Innovation at the Speed of Thought

With the advent of generative AI, artificial intelligence has transitioned from the realm of technology to the social realm. There have been stunning advances in model sophistication for the past few years that have been progressing at breakneck speed in the background, giving rise to large AI models called Foundation Models. That progress has suddenly been thrust into the foreground by easy access to the power of Foundation Models via chatbot interfaces like chatGPT. What has been in the works for years but was not on everyone's radar is now part of most people's everyday social conversation. Generative AI presents an opportunity for us to experience AI intuitively at scale and with high impact.

From a business perspective, machine learning is no longer limited to the purview of Data Science/IT organizations. The traditional route of ML practitioners gathering requirements and then building models to satisfy that specific need is transitioning to a fast iterative approach. Line-of-business decision-makers at the highest levels are seeking to infuse AI in every facet of their business including their products, customer experience, business processes, operations, human capital, and compliance. Organizations of all sizes are envisioning options to leverage generative AI to accelerate their top-line growth, improve their bottom line through productivity gains, and manage risk effectively. The ideation of AI use cases and momentum for AI adoption is increasingly being driven by the LoB leaders.

One of the paradigm shifts with generative AI and foundation models is that data has moved from being a prerequisite for machine learning to a value-add when available for conversational, classification, entity extraction, summarization, personalization and similar use cases. Having a large corpus of clean, labeled data was a potential barrier for organizations before they could create ML models and integrate them into their solutions. Foundation models have leveled the playing field for organizations that don't have decades of accumulated, cleaned, labeled, and organized data. They can just as easily access a foundation model from a model provider and build ML into their applications and solutions. By comparison, where an organization does have a data advantage, they can apply that data to add value in combination with a foundation model.

Generative AI is ushering in the era of access to high-quality, multimodal, foundation models at the API level for embedding into solutions just as easily as any other API. You don't need sustainably provisioned GPUs and a team of highly skilled data scientists to infuse AI into your end applications. Access to ML models is becoming a service that can be called upon as needed. This presents an opportunity for organizations with the data and skills advantage to monetize this advantage by participating in the AI-as-a-Service business category and becoming part of the AI economy of the future.

This is an exciting time in the world of AI! While all the ways in which generative AI will influence the world's technology, economy, and society are not fully known, there are clear signs that the effects will be profound.