Designing an AI Assistant for Smarter Sales Workflows
Design a modular AI platform to empower teams, starting with CGX’s sales team, by automating repetitive tasks, surfacing timely insights, and supporting strategic decision-making.
Client
Clear Grain Exchange
Services
- UX/UI Design - Design System Setup - User Story & Documentation - Product Strategy Support
Industries
Agritech, Supply, Wholesale, Retain SaaS
Overview
Design a modular AI platform to empower teams, starting with CGX’s sales team, by automating repetitive tasks, surfacing timely insights, and supporting strategic decision-making.
Problem
Sales reps at CGX are responsible for managing hundreds of grower relationships. Key challenges included:
Manually tracking who to contact and when
Following up across inconsistent channels (calls, SMS, email, Slack)
Drafting repetitive emails and messages
Lack of shared visibility across the team
Missed opportunities due to timing, pricing, or offer gaps
Goals
To build an Agentic AI that acts as a central, intelligent assistant, proactive, context-aware, and deeply integrated, helping teams identify opportunities, prioritize follow-ups, and understand market dynamics.
This AI empowers users to:
Gain instant visibility into key sales and support conversations
Receive smart follow-up recommendations and timely nudges
Automatically draft emails, SMS, and call scripts personalized to clients
Surface trends from offer activity, grower behavior, and pricing shifts
Automate repetitive tasks and route action items to the right tools
Collaborate better through unified insights across teams
Solution
We set out to build an Agentic AI, a central, intelligent assistant designed to transform how sales teams work.
Instead of just reacting, this AI acts: listening, learning, and proactively surfacing what matters most.
Whether it’s spotting a sales opportunity hidden in yesterday’s calls, reminding you to follow up with a silent lead, or flagging a pricing trend across grower conversations, the AI becomes your extra pair of eyes, ears, and hands.
The solution enables teams to:
Stay ahead of their pipeline with real-time nudges and summaries
Draft follow-ups, SMS, and call scripts tailored to client sentiment
Organize chaos from Slack, CRM, and calls into a single, actionable feed
Detect deal risks, market shifts, and customer intent before it’s too late
Reduce manual admin, so reps can focus on building relationships
Collaborate better across sales, support, and marketing with shared AI insights
Key Features
l. Action-oriented widgets
Prioritized tasks (Urgent vs Reminder)
Embedded context (who to contact, why)
ll. Summarized insights & metrics
Daily summary based on Slack logs
KPIs: Calls, SMS sent, urgent replies, trades closed, etc.
Helps sales team prioritize daily work
lll. Proactive AI Advice
Pre-filled email templates based on recent interactions
AI-suggested email drafts and contextual advice
Personalized content (buyer name, topic, bid, etc.)
lV. Dashboard & navigation
Centralize access to assistant-driven tools
Activity Log
Grower Lookup
Drafts & Templates
Tasks & Automations
Knowledge Assistant
Settings & Support
Process
Discovery & Research
I conducted interviews with CGX sales reps and analyzed:
Slack message flows
Call transcripts
CRM usage patterns
Email follow-ups
1. Discover
I analyzed real Slack interactions, interviewed users, and tried to identify key pain points like: missed follow-ups, scattered data, and time-consuming comms by analyzing:
Slack message flows
Call transcripts
CRM usage patterns
Email follow-ups
2. Define
I mapped user flows, built the information architecture, and prioritized MVP featured, focusing on assistant suggestions, summaries, and Slack integration.
3. Ideate
Early wireframes and card-based UI explored how AI could proactively surface insights and draft follow-ups in a dashboard-first model.
4. Design
I developed high-fidelity mockups, agent creation flows, and an interactive prototype simulating daily workflows.
5. Test
User feedback sessions focused on clarity, usefulness, and trust. I refined the information layout and assistant suggestions based on real sales team input.
Onboarding Flow
New users complete a smart onboarding flow:
Define role and goals (e.g., “I want to automate follow-ups”)
Connect Slack and email
Upload tone of voice docs (emails, brand book)
Answer key business context questions
Based on this, personalized agents are activated.
Results
Time saved
2.5 hrs/week saved per rep
Measured via time comparisons between manual vs. AI-generated follow-up messages.
Follow-up completion
+22% more follow-ups sent within 48h
Tracked via Slack/CRM logs pre- and post-assistant usage.
Collected insights
3× more actionable buyer signals identified
AI flagged 9 insights/week vs. 3 manually; validated in user feedback.
Draft adoption
68% of messages started from AI drafts
Based on in-app analytics of “Use Draft” button engagement.
User trust & Usefulness
86% found the dashboard useful,
73% trusted AI suggestions
Results from post-testing surveys.
Faster lead identification
30–50% reduction in time to decide who to contact next
Measured during timed usability tests using the AI dashboard vs. Slack review.
Conclusion
The AI assistant helped the sales team stay on top of follow-ups, spot key signals from conversations, and save time on writing and planning. It fit into their existing tools and actually made their work easier. The early results show it's solving real problems, and there’s clear potential to scale it further.