The public-facing version of ChatGPT reached 100 million users in just two months. It democratized AI in a manner not previously seen while becoming by far the fastest-growing app ever. Its out-of-the-box accessibility makes generative AI different from all AI that came before it.

While foundation models serve as the “brain” of generative AI, an entire value chain is emerging to support the training and use of this technology (Exhibit 2).1 Specialized hardware provides the extensive compute power needed to train the models. Cloud platforms offer the ability to tap this hardware. MLOps and model hub providers offer the tools, technologies, and practices an organization needs to adapt a foundation model and deploy it within its end-user applications. Many companies are entering the market to offer applications built on top of foundation models that enable them to perform a specific task, such as helping a company’s customers with service issues.

Transformative use cases that offer practical benefits for jobs and the workplace already exist. Companies across sectors, from pharmaceuticals to banking to retail, are standing up a range of use cases to capture value creation potential. Organizations can start small or large, depending on their aspiration.
Costs of pursuing generative AI vary widely, depending on the use case and the data required for software, cloud infrastructure, technical expertise, and risk mitigation. Companies must take into account risk issues, regardless of use case, and some will require more resources than others.
While there is merit to getting started fast, building a basic business case first will help companies better navigate their generative AI journeys.