Published on February 2, 2024, 2:14 pm

Generative AI, also known as genAI, has gained significant importance in the business world since the release of ChatGPT in November 2022. According to a PwC report, 84% of Chief Information Officers (CIOs) expect to use genAI to support new business models by 2024. This technology is undoubtedly transformative, but it’s important to remember that it is just one type of AI and not necessarily the best solution for every use case.

The definition of AI has evolved over time. What was once considered artificial intelligence, like a tic-tac-toe-playing program, may no longer be classified as such today. However, AI history can be broadly categorized into three different types.

While many organizations recognize the potential of generative AI, data shows that it currently accounts for about 15% of use cases and models. Predictive AI remains the dominant force in model-driven businesses, and future models are likely to combine both predictive and generative AI capabilities. In fact, there are already cases where these two types of AI work together seamlessly. For example, radiology images can be analyzed using both predictive and generative AI to generate reports on preliminary diagnoses. Similarly, stock data can be mined using these technologies to generate reports on stocks most likely to increase in the near future.

As a result, CIOs and CTOs should consider adopting a common platform that facilitates the development of complete AI solutions. Building separate stacks for each type of AI is unnecessary in most cases. While genAI may require additional computing power like GPUs and optimized networking for enhanced performance, it usually doesn’t require building a whole new infrastructure from scratch unless it’s on an extensive scale like Meta or Microsoft.

When it comes to governance and testing processes for genAI projects, organizations don’t have to start from scratch either. Similar principles apply for mitigating risks associated with genAI as well as predictive AI models. For instance, mortgage risk models powered by predictive AI necessitate rigorous testing, validation, and constant monitoring, just like genAI’s large language models. Although there are specific nuances such as genAI’s susceptibility to “hallucinations,” the overall risk management processes remain comparable.

Domino’s Enterprise AI platform is trusted by many Fortune 100 companies to manage their AI tools, data, training, and deployment. This platform allows AI and MLOps teams to oversee complete AI development and management from a centralized control center. By unifying MLOps under a single platform, organizations can streamline the process of developing, deploying, and managing both predictive and generative AI applications.

To learn more about how to maximize the benefits of genAI projects while effectively managing the associated risks, Domino offers a free whitepaper on responsible genAI. This comprehensive resource will provide valuable insights into leveraging genAI technology responsibly and ethically.

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