Unlock Seamless Project Management with AI: How Lantern Used Generative AI for Quick and Accurate Information Retrieval

Lantern revolutionized its project management by developing an AI-driven solution using Copilot Studio. Faced with the challenge of disparate project information spread across multiple systems, Lantern integrated these data sources into a single, seamless interface. By leveraging generative AI and Power Automate flows, the solution enables quick and accurate information retrieval, significantly saving time and enhancing team efficiency.

This internal case study delves into the development, implementation, and impact of our approach, highlighting the potential of AI to transform project management processes.

The Challenge

Like many organizations, we faced significant challenges in managing project information. Our data was dispersed across multiple systems, making it difficult to access comprehensive and accurate project details quickly. Employees had to navigate through various platforms, including the Projects system for project descriptions and client details, the Beacon system for project outcomes and feedback, and the People system for internal CVs and skillsets. This fragmented approach led to inefficiencies, time wastage, and potential errors in retrieving critical information.

The AI-Driven Solution

Inspired by the capabilities of ChatGPT, the idea was to create an internal chat-based information retrieval system that could integrate data from all relevant sources. The goal was to provide a one-stop shop where employees could effortlessly query project details, HR policies, and other internal data, thereby saving time and improving accuracy.

Leveraging Microsoft’s generative answers and Power Automate flows, our team set out to build a chatbot that could provide quick and accurate information retrieval.

“The big value add here is for the team to have one place to go to find out information about projects, dig into the projects a little bit more, get a better understanding, and have easy access to the information that’s backing up the answers.”
– Wisam Fawzi, Solution Architect

Technical Implementation

The chatbot was built using Copilot Studio, a low-code platform that facilitated quick development and deployment. Here are the key components of the technical setup:

• GraphQL API: Connected internal apps (Projects, Beacon, People) to fetch data.
• Power Automate Flows: Automated data retrieval and centralization.
• Backend Setup: The backend utilized Cosmos DB and Azure SQL, with third-party integrations like Harvest for time tracking and Microsoft Entra ID for identity and access management.
• Data Manipulation: Ensured the data was in the correct format for Copilot’s generative actions.

“This solution saves significant time by providing a one-stop shop for project information, reducing the need for manual searches across multiple systems. It’s easier, more efficient, and makes the whole process more enjoyable for our employees.”
– David Cronin, Digital Studio Executive

Features and Functionality

The AI solution offered:

• Centralized Information Hub: A single interface for project details, team information, and client data. Users no longer need to switch between multiple systems, making the process more efficient.
• HR and Admin Integration: Easy access to HR policies and administrative procedures via SharePoint. Employees can quickly find information on processes like vacation requests and timesheet submissions.
• Generative Answers with Citations: Accurate responses with proper citations for transparency.
• Personalized Interactions: The chatbot includes personalized interactions, such as animated GIFs, to make the experience more engaging and user-friendly.

Impact and Benefits

The implementation of the AI-driven solution brought about several significant benefits:

• Time-Savings: Centralized information reduced time spent searching across multiple systems.
• Improved Efficiency: The ability to retrieve accurate information quickly enhanced overall productivity and reduced the risk of errors.
• Enhanced User Experience: The chatbot’s intuitive interface and personalized interactions made it more enjoyable for employees to use, leading to higher satisfaction levels.
• Scalability and Reusability: The solution’s architecture allows for easy scalability and potential reuse across other departments and applications within Lantern.

Future Prospects

The success of the AI-driven solution has opened numerous future prospects for us. The team plans to expand the solution’s capabilities by integrating additional data sources and functionalities. Potential enhancements include:

• Dynamic Learning Paths: Integrating with LinkedIn Learning to create personalized learning paths for employees, helping them improve their skills and advance their careers.
• External Applications: Leveraging the solution’s flexibility to create customer-facing applications that provide similar benefits in external environments, such as client portals or customer support systems.
• Further Integrations: Exploring additional integrations with other internal and third-party systems to continually enhance the solution’s functionality and value.

“By integrating multiple data sources into a single chatbot interface, we revolutionized our project management, offering a seamless user experience and significantly improving efficiency. The AI-driven solution has opened up numerous future prospects for further enhancements and scalability.”
– Adam Tobias, Solution Architect

Conclusion

Our journey in transforming project management with AI showcases the power of innovative technology to address real-world challenges. By developing an AI-driven solution using Copilot Studio, we successfully centralized project information, streamlined access, and significantly improved efficiency. The solution not only saved time but also enhanced the user experience, demonstrating the potential of AI to revolutionize project management processes.