the discriminator.

• Discriminator - also a neural network to identify between fake samples created by Generator and original data from the training dataset.

The Discriminator uses a binary classifier to detect a difference between fake and real. So, if the classification result is close to 0, it is considered fake; while it is closer to 1, it is real.

So, whatever the result is, it is updated in any of the neural networks and provides a template for both to learn.

The Discriminator neural network learns from the negative sample results that the Generator produces and thus improves its accuracy in identifying real data. And vice versa, the Generator NN does the same to fake data.

A plethora of functions can be accomplished using GAN-based Generative AI models that include,

• Removing noise from data,

• Image-to-image translation,

• 3D object generation,

• Face frontal view generation, etc.

All of these applications can provide massive opportunities for healthcare use cases.

Transformer Models

When discussing transformer models, they are widely used now for a variety of use cases across various business functions.

Transformer models are basically built with massive datasets or deep learning neural networks known as large language models or natural language models to parse human language.

More often, Transformer models are also called Foundation modelsdue to the flexibility to fine-tune with industry-specific data and use them to perform business-specific tasks.

The most common example of a Transformer model is none less than the popular Generative AI chat interface - ChatGPT.

Look at its suffix. GPT stands for Generative Pre-trained Transformer.

It means GPTs are pre-trained with a huge corpus of data to parse any natural language and mimic human intelligence to produce accurate output.

However, the more accurate data is the more accurate the output. If a GPT contains wrong or faulty data, it produces wrong information.

Transformer models synthesize data using their encoder and decoder components to provide the output.

Simply put, a transformer model encodes input data, then decodes it with the expected output result, and then generates an output.

The underlying functioning of the transformer model is to parse data sequence-to-sequence using semi-supervised or unsupervised learning and produce the response.

When it focuses on sequence-to-sequence learning, it means a transformer model is trained to identify the next sequential word or phrase in a query and to produce output based on the same theory.

At the core, a Generative Pre-trained Transformer model identifies contexts in the input rather than finding words that match the input.

For example, if a user wants to ask, “Who is the President of the US?”, the transformer model would encode these words and sends them for decoding. A transformer model would then search its database, build a relationship with the input, and provide the right output: "Joe Biden is the present President of the US.”

A transformer model consists of two parts 一

• An encoder converts input sequences into tokens, turns them into vector embeddings or numeric representations, and transfers them to the decoder.

• A decoder matches contexts and sequences between encoded inputs and probable output sequences and then produces the right output.

OpenAI’s ChatGPT or GPT-3, BERT, or RoBERTa are a few examples of transformer models.

These models can either be fine-tuned with specific industry data or integrated with the business applications through the API layer to perform specific business operations.

GPTs can be used to apply to various use cases, such as,

• Answering questions and answers to users

• Aiding customer support to provide product recommendations

• Solving customer problems in real-time

• Drafting a sales document in various formats, i.e., excel, slides, etc

• Automating various tasks such as text generation, email copy generation, etc

The sudden rise in popularity of ChatGPT has reopened the discussion of Generative AI or large language models and triggered an AI race.

Generative AI is not a new phenomenon. Businesses of all sizes are keen to evaluate the best aspect of this emerging technology and apply it to their workflows to maximize business benefits.

Let’s find out how Generative AI has evolved.

Generative AI milestones or timeline

• 2014 - GANs or Generative Adversarial Networks introduced by Ian Goodfellow

• 2015 - An attention model by Dzmitry Bahdanau to reduce the complexity of recognizing longer sentences by considering only words to generate the right response

• 2017 - A transformer model by Ashish Vaswani to work based on attention mechanisms and deep neural networks instead of recurrent neural networks

• 2018 - GPT 1 by OpenAI built with supervised learning via manually labeled data

• 2019 - GPT 2, the second foundation series from OpenAI, with 1.5 billion parameters 一, a 10-fold increase in parameter count, and training datasets

• 2020 - GPT3, a decoder-only transformer model, trained on 175 billion parameters that use between zero-shot and few-shot learning

• 2022 - GPT 3.5 Turbo, popularly known as ChatGPT, to provide responses from the Internet up to 2021

• 2023 - GPT 4, the extended version of GPT 3.5, known to be a large multimodal model to receive inputs in the form of texts and images and produce text output

As soon as GPT 3.5 was released, several other companies join the large language model race.

• Image generation transformer model Stable Diffusion arrived as a surprise. Mid-journey and DALL-E hold a similar promise to reimagine image creation from scratch.

• GitHub’s Copilot emerges as a great companion for developers to ease their coding jobs.

• Salesforce integrates Generative AI to reimagine their CRM applications for marketers.

From supply chain to logistics, finance, healthcare, to fashion, Generative AI has massive scopes to apply across various business functions and tap into real-time benefits.

Let’s discuss enterprise use cases of Generative AI for your business functions.

Customer support redefined

With the application of question and answer, customer support can reimagine how they interact with customers and address issues. One specific use case is automating conversations with customers and allowing them to self-service their issues.

Examples

Example 1: If a customer wants to review a product with different model versions, a Gen AI-based chat interface can help recommend better options by analyzing her current conversation patterns and surfaces models that align with her objectives. For example, if it is observed from the ongoing conversation that the customer can spend a good amount, the support platform recommends a choice based on the observation and help place an order.

Example 2: Generative AI aims to cut short the customer response time by allowing the customer support agent to grab instant chat history and help prepare a better response to handle the call. As a result, unlike traditional customer support, a customer receives better clarification of their questions, and on the other hand, there is no wait time for other customers.

Enhancing employee experience

Enterprises can build seamless employee experience with Generative AI built on their enterprise workflows.

Internal employee communication channels or ITSM tools that integrate Gen AI solutions can empower employees to solve their problems on their own.

Examples

Example 1: A conversational AI platform from Workativ applies Generative AI properties to its underlying architecture to help enterprise leaders build their own LLM-powered KB and provide industry-specific answers in a straightforward manner to solve employee issues in real-time. The best thing about the conversational platform is that it offers straightforward responses in the chat interface so employees can access information and use it to solve their problems.

For example, if a user needs to troubleshoot a paper jam in the printer, a Gen AI-powered chatbot would ask several related problems and then offer the right answer that helps address the issue.

Example 2: if an employee wants to know how to claim insurance for a particular treatment, a workplace chatbot powered by Gen AI will surface the information to help him raise a claim without friction.

Easing IT operations

For IT teams, IT operations are an everyday hardship. They need to be steady in ticket handling and provide rapid yet meaningful assistance that solves problems quickly.

But, tasks such as IT asset management are repetitive and time-consuming, causing IT fatigue.

With applications layered with Generative AI properties, IT teams can streamline workflows, more flexible and agile than traditional AI, and cut off friction from the IT journeys.

Examples

Example 1: An ITSM manager can use Generative AI and steadily track employee devices and warn them of serious issues with their devices. If it is predicted that an employee is approaching a password expiry, a notification is escalated for the employee to help him set his password before it expires and causes disruptions in operational efficiency.

Example 2: Generative AI helps with real-time monitoring and automation. So, an incident is detected, it streamlines workflows by sending an incident mitigation plan to the right person and helps reduce the impact.

Freeing up the HR team

Onboarding and offboarding is the most mundane work for HR teams. An HR team can be seen juggling administrative work and onboarding operations. The result is either of the work can happen haphazardly.

The wait time for new hire onboarding increases, making him impatient, whereas administrative tasks pile up, creating a backlog.

Examples

Example 1: Generative AI streamlines the onboarding processes by automating mundane tasks such as facilitating documentation, scheduling new hire introduction meet, etc.

Example 2: Generative AI makes the knowledge base easily accessible to help a new hire learn company policies and various operation aspects.

Sales and marketing

There are myriad ways sales and marketing can benefit from Generative AI. Marketing and sales must build brand awareness through different materials for social media posts or website publications. Generative AI helps reduce the time to create and polish a draft with meaningful content.

Examples

Example 1: Sales and marketing must communicate with the prospects with different types of email templates. Generative AI helps reduce the labor of creating a draft from scratch, generating it instantly to revise and use for communications. As a result, sales and marketing can focus more on the human aspect of collaboration and building relationships.

Can Generative AI be trustworthy?

Generative AI or its foundation models are built based on unsupervised learning. The chances are high for the models to hallucinate. Or it can even lack explainability, meaning Generative AI cannot explain how it arrives at decisions or predictions due to its deep learning models with billions of parameters.

The best way to use Generative AI ethically is to provide enough human oversight to ensure it does not surface biased or harmful responses. It can be done during the model training or fine-tuning.

Also, model training should be based on clean data, a high-level task requiring intense human oversight.

In addition, the model deployment is not the end of the iteration. The process should be ongoing for performance review and then for making changes to the model to enable it to perform without bias.

Different scenarios can determine whether a business needs a custom, a fine-tuned, or a pre-trained model.

• Custom solutions provide use-case-specific solutions. So, you need quality data to create your workflows. It gives you much control and makes it easy to update the model workings whenever needed.

• Fine-tuned models allow us to tweak only the final layer of the LLM models. This model solution effectively implements one or two use cases for a small project. It requires less computational power and hardware investment.

• Pre-trained models such as ChatGPT, Microsoft AI copilot, and Claude are useful if your purpose is generic, such as Q&A only. Through an API integration, you can implement pre-trained models and gain good business results.

So far, the article aims to provide a basic understanding of Generative AI, and we have supplied all the necessary information to make you aware of the technology and its capabilities.

In the enterprise setting, Generative AI can offer multiple use cases that can be applied across a wide range of business functions and streamline complicated work processes.

If you want to savor the taste of Generative AI, you can start small, and API-layered Generative AI solutions are a good option.

Want to empower your HR and IT operations with Generative AI? Workativ virtual assistants can complement your business objectives.

Get in touch with us for a personalized demo today.