Generative AI has taken the world by storm. This AI-based technology is evolving quickly, and we’re seeing a volley of innovations and applications in this sector. The pace at which generative AI is growing is indeed rapid, a lot has happened in the past month. We’ve had the new NYPD pilot project, testing robots to patrol Times Square, we also have the Hugging Chat, the new open-source alternative to ChatGPT, and then we also saw Databricks releasing Dolly 2.0, their first open-source, instruction-following Large Language Mode (LLM) and well, a lot more.

We can safely say that AI-driven innovation is at its peak. The dynamic developments in generative AI may seem startling, but if you’ve carefully observed AI trends, you’d already know this was coming. Even for those still hesitant about generative AI, you need to know that generative AI is here to stay. And the reason is simple - the pressure from consumers for corporations to evolve is tremendous.

Consumer preferences and requirements are simple - we want to get done what we need. They do not care about the limitations of conventional software of these companies - and that’s precisely why organizations need to buckle up. Change is here, and we need to accept, adapt and implement accordingly to ensure we’re still in the race. 

In this detailed overview of generative AI and how large corporations can leverage it, we will give you a comprehensive perspective of generative AI - right from the benefits it offers to the risks it renders - to help you understand how to navigate through this AI-based technology.

Let’s dive into the world of generative AI.

Benefits of Generative AI for Large Corporations

To know the potential of generative AI, we first need to understand the benefits of generative AI and how it can transform the way businesses function. Generative AI can not just help enhance workflows but also boost customer experience - both of which are essential aspects for a business. So let’s have a look at some of the key benefits:

Personalized User Experience

Generative AI is assisting businesses to offer enhanced personalization to their customers via creative problem-solving. AI algorithms are dynamic and quick to analyze customer data and come up with accurate predictions with respect to customers' tastes, choices, and preferences. With this data, it is easier for businesses to generate accurate and relevant content for their customers, thus improving user experience and overall customer satisfaction.

Efficient Business Processes

AI-powered algorithms can help businesses identify inefficiencies and issues in their current workflow. Generative AI does not stop at just identifying issues; it also suggests improvements and solutions. This helps businesses in better, more accurate, and efficient decision-making and improves productivity and quality of work.

Improved Output Quality

AI can help businesses be more creative without putting in too much time and effort. From the existing pool of data, generative AI can generate new concepts and ideas, which businesses can materialize into creating new products and services. Additionally, AI can also be used to create high-quality, visually-enhancing content in the form of images and videos as compared to manually created content. As AI-generated content is more relevant to the target customer base, it also helps boost customer engagement.

Automation of Processes

We now have AI algorithms and AI-powered robots taking over repetitive, mundane manual tasks in companies. AI-enabled automation not only helps boost efficiency in organizations but also reduces labor costs and saves money. Automation also frees up the workforce to focus on more creative and complex tasks.

Use Cases of Generative AI

Now that we have a fair idea of the benefits of Generative AI let’s delve into understanding its industry use cases  in different sectors:

Marketing

Generative AI is an extremely beneficial tool for marketing. It can collect, analyze, and segment data and develop relevant, accurate responses and predictions. These, in turn, can be used to curate a marketing strategy that can aptly target the customer base, thus helping improve the visibility of a business and boost its sales.

Healthcare

Accurate diagnosis is the basis of good healthcare, but with hazy Z-rays and MRI scans, a proper, conclusive diagnosis can be challenging for healthcare professionals. With generative AI, it is possible to convert X-rays and CT scans into clear, more realistic images, contributing to a quick and accurate diagnosis.

Content and Media Creation

Generative AI can create text, images, videos, and even music! With the right set of inputs, generative AI can quickly and easily create articles, summaries of articles, product descriptions, and a lot more. Likewise, from existing data, it can curate images, logos, social media posts, and videos that can be used for marketing or other purposes. AI algorithms can also help you create quirky and catchy tunes for music videos, advertisements, or other music projects.

Customer Service

Generative AI can help automate customer service, making it quicker and more accurate as compared to human customer service executives. With generative AI, businesses are now opting for chatbots that are effortlessly able to handle bulk customer queries efficiently and promptly. With email automation, you can cut down the delays in replies and ensure prompt responses to customer emails. Likewise, AI offers multilingual customer service support, helping cater to a large and diverse customer base without additional investment.

Legal Work

In the legal field too, generative AI has many applications - from compliance and regulatory monitoring to legal chatbots to other useful services in the legal domain. Generative AI can help with quicker and more accurate drafting of documents with pre-defined templates and input data. Additionally, it can also be used for due diligence, wherein large volumes of documents need to be assessed. Additionally, it can also help in intellectual property management, specifically with tasks such as trademark searches, patent analysis, and also infringement detection.

Research and Development

Advanced generative AI can process scientific research and datasets to assist professionals in different sectors in the process of research and development. For example, generative AI can create new drugs for the treatment of illnesses. It can also help us find solutions to various other brewing issues in society, making the process of research and development faster, more efficient, and more constructive too.

Opportunities in the Generative AI Value Chain

From hardware providers to application builders, generative AI is adding value to different essential aspects of businesses. As the development and deployment of generative AI systems are rapidly gaining pace, we are now witnessing the emergence of a new value chain. At first instance, it may seem that this new AI value chain is very similar to the traditional AI value chain; the observation is absolutely correct except for the newest addition to the value chain - foundation models. 

Let’s quickly look at the new generative AI value chain, which includes Services, Foundation Models, Applications, Model hubs and MLOps, Cloud Platforms, and Computer Hardware.

The Services aspect of the AI value chain focuses on leveraging generative for the benefit of different sectors. Services are inclusive of training, feedback, and reinforcement learning. They help in understanding problems, defining goals based on them, and curating a detailed line of action to tackle the problems.

Next, you have the Foundation Models, which are AI technology trained on huge amounts of unlabeled data. These have many possible applications and can be adapted into a wide range of tasks and operations. Foundation models form the basis of the development of B2B or B2C AI-based Applications, which are used to help ease and enhance the workflow of businesses. In the entire AI value chain, applications are the most significant and have high scope for development.

Model Hubs and MLOps are tools that help fine-tune foundation models as per the requirements of the applications. Lastly, coming to the two main aspects - Computer Hardware, which is optimized for training and running models, and Cloud Platforms, which play an integral role in providing access to computer hardware.

Generative AI systems are more complex than traditional AI systems and require extensive time, cost, and expertise to deliver them. A deeper understanding of the value of the generative AI market is booming. There is significant scope in the value chain, with most opportunities concentrated in the AI applications sector.

Platform Enterprise Offerings for Generative AI

The vast scope of generative AI has resulted in a boom in the number of generative AI startups. These are only additions to the already existing generative AI-based enterprises. Any organization looking to adopt AI for their processes face many teething difficulties and extensive costs in setting up and running AI systems.

Considering these issues, AI companies have come up with a whole new category of enterprise software known as AI platforms. These enterprise-ready platforms afford a seamless integration of AI in any corporation. Let’s have a look at some of the most popular enterprise-ready AI platform offerings that you can leverage for your business:

AWS AI Services

Amazon Web Services (AWS) has made immense progress in the fields of Artificial Intelligence (AI) and Machine Learning (ML). AWS AI services offer comprehensive assistance to clients, ensuring they have access to the full range of AI services, resources, and infrastructure for seamless integration of AI in the client’s organization.

The Amazon AI platform can help make predictions on the basis of data, train machine learning models at scale as well as host trained models on the cloud. Amazon has recently launched ‘Bedrock’, which is a compilation of generative AI tools. These tools can help AWS users to operate chatbots, generate and summarize text and also create and classify pictures on the basis of specific prompts.

Google AI Services

Google Cloud is helping businesses unlock the power of generative AI, giving them the resources and infrastructure required to create, recommend, analyze, and engage with generative AI in a seamless and responsible manner. Generating texts, audio, videos, codes, and a lot more from simple language prompts is now possible, thanks to the Google Cloud range of AI services.

Consider Google Bard - Google’s first AI-powered chatbot that offers responsiveness and conversationality - the very features that set it apart from Google Search, which already uses AI. Bard is a large language model and has been trained with huge amounts of text so that it is in a position to process natural language effortlessly. Using this tool, you can explore different topics, strengthen your understanding and make logical decisions.

Azure Open AI Services

Azure offers large-scale, generative AI models that feature a deep understanding of both code and language. These pre-trained AI models are enterprise-ready, and can be used to unlock new scenarios. Azure Open AI also offers custom AI models that are fine-tuned to the requirements of your organization and trained with relevant data and parameters. Some of Azure’s AI-based tools are Azure Cognitive Services, Content Moderator, Anomaly Detector, Azure Databricks, Azure Video Indexer, Azure Immersive Reader, and a lot more.

Risks of Generative AI

The benefits of generative AI are tremendous - but unfortunately, it comes with its set of risks too. Its benefits overshadow the risks it brings, but as responsible businesses, we need a fair idea of generative AI before incorporating it in workflows and other aspects of the business. These risks have prompted some countries to take immediate cognizance and ban the usage of ChatGPT as a whole - which explains how serious they are.

Let’s understand better the top 4 risks of generative AI:

Cybersecurity Threats

Cyber crimes are already on the rise - and the inception of AI in different sectors will only exacerbate this threat exponentially. From sending out phishing emails that appear to come from reliable sources and using deep fakes to helping cyber criminals identify vulnerable systems or weak links in security mechanisms, - generative AI can do it all. Apart from this, attackers can also use generative AI to create malicious code easily.

Deep Fakes

Deep Fakes uses generative AI to create realistic audio, video, and image hoaxes. Generative AI has pushed the boundaries of video and image manipulation. Unfortunately, most deep fakes are being made by everyone, from academic and industrial researchers, porn producers, and visual effects studios to governments. Creating and spreading misleading information is the primary objective of the majority of the people creating deep fakes.

Intellectual Property Issues

Copyright is a burning issue for AI-generated art, music, images, and other media. Generative AI models feed on data collected from different sources and are trained to deliver an output. This output may not be completely original and may be sourced from the original creator’s work too. The issue with generative AI is that it very casually picks data from the original source without crediting the original creator or seeking permission from them, which may result in an infringement of copyright.

Data Privacy

Breach of data privacy was the prime factor that pushed Italy to completely ban the usage of ChatGPT. Privacy comes into the picture because generative AI stores user data for model training. Issues revolve around the type of data stored, its confidentiality, the method of gathering and storing it, and its usage. Apart from compromising user confidentiality, generative AI may also result in the misuse of this data by cyber attackers, resulting in security breaches.

Ethical Concerns

There are a number of ethical concerns raised revolving around generative AI. These particularly involve deep fakes and synthetic media, which can be used to create fake or misleading content. The ethical concerns of generative AI have a subsequent negative impact on privacy, security, and trust. Apart from this, one of the major concerns is the effect of generative AI on humans. Its usage to commit cyber crimes is a major red flag for generative AI as well. 

How to Mitigate Risks of Generative AI?

Considering the risks of generative AI, organizations often hesitate to incorporate these AI-based applications into their existing systems. But from a futuristic perspective, avoiding generative AI completely isn’t the solution. Rather, it’s essential to introduce a comprehensive, enterprise-wide strategy for generative AI risk management and mitigation.

Here are some ways to mitigate the risks of generative AI:

Proper Designing

Responsible designing and training of AI models is essential to prevent the risk of bias. At this stage itself, it is crucial to train generative AI systems with diverse and representative data. Additionally, it is important to evaluate and analyze the quality, accuracy, and relevancy of the generated output. If the output isn’t in part with what’s expected, the issue needs to be addressed at the initial stages itself to prevent bias of any type.

Educate Stakeholders

Most of the risks and issues with generative AI are caused due to human error. The first thing is to train stakeholders about the applications and limitations of generative AI. It is crucial to educate them about the importance of securing their systems and the type of data that can be safely shared with generative AI. They also need to be cautious enough to proofread the input information and test it before implementation. Proper training and systematic guidance can help reduce incidents of unintentional compromise of organizational data.

Implement Mandatory Safeguards

Implementing stringent review processes can help prevent the usage of generative AI systems for malicious purposes. Apart from monitoring the stakeholders using generative AI, it is also essential to consistently monitor and evaluate the performance of AI systems. This would help ensure that there are no discrepancies in the way the systems are functioning, and if at all there are any glitches, they are mitigated instantly.

Generative AI - What’s Coming Next?

Generative AI has already developed extensively, and that brings us to the question - what’s coming next? As of now, the largest breakthrough for generative AI is the development of foundational models. According to experts, we can expect multimodal generative AI to be out in the market soon. Multimodal AI can function with multiple inputs and deliver multimedia output post-implementation. From the reception perspective, the enterprise adoption of AI across the globe is expected to compound at an annual rate of almost 38-40% till 2030.

In this overview, we’ve summarized everything you need to know about generative AI. It is indeed a disruptive technology, and its implementation will lead to the redundancy of many jobs - but the bright side is that it will also result in the creation of new jobs. As an organization, if you’ve been considering implementing AI-based applications to improve your workflow, remove bottlenecks and enhance productivity, it's time to take the plunge into the world of AI - albeit with due care.