Amazon Seller Assistant and Canvas: agentic AI reaches seller operations

In March 2026, Amazon introduced Canvas, a Seller Central experience that combines conversation with Seller Assistant and dynamic visualisations tailored to each seller’s business. The change is significant: AI moves beyond answering questions and begins to relate signals in order to analyse and prepare decisions.

Amazon says Canvas can work with sales, traffic, advertising spend, impressions, conversion, inventory and launch scenarios. The aim is to give sellers one space to investigate an issue, compare options and turn analysis into next steps.

What Canvas adds

A conventional conversation returns a series of messages. Canvas adds a visual area that can organise charts, tables, comparisons and recommendations for the question at hand. The result is easier to review and users can explore further without losing context.

Amazon’s examples include comparing spend, impressions, conversion and sales lift; finding products that drive or slow performance; assessing inventory for a campaign; exploring scenarios; and preparing product launches.

The principal contribution is not a new chart. It is the ability to bring together information that previously required multiple reports and turn it into an explanation directed at a decision.

From information assistant to operational agent

Amazon positions Canvas on Seller Assistant’s agentic architecture. An agentic system does more than write an answer: it can divide an objective into steps, consult sources, use tools and propose actions.

That advance needs careful interpretation. A recommendation is only as good as the data, objectives and constraints behind it. The seller remains in control, and actions with commercial impact require review.

How a brand should prepare

Five areas matter:

1. Catalogue: relationships, variations, attributes, costs, images and content must be coherent.
2. Unit economics: sales are not profit. Include product cost, fees, fulfilment, returns, promotions and advertising.
3. Inventory: coordinate campaigns with stock, replenishment lead times and coverage.
4. Objectives: revenue, margin, launch, rotation and new-customer acquisition require different decisions.
5. Governance: define who reviews recommendations, which actions the tool may prepare and which need approval.

If product relationships or costs are incomplete, the analysis inherits the weakness. Accelerating demand for an item about to run out can also damage sales and visibility.

Availability: check before planning

In the 3 March 2026 announcement, Amazon said Canvas was available to sellers in the United States and United Kingdom, with additional countries and languages planned. That describes the launch. Access may later vary by market, account or rollout, so businesses should check Seller Central directly.

Spanish and European brands should not wait to organise their operations. Catalogue, costs, inventory, analytics and processes create value with or without immediate Canvas access.

A method for using AI recommendations

Use a disciplined cycle:

1. State the decision clearly.
2. Verify the data and period used.
3. Compare the recommendation with cost, stock and restrictions.
4. Review scenarios and consequences.
5. Approve, adjust or reject the proposal.
6. Record the decision and measure the outcome.

AI then supports judgement rather than replacing it, providing more context and reducing the time required to reach a conclusion.

Where Sitelicon fits

Sitelicon manages Amazon as a complete channel. Our ecommerce operations cover catalogue, marketplaces, content, campaigns, incidents, logistics and analysis. We also connect information with ERP, PIM and reporting systems so decisions are not based on a partial view.

Canvas points towards a more conversational and proactive operating model. Brands with reliable data and clear processes will be better placed to benefit. Businesses with scattered information will receive faster answers about an operation that remains disorganised.

Editorial note: originally published in March 2026 and reviewed on 11 September 2026 to preserve the continuity of the editorial archive.