AI can crunch 18 months of multi-channel sales data, but only if you hand it the keys first.

The analytical structure — segment by category, compare channels, correlate bundles with AOV and refund rates — is identical every time this report is run. This is a strong candidate for a recurring automated workflow.

Core metrics like revenue and refund rate are well-defined, but 'most profitable' requires a margin definition (gross vs. net, including fulfillment costs?) that the agent may not have. Bundle correlation analysis also needs a threshold for what counts as meaningful correlation.

Shopify and Amazon both have APIs, but Amazon Seller Central's data access is notoriously fragmented and requires MWS/SP-API credentials plus careful rate-limit handling. A unified data pipeline must exist or be built before the agent can run — this is the single biggest execution risk.

Analytical errors here are low-stakes in the sense that no irreversible action is taken, but a miscalculated profitability ranking could misdirect inventory investment or promotional spend. The output is advisory, not transactional, which limits damage.

Statistical patterns are machine-readable, but interpreting why a category underperforms on Amazon versus DTC (brand positioning, pricing strategy, competitor context) requires business intuition the agent lacks. A human should validate findings before acting on them.