The implementation of completely new procedures and infrastructure is not necessary for generative AI.
Enterprise CEOs and boards of directors have made generative artificial intelligence (genAI) a top focus since the introduction of ChatGPT in November 2022. For example, according to a PwC survey, 84% of CIOs anticipate using genAI in 2024 to support a new business model. Without a question, genAI is a truly revolutionary technological advancement. However, it’s also critical to keep in mind that this is only one type of AI and that not all use cases will benefit from its utilization.
The definition of artificial intelligence evolves with time. A program that played tic tac toe would have been considered a form of artificial intelligence fifty years ago; not so much these days. However, the history of AI can be broadly divided into three groups.
Conventional Analytics: For the past forty years, businesses have employed analytical business intelligence (BI). However, as technology has evolved and grown more complex, the term “BI” has been replaced with “analytics.” In general, analytics uses historical data to uncover insights about past events.
Predictive artificial intelligence (AI) is a forward-thinking technology that uses historical data analysis to identify patterns that may be applied to the present to produce precise future projections.
Generative AI: GenAI examines text, photos, audio, and video content to create new content based on user requirements.
“We work with a lot of chief data and artificial intelligence officers (CAIOs),” said Thomas Robinson, COO at Domino, “and, at most, they see generative AI accounting for 15% of use cases and models. Predictive AI is still the workhorse in model-driven businesses, and future models are likely to combine predictive and generative AI.”
Predictive and generative AI are actually already being used in tandem in certain use cases. For example, reports on preliminary diagnoses can be generated by studying radiological pictures, or reports on stocks that are expected to rise in the near future can be produced by mining stock data. This means that companies will require a shared platform for creating fully functional AI, according to CIOs and CTOs.
Every kind of AI has its own stack and is not treated as such in complete AI development and deployment. It’s true that genAI might need a little more power from some GPUs, and networking might need to be strengthened for better performance in some parts of the system, but constructing a new stack from the ground up isn’t necessary unless a company is operating a genuinely massive genAI deployment on the order of Microsoft or Meta.
Additionally, testing and governance procedures don’t have to be entirely redesigned. Predictive AI-powered mortgage risk models, for instance, need to undergo extensive testing, validation, and ongoing oversight, much as genAI’s large language models (LLMs). Once more, there are distinctions, like the well-known issue with “hallucinations” with genAI. However, risk management procedures for genAI will typically resemble those for predictive AI.
One in five Fortune 100 firms rely on Domino’s Enterprise AI platform to handle AI tools, data, training, and deployment. Teams working on MLOps and AI can use this platform to manage all aspects of AI, including generative and predictive AI, from a single control center. Organizations may enable full AI development, deployment, and management by consolidating MLOps under a single platform.
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