Today
- GTM Signals
- Human interpretation
- Should we care?
- What should we do?
- Manual updates
- Time lost
Project Overview
GTM teams receive more signals than ever before. The challenge is no longer collecting data, but deciding which signals deserve action, why they matter, and how to engage safely. That gap is where opportunities are missed and time is lost.
I designed a Decision Quality Agent that acts as the reasoning layer between GTM signals and GTM execution through explainable AI, human approval, and continuous learning.
Role
AI Product StrategyProduct DiscoveryUX/UI DesignAI Interaction Design
Collaboration
Customer discovery, product strategy, UX/UI, AI interaction design
Maryna LievshynaTechnical architecture, integrations, full-stack development
Théo BoutronWorkflow
The Problem
With Decision Quality Agent
- Signals
- Evidence-based reasoning
- Human approval
- Prepared execution
- Learning loop
- Better decisions
Sales teams don’t struggle with finding signals anymore—they struggle with deciding which ones deserve action before time, trust, and opportunities are lost.
The Solution
Instead of generating outreach immediately, the Decision Quality Agent evaluates evidence, identifies unknowns, recommends the safest next action, and prepares execution that always requires human approval.
Explainable AI
Every recommendation is supported by transparent reasoning instead of black-box automation.
Human Approved
Nothing customer-facing is sent automatically. Humans remain responsible for every decision.
Learns Over Time
Approved decisions become reusable business knowledge that continuously improves future recommendations.
Product Walkthrough
This walkthrough follows one real GTM signal from detection to human-approved execution.
Key Product Decisions
Executive Summary
Decision-makers receive an explanation before details, allowing faster and more confident decisions.
Explainable AI
Recommendations clearly separate verified evidence from remaining uncertainty instead of hiding ambiguity.
Decision Quality
Not every signal deserves outreach. Sometimes the best decision is simply maintaining CRM quality.
Message-Market Fit
Communication recommendations are built around customer context, pain, timing and proof—not generic AI copy.
Execution Strategy
Approved recommendations become drafts, reminders and manual tasks while preserving human control over customer communication.
Continuous Learning
Human feedback becomes reusable business rules that improve future decision quality.
Design Process
- 01
Customer Discovery
Mapped how GTM teams interpret signals, coordinate ownership, and decide when to act.
- 02
Problem Framing
Reframed the opportunity from generating outreach to improving the decision before outreach.
- 03
Information Architecture
Structured signals, evidence, unknowns, recommendations, and actions into a clear hierarchy.
- 04
AI Interaction Design
Made reasoning inspectable and gave uncertainty a visible, useful role in the interface.
- 05
Workflow Design
Connected detection, evaluation, approval, execution, and learning without removing human control.
- 06
Prototype Iteration
Tested progressively higher-fidelity flows across executive and operator views.
- 07
User Testing
Validated whether users could understand the recommendation and confidently choose a next step.
- 08
Refinement
Reduced noise, clarified decision ownership, and strengthened approval and feedback moments.
Reflection
Building this product changed how I think about AI products.
Most AI systems focus on generating answers.
This project explored a different question:
How can AI improve the quality of human decisions instead of replacing them?
Rather than automating outreach, I focused on creating transparency, explainability, and confidence before execution begins.