Top 5 Ways Generative AI and RPA Can Help Your Business

In November of 2022, a significant milestone occurred in the technology industry as ChatGPT 3 was introduced, rapidly gaining widespread recognition in a matter of weeks. Subsequent to its launch, numerous enhancements have been implemented, and multiple vendors are preparing to unveil their own variations of generative AI products. The concept of Generative AI has captured the attention of various sectors in the market – ranging from finance and banking to healthcare and manufacturing – all contemplating ways to leverage this evolving technology to their benefit.

However, this convenience also introduces potential challenges related to intellectual property, adherence to compliance laws, maintenance of safety standards, preservation of data privacy and security, and evaluation of workforce implications.

Previously in our blogs, we have discussed the potential of Generative AI and its convergence with RPA.
But, despite the high potential of Generative AI for industries, there’s a lot of fear surrounding the unknown aspects of generative AI, how can we use it? And more importantly, how can we utilize it securely in our businesses? Let’s find the answer in our blog.

A Conversation with ChatGPT

How Generative AI and RPA Can Help Your Business?

In our previous blog, we discussed how the convergence of Generative AI and RPA can unlock a new level of innovation for businesses. Right from giving data insights to creating human-like interactions, Generative AI and RPA can scale up the business process. Even Gartner in its report stated that By 2024, 40% of enterprise applications will have embedded conversational AI, up from less than 5% in 2020. Let’s have a look at the function of Generative AI and RPA across business operations.

1. Data Massaging

   Data massaging, often referred to as data wrangling or data preprocessing, involves the process of cleaning, transforming, and enriching raw data to make it suitable for analysis or other downstream tasks. Using generative AI and automation convergence in this process refers to using tools, scripts, or software to streamline and expedite these data preparation tasks.

   Also as per the requirements, these tools can be programmed to perform tasks like imputing missing values, removing duplicates, and correcting formatting errors without manual intervention. On one side RPA can extract data from multiple sources and create structured data, whereas Generative AI on the other hand assists in matching records across datasets using techniques like record linkage or deduplication.

2. Knowledge Base Management

   Generative AI offers valuable capabilities for constructing and enhancing knowledge bases, which are structured repositories of information. It achieves this through content generation, summarization, question-answering, and data gained from customers. Also, Generative AI can efficiently fill gaps, expand domains, and personalize content for various audiences.

   With the ready knowledge base, service desk staff get all the data in centralized manner and it makes it easy to offer instant resolution to query. Although it streamlines knowledge base development, human oversight is essential to ensure accuracy and ethical considerations. Remember, while generative AI is a potent tool, it benefits from human refinement and responsible usage.

3. Customer-Employee Interaction

   As per Gartner, customer service is one of the primary focuses of Generative AI initiatives. This is the reason, that businesses are using AI chatbots to facilitate the customer-employee interaction.

   Generative AI with the power of natural language understanding can create a seamless experience for customers and employees as well. Suppose when a customer raises a service ticket, employees require information to resolve it. Instead of humans spending time extracting information, generative AI can create knowledge-based articles as per customers’ past queries.

   This way, When employees need to provide customers with resources or solutions, the AI can generate informative content, helping customers find answers quickly. The convergence of Generative AI and RPA allows highly personalized efficient and scalable customer experience

4. Data Analysis

   Generative AI is fundamentally reshaping the landscape of data analysis, streamlining and expediting the extraction of insights from extensive datasets. By leveraging this technology, computers are able to discern patterns within data and apply this acquired knowledge to generate novel content or forecast outcomes.

   Conventionally, the task of data analysis necessitated a team of experts who meticulously combed through vast datasets in search of noteworthy trends. However, the advent of generative AI algorithms has automated these processes. As a result, enterprises can swiftly pinpoint crucial indicators and make well-informed decisions based on up-to-the-minute information.Furthermore, generative AI empowers companies to delve deeper into understanding customer behaviors by analyzing copious amounts of unstructured data, such as social media posts and online reviews. Subsequently, businesses can harness this data to devise precisely targeted marketing strategies and enhance customer experiences.

5. Chatbot and Virtual Assistance

   Generative AI, powered by models like GPT-3, enables machines to generate human-like text based on the input it receives. This technology is great for creating chatbots and virtual assistants. To create a comprehensive chatbot or virtual assistant, you can integrate both generative AI and RPA technologies. Generative AI can handle the natural language understanding and generation aspects, while RPA can take care of backend processes and transactions. For instance, a virtual assistant could use generative AI to provide information and answer questions, while also utilizing RPA to perform tasks like updating user profiles or making reservations.

Getting Started with Generative AI

Considering the endless number of use cases for generative AI, it’s clear that this technology is game-changing, and you don’t want to be left behind.
To prepare yourself to use generative AI, consider these questions before you get started:

1. 1. What is the goal of your project?
   2. What kind of input data do you have?
   3. What is your level of expertise in AI and RPA?
   4. Do you have the necessary capabilities?
   5. What is the scalability and maintenance plan?
   6. How will you integrate Generative AI into your existing model?

The rapid growth of Generative AI technologies and their increasing integration into various business aspects necessitates a profound comprehension of their potentials, applications, and associated hazards. This mandates leaders across all hierarchies to familiarize themselves with Gen AI. To achieve this, a crucial first step involves formulating pertinent inquiries about AI, enabling a comprehensive grasp of its scope, utility, and potential pitfalls.