Amazon rolls out 24/7 AI agent for seller operations, from inventory to ads

Amazon introduces an always-on AI agent for seller operations

Amazon has upgraded Seller Assistant into a proactive AI agent for third-party sellers. It monitors your account 24/7, recommends actions, executes routine tasks with approval, and adds strategic input. You stay in control of what gets implemented.

Amazon Ads is also getting conversational AI, so teams can create campaigns from simple prompts. For operations leaders, this compresses timelines across inventory, compliance, and marketing execution.

What's new inside Seller Assistant

The tool moves from a dashboard to an agent that works continuously, flags issues early, and proposes next steps you can approve.

• Inventory analysis: Identifies slow movers before long-term storage fees hit and suggests price cuts or removals.
• Demand forecasting: Spots product trends and recommends shipment strategies.
• Regulatory compliance: Scans listings for potential policy issues and guides fixes across markets.
• Proactive alerts: Monitors account health and raises operational risks early.

On the ads side, prompt-based campaign creation reduces time-to-launch and cuts the manual setup workload.

Why operations teams should care

Agent-based automation is moving from concept to daily utility. The assistant handles repetitive, cross-functional tasks while your team retains final approval.

This shift also tightens platform lock-in. If inventory, compliance, and ads run smoothly inside Amazon's native stack, external tools and manual workflows lose ground.

Impact on your operating model

• Efficiency is the baseline: Automate inventory strategy, replenishment planning, and ad setup. If your team is still manual here, you are ceding speed. Tip: If you use tools like Helium 10, map overlaps vs. gaps with Amazon's native AI.
• Tighter ops-marketing sync: Pricing, shipping, and listing health now influence search rank and campaign outcomes in near real-time. Create a shared weekly cadence and SLAs.
• Audit the AI: Review suggestions, approvals, and outcomes. Validate that actions align with brand, margins, and policy. Keep compliance and reputation checks in the loop.

30-60 day implementation playbook

• Week 1-2: Set guardrails - Define approval rights, price floors/ceilings, promotion windows, and forbidden edits (titles, imagery, claims). Turn on proactive alerts for account health and policy.
• Week 1-2: Data hygiene - Fix missing attributes, reconcile ASIN variations, clean duplicate listings. Better inputs mean better AI suggestions.
• Week 2-4: Pilot - Select 10-20 SKUs across demand profiles. Compare against a control group. Track: excess inventory %, OOS rate, stranded/suppressed listings, ad launch time.
• Week 3-6: Ads workflow - Build a prompt library for new launches, seasonal pushes, and defensive campaigns. Standardize negative keywords and budget caps.
• Week 3-6: Meeting rhythm - Ops + Marketing weekly standup: decisions approved, actions queued, exceptions, and learnings.
• Week 4-8: Scale - Roll out to more SKUs, keep approvals on for pricing and removals until variance stabilizes.

Risk controls and governance

• Approvals matrix: Auto-approve low-risk fixes (typos, missing attributes). Require human sign-off for pricing, removals, large shipment changes, and sensitive copy.
• Policy and brand checks: Maintain a claims blacklist by category and market. Lock brand voice guidelines for bullets and A+ content suggestions.
• Audit trail: Log every AI suggestion, decision, and outcome. Review weekly.
• Financial guardrails: Price floors/ceilings, max daily budget changes, ROI thresholds for campaigns.
• Incident playbook: Escalation path for account health threats, listing takedowns, or ad overspend.

Metrics to watch

• Inventory: excess inventory %, LT storage fee exposure, OOS rate, aged units, IPI trend
• Listing health: stranded/suppressed count, time-to-fix, policy flags per week
• Ads: time-to-launch, ROAS, TACOS, wasted spend (search terms with zero sales)
• Operations: average approval time, % AI suggestions accepted, variance vs. forecast, chargebacks/defects

What this signals for ops leaders

This is a shift from dashboards to agents that get work done with your oversight. Treat the assistant as an automation layer that reduces toil and raises decision quality.

Start with guardrails, run a tight pilot, and scale with clear metrics. Teams that move now set the pace on speed, compliance, and margin discipline.

Next steps and upskilling

• Create a one-page policy for approvals, price bounds, and copy rules. Share it with every operator and brand manager.
• If your team needs a practical AI automation track, review courses by job roles at Complete AI Training or explore workflow ideas under Automation.