Join our waitlist
We are currently in a private beta. Join our waitlist to get priority access. Beta users also have a special pricing when we launch. We promise we won't spam.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Why Generative AI is not ready for Enterprise Adoption

July 28, 2023

Why Generative AI is not ready for Enterprise Adoption

Generative Artificial Intelligence (AI) models is getting a lot of hype after the release of OpenAI's ChatGPT. The world is seeing the automation of some skills around creativity and imagination sooner than many expected. Despite all the development and promises, Generative AI at its current state is not ready for Enterprise adoption and here's why:

The Promise of Generative AI

Generative AI offers the potential to revolutionize numerous industry sectors. At its core, it's about teaching machines to create. This extends from generating text, images, and music, all the way to designing new molecules for drug discovery. A notable example of this technology is EnterpriseGPT, a generative model that excels in creating human-like text. It can generate anything from poems to code, making it a versatile tool with seemingly boundless potential.

The future of enterprise artificial intelligence could be shaped significantly by generative AI. It can automate tasks, improve efficiencies, provide insightful analytics, and even drive innovation. The goal is to have AI not just as a tool that replicates human intelligence, but one that could potentially supersede it, creating novel solutions to complex problems that we wouldn't have thought of ourselves.

The Roadblocks to Enterprise Adoption

However, despite the incredible potential and excitement surrounding generative AI, its transition into widespread enterprise adoption is not without significant hurdles. The truth is that generative AI, in its current state, has several issues that limit its readiness for enterprise deployment.

The first of these concerns is 'LLM hallucination.' This term refers to instances where Language Learning Models (LLMs), like EnterpriseGPT, generate information that appears plausible but is in fact, incorrect or nonexistent—a fabrication of the AI. This can be a minor issue when generating a poem or a piece of music, but in an enterprise context, the stakes are much higher. A financial report, a legal document, or a piece of medical advice that contains inaccurate or fabricated information could have severe consequences.

A lack of transparency and explainability is another obstacle to enterprise adoption. Often, generative AI models function as black boxes—their inner workings and decision-making processes are difficult to comprehend, even for experts in the field. In an enterprise environment, where decisions can have significant impacts, this lack of transparency can be a substantial risk. Enterprises require AI systems that offer clear, understandable explanations for their outputs, something generative AI often struggles to provide.

Data privacy and ethical issues also present significant roadblocks. Generative AI models require massive amounts of data to train effectively. Collecting this data without infringing on privacy rights is a significant challenge. Additionally, there are ethical considerations surrounding the use of AI-generated content. Is it acceptable for a generative AI to write an article, compose a piece of music, or create a design without human involvement? How should intellectual property rights be managed in such cases? These are questions that still lack clear answers.

Finally, the implementation of generative AI in the enterprise setting requires significant resources and expertise. The process of integrating such AI systems into existing workflows, and ensuring they are robust, reliable, and secure is complex and costly. For many businesses, the investment needed to properly deploy and maintain these systems may not align with the perceived benefits.

In conclusion, while the promise of generative AI is immense, we're still some way off from it being ready for widespread enterprise adoption. Issues surrounding LLM hallucination, transparency, data privacy, ethics, and implementation must be addressed before we can fully unleash the potential of generative AI in the enterprise. The journey may be longer than we'd like, but the destination—where AI drives innovation and growth in businesses—is undoubtedly worth the wait.

Join our waitlist