Introduction
AI-driven workflows are quickly becoming a new operating layer for marketplace management. In this roundtable discussion, experts from Intentwise, Levanta, SmartScout, and Voartex came together to discuss what happens when Amazon brands and agencies connect their tools, data sources, and workflows to AI agents like Claude, ChatGPT, and Copilot.
The conversation centered on a simple but important idea: AI becomes far more useful when it can access the right business context. For Amazon teams, that means connecting ad performance, retail data, creator and affiliate performance, competitive intelligence, inventory signals, marketplace context, and operational workflows into one place where AI can help analyze, diagnose, and recommend next steps.
AI Agents Need Better Context, Not Just Better Prompts
The panel opened by discussing what each platform contributes to an AI workflow.
Intentwise brings together core Amazon and e-commerce data, including Amazon Ads, Amazon DSP, Seller Central, Vendor Central, inventory, finance, and other marketplace datasets. This gives AI agents the context they need to analyze business performance more accurately.
Levanta adds creator and affiliate performance data. For affiliate and creator managers, this means AI can help surface performance patterns, identify opportunities, and reduce the time spent manually reviewing dashboards.
SmartScout contributes structured Amazon marketplace intelligence, including brand-level market share, keyword trends, competitive positioning, and category-level context.
Voartex and Helm focus on orchestration, helping route questions to the right data source, execute workflows, and provide evidence around which tools were used and why.
The main takeaway: AI agents become more powerful when they are not limited to one dashboard or one dataset. When multiple tools are connected, teams can ask bigger, more strategic questions.
What Becomes Possible When Data Sources Work Together
A major theme of the discussion was the value of combining data sources that traditionally live in separate tools.
For example, a brand manager could bring together SmartScout market share data, Intentwise ad performance data, and Levanta affiliate data to ask questions like:
Where are we gaining organic momentum, but underinvesting in ads or affiliate support?
Which products are creators already promoting successfully, but are not receiving enough paid media investment?
How did a stockout impact our market share, ad performance, and creator-driven traffic?
Before AI-connected workflows, answering those questions could require multiple dashboards, CSV exports, team handoffs, and manual analysis. With MCPs and AI agents, those questions can increasingly be explored in one workflow.
Instead of jumping between tabs, teams can begin to use AI as a single surface for analysis.
Start With the Workflows You Already Repeat
The panel emphasized that brands do not need to start with a massive AI transformation project. The best starting point is usually a workflow the team already does repeatedly.
Examples included:
- Weekly campaign performance reviews
- Recurring reporting
- Listing monitoring
- Market share analysis
- Retail readiness checks
- Affiliate performance reviews
- Competitor research
- Suppressed listing monitoring
One recommendation was to identify the tasks that require multiple datasets and happen every week or every month. Those are often the best candidates for AI-driven workflows because they already consume time, already have a defined process, and already require repeated analysis.
The advice was simple: start with the itch. Look for the recurring work that feels repetitive, time-consuming, or fragmented across tools.
AI Discoverability Is Changing the Buying Journey
The group also discussed AI discoverability and how product research is changing as shoppers begin using AI tools for recommendations.
When a shopper asks an AI assistant for the “best product under $50,” some brands get surfaced and others do not. That raises a new question for brands: what makes a product or brand visible in AI-powered search experiences?
The panel discussed how AI systems often rely on third-party sources, reviews, creator content, affiliate content, publisher coverage, Reddit, YouTube, and other signals to determine whether a brand is relevant and credible.
This makes affiliate and creator content more important, not less. Human-generated reviews, product comparisons, videos, and authentic recommendations can help shape how AI tools understand a brand.
The group also noted that Amazon listing quality still matters. AI tools often cite Amazon because it remains a high-authority source for product information. That means optimizing Amazon content, attributes, reviews, and product detail pages can influence visibility both on Amazon and in external AI-powered shopping experiences.
Data Quality, Trust, and Human Review Are Critical
A recurring point throughout the discussion was that AI workflows are only as trustworthy as the data behind them.
The panel stressed that marketplace data needs to be clean, consistent, and reliable before teams can safely use AI for recommendations. Unlike creative tasks, data analysis needs to be deterministic. If a team asks the same performance question twice, the answer should not change randomly.
That is why source trust, auditability, permissions, and feedback loops matter. Teams need to understand what the AI checked, which data sources it used, what it found, and where human review is still required.
The panel agreed that human review should remain in place, especially when AI recommendations could trigger meaningful business actions. For example, pausing campaigns, changing listings, adjusting budgets, or making inventory decisions should not be fully automated without approval.
A useful rule of thumb from the conversation: AI can recommend, summarize, and diagnose, but teams should be careful before allowing it to execute high-impact actions automatically.
The Future of Marketplace Management Is More Connected
Looking ahead, the panel pointed to a future where marketplace managers rely less on disconnected dashboards and more on connected AI workflows.
That does not mean dashboards go away. Instead, AI may become the layer that helps teams interpret data across dashboards, tools, and platforms.
The role of the marketplace manager may shift from manually gathering data to asking better questions, validating recommendations, reviewing evidence, and deciding what actions to take.
The panel also discussed the rise of more personalized AI shopping experiences. As AI agents better understand shopper intent, budget, preferences, and past behavior, brands may need to think differently about how they position products, build authority, and show up across channels.
Key Takeaways
The biggest takeaway from the roundtable is that AI-driven marketplace management is not about replacing human teams. It is about helping those teams connect fragmented data, reduce repetitive analysis, and make better decisions faster.
For brands and agencies getting started, the panel recommended:
- Connect reliable data sources first
- Start with workflows your team already repeats
- Use AI to reduce manual analysis, not create more work
- Keep humans involved for important decisions
- Focus on source trust, data quality, and evidence
- Think beyond dashboards and toward connected workflows
- Prepare for AI-powered product discovery and shopping behavior
AI workflows are evolving quickly, but the direction is clear. The teams that learn how to connect the right data, ask better questions, and build repeatable review processes will be better positioned to manage Amazon and broader marketplace growth in an AI-driven world.
