Best AI Use Cases Awards 2023

Best AI Use Cases Awards 2023


A few years ago, artificial intelligence (AI) was considered rocket science, very difficult for non-experts to use and understand. In recent years, AI has become more and more accessible and is being used in many applications. The UiPath Business Automation Platform is the ideal deployment vehicle for AI technologies, which are used in almost every component.

Today we announced the first-ever UiPath AI Awards celebrating the achievements of AI automation community. AI is used in automation tasks, in end-to-end business processes, offering solutions to complex document processing, and general process improvements. The AI awards are proof that ‌UiPath users from developer communities across the world are using AI in automations. This is a paved way in demonstrating the limitless opportunities for offering solutions to extremely complex problems.

The UiPath AI Awards 2023 consists of two categories: AI Ambassador of the Year and Best AI Use Cases. You can read more about the AI Ambassadors of the Year winners in the community blog. In this article, I’ll tell you more about the "Best AI Use Cases" category.

Automation experts across the world are finding more and more ways to apply AI across various industries and domains. The Best AI Use Case awards were launched to recognize RPA developers who are using AI to innovate and transform business processes. The participants were asked to submit their most impactful and interesting AI use cases with UiPath AI products: AI Center, Document Understanding, AI Computer Vision, Communications Mining, and Task Mining. Then, the community members and UiPath jury voted to select the top ten most innovative and value-driving use cases.

As one of the UiPath jury members, I would like to mention that all the participants created impressive automations, and it wasn’t easy to decide on the winners. Entries that included a comprehensive report of their experience working on a real-life scenario, supported by relevant metrics and an explanation of the actual or potential advantages, had a higher likelihood of receiving the award.

Now, I’m happy to share the winner list, kudos to the winners!

  1. Efficient Product Sequencing Recommendation Model by Pradeep Shukla, Peraton, US

  2. Early Detection of Foodborne Illness Outbreaks by Michael Sebastian, SimplifyNext, Singapore

  3. Crop Recommendation Using TPOT Model by Pradeep Chinnala, WonderBotz, India

  4. Early Disease Detection and Prevention by Ikshit Dhawan, SimplifyNext, India

  5. Bill of Lading Automation by Imran Loon, Tech Mahindra, UK

  6. Improved Retail Demand Forecasting by Oumayma Lajili, OLCYA, France

  7. Procure to Pay - Invoice Processing by Sumit Sharma, Tata Consultancy Services, India

  8. Chronic Disease Prediction Using Lab Data by Vishal Kalra, Applications Software Technology (AST), India

  9. Automated Medical Coding by Chaitanya Kulkarni, JPMorgan Chase & Co, India

  10. AutoMate One-Stop Auto Insurance & Auto Claim Settlement App by Mohd Faiz Khan, The Silicon Partners, India

Best AI use cases per industry

The interesting fact is the diversity of themes addressed by the winners. I was amazed by the variety of AI applications and how the participants found innovative ways to use UiPath AI products. Based on the areas of application, the ten winners of the Best AI Use Case awards included:

  • four use cases were in healthcare

  • two use cases presented AI based optimization solutions for eCommerce

  • two use cases were solutions for document processing in logistics and finance

  • one use case was in agriculture optimization

  • one use case in insurance claims processing

Healthcare use cases

The domain that has the most use cases is healthcare. It is well-known that healthcare is a vertical that has embraced automation a lot because it dramatically improves the quality of services offered to patients, which is vital for their health. These are use cases #2, #4, #8, and #9.

Three of them deal with the early detection of foodborne illness outbreaks (#2), and two of them deal with the early detection and prevention of illnesses (#4 and #8).

Use Case #2 leverages NLP techniques such as Text Classification and Named Entity Recognition (NER), and RPA automation with Python libraries such as Scrapy and Selenium. It extracts and analyzes large volumes of data from multiple sources to identify potential outbreaks of foodborne illnesses from social media posts. Additionally, the use of UiPath out-of-the-box ML models such as Sentiment Analysis, Text Classification, and NER help in managing and analyzing the extracted data more efficiently and accurately.

Use Case #4 integrates the FitBit Smartwatch APIUiPath platform, ML models, and WhatsApp API to enable automated data collection and analysis, personalized recommendations, and real-time communication for early disease detection, improved health outcomes, and time and cost savings.

Use Case #8 is similar to#4, with the difference that it uses available lab data for the ML models to predict diseases. The two use cases are complementary.

Use Case #9 involves natural language processing and human in the loop verification in an AI application for a home healthcare organization. This use case is a creative solution that improves accuracy and efficiency in clinical coding, which is a very difficult, costly, and challenging task.

eCommerce use cases

The eCommerce automations (#1 and #6) created solutions to optimize the sequence of the presentation of products on an eCommerce website. The other one predicted product consumption in order to optimize stock levels in the warehouse. Both are extremely creative and useful.

The winning Use Case (#1) implements ML-based models for product placement on a website. It has brought significant value to the eCommerce client. The previous rule-based approach was static and dependent on human judgment, making it difficult to keep up with changing customer behavior. In contrast, the ML models are dynamic and can adapt to new buying patterns, making them more effective at ranking products.

Use Case #6 leverages UiPath automation capabilities and machine learning algorithms to provide retailers with a data-driven solution for demand forecasting and inventory management. By integrating data collection, preprocessing, model development, and deployment into a single platform, UiPath enables retailers to optimize their inventory levels and reduce waste while improving customer satisfaction.

Logistics and finance use cases

The logistics and finance use cases (#5 and #7) create a solution to solve two complex general business problems, related to the bill of lading processing for logistics companies and the process to pay for large companies that have thousands of different invoice types. Both automations bring substantial ROIs for the customers.

Use Case #5 is an UiPath-based automation with a focus on AI and machine learning for bill of lading processing. The use of automation technology will definitely save time, improve quality, and reduce errors that could lead to loss of time, money, and brand image due to customer experience. This is an end-to-end process that extracts the data and makes it actionable in the downstream systems.

Use Case #7 is an automated solution that addresses the challenge of processing a large number of rental invoices in various formats manually, resulting in delays and long processing times. The solution is designed to leverage AI, OCR, and RPA technologies. The uniqueness of this is that this is an on-premise instance (AI Center and Orchestrator- Air-gapped solution using Automation suite) of UiPath. There are not too many on-premises deployments of this end-to-end solution to a complex business automation problem that has a great ROI.

Agriculture use case

The agriculture use case (#3) presented an ML-based optimization, that determines the optimal distributions of cultures to maximize the culture’s productivity.

The crop recommendation system is a machine learning-based solution that helps farmers make informed decisions about the crops to grow on their farm. It takes into account several parameters like N, P, K, temperature, humidity, pH, and rainfall to recommend the most suitable crop to grow. One of the winners’ use cases is from an insurance vertical (#10), another industry that already sees enormous profits by using the intelligent automation offered by UiPath technology. It is related to the auto-claim settlement process. I didn’t see too many use cases with automations in agriculture. I believe this is also a great area where improvements are needed. Overall, this use case of a crop recommendation system based on machine learning is creative as it combines the traditional knowledge of farming with modern technology to provide farmers with data-driven insights and personalized recommendations.

Insurance use case

This use case is in the auto insurance and auto claim settlement domain. The UiPath ML model utilizing object detection technology can improve the efficiency and accuracy of the auto insurance claims process by identifying and assessing specific objects within images or videos of the insured vehicle. This can help identify potential safety hazards ultimately leading to better customer experience.


AI has become a commodity offering substantial ROIs for complex problems. AI-driven automation is the most efficient way to automate and optimize end-to-end complex business processes, and UiPath Business Automation Platform offers the ideal tool to do so.

Congratulations to all the participants who showcased their amazing automation skills and their substantial impact on industries worldwide. UiPath AI Awards celebrate your achievements, and we aim to make this an annual tradition. We encourage you to share more of your AI successes widely, educate others on the role and value of intelligent automation, and submit your contributions to compete for the UiPath AI Awards next year!


George Roth
George Roth

Technology Evangelist for Document Understanding, UiPath