Background: The Data Bottleneck in Ad Optimization

TRADR generates vast amounts of campaign data daily, covering performance metrics, audience engagement, and cost efficiency. Despite having sophisticated dashboards, users still face significant friction in extracting meaningful insights:

• Technical Traders spend too much time manually querying data, limiting their ability to act on optimizations quickly.
• Client Services Teams struggle to interpret complex metrics and translate them into clear client narratives.

These inefficiencies slow down decision-making, creating bottlenecks in both campaign execution and client communication.

Vision: A Conversational AI for Real-Time Insights

To address these challenges, I'm spearheading the vision and planning for a Gen-AI Insight Assistant, designed to let users interact with their data conversationally—minimizing the need for manual analysis.

With this AI assistant, users will be able to:

• Ask natural-language questions like "Which campaigns have the highest margin?" or "What budget reallocation will improve performance?"
• Receive instant, data-driven insights without sifting through dashboards.‍
• Generate digestible reports for internal strategy and client-facing presentations.

The Role

As Director of Product Management, my focus has been on defining the strategic vision, user needs, and execution roadmap for this initiative—ensuring a strong foundation before prototyping begins.

Key Responsibilities & Planning Strategy

• Defining the Problem & Opportunity:
  To ensure this solution addressed real pain points, I conducted stakeholder interviews with technical traders and client services teams. This allowed me to map out inefficiencies in data access and confirm that existing solutions were not meeting their needs. Additionally, I framed the AI assistant as a competitive differentiator that could enhance TRADR’s analytics capabilities by making insights more accessible across teams.
• Establishing Product Vision & Scope:
  I defined the core user groups (Technical Traders & Client Services Teams) and outlined their distinct needs, ensuring the AI assistant would provide value. To balance feasibility with impact, I worked with internal teams to prioritize key capabilities such as natural-language query handling, insight summarization, and real-time data access. Additionally, I established key success metrics—time savings, adoption rates, and insight accuracy—to track the effectiveness of the solution.
• Cross-Functional Planning & Alignment:
  To prepare for execution, I engaged engineering, data science, and UX teams early in the process to validate the technical feasibility of our approach. I also worked with business stakeholders to align the project with revenue goals and client engagement strategies, ensuring that the AI assistant would have a measurable business impact once deployed.
• Preparing for Prototyping & Execution:
  Before moving into prototyping, I structured a phased development plan, starting with a minimum viable AI assistant that could address the highest-priority use cases. Additionally, I outlined an early testing and iteration framework to ensure that user feedback would shape future development stages, making the AI assistant more effective over time.

Next Steps & Future Direction

With the vision, planning, and execution roadmap in place, the next phase is transitioning into prototyping and early validation. This stage will focus on ensuring that the AI assistant can effectively process natural language queries, generate meaningful insights, and seamlessly integrate with TRADR’s data infrastructure.

Immediate Priorities

• Refining Natural Language Query Handling:
  The AI assistant needs to interpret a range of user queries with high accuracy, ensuring that both technical traders and client services teams receive relevant, contextual responses. Early tests will focus on how well the model understands intent and retrieves actionable insights.
• Defining Initial Data Integration Workflows:
  The assistant must connect to TRADR’s existing data sources and analytics pipelines. This requires aligning with engineering teams to identify key datasets, establish secure data access, and ensure real-time availability of insights.
• Creating a Feedback Loop for Iteration:
  As early users interact with the prototype, gathering qualitative and quantitative feedback will be critical. By monitoring query success rates, response relevance, and usability, we can iterate on the assistant’s functionality and refine its outputs to better match user expectations.

Long-Term Goals

• Expanding AI Capabilities to Predictive Analytics & Automated Optimizations:
  Beyond responding to direct questions, the assistant should evolve into a proactive insights engine, surfacing trends, anomalies, and optimization opportunities without requiring users to ask.
• Integrating the AI Assistant into TRADR’s Core Platform:
  A seamless user experience will be key to adoption. The assistant should be embedded directly into TRADR’s existing workflow, reducing friction and making insights accessible without leaving the platform.
• Scaling Adoption Across Internal Teams and External Clients:
  While initial development is focused on internal users, there is strong potential to extend this technology to TRADR’s advertisers and partners. Educating users on its capabilities and demonstrating tangible value will drive broader adoption and long-term engagement.

By focusing on execution, user experience, and long-term strategic value, this initiative positions TRADR as a leader in AI-driven decision-making. The next phase will determine how effectively we translate this vision into a high-impact, widely adopted product.

Key Takeaways & Learnings

Developing an AI-powered insights assistant requires careful planning, strategic alignment, and cross-functional collaboration to ensure its success. Key lessons from the planning phase include:

• Strong planning leads to strong execution.
  Defining clear user needs, goals, and success metrics at the outset ensures that development stays focused on high-impact functionality rather than experimental features that don’t solve real problems.
• Cross-functional collaboration is key.
  Engaging stakeholders early, including engineering, data science, UX, and business teams, helps surface potential roadblocks before development begins and ensures that technical feasibility aligns with business goals.
• AI adoption depends on usability.
  A conversational AI tool must feel intuitive, fast, and reliable to drive real user engagement. While the technology itself is powerful, its adoption will ultimately depend on how well it integrates into existing workflows and delivers tangible value.
• Continuous iteration will be necessary.
  The AI assistant's effectiveness will improve over time as it learns from user interactions. Establishing a structured feedback loop for monitoring performance, refining responses, and optimizing outputs will be crucial to long-term success.

Final Thoughts

This initiative represents a fundamental shift in how users interact with data—moving from manual dashboard exploration to AI-powered conversational insights. By focusing on strategic planning, cross-team coordination, and user adoption, I am ensuring that the transition into prototyping is structured, outcome-driven, and aligned with business objectives.

The next steps will determine how well this AI-powered tool delivers on its promise of improving efficiency, streamlining decision-making, and enhancing the overall TRADR experience. With a clear roadmap in place, the foundation has been set for a high-impact, scalable product that can redefine data accessibility within the organization.