The Emergence of a Generative AI Ecosystem: Enabling Its Business Potential

Introduction

In the last year, generative AI has made significant progress, captivating investors, business leaders, and society with its ability to produce human-like text and images. OpenAI’s ChatGPT, for example, attracted one million users in just five days, faster than any previous technology adoption, while other generative AI tools are also experiencing high levels of interest.

 

These tools are already finding use cases in different industries. Morgan Stanley is using generative AI to improve financial advisers' insights, and the Icelandic government is partnering with OpenAI to preserve its language. Salesforce has integrated the technology into its popular CRM platform.

 

However, the rapid growth of the generative AI industry has left investors and business leaders struggling to keep pace. To provide a starting point for assessing investment opportunities, it is necessary to understand the generative AI value chain composition.

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What is Generative AI

Generative AI is a breakthrough technology that can create new content in various forms, such as text, images, videos, and 3D representations, unlike traditional AI technologies that can only analyze, predict or prescribe based on existing content. The key to this technology's success is training neural networks, which are deep learning algorithms, on vast amounts of data and applying attention mechanisms to help AI models understand what to focus on. With attention mechanisms, generative AI systems can identify patterns, relationships, and context in user prompts, which allows them to create new and original content. Multimodal models are also emerging that can create content in different formats. The outputs of generative AI models have been impressive, winning digital art awards and scoring high in tests such as the US bar exam for lawyers and the SATs.

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Understanding the generative AI value chain is critical in assessing investment opportunities in this fast-paced industry. To better grasp the generative AI value chain, it is essential to comprehend how this technology differs from traditional AI. While traditional AI is good at predicting client churn, forecasting product demand, and making next-best-product recommendations, it cannot create new content. On the other hand, generative AI can create entirely new content that resembles human-made text and images. This difference in capabilities is due to generative AI models being trained on more extensive data sets and attention mechanisms.

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Varied Applications

Our research indicates that while generative AI will likely have an impact on most business functions in the long run, there are certain areas that are particularly ripe for the first wave of applications. These include information technology, marketing and sales, customer service, and product development.

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In the field of information technology, generative AI has already been used to help teams write code and documentation. Automated coders on the market have already boosted developer productivity by over 50 percent, helping to accelerate software development. This trend is likely to continue as generative AI becomes more sophisticated.

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For marketing and sales teams, generative AI applications can be used to create content for customer outreach. In fact, within the next two years, it's expected that 30 percent of all outbound marketing messages will be developed with the assistance of generative AI systems. This can help companies to increase efficiency and reach more customers with personalized messages.

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Customer service is another area where generative AI can have a significant impact. Natural-sounding, personalized chatbots and virtual assistants can handle customer inquiries, recommend swift resolution, and guide customers to the information they need. Already, companies such as Salesforce, Dialpad, and Ada have announced offerings in this area.

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In the realm of product development, generative AI can help companies rapidly prototype product designs. For example, life sciences companies have already started to explore the use of generative AI to help generate sequences of amino acids and DNA nucleotides, which can shorten the drug design phase from months to weeks.

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While all industries stand to benefit from generative AI, some are better positioned to take advantage of these applications than others. For instance, the media and entertainment industry can become more efficient by using generative AI to produce unique content and rapidly develop ideas for new content and visual effects for video games, music, movie story lines, and news articles. On the other hand, banking, consumer, telecommunications, life sciences, and technology companies are expected to experience greater operational efficiencies due to their significant investments in IT, customer service, marketing and sales, and product development.

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