AI-assisted advertising reports can make it easier to ask a performance question. They do not remove the need to decide what the answer means. For brands managing Amazon retail media, the useful opportunity is faster investigation with a consistent standard of evidence.
Our recommendation: establish that standard before your team starts turning conversational answers into budget changes. A polished explanation should lead to a better decision, not skip the work needed to support one.
What Amazon announced
In its September 29, 2026 Amazon Ads Agent announcement, Amazon described expanded conversational capabilities for media planning, targeting, and analytics. The announced analytics experience includes performance questions, trend diagnosis, peer benchmarking, and visual analysis across campaign types.
Amazon said these conversational capabilities would roll out over the coming months. That is a rollout announcement, not confirmation that every advertiser can use every feature today. Check what is available in your account before building a process around it.
Ask a question that can support a decision
Start with the decision your team needs to make. “How are we doing?” leaves too much room for interpretation. A more useful question names the campaign group, date range, comparison period, and measure being reviewed.
For example, ask which campaigns changed most in spend and attributed sales between two complete periods. Then investigate the underlying products and conditions. This is our suggested review approach, not a promise about a particular command or feature supported by the platform.
Keep the original question with the result. If a second reviewer cannot tell what population and period the answer covers, the analysis is not ready to guide a consequential change.
Confirm the definitions behind the numbers
Before comparing results, verify the attribution window, reporting dates, campaign scope, and metric definition in the underlying reporting. Check whether recent results are still developing and whether the comparison periods are genuinely comparable.
Distinguish attributed sales from total business sales. Neither measure should quietly stand in for profit. If the decision concerns profitability, bring the relevant product and order economics into the discussion separately. Advertising efficiency and contribution answer different questions.
Investigate what else changed
A movement in campaign results can coincide with changes elsewhere in the business. Review product availability, price, promotions, listing content, and assortment alongside the media data. Document any meaningful differences between the periods.
We recommend treating an AI-generated explanation as a hypothesis until the evidence supports it. If the answer points to a specific campaign or product, open the relevant report and check the comparison. If it lacks enough detail to reproduce the finding, narrow the question rather than treating confident language as proof.
Record the action and the review date
For each approved change, record the reason, the owner, the expected outcome, and when the team will review it. Keep a note of other changes happening at the same time. This gives the next reviewer a useful history instead of a series of disconnected recommendations.
Start with a recurring reporting question your team already understands. Compare the assisted analysis with the established review, then expand the workflow when it is dependable. The goal for Amazon Ads analytics is a shorter path from question to verified decision, with clear accountability for what happens next.