What is Gen AI? An In-Depth Guide to Understanding its Functionality and Best Practices

Generative AI has become the driving force at enterprises and is emerging as one of the most frequently deployed AI technologies in organizations. Based on a survey, amongst several applications of generative AI, 25% of its use cases account for customizing data models with prompt engineering. Let’s dive further to understand what is gen AI, how it translates into business opportunities for enterprises, its limitations, and best practices to thrive in the digitally connected world.

What is Gen AI in Simple Terms?

Gen AI refers to a technology and specific subset of AI based on machine learning and deep learning models. These models are algorithms (such as ChatGPT) that process large data sets and detect patterns similar to a human’s learning & decision-making processes. They are programs that identify patterns in data, use that information to understand instructions (known as prompts), and generate new content.

Gen AI is being extensively used to generate similar content such as videos, texts, audio, images, and software code in response to a user’s natural language request. This technology was first invented in the 1960s with the origin of chatbots, and it was not until 2022 that the world awakened to the transformative potential of ChatGPT.

Generative AI models are capable of generating graphs that demonstrate new chemical compounds and moldecules that assist in drug discovery. These models are being applied to design logos and create realistic images for virtual or augmented reality. They produce 3D models for video games and enhance or edit existing images, based on the user’s prompts.

The chart below illustrates the relevance of the above use cases and more sophisticated applications of generative AI beyond just ChatGPT.

Source: Gartner, January 2023

It is apparent that apart from material science and generative design, synthetic data is gaining prominence in most industries like healthcare and manufacturing to mimic real data for software testing & data science projects.

History of Generative AI

The inception of Gen AI dates back to the mid-1950s when researcher Alan Turing experimented with mathematical concepts for building AI. A crucial breakthrough in the genesis of AI was the first chatbot, ELIZA, invented by British scientist Joseph Weizenbaum in 1961. Advancements in machine learning algorithms fueled the 1980s to 2010s, helping machines learn from data and improve outcomes.

The late 1980s saw the introduction of Recurrent Neural Networks (RNNs), and in 1977, Long Short-Term Memory (LSTM) networks improved AI systems’ capability to process sequential data. The evolution of gen AI gained momentum during the 2000s with developments in machine learning and deep learning to create neural networks. Artificial neurons are nothing, but software modules and artificial neuron networks are algorithms that use computing systems to derive answers from mathematical calculations.

In 2014, Ian Goodfellow and his colleagues created a specific type of neural network called Generative Adversarial Network (GAN) that proved a game-changer for image generation. This technique combined two neural networks into an architecture, namely a generator and a discriminator, competing to improve performance in generating responses. Currently, researchers are innovating in natural language processing (NLP) and variational autoencoders (VAEs) amongst other use cases to develop AI system’s ability to produce new and creative content.

What is the Difference Between AI and Gen AI?

The difference between AI, also called traditional AI and generative AI is that the former is a system that is majorly used for analyzing data and suggesting predictions. Whereas, gen AI is an advanced system that creates new content similar to its training data. Traditional AI is suitable for pattern recognition and gen AI helps in pattern creation. Though these two systems may have may differences, many applications combine their power to develop cutting-edge solutions. The traditional AI in product recommendation solutions analyzes user behavior whereas gen AI uses this analysis to suggest tailored product recommendations to customers.

AI is broadly categorized into traditional AI, also known as narrow or weak AI, and the other is general AI, which might or might not include generative AI, depending on specific use cases. Examples of traditional AI include task-specific applications like spam filters in email services,  recommendation systems in e-commerce platforms, or chess-playing programs.

Despite the hype created by ChatGPT’s genesis, it belongs to the classification of Artificial Narrow Intelligence (ANI) rather than Artificial General Intelligence (AGI). This is because ChatGPT is task-specific and is primarily focused on natural language understanding & generation.

Examples of AGI include self-driving cars, drone robots, and financial trading solutions that can execute a multitude of tasks simultaneously.

How Does Generative AI Work?

Gen AI primarily works in three phases: i) Training, ii) Fine-Tuning, and iii) Generation, evaluation, and RLHF.

1. Training: This creates the foundation for developing a gen AI model by exposing it to a vast amount of data, allowing it to understand and generate original content. Since the training process is compute-intensive and expensive, open-source foundation model projects like Meta’s Llama-2 are being leveraged by AI teams to navigate bottlenecks around developing models.
2. Fine-Tuning: This phase is crucial to allocate the best settings to an algorithm for learning. Since relying on default parameters in model tuning for real-world applications can be erroneous and risky, data scientists use trainable and hyper parameters to adapt the foundation model to a specific gen AI application.
3. Generation, evaluation, and RLHF: Developers and users are focusing on safety, accuracy, and governance to continually assess the results of gen AI apps. They are tailoring evaluations to specific questions or scenarios to enable more targeted assessments.

To further leverage evaluations, data specialists are using the Reinforcement Learning from Human Feedback (RLHF) technique that uses human feedback to enhance machine learning models. This technique helps to emulate humans closely while completing complex tasks and is being applied in self-driving cars, stock market predictors, and natural language processing.

Common Myths About Generative AI

Since their proliferation in 2022, generative AI tools have garnered a massive user base in mere months. However, business leaders must be informed about certain myths that could affect their adoption in business operations.

1. Gen AI tools can be used free of charge or at minimal cost: Building proprietary generative AI models helps leaders precisely align with business processes and workflows. However, the development process requires considerable investment in infrastructure, talent, and resources. Thus, partnering with specialized professionalsprovides a more cost-efficient approach to achieving business goals.
   
2. Generative AI is not expected to influence my business: According to a study, 66% of finance leaders anticipate that gen AI will soon play an instrumental role in explaining revenue data classification and understanding management reports.

In the chart below, forecast and budget variance explanations are considered the most anticipated use cases for gen AI in finance in 2024.

Source: Gartner, June 2024

It’s clear that gen AI is quickly entering applications, and employees are already experimenting with publicly available tools to evaluate its implementation in business.

Business Use Cases for Generative AI

Gen AI systems can produce content that is on par with humans. Before using this technology, certain things to consider include utilizing the right tech stack, model matchmaking, and teamwork.

a) Sales & Marketing: A majority of marketing-related gen AI use cases in businesses focus on content creation. Organizations should tap into the predictive power of recommendation engines and intelligent ad placement to empower sales & marketing initiatives.

b) Democratizing Data Analytics: Data democratization makes information and insights available to all employees and stakeholders, regardless of their technical expertise. Organizations can help clients enhance AI-augmented analytics systems and self-service business intelligence by leveraging generative AI.

c) AI Agents: An AI agent is a computer program, similar to a digital assistant, that performs tasks autonomously based on inputs, its environment, and predefined goals. These agents are emerging as a transformative force, capable of making decisions for tasks like managing intricate system processes, navigating the web, and even engaging in transactions. Crew AI is emerging tool that automates workflows quickly and with simplicity for users sans technical know-how.

d) Product Development: To fully take advantage of generative AI, developers and product designers use generative designs from the initial concept to manufacturing and procurement. This helps to optimize design concepts on a large scale. Product managers are deploying gen AI to incorporate user feedback for product improvements, minimize the use of materials, and significantly reduce costs.

e) Machine-generated Events Monitoring: Gen AI is used along with predictive maintenance to identify possible equipment failures before they happen. According to a research, predictive maintenance helps to increase productivity by 25%, minimize maintenance costs by 25%, and lower breakdowns by 70%. AI-generated events monitoring has the capability to recommend potential solutions in case of downtime and help manufacturing engineers gain operational efficiencies.

f)Virtual Field Assistant: Engineers often work in challenging environments due to the lack of manuals and information needed to identify problems in industrial areas. In such scenarios, gen AI-enabled virtual field assistants play an instrumental role in addressing issues and spending less time in fields exposed to potential environmental hazards. They help serve as a reference tool and offer access to technical information. Engineers can describe the problem and the assistant responds with a step-by-step guidance for resolution.

What are the Benefits of Generative AI?

Gen AI holds promising opportunities for business transformation. Enhanced creativity, faster decision-making, and dynamic personalization are just some of the benefits of gen AI.

1. Personalization: Generative artificial intelligence is fueled by neural networks to analyze existing data and discover patterns in customer interactions. Smart recommendation solutions help to suggest offers based on individual preferences. These developments make experiences more human-like in comparison to traditional marketing tactics.
   
2. Speed and Performance: Automating tasks like generating new product concepts and designs that previously required human intervention is now accelerating time to market. Gen AI solutions help to identify patterns in stakeholder feedback and market trends, thus speeding up the design process. According to research, 82% of leaders predict that AI will boost employee performance, allowing them to focus on more mission-critical tasks.
3. Adaptive Learning and RLHF: Gen AI systems and models can persistently learn and adjust based on new data and feedback. This has given rise to adaptive AI that can revise its own code and respond to changes in data or environment. Personalized education uses adaptive AI to dynamically adjust learning materials to match individual student needs and preferences.Moreover, RLHF technique is gaining prominence because it can incorporate human feedback for training AI systems that mimic human behavior, responses, and decision-making. This technique primarily ensures large language models (LLMs) generate helpful content and is used for gen AI applications like creating music that matches specific moods or activities.
4. Data Synthesis: Data professionals in the finance sector are developing gen AI models to synthesize large amounts of data to generate valuable insights, analyze market trends, and study economic indicators. These markers help businesses make well-informed investment decisions.
   
5. Sentiments and Intent Analysis: Since natural language understanding is a growing demand in any industry, data scientists are building GenAI models that accurately comprehend sentiment and intent. This is possible with a combination of pre-training and fine-tuning processes. Understanding the context, intent, and tone of prompts across use cases is expected to benefit enterprises that are planning to invest in GenAI technologies.

Limitations of Gen AI

Artificial intelligence enhances business efficiency and infuses the creative process with renewed energy. However, limitations in its implementation are grabbing the attention of data teams. Let’s learn mitigation strategies for the challenges below.

1. Data Privacy and Security: Generative AI models largely depend on huge datasets to generate meaningful insights. However, issues in managing large data volumes and sensitive data could hinder AI adoption. Hence, enterprises are adhering to ethical guidelines and ensuring compliance to protect sensitive data from potential breaches.
   
2. Integration with Existing Systems: Factors like individual training and changes in existing workflows are impeding the use of AI for business processes. To overcome these, businesses should provide the necessary training and emphasize individual growth to seamlessly integrate AI solutions with existing systems. They should also develop change management strategies to establish a strong technical know-how of AI models.
   
3. Using Managed AI Services: Gen AI models require significant computational resources, such as memory and high-performance GPUs ( Graphics Processing Unit). Managed AI services help in cost optimization and eliminate the need to manage the infrastructure and model training.

Examples of Generative AI Tools

Modern customers value faster response time, personalized experiences, and meaningful relationships. In this competitive environment, businesses are using gen AI tools like ChatGPT, Dall-E, and Gemini to meet clients’ high expectations.

1. ChatGPT: Since this tool gained a vast customer base after its release in 2022, we inevitably discuss its advantages, especially for small businesses and content creation. ChatGPT uses the power of conversational AI to provide answers to a variety of questions. It’s built on a large language model to interact with users using prompts and translate texts across diverse languages.
   
2. Dall-E: Another well-known example of a gen AI tool is Dall-E, which turns text prompts into images. Its ability to process natural language eliminates the need for special coding or image-editing skills. As of 2022, with over 1.5 million users generating over 2 million images daily, Open AI has launched Dall-E 2 and Dall-E 3, which help users create higher-resolution images.
   
3. Gemini: Google’s Gemini has grown popular as ChatGPT’s rival. Previously known as Google Bard, is an AI-powered chatbot that uses ML and natural language processing to provide outcomes to text, audio, and image prompts.
   It is debatable which of the two is better for 2024 since Gemini produces free AI images, whereas ChatGPT has made this feature available only to paid subscribers with Dall-E 3. On the other hand, ChatGPT leverages conversational learning to hold context while interacting with users, but Gemini performs this with limitations.
   
4. Sora from OpenAI: This one-of-its-kind text to video generative AI model is expected to revolutionize how videos are made. Its foundation is based on combining diffusion and transformer models to determine the details and high-level layout of the video frames.
   
5. GitHub Copilot: This AI coding assistant helps developers write code faster and with less effort. It boosts developer productivity by offering code snippets and context-based guidance.
6. Code Rabbit: This tool is an AI-first code reviewer. It integrates GitHub and GitLab repositories to perform continuous and incremental reviews for each commit within a pull request.
7. Microsoft Copilot: Copilot is an AI assistance technology built for personal and business products. It helps in content generation and supports outside of office suite tools by enabling AI assistance for sales workflows and data analytics.

Best Practices for Using Generative AI

The magnitude and frequency of change while implementing Gen AI make it challenging for business leaders to realize its actual ROI. Stakeholders should use a more purposeful and systematic approach to implementation and minimize ad hoc experimentation.

a) Develop a Comprehensive Gen AI Strategy: Organizations should lay out strategic gen AI themes considering their business model, products, and services that align with their long-term business objectives. As in this graph below, each stage of successful gen AI implementation increases the chance of achieving better ROI.

Source: : BCG, October 2022

b) Determine and Prioritize Use Cases: Though an organization might have several AI use cases, it’s advisable to have a master list of existing activities with feedback from data teams. This inventory of activities can be mapped according to different customer journeys and enterprise processes to identify gaps or further opportunities to improve gen AI implementation.

c) Make ROI a key topic from the beginning: As much as it’s necessary to focus on providing value to clients, it’s equally important to maintain a systematic ROI calculation spreadsheet to identify investment in tools according to an organization’s business use cases. This involves making a list of value propositions for client-facing products and efficiency in time and cost savings for internal processes.

d) Establish Data Privacy Guidelines: GenAI algorithms require data governance frameworks to prevent unauthorized access to data and data breaches. Since training, testing, and case review require skilled data specialists, organizations should partner with reliable vendors for responsible AI deployment.

e) Undertake Purposeful Experimentation: Since gen AI is a constantly evolving technology, it is easy for professionals to lose focus and divert away from the end business goal. To avoid this, specialists should test the AI models with different use cases and small controlled test groups to evaluate and compare their potential. This gives employees and clients an opportunity to familiarize themselves with different AI applications.

f) Build a Center for Excellence: Deploying technologies like gen AI and predictive AI requires human oversight and careful monitoring to be effective. Enterprises should invest in establishing a center of excellence to build a skilled team that overseas AI initiatives and support employees & departments to embrace innovations.

Future of Generative AI

Industry 4.0 is advancing in ways we never thought were possible. Gen AI holds the potential to boost the global economy. According to a study, this technology can boost labor productivity in the coming years. By bridging the gap between skills and training, enterprises and nations can catapult GDP growth owing to improvements in productivity levels.

Gen AI adoption is gaining momentum in industries like energy and materials, advanced industries, healthcare, etc. Developers using AI tools are happier and more satisfied due to their ability to automate several tasks. This helps organizations retain employees.

Marketing and sales leaders are enormous value drivers for gen AI. These industry leaders are most enthusiastic about personalized outreach, lead identification, and marketing optimization. Venture capital investment in AI has increased 13-fold over a decade. This has augmented the availability of readily usable data to formulate insights and recommend tangible actions.

n the next five years, organizations will witness an increase in tailored gen AI applications that foster innovations in products and services. For instance, Insilico Medicine, a biotechnology company based in Hong Kong, uses big data, genomics, and deep learning to reduce the cost of development and drug discovery.

Generative AI V/S Predictive AI V/S Conversational AI

All three technologies fall under the broader category of artificial intelligence. Let’s individually understand each technology and its application for business.

Generative AI helps create new content and is particularly significant for design, marketing, and entertainment teams. Gen AI tools provide output to natural language prompts in the form of text, charts, audio, or relevant videos.

Predictive AI foresees future outcomes based on historical data to assist businesses in decision-making. Data professionals are developing predictive recommendation solutions, fraud detection systems, and equipment maintenance solutions to improve business operations.

Conversational AI improves business interactions through virtual assistants and chatbots. Typical use cases of this technology are prominent in customer service to enhance customer engagement and HR processes to facilitate personalized employee onboarding.

To develop a holistic AI strategy, employees and clients should integrate all three types of AI to improve business efficiency, encourage innovations, and improve customer satisfaction.

Eliminate Guess-Work and Explore Gen AI Possibilities with AIT Global Inc

Enterprises are maximizing value out of industry-specific generative AI models against general-purpose models to handle specialized tasks with precision. Task-specific models help in context understanding of prompts, retaining the memory of past queries, and generate accurate outcomes.

Due to the rapid pace of change in the digital environment, partner with us to implement the correct methods for developing an adaptive gen AI strategy. Leverage our AI & ML solutions and update your strategies regularly to encourage continuous learning among employees & clients.

Our specialists consult crucial measures, such as maintaining an ROI calculation spreadsheet that helps clients and employees track the before-and-after progress of gen AI implementations. If you have found these insights helpful, share this blog with your network. Also, check our recent blog on AI use cases and their relevance in industries like healthcare, finance, IT, and others.