Innovation in Language: Powering Business Processes Through LLMs

Manoj Chaudhary, the CTO and SVP of Engineering at Jitterbit, explains how Large Language Models (LLMs) can power your company’s business processes. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Large Language Models (LLMs) rose to prominence with the advent of OpenAI’s ChatGPT generative AI service. Now, business leaders are trying to use the magic of LLMs to streamline as many business processes as possible. However, there are countless applications for LLMs in business, and there is nuance to how the apps can be used effectively—namely, that they are somewhat limited unless paired with specific applications. How can business leaders parsing the LLM landscape use AI most effectively to automate and thereby accelerate their business practices? 

Uses for LLMs in Business Processes 

Consider a company that wants to streamline the creation of its “opportunity to order” process or the steps of guiding a customer through finding and purchasing a product. This is a good area to leverage LLMs because the development of such a framework traditionally relied heavily on manual methods such as high-code programming.  

With the addition of LLMs, businesses relied less on manual methods, as they no longer needed to start from scratch with every application. LLMs host vast databases with examples of common processes fit for automation, providing a head start to developing solutions. Existing solutions from previously developed frameworks within the LLM’s databases can be modified to fit the specific needs of each business to speed up the process significantly.   

Moreover, natural language interaction improves the end-user experience. Let’s say the user expresses an interest in creating an “opportunity to order” automation framework tailored to their customer relationship management (CRM) and enterprise resource planning (ERP) tools using an Integration Platform as a Service (iPaaS). The LLM can take this request and quickly generate programs that connect and automate the process, creating an operational solution that requires only slight fine-tuning. 

In a second example, consider how an e-commerce business that uses electronic data interchange (EDI) technology could leverage LLMs. This business wants to create an application to check incoming orders before those orders are integrated into an ERP system. Building this app from scratch or using a low-code platform, as would usually be done, would be labor-intensive and time-consuming. An LLM can interpret the requirements from the user’s request and generate an app, a revolution in efficiency. 

LLMs also open avenues of technology to those without tech sophistication. A farmer might have invested in technology in the agriculture industry but could lack the information needed to apply it effectively. An LLM could help this person design an application using natural language to monitor their carbon footprint across seasons without the depth of coding knowledge ordinarily required. The LLM, utilizing its database of apps for data representation and tracking needs, can develop a tailor-made application. 

These examples show some of the range of uses for LLMs in business, automating considerable portions of tasks and removing the need for intricate tech knowledge to generate custom solutions. Across industries, the applications for LLMs are endless.  

How to Get Started With LLMs 

LLM-related technologies have been booming in recent months, and their efficiency means businesses must consider taking advantage of them to remain competitive. Those who wish to look into these tools should consider the following: 

Learn More

Preparation is critical, especially when implementing new technology for your business. LLMs are part of a rapidly evolving landscape, and it is pivotal to know which tools are available and where they stand. OpenAI was an innovator in the generative AI space with variations of ChatGPT, but other major developers like AWS and Google, as well as open-source iterations such as LLaMa 2 and Bloom, are leaning on LLMs to provide new tools for business app development.

Know the Options

With so many options and possibilities at play, businesses must familiarize themselves with the range of vendors and choose the LLM that best caters to their needs. AWS Bedrock, Google’s newly introduced Gemini, and many others have options for integration and app development, and each of them has strengths and weaknesses to research before applying them to your business. 

Exercise Caution

When you consider how quickly the AI industry is growing, businesses must keep up to date. Governance and security are a must, as seen in OpenAI’s recent statement indicating its responsibility to address issues that arise from API-driven applications. The evolution of the tools — and regulations surrounding them — should be closely monitored to ensure that businesses are using systems that align with their needs and values and to implement security measures as needed.  

The importance of AI and LLM in the future of business is indisputable, so the only question is how best to leverage these advancements for success while still exercising caution and selecting the tools that align with both business objectives and security requirements. 

Nobody wants to fall behind in adopting new technology for fear of losing the competitive edge, but there are risks with any new system. Just as it is important to stay on the cutting edge of new technology, it is crucial to stay informed to avoid investing in initiatives that fall flat or cause unintended consequences. The best practice is to strike the perfect balance between early adoption and caution, and those organizations that can do so put themselves in the best position to succeed.