×

Subscribe

Apr 18, 2023 511

Is Generative AI ready for enterprise?

"OpenAI’s ChatGPT has taken the world by storm, with over 100 million active users in just a few short months. The technology can generate human-like, grammatically correct responses, and related technologies can produce artwork and code by entering a description of what you want. You can even interact with the AI after your initial question, making adjustments to your picture or code so it more closely matches your vision. All of this happens instantly without the help of a subject expert, an artist, or a coder.

However, there are issues that come with generative AI, including the sourcing of the data used to train the underlying AI model, the currency of that training data, a lack of permissions to use the source data, bias in the model, and the accuracy of the responses, which are sometimes laughably wrong."

Despite these issues, enterprise software companies are taking the generative AI plunge. Salesforce, Forethought, and Thoughtspot have all recently announced betas of their own flavors of generative AI. Salesforce is adding generative AI across the platform, Forethought is aiming at chatbots, and Thoughtspot wants to use AI for data querying. Each company took the base technology and added some algorithmic boosters to tune the tech for their platform’s unique requirements.

Microsoft also announced that its OpenAI service aimed at enterprise users on Azure is generally available as a managed service. Throughout this year, we can expect to see many more companies joining in, but the limitations are real, which makes us wonder: Is the technology — as early and raw as it is, no matter how cool it looks on its face — really enterprise ready?

One of the biggest issues with generative AI is bias. The data used to train these models is often biased towards certain groups or demographics, leading to biased responses. This can be particularly problematic in industries such as finance or healthcare where biased decisions can have serious consequences.

Another issue is the accuracy of the responses. While generative AI can produce human-like responses, it is not always accurate. This can be a problem when dealing with sensitive information or making important decisions based on the AI’s output.

There is also the issue of data privacy. Generative AI requires large amounts of data to be trained effectively. However, this data often contains sensitive information that may not be suitable for use in AI models. Companies must ensure that they have the necessary permissions to use this data and that they are not violating any privacy laws.

Despite these issues, there are many potential benefits to using generative AI in enterprise settings. For example, it can help automate tasks that would otherwise require human intervention, freeing up employees to focus on more complex tasks. It can also help improve customer service by providing quick and accurate responses to customer inquiries.

In conclusion, while generative AI has enormous potential in enterprise settings, there are still many issues that need to be addressed before it can be considered truly enterprise-ready. Companies must be aware of these issues and take steps to mitigate them if they want to successfully implement generative AI in their operations.