Unlocking GTM's Potential: Connecting the AI Puzzle Pieces

UX Research | Deep Dive into PipeIQ's GTM Transformation

Project Overview

Introduction

PipeIQ is an AI-driven Go-to-Market platform, transforming how business work

Project Goal

To architect AI innovations directly addressing key Go-to-Market needs

Timeline

Mar – April 2024

My Role

UX Researcher

Background and Challenge

Background:

PipeIQ is an AI-powered GTM orchestration platform that helps B2B teams align marketing, sales, and operations through AI digital workers and account-based workflows. It replaces fragmented processes with automation and strategic clarity, enabling faster and more focused execution.

In early 2024, the waves of AI were tight, from RAG to AI agents, sparking innovation across industries. In martech, where potential meets intense competition, this shift became both a challenge and an opportunity.

Challenge:

My task was challenging but inspiring: understand the existing GTM ecosystem, uncover pain points within current solutions, and explore how emerging AI capabilities could unlock a more streamlined, intelligent GTM experience. This UX research project was a meaningful exploration, focused on redefining how companies launch, scale, and ultimately win in an AI-first future.

Research Methods

Research Methods:

To explore how AI could elevate the GTM experience, I conducted a multi-layered UX research process that combined market analysis, competitor benchmarking, user interviews, and internal stakeholder insights:

1. Landscape & Competitor Analysis
I reviewed existing GTM tools, from sales automation to ABM platforms, to understand their positioning, features, and user gaps. This helped identify white space opportunities where PipeIQ could stand out with AI-driven differentiation.

2. User Interviews
I interviewed 20+ GTM operators across different comapnies to understand their workflows, frustrations, and unmet needs. These conversations revealed recurring pain points, such as tool fragmentation, manual processes, and lack of strategic alignment.

3. Synthesis & Opportunity Mapping
After collecting qualitative and competitive data, I synthesized findings into user personas, key pain points, and opportunity areas. This formed the foundation for experience principles and design directions in the next phase.

Persona

In every sales funnel, especially B2B, multiple roles collaborate across touch points, each with distinct responsibilities, tools, and challenges. As a GTM platform, one of the fundamental strategic questions PipeIQ faced was whether to build horizontal features that serve everyone in general, or vertical solutions tailored to the unique workflows of each role.

Embracing the momentum of AI agents, we chose the vertical path: to create specialized AI agents that deeply understand and support specific GTM roles. This made it critical to first understand the day-to-day reality of each role we planned to support.

I conducted in-depth research on five key positions that represent the backbone of a GTM motion:
1. SDR/BDR (Sales Development Representative / Business Development Representative)
2. Campaign Manager
3. AE (Account Executive)
4. SE (Sales Engineer)
5. Business Analyst

For each, I built a detailed persona that mapped their workflows, goals, pain points, and decision-making patterns. These personas serve as strategic thinking tools — helping us cut through the noise, recognize the nuances across roles, and find common threads where AI can deliver real, contextual value.

User Journey

We chose SDR/BDR as the first role to focus on, the critical entry point of the GTM funnel. To deeply understand their workflows, I conducted interviews with over 10 SDRs/BDRs. To bridge the gap between user insights and product development, I created workflow charts that mapped the SDR/BDR process, to help our internal team clearly visualize their day-to-day journey.

SDR/BDR General Workflow

The final user journey included 4 main parts:
1.Key Task Breakdown: A clear outline of the core responsibilities SDRs/BDRs handle throughout their workflow, from prospecting to qualification.
2. Pain Points: A detailed look at the common friction points and inefficiencies they encounter at each stage, grounded in real user insights.
3. Tool Audit: A competitive snapshot of the tools most frequently used for each task (e.g., outreach platforms, CRMs, enrichment tools), including feature comparisons and limitations. Interactive logos link directly to detailed tool breakdowns for deeper exploration.
4. Opportunity Mapping: Strategic insights highlighting where PipeIQ’s AI agents can drive the most value — by automating repetitive tasks, improving timing, and enabling smarter personalization.

User Journey of SDR/BDR

Ideation

Considering the distinct work requirements of GTM teams, especially SDR/BDR, and leveraging the unique characteristics of Artificial Intelligence, I organized potential AI solutions under five core principles. These principles served as guiding pillars, ensuring that ideated features would not only be innovative but also practical, scalable, and genuinely beneficial for GTM professionals:

•Collaboration: How AI can foster seamless interaction and information exchange, enabling GTM teams to work more cohesively and efficiently, meanwhile facilitating effective human-AI partnership.

•Adaptation: The capacity for AI solutions to continuously learn from user behavior, evolving market trends, and performance data, allowing for dynamic refinement of strategies, content, and recommendations.

•Interoperability: Ensuring AI solutions can integrate effortlessly with existing GTM tools and platforms (CRMs, sales engagement tools, marketing automation), creating a unified and frictionless ecosystem.

•Evolvability: The built-in ability of the AI solution to grow, improve, and incorporate new datasets, algorithms, and functionalities over time, future-proofing its value without requiring disruptive overhauls.

•Reliability: Guaranteeing that the AI's outputs—from data insights and predictive analytics to automated actions and recommendations—are consistently accurate, trustworthy, and dependable for critical GTM decision-making.

This ideation phase was an intensive period of translating research insights into tangible concepts. The attached visual compilation reflects extensive research synthesis, detailed conceptual mapping, and the exploration of initial solution visualizations. This iterative process distilled complex information into foundational frameworks, charting how PipeIQ's AI capabilities could redefine the GTM experience.

Insight-to-Solution Framework

Impact and Reflection

Strategic Impact:

By uncovering the nuances of GTM roles and workflows, the research set a strategic blueprint for how PipeIQ’s AI agents could be built, evolve, and differentiate. It uncovered role-specific insights that sharpened our product direction, transformed complex workflows into clear visual maps for the team, and laid the groundwork for investor-ready narratives. The findings also informed future GTM directions, such as exploring AI-guided messaging and more contextual engagement strategies, that now help shape how we position ourselves in the market.

Reflection:

This case challenged me to go beyond typical UX research. Here are a few key learnings I carry forward:

1. Translating AI to Tangible Value: A key learning was translating AI's abstract potential into concrete, user-centric solutions. Rigorous validation, focusing on whether AI truly solved specific pain points, was crucial for user adoption and success.

2. Agility in a Rapid Landscape: The swift evolution of MarTech and AI reinforced the need for agile research. Quickly synthesizing new breakthroughs and assessing their applicability to user needs was essential for delivering cutting-edge, future-proof solutions.

3. Personas as Strategic Tools, Not Just Empathy Maps: In this project, personas weren't just for empathy. They became a diagnostic tool — helping us decode workflow friction, spot automation opportunities, and map out where human-AI collaboration makes the most sense.

4. Vertical Depth Creates Strategic Clarity: While horizontal platforms aim for scalability, verticalized AI agents create focus. They let us design around actual job logic, not just generic features — and that unlocks deeper value for users and clearer positioning for the product.