AI Sales Assistant
An AI-assisted sales workspace that converts account information into risks, actions and follow-up recommendations.
Challenge
What needed to be solved
Sales information is spread across targets, account notes, regional performance and individual follow-ups. The difficulty is not accessing data, but turning it into a coherent view of what needs attention now.
Approach
How the product was framed
The system organises information by business question rather than by source. It highlights the current account state, explains the main risk and converts that assessment into an action list with follow-up suggestions.
01 · Information model
Organise around decisions
Instead of reproducing dashboards, the experience groups information into a small number of decision layers: target progress, regional context, account risk and next action.
- Current performance against target
- Country and regional context
- Priority customer signals
- Recommended follow-up sequence
02 · AI role
Move from summary to recommendation
AI first extracts the most relevant risk, then explains why it matters and generates follow-up actions. The recommendation remains connected to its supporting context so users can review rather than blindly accept it.
03 · Interaction
A workspace, not a chatbot
The primary experience is a structured account page. Conversational AI supports the workflow, but the product remains navigable and legible without requiring the user to formulate every question from scratch.
Outcome
The concept clarified how an AI-native layer could sit above existing sales data: not replacing source systems, but translating scattered signals into a prioritised operating view.
Learning
A useful enterprise AI experience must show the path from source signal to recommendation. Explainability is not an additional panel; it is part of the core workflow.