Create, Adapt, Consume: Engaging With AI to Meet Business Goals

Many organizations are eager to take advantage of generative AI. This article explores how businesses can engage with AI to amplify transformation and meet business goals, focusing on three main approaches: creating, adapting, and consuming AI models.

Creating AI Models

Creating AI models from scratch is a big undertaking. It needs huge amounts of data, sometimes trillions of data points. All this data has to be cleaned up and organized. This work involves data scientists, data architects, and data engineers. It also requires a lot of computing power, networking, and storage.

Companies like OpenAI, Google, Mistral, and Anthropic are building these foundational models. They are creating the basic tools that other businesses can then use.

Adapting AI Models

Adapting AI models means changing parts of them to fit your organization's specific needs. There are two main ways to do this:

• Using Your Knowledge Base: You can give the AI model your company's knowledge or information about your environment. This helps the AI respond better to your prompts.

• Fine-Tuning: If you want the model to learn new tasks, you can use a process called fine-tuning. This adjusts the model's settings so it can learn new things. This way, you can teach it new tricks, like creating marketing material, without having to write a very long prompt every time.

Consuming AI Models

Consuming AI models means using AI tools that already exist. This is often done through prompt engineering or by chatting with your data. Most people think of consuming AI when they hear about large language models, like the one from OpenAI.

Many applications are now building AI directly into their systems. You can see this with tools like Microsoft Co-pilot, Adobe Firefly, and Salesforce. The hard part isn't the AI itself, but learning how to talk to these AIs and how they will change your organization. This involves:

Key Takeaways

• Adoption

• Change management

• Education

These things can be tough to do, but they are important for using AI effectively.