Making Lead Management Easier Through Voice and Conversation
An AI-powered conversational experience that helps Financial Advisors manage lead updates and follow-ups using natural language in Hindi or English.
AMS/LMS helps Financial Advisors manage leads and their daily activities. Updating a lead or scheduling a follow-up, however, can mean navigating the app and completing several manual steps.
I designed an AI Voice Assistant as an integrated feature of AMS/LMS. Agents speak or type a request, give the details, review the proposed change and confirm it, in Hindi or English.
Financial Advisors regularly check lead information, update customer statuses and schedule follow-ups. Doing this through conventional screens can interrupt their work, especially in the field or while talking to customers.
Agents may need to move between screens to find and update a lead.
Updating a lead means identifying the record and providing the new information.
Scheduling a follow-up requires capturing the relevant date and time.
Agents should be able to interact in Hindi or English, not only typed commands.
I explored a conversational approach where agents use natural language to start common lead tasks. The assistant asks for the lead ID or mobile number, finds the lead and guides the next action.
For follow-ups, the agent provides the date and time, reviews the summary and confirms.
Voice, text, contextual prompts and conversational confirmation work together to guide agents through lead tasks.
Agents can speak or type their request.
The assistant asks for what it needs, such as a lead ID or mobile number.
After finding the lead, it shows the current status and offers the next action.
The agent gives a date and time, and the assistant summarizes before asking to confirm.
After confirmation, a completion message shows the outcome.
This flow reduces unnecessary navigation while keeping each step clear.
Agents can talk in Hindi or English using familiar expressions, without remembering exact commands or technical terms.
The assistant asks clear, contextual questions and guides the user toward the next action. The same interaction works in either language.
Illustrative playback of the conversation, based on the final screens.
Users cannot always tell whether a voice system is listening, processing or preparing a response, so I designed a distinct state for each.
The assistant first asks which lead to update. Once it has the lead ID or mobile number, it shows the matching lead and asks what to do. For a follow-up it asks for the date and time, then summarizes the lead ID, status and schedule before applying the change.
The assistant asks for information as it becomes relevant to the task.
Short, contextual prompts show what is needed next.
Listening, processing and responding states show the system's status.
A summary of the lead and follow-up lets the agent review before confirming.
Hindi and English make the experience flexible for different language preferences.
The visible conversation helps agents follow the questions, answers and proposed actions.
The AI Voice Assistant adds an alternative to conventional screen navigation. Agents can start a task by voice or text and follow a guided conversation to complete it.
It extends AMS/LMS with a conversational interface focused on reducing friction, improving clarity and supporting bilingual communication.
The design creates opportunities to test whether conversational interaction improves the agent experience.
Share of lead updates and follow-ups completed successfully.
Time to complete an update compared with the manual workflow.
How often voice input is misunderstood and needs correcting.
Whether agents correctly identify the proposed change before confirming.
Successful completion of equivalent tasks in Hindi and English.
Whether agents understand the assistant's status and the result of their actions.
This project explored how conversational interaction can extend an existing enterprise application without replacing its core workflows.
The focus was not just adding voice input. It was designing an end-to-end experience that understands the request, gathers the details, guides the next step and confirms the result.