Building a brand-new large language model (LLM) from scratch is an option for companies, but it can be extremely expensive and complex. Thankfully, there are other ways to use AI that are faster, easier, and much more affordable.
AI is advancing at an incredible speed. The use of generative AI, which can produce text, graphics, and even music, is growing quickly. According to a recent survey, 11% of American workers use AI on a regular basis, while roughly 25% utilize it at work. AI adoption is occurring at a rate that is almost twice as rapid as the expansion of the internet. AI is already being used by the vast majority of enterprises, and many of them have completely incorporated it into their daily operations.
One kind of AI that specializes on text and code is called an LLM. OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Gemini, Meta’s Llama, and Mistral’s open-source models are a few of the most well-known models available today. These models assist companies in increasing decision-making, automating processes, and improving customer service.
Numerous companies are attempting to determine the most efficient strategy to apply AI. Depending on how much customization a business desires, adopting AI might be complicated at different levels. Let’s examine some typical applications of AI being used by businesses today.
Chatbots are the simplest way for companies to begin utilizing AI. These tools can help staff members with a variety of duties, provide information summaries, and respond to inquiries. While enterprise versions provide more protection, some chatbots are freely accessible to the general public. AI-powered chatbots are now used by most enterprises; the most common options are Google Gemini, Microsoft Copilot, and ChatGPT.
Numerous platforms for business software, including Salesforce and Grammarly, are incorporating AI into their goods as well. More than 80% of commercial software will have AI capabilities by 2026, according to experts. Search engines, commercial apps, and customer support systems are increasingly using AI assistants.
APIs, which enable the addition of AI features to pre-existing software, are another method businesses employ AI. For instance, an API could be used by a meeting management program to automatically create discussion summaries. This method is economical and enables companies to integrate AI without creating custom models.
Retrieval-Augmented Generation (RAG) is a strategy used by several businesses seeking to personalize AI. This entails keeping data unique to the organization in a vector database, a unique kind of database. In the event when a user queriesBefore sending the query to the AI model, the system first determines what data is available and obtains pertinent details. This guarantees that the AI bases its answers on precise and pertinent business facts.
AI can swiftly and effectively locate the most pertinent responses with the aid of vector databases. These databases are being used by businesses more and more to personalize and enhance AI encounters. Vector databases have been effectively used by businesses such as Salesloft to improve AI-powered client interactions.
Instead than depending on outside AI providers, several companies would rather use locally executable, open-source AI models. Businesses can have greater control over their data and expenses thanks to open-source models like Google’s AI tools, IBM’s Granite, and Meta’s Llama.. This strategy is gaining traction, particularly among companies looking to cut expenses associated with adopting commercial AI services.
To protect its data, a business might, for instance, build its own AI model on local servers. Certain businesses, such as PwC, have developed their own AI tools that enable staff members to use AI for particular business tasks, such creating job descriptions. These models are carefully designed to follow company policies and branding.
Fine-tuning AI models is another option for businesses that want AI customized to their needs. Instead of using a general AI model, companies can train it on their own data.This is particularly helpful for sectors where accurate information is essential, such as healthcare and finance. Although it takes more work, fine-tuning can increase AI’s effectiveness for particular corporate applications.
Even with all of these choices, most businesses still find it unfeasible to create an AI model from the ground up. Millions of money and a vast amount of processing power are needed to train a sophisticated AI model. The development of the largest AI models, such OpenAI’s GPT-4, costs more than $100 million. Most businesses would rather improve on current AI or leverage AI through APIs and integrations than develop new models.
Some of the most advanced businesses are now using multiple AI models at the same time. They create a system where different AI models handle different tasks based on their strengths. This approach, known as a “”model garden,”” allows companies to switch between AI models to optimize cost and performance. Even though there are currently very few businesses employing this tactic, analysts predict that it will soon become widely used.
Businesses must be flexible in the rapidly evolving field of artificial intelligence. Businesses should establish robust data management and governance procedures rather than concentrating on a single AI model. As technology advances, they can quickly transition to more advanced AI models. Understanding AI’s potential and choosing wisely how to incorporate it into corporate operations are essential for success.
