5 Generative AI Use Cases You Need to Apply to Your Processes

Ready to implement generative AI in your business processes? Starting with the right generative AI use cases is key to your success. You’ll want to find areas where you can achieve quick wins as you grow toward your larger AI vision. In this article, we’ll highlight five use cases where you can incorporate generative AI for increased process efficiency.

Also, for our Appian customers, keep in mind that these five use cases are all things you can accomplish today by adding the Appian Azure OpenAI plug-in to your applications. 

One effective generative AI use case is to build internal chatbots directly connected to your knowledge base. Employees can pose questions and receive instant, accurate responses, not only saving time but also ensuring that the valuable insights and resources they need are just a message away. 

Example: The Texas Department of Public Safety implemented an AI-powered chatbot that mines their proprietary documents to provide answers to employees. Employees can now go to the chatbot for answers to questions that the agency director used to have to field.

Elevate decision-making and reap the benefits of intelligent document automation with automated document summaries. Generative AI can summarize key facts and action items from documents based on predefined settings, letting you get the information you need from even large volumes of documents much more quickly.

Example: A government agency used generative AI to process critical identity document applications. These used to take weeks to review because employees had to match job descriptions on the job applications to predefined job codes. But by using a generative AI plug-in, the agency was able to quickly create an application to automate this matching process. 

Their new AI-powered application takes the unstructured text of the job description and instantly recommends the top three codes for that job. Thanks to this AI-improved process, the agency has significantly reduced the time it takes to process applications, allowing for increased capacity and enabling reviewers to focus on more critical tasks.

Generative AI comes in handy for generating internal and external email responses. In Appian, you can set predefined requirements for tone, length, language, and type before placing this skill into your workflow. 

Example: A global investigative service organization uses generative AI to process incoming tips. The AI swiftly extracts key details, matches them with the right case, and generates an email for the employee to send back to the person who submitted the tip—providing a faster response while maintaining human oversight. With this new tool at the ready, the organization can act on critical tip information much faster as well as handle more cases. 

Create custom chatbots that match your brand's color palette and iconography for customers to self-service. This use case for generative AI not only enhances the user experience but also frees up time for your support teams to focus on more complex issues.

Example: A litigation insurance company uses AI to provide a seamless and efficient claims management process. First triaging incoming emails with email classification and document extraction, employees then use a generative AI–powered chatbot to ask questions and address outstanding issues.

Learn about the differences between generative AI and large language models. 

Imagine the efficiency gains that could arise when AI extracts the most relevant data from text, categorizes it, and plugs it into other systems for data analysis preparation. When you’ve got large volumes of information to analyze, this use case is particularly potent.

Example: Leroy Merlin, a major home improvement retailer, needed to expedite the return, refund, and exchange procedures. Manual approvals, a lack of oversight in fulfillment, and data silos between systems delayed approval processes and caused frequent order cancellations, leading to declining customer satisfaction ratings. Additionally, data inaccuracies contributed to financial losses.

To address these issues, the retailer implemented process automation. They utilized robotic process automation (RPA) to automate refund payment transactions, capturing data from diverse payment portals. Next, and this is where generative AI came in, they used AI-powered document processing to extract essential customer information required for initiating refunds. This automation successfully streamlined up to 90% of previously manual processes.

Looking for more ways to use gen AI? Check out these seven AI/ML use cases.

In a world where AI is on the precipice of transforming everything, not every business will end up on the winning side. You need certain capabilities to support AI operationalization—specifically, data and process. Data is the intellectual fuel for AI, making it smarter and more informed, while process turns insights into action across the enterprise.

To start implementing AI effectively in use cases like the ones in this article, focus on these four elements:

• Workflow integration: Make sure AI is seamlessly integrated into your processes and set up to collaborate with human decision-makers.
• Task automation: Streamline operations by automating manual tasks, such as searching for, matching, and summarizing data. This boosts efficiency and frees up human resources for more strategic work.
• Data: Invest in a strong semantic layer over your enterprise data to ensure you can use it to train your algorithms and/or query it in real time to get fast answers to your questions. A data fabric is a great way to do this.
• Security and privacy: Prioritize the security and privacy of your AI implementations. Take measures to protect sensitive information, particularly with a secure and trustworthy AI integration.

We’ll leave you these words of guidance from Piyush Kumar, Global Head – Strategy, Strategic Partnership & Solutions at Wipro: 

It all boils down to the basics. You should have your data strategy dialed in. The best organizations take a top-down approach, where the c-level buys into the critical business value and differentiation AI can drive. This may be easier to achieve with the news hype and promise of generative AI. Picking the right use cases to showcase the value has always been key. Focus on early wins with low-hanging opportunities while you build and invest toward the bigger vision.