Taking control of generative AI

The hype surrounding the capabilities of generative AI is immense. While generative AI has been around for some time, the launch of foundation models like ChatGPT has sparked a surge of interest in the technology, leading to an unparalleled adoption rate.

According to Gartner, 70% of global organizations are currently exploring Gen AI.

While the market is already shifting to bring Generative AI closer to business use, organizations have several factors to consider in order to get the most out of these large language models.

Leveraging foundation models for business outcomes

In this point of view we explore how foundation models can be tuned, integrating company knowledge to create tailored intelligence that meets business needs, producing the right outcomes while guaranteeing security and data privacy.

Building on AI foundations

The latest generative AI foundation models can have 10 billion, and even 100 billion parameters, which are significantly more than the earlier versions.

Hyperscalers are competing to create the best AI foundations that businesses can use to build upon. This allows companies to focus more on vendor selection and then their business challenges rather than having to build the whole foundation from scratch. This means AI is moving from custom development into the package age.

Getting customized intelligence from generative AI 

Foundation models are really good at creating text and pictures and can outperform humans in areas such as translation and the processing of handwriting, images, and speech. But they do this in a statistical way and can be prone to hallucinations, generating false information from the underlying data. These are models that do not have general intelligence or indeed any intelligence beyond what is provided to them.

However, foundation models can be tuned. Integrating company knowledge into a model results in tailored intelligence that meets the individual specificities of an organization. This guarantees that the model produces the right outcomes and works within set boundaries.

Creating business value with company data

AI can help create customer profiles, identify trends, and unearth new business opportunities through data analysis. However, developing accurate and reliable AI models requires a substantial amount of data, which presents challenges regarding data quality and quantity.

Generative Adversarial Network (GAN) models can produce synthetic, artificially generated data based on real data sets. This allows organizations to perform data analysis while remaining compliant with security regulations.

Trust is the biggest challenge in generative AI

Data has value, and organizations have a responsibility to protect their data and the data of their customers. The uncontrolled usage of generative AIs could result in data suddenly being available for public consumption.

Establishing a model hub that implements a testing and trust layer to monitor any potential model usage leakages makes sure AI tools are being used in a secure and private way. It also ensures consistency and accuracy.

Generative AI is an undeniably powerful tool, especially when pre-trained foundation models are combined with a company’s knowledge. If a company wants to use generative AI with their own data, they need a reliable partner who can help build the necessary infrastructure, give guidance, and help them scale up.