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Whether you’re working in a small startup, or in a large transnational corporation, there’s a good chance that you’ve already heard of workflow automation. In fact, there’s probably an even greater chance that you’ve interacted with tools and elements that automate some part of your workload, to an extent. From aiding in tasks like sorting and indexing emails; inputting data in a sheet, or managing your work-vital digital documents, to entirely automating crucial business processes, workflow automation has increasingly become an essential tool for day-to-day life in successful businesses.

However, traditional workflow automation processes are not without their limitations: For instance, they depend on a strict set of rules, which by definition are limited in scope and scalability, and will often require human input to perform effectively. Moreover, since they require human input, this opens way to human error, not to mention that these tools also can’t aid in decision making reliably. This is where AI and Large Language Models come into play, as integrating chatbots like ChatGPT into the workflow automation process can exponentially increase the effectiveness and efficiency of these tools.

The Role of AI in Workflow Automation

In the past, workflow automation was limited to the constraints of its scripts and overall programming. As such, these tools always required at least a modicum of human monitoring and interaction to ensure that they are working as intended, which defeats the purpose of automation. Furthermore, tasks that would require more complex interactions such as predicting outcomes based on data inputs, and analyzing data patterns to detect and protect from fraud, to name a couple, are all out of reach when it comes to these traditional workflow automation efforts.

By incorporating artificial intelligence to the field of workflow automation, we can cover a wider range of tasks, and even address processes that otherwise would’ve been impossible in the past, such as the ones mentioned above. Other benefits of implementing artificial intelligence into the workflow automation processes include improved decision-making; predictive analytics; image and speech recognition, and robotic process automation, among others.

A good example of this implementation is how Nanonets uses AI to automate email parsing, reducing turnaround times and manual effort required to complete this standard task. One of the core applications of Nanonets revolves around simplifying data capturing efforts through the use of artificial intelligence. Specifically, our AI enables collecting the exact information you need from any document—even from the ones that don’t follow standard templates—, and validating and exporting it as per your requirements.

This specific component of our AI greatly streamlines and optimizes the document management workflow, while also producing clean information with reduced chances of human error.

What is an LLM?

An LLM, or Large Language Model, is an advanced type of artificial intelligence that can generate human-like text based on a given input. These models, such as OpenAI's GPT-4, are trained on vast amounts of data to understand context, generate meaningful responses, and perform complex tasks. By leveraging LLMs, businesses and individuals can automate various aspects of their workflows, enhancing productivity and reducing human error.

How LLMs Help to Improve Workflow Automation?

Even with the advances that artificial intelligence has seen in the past few years, and despite its growing role in workflow automation, this tool still has a few crucial limitations in what it can achieve. More specifically, AIs by themselves lack the ability to process natural language inputs, and have limited methods of producing personalized data catered to the user’s exact needs.

This is where Large Language Models (LLMs) come into play, giving AIs an extra layer of depth, allowing them to not only process large amounts of data, but also understand the requirements of the user based on natural language inputs, in order to process and present the data in an effective and user-friendly manner. Recent developments in chatbots such as ChatGPT have allowed the integration of the GPT-4 LLM with certain workflow automation endeavors. Businesses such as Zapier have recently incorporated this technology into their existing offerings, giving them much more flexibility and overcoming most of the past limitations of its AI solutions.

The ability to process language inputs opens the field for more automation endeavors, particularly when it comes to user interactions and engagement. As such, this development paves the way for more hands-on uses, such as using AI to directly interact with users and clients.

A good example of these developments is how Uber is using AI and LLMs to streamline comms between users and drivers. The way this works is that, whenever a user or a driver inputs a query through the chat feature, the natural language processing component of its Michelangelo AI will process the text to discern the intent, and produce responses that the users can choose with a single tap. This makes the journey much safer for the driver, as they can keep their attention on navigating, without having to manually respond to texts or calls, while also ensuring that the clients receive timely responses to their texts.

In the same vein, Coca Cola has also been dabbling in AI with their modern vending machines, which connect with the Coca Cola Freestyle app to facilitate PoS operations when purchasing drinks from these machines. The implementation also helps to capture important data like individual purchases, which in turn can be automatically captured and used by the internet-enabled vending machines to encourage stocking the most popular drinks in that area, improving sales. Additionally, AI also adds a “gamification” aspect to the user engagement workflow, by allowing users to interact with its onboard chatbot via Facebook Messenger, which uses NLP to adapt its language and personality on a per-user basis.

However, not all these innovations are related to improving user engagement and marketing. Case in point, IBM Watson’s AI platform uses LLM to incorporate natural language processing capabilities to its artificial intelligence solution, giving it the capability to service a wide variety of industries including healthcare, finance, and customer service fields. The AI is capable of understanding natural language inputs; capturing data to establish patterns, and providing a wide variety of insights to enhance the workflow automation of its users.

AI and LLM have also become instrumental in the field of pharmaceuticals, as companies like Johnson & Johnson once adopted their use in order to process and analyze vast volumes of scientific texts and literature. The expectation was that, through natural language processing and machine learning algorithms, the AI could highlight and suggest potential methods for developing new drugs, which in turn is a massive boon in the workflow automation of the drug discovery process. While the product itself has been discontinued as of 2019 due to poor financial performance, it highlights the potential uses of these technologies in the field of drug discovery.

Using LLMs to Automate Workflows

Leveraging the power of Large Language Models (LLMs) can greatly simplify workflows and save time. From drafting emails and generating content to automating project management and providing customer support, LLMs can understand and interpret user inputs to generate contextually relevant outputs. Here are some common use cases where LLMs can greatly help improve productivity.

Drafting Emails and Other Communications

LLMs can be used to draft emails, social media updates, and other forms of communication. By providing a brief outline or key points, the LLM can generate a well-structured, coherent, and contextually relevant message. This saves time and ensures that your communications are clear and professional.

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Content Generation

Whether you need to create blog posts, product descriptions, or marketing materials, LLMs can help by generating high-quality content. Simply provide an outline or topic, and the LLM will use its vast knowledge base to craft content that is engaging, informative, and well-structured.

Task Automation

LLMs can be integrated with various task management systems, like Trello, Asana, or, to automate project and task management. By using natural language processing, LLMs can understand and interpret user inputs, creating tasks, updating statuses, and assigning priorities without the need for manual intervention.

Data Analysis and Reporting

LLMs can be used to analyze large datasets and generate reports or summaries. By providing the LLM with relevant information, it can identify trends, patterns, and insights, transforming raw data into actionable intelligence. This can be especially valuable for businesses looking to make data-driven decisions.

Customer Support

By integrating LLMs into your customer support systems, you can automate responses to frequently asked questions, reducing the workload on your support team. LLMs can understand the context and intent of a customer's query, generating helpful and accurate responses in real-time.

Programming Assistance

LLMs can be used to generate code snippets, provide suggestions for debugging, or offer guidance on best programming practices. By leveraging the LLM's vast knowledge of programming languages and frameworks, developers can save time and ensure their code is optimized and efficient.

Best Practices for Implementing LLMs

Identify Suitable Use Cases

Before integrating an LLM into your workflows, it's essential to identify tasks that are well-suited for automation. Tasks that involve repetitive processes, require natural language understanding, or involve generating content are ideal candidates.

Start with a Pilot Project

When implementing LLMs, it's a good idea to start with a small pilot project. This allows you to gauge the effectiveness of the LLM, refine your approach, and identify any potential challenges before scaling up.

Monitor and Optimize

As with any AI-driven technology, LLMs may require fine-tuning and optimization to ensure they meet your specific needs. Regularly monitor the performance of the LLM, gather feedback from users, and make necessary adjustments to improve its effectiveness.


We’ve barely just scratched the surface when it comes to how LLMs like GPT-4 are revolutionizing the field of workflow automation. All this evidence points to the fact that the future of business will see a much larger AI involvement as a tool to support the tasks and endeavors of both the personnel, as well as their prospective clients and users.

Have you interacted with any LLM-based workflow automation tools? Feel free to share your experiences and thoughts with us!