What’s the Difference Between Regular and Enterprise AI?

When are tools like ChatGPT and Bing safe to use?

For years AI quietly assisted us in the background, largely ignored. You don’t blink twice when Gmail predicts the next phrase you’ll write. Hoping to get help with your cable subscription? A chatbot probably triaged your question before connecting you with an agent. Yet when OpenAI launched ChatGPT in late 2023, we became hyper-aware of AI’s potential to change how we do business. 

With AI development and adoption moving at light speed, it’s been tough to grapple with or even understand the tools we use daily. AI companies promise miracles while scary headlines appear about leaked data or lawsuits. Leaders want to know, “Is AI a problem or an asset?” The answer, of course, is, “It depends.” 

In this post, we’ll break down one of the most popular AI tools—the large language model (LLM)—of which ChatGPT, Bard, and Bing are famous examples. We’ll review the difference between standard and enterprise models and consider some questions to ask when considering enterprise AI platforms.

What’s a large language model, and how does it work?

Artificial intelligence (AI) is an umbrella term often used for machine learning, a type of AI that uses algorithms to predict and classify patterns. Generative AI takes it one step further by using that information to generate something new. There are different types of generative AI:

• Large language models (LLMs) use natural language processing (NLP) to respond to queries or “prompts” with a text answer. ChatGPT, Bing, and Bard are the leaders here.
• Text-to-speech changes text prompts to a realistic voice and may also offer translation. For this type, you might think of ElevenLabs, WellSaid, or LOVO.
• Text-to-image converts typed prompts into images. Examples include DALLᐧE2, Stable Diffusion, and Midjourney
• Text-to-video—you guessed it—transforms text into a video. Imagen or Make-A-Video accomplish this task. 

Some of these models may combine generative AI models. For example, text-to-image draws information from an LLM and a generative image model to create results. 

Organizations considering AI to increase productivity and creativity will likely explore some or a combination of these generative AI types. Knowing the difference between the tools is important before digging into the risks and rewards. We’ll stick to LLMs for this post, but the information here applies to any generative AI tool types listed. 

Some people who tested these tools when the market exploded last year criticized the output quality and safety. However, the tools have evolved in an incredibly short time, so it’s worth looking again at your options when considering how to streamline your business processes.

Public or standard LLMs

When a child learns to speak a language, they gather input from everywhere—their family, the television, or their care provider. Eventually, they master the language well enough to create their own sentences, but it will take time before those sentences become masterpieces (just don’t tell Grandpa). Along the way, they’ll gather and incorporate new input into their personal lexicon. Sometimes, they may repeat things they shouldn’t, like a naughty word mom said. 

Now imagine an LLM following a similar path. At first, the LLM learns from controlled inputs like websites, books, and articles on the internet. Then, it masters natural-sounding language well enough to create plausible, conversational responses to questions. 

As models like ChatGPT gained billions of users, they continued learning from users and the tool’s builders. Sometimes, an unaware user told the LLM information they shouldn’t have, risking that information—which might be proprietary, inaccurate, or even harmful—appearing in other users’ responses. 

Luckily, LLMs and their creators have learned a lot in the past year and added more guardrails to protect their users. These will continue to evolve over time to improve tool functionality and output. One important development is enterprise LLM

What is an enterprise LLM?

Enterprise LLM can mean several things. First, it can mean that a company develops its own LLM using its proprietary data. These models likely include specialized training data related to the business. For example, a customer service center may have an LLM chat trained on the product support manual so that the agent can query that specialized database while assisting a customer in real time. 

Enterprise can also mean an enterprise-level paid subscription to an LLM provider. This subscription typically includes additional protections and guardrails to protect the subscribers’ data and sensitive information. The enterprise software may offer additional computing speed or other benefits.

It can be very expensive to create an enterprise AI platform from scratch, so some organizations may choose the best of both worlds and use established models as a foundation for their own enterprise tools.

In all cases, enterprise AI applications aim to create a closed loop where information employees input into AI can’t get out into the world for general AI users. 

Can I use LLMs like ChatGPT at work?

LLMs represent a huge opportunity to increase productivity and employee performance. On the other hand, organizations understandably have concerns about using LLMs and other AI tools at work. It’s important to bring cross-functional leadership together to explore the best use cases for AI and potential risks. This exercise will lead to a better understanding of AI use at work and set appropriate boundaries and policies for your industry and use case. 

For work, an enterprise-level LLM places the appropriate guardrails to ensure employees keep data and inputs secure at the company. It’s not wise to put sensitive information in an LLM without these measures in place. 

Questions to ask AI-assisted tool providers

Whether you consider subscribing directly to an LLM provider or a tool powered by an enterprise LLM model, these questions can help you determine if it’s safe to proceed. 

• How is data transmitted and stored? The provider should not retain your data or store it on subprocessors. They should delete the data immediately after you’ve finished generating the new content. 
• Does your tool use my data to train the AI model? Look for a tool that does not use your data to train a public model. That way, you can be assured sensitive data isn’t shared. 
• What speed does the tool offer? One of the benefits of enterprise AI solutions—beyond security—is often additional speed. 
• Does the tool follow any security protocols or certifications? Enterprise-level tools should provide you with their data protection protocols, such as GDPR, SOC 2, and ISO 27701.
• How will the tool’s output incorporate accessibility and DEI best practices? While LLMs aren’t at the point of generating public-ready content, the tool provider should have a plan for incorporating best industry practices.

For tools that use an LLM model, the following additional questions can provide key insight:

• What model(s) was the tool built on, and what standards above do they follow? A provider should be transparent about their partners and partners’ standards.
• Can I use the tool without AI if I choose? You should have the right to toggle AI on and off within a tool. 

Understanding is key to making good AI decisions

Every new technology ushers in both fear and excitement. It’s hard to imagine a time before Chipotle’s epic social media presence, but once upon a time, corporations considered social media a risky venture. In a few years, work powered by AI and machine learning models will be as commonplace as social media. Until then, during this time of digital transformation, it’s wise to explore AI solutions methodically to ensure that you maximize the benefits while minimizing risk exposure. 

Aligning the team to create an AI policy is a great first step to successfully adopting AI. For more information, get our guide to drafting an AI policy.