Your Amazon Data Is AI-Ready. Now What?

Connecting your Amazon data is only step 1. Our webinar session unpacks the advertising guardrails and high-value workflows you need to implement next with your AI agent.

Kenton Snyder

Product Manager

Brett Bohannon

Founder

May 20, 2026

Transcript

Introduction

Welcome to the webinar titled "Your Amazon Data Is AI-Ready. Now What?" featuring Kenton Snyder and Brett Bohannon. This session explores how businesses can leverage AI tools with their Amazon data to streamline workflows and improve decision-making.

Understanding AI Workflows with Amazon Data

In this webinar, Kenton Snyder, Product Manager at Intentwise, and Brett Bohannon, Founder of Voartex, discuss practical steps for utilizing Amazon data in AI workflows. The focus is on how to effectively integrate data into AI tools like Claude, ChatGPT, and Copilot, avoiding unnecessary work and maximizing efficiency.

Key Topics Discussed

Introduction to MCP (Model Context Protocol): A bridge for integrating Amazon data with AI tools to facilitate data-driven decisions.
Challenges and Solutions: The main pain points for brands and agencies when connecting their data to AI and how to overcome them.
Setting Up Workflows: Guidance on establishing simple, effective workflows that utilize AI to automate repetitive tasks.
Importance of Clean Data: Ensuring data quality to prevent issues such as AI hallucinations, which can lead to incorrect outputs.

Practical Steps for Implementation

Step 1: Connecting Your Data

Use Intentwise's AI Gateway to connect your Amazon data to AI platforms efficiently.

Step 2: Establishing Guardrails

Set specific goals (e.g., ACOS, TACOS) and parameters that guide the AI's decisions, ensuring outputs align with business objectives.

Step 3: Creating Repeatable Workflows

Start with small, manageable tasks, like monitoring inventory or checking ad performance, and gradually expand as familiarity with the system grows.

Step 4: Incorporating Feedback Loops

Continuously assess the AI's performance against established goals and adjust guardrails accordingly to maintain relevance and accuracy in outputs.

Recommended Tools and Resources

Blueprint Toolkit: A free resource provided to help users set up their workflows easily, including templates for guardrails and feedback logs.
Cloud Cowork: An accessible platform for creating AI projects without needing extensive technical knowledge.

Conclusion

Kenton and Brett emphasize the importance of starting small and experimenting with AI tools to enhance operational efficiency. With the right data and strategies in place, businesses can leverage AI to make informed decisions and optimize their Amazon strategies effectively.

For further assistance, feel free to reach out to Intentwise to explore the AI Gateway and other resources to get started on your AI journey.

Once brands and agencies can access their Amazon ads data through their AI agent, the question becomes: what’s next? How do you start building practical agentic workflows—and critical guardrails—around that advertising data?

In this webinar session, Brett Bohannon, founder of Voartex, will show brands and agencies how to leverage (and de-risk) their AI agent for Amazon performance. He’ll walk through how to:

Layer in brand and client guardrails. Add in the client’s goals, margin profile, budget limits, launch priorities, branded vs. non-branded strategy, or acceptable ACOS and ROAS thresholds.
Building high-value ad workflows. Instead of trying to build one agent that answers everything, start with workflows like budget pacing, campaign performance reviews, search term analysis, wasted spend checks, bid adjustment opportunities, branded vs. non-branded performance, and weekly client reporting.
Create useful feedback loops. The best agent setups will not be static. They should log outputs, capture misses, classify bad recommendations, and improve instructions, routing, and workflows based on real usage.

In this webinar session, Brett Bohannon, founder of Voartex, will show brands and agencies how to leverage (and de-risk) their AI agent for Amazon performance. He’ll walk through how to:

Layer in brand and client guardrails. Add in the client’s goals, margin profile, budget limits, launch priorities, branded vs. non-branded strategy, or acceptable ACOS and ROAS thresholds.
Building high-value ad workflows. Instead of trying to build one agent that answers everything, start with workflows like budget pacing, campaign performance reviews, search term analysis, wasted spend checks, bid adjustment opportunities, branded vs. non-branded performance, and weekly client reporting.
Create useful feedback loops. The best agent setups will not be static. They should log outputs, capture misses, classify bad recommendations, and improve instructions, routing, and workflows based on real usage.