Logility: Logility Expert Advisor (LEA)
AI Powered Guidance
My Role: UX/UI Designer and Strategic Collaborator
Introduction
As the lead UX designer, I was tasked with designing a natural-language interface for our enterprise supply chain platform, enabling users to both ask high-level logistics questions and run AI-assisted analysis on their real-time data. The company had an AI-based chat that was already in place in one of their products. I needed to update the look and feel of the chat, add features, improve usability and expand the chat to enable inquiries that would work in and across multiple software solutions. This AI chat would be known as Logility Expert Advisor (LEA).
Process
Problem
Supply chain software is essential for companies to streamline and optimize their operations with users having to navigate multiple reports and dashboards to get answers. It can be a tedious and confusing process. Our goal was to reduce cognitive load and make querying supply chain data as intuitive as messaging a teammate.
​
There are many common problems that users of supply chain software might face, prompting the implementation of an AI chat to assist:
Demand Forecasting
Issues
Shipping Delays
Inventory
Management
Supplier
Coordination
Cost Management
.png)
How can I accurately predict demand?
How can I avoid
and/or manage unexpected delays?
How can I keep
track of inventory levels and ensure timely restocking?
How can I manage relationships and communications with multiple suppliers?
What is the impact
of rising costs
in logistics, raw materials and labor?
Quality Control
Data Analysis
Customer
Service
Risk Management
Regulatory
Compliance
How can I ensure
consistent product quality across the supply chain?
How can I use data
to gain insights and make informed decisions?
How can I meet customer expectations for speed and quality
with changing demands?
How can I identify
and mitigate risks?
How can I navigate and comply with regulations across different regions?
UX Goals & Challenges
Implementing an AI chat can help to address many of these issues by providing real-time insights, automating responses, and offering predictive analytics to optimize supply chain operations. A core UX goal was to make AI feel like a helpful partner. To get started, I analyzed the current chat that was present in one of our solutions to see how it functioned and what issues were present with the current design.
Previous Design: Issues
It is unclear what this small trash can icon with an "x" does. The design already has a close icon and as a persistent chat, it is not something a user would delete.
The "Write your message" prompt is hard to see due to the lack of contrast between the blue of the text on top of a darker blue background.
The dark blue Q & A is not centered in the chat window.
The color scheme of the chat is hard to view for long periods of time and the blue on blue is an accessibility issue.
The use of a transparent background here is distracting, making it harder for the user to concentrate on the relevant information.
The aqua outline buttons on a similar toned grey blue background is hard to see.
The dark send arrow is not visible on the dark background.
**Using a contrast checker, both the aqua arrows and the aqua outlined arrows fail with ratios of 4.14:1 and 1.71:1 respectively**
When you click on one of the questions above, the question/prompt comes into the message window instead of just going to the information.
The dark send arrow is replaced with an aqua arrow once a message is typed.
There is a close "x" icon here. The reason is unclear and it is hard to see (accessibility issue).
This color text (#FFFFFF) on the (#5391F7) colored message bubble fails the contrast checker with a ratio of 3.11:1.
Using multiple shades and tints of blue make it hard to decipher between message, message box and background.
Previous Design: Reaction Issues
Competitive Analysis
As part of the UX design process, I conducted a competitive analysis by evaluating multiple AI chat platforms to inform the redesign of our company's AI chat. I analyzed user flows, interface designs, conversational styles, and key features across leading AI chat tools to identify industry best practices and areas for improvement. This research provided valuable insights into what makes an AI chat feel intuitive, engaging, and trustworthy. By comparing strengths and weaknesses across different platforms, I was able to define key opportunities for differentiation and ensure our AI chat would deliver a seamless, user-centered experience that aligned with our brand's goals.
User Flow
This user flow represents the MVP LEA chat representative of the first release. Over time this user flow will become more complex as additional features including data analysis, product filtering, Query ID, and credit management are added.
New Chat Design
In the new design created in Figma, the chat feature is seamlessly integrated into the Logility platform, serving as a centralized hub for accessing the full suite of Logility products. LEA can be launched via an easily accessible icon. The home screen is designed with product categories, suggested questions and prompts, a chat window with the capability to tag specific products, and icons to minimize, maximize, or open in a new window. Additionally, a slide-out sidebar provides access to chat history. The home screen boasts a clean, modern, and user-friendly interface that adheres to accessibility guidelines.
Once the user selects a product category the chat will open with a prompt to then choose a subcategory to learn about. Choosing the subcategory will activate a list of relevant commands and questions.
The user has the option to minimize the chat which will then display the chat as an overlay contained to the righthand side of the screen:
Manage Chat History
Users needed the ability to manage their chat history by being able to edit the chat title, delete specific chats and clear the entire chat history. Being able to prioritize and order the chat was also implemented through the ability to star specific chats and drag to reorder.
Collapsed Tabs
LEA is heavily data driven with charts, tables and other diagrams. Users needed the ability to both expand a preview inside the chat window, and to open the full chart, table or diagram in a separate overlay.
Credit Management
LEA runs on a credit-based model so users would need the ability to view and manage the amount of credits they have in their account. I created an easy to access button located in the header that would trigger a dropdown that showed remaining credits, housed a credit usage log, gave the user the ability to set a credit cap and set a notification threshold. Credits can be color coded to indicate when a specific threshold had been met.
If the user activates the slider it triggers a dropdown where the user can enter specific information for the credit cap and/or the notification threshold. the user can then save the information which will then exit the dropdown. If the credits are above the set threshold it will display the number in primary blue. If the number is below the threshold it will display the number in a warning indicator orange.
If user chooses "View Credit Usage" or "Manage Credit Settings" they will be taken to an overlay that displays a usage log or editable credit settings.
Fork Chat
Fork chat enables the user to create a copy of the chat that includes all previous messages. The fork chat pop up is triggered by an icon underneath the cha response. Once the chat is forked, it will appear in the chat history and a fork icon will replace the chat icon.
Imbedded Feedback
In order to receive real-time ratings and feedback about the quality of the responses, I added thumbs up and thumbs down icons underneath each response. Each icon triggers an overlay form that prompts users to detail what they like or dislike, include a screenshot, learn more about what is collected and approve information shared.
At this time we are a few months out from releasing the first version to a small sample of clients and business consultants. Upon release I will plan user testing to gather insights on usability, functionality and features. This information will be used to design improvements to the existing design and to plan for new features.
​
This project not only showcased the potential of AI-driven user experiences but also deepened my understanding of designing intuitive, data-rich interfaces. By translating complex supply chain data into a conversational format, I helped make planning more accessible and actionable for users—ultimately improving efficiency and decision-making across the board.
