0106
Map
Identify owners, source systems, data boundaries, and decision paths.
ControlSource
Retail & Commerce Operations
Resolve retail exceptions with the right inventory, order, and customer context.
ZenAI assembles approved product, inventory, order, store, and customer data into a review-ready exception. Merchandisers, planners, store leaders, and service owners decide the commercial response and approve every system update.
Service types
Put AI into the retail queues where context is scattered.
Start with one recurring exception whose product, inventory, order, policy, and ownership data already exist across known systems.
- Current retail context
- Human decision gates
- Reviewed system updates
Merchandising
Merchandising intelligence workflows
Bring product performance, assortment context, supplier information, and approved commercial rules into a consistent review packet.
Workflow
Prove one exception from signal to approved update.
A useful pilot takes a real retail signal from source systems to a named reviewer and an approved update. Unknowns stay visible, and each system keeps its existing authority.
0206
Connect
Connect the minimum approved systems and context.
ControlReview
0306
Govern
Define permissions, review, logs, and actions the model cannot take.
ControlSource
0406
Build
Ship the workflow interface, integration layer, and review queue.
OpsReview
0506
Validate
Run beside the current process and measure speed, quality, and adoption.
OpsSource
0606
Scale
Expand only after the first workflow has stable ownership and value.
OpsReview
Platform scope
Give every retail exception current context and a clear commercial owner.
ZenAI works around the systems already responsible for products, transactions, inventory, orders, customers, and store operations.
01
Merchandising and digital commerce
Prepare product, assortment, supplier, pricing, and promotion context for category and commerce teams without transferring commercial authority to AI.
PIMCommerceCategories
02
Inventory and fulfillment operations
Connect ERP, WMS, OMS, forecasts, purchase orders, and location signals into exception queues owned by planners and fulfillment teams.
InventoryOMS / WMSPlanning
03
Stores and customer operations
Bring procedures, tasks, transactions, cases, returns, and promise context into review paths for store leaders and service owners.
POSStoresCRM
PIMCommerceCategoriesInventoryOMS / WMSPlanningPOSStoresCRM
Before
Evidence scattered across systems
- Planners reconcile forecasts, stock, purchase orders, transfers, and store requests across separate systems
- Stockout and overstock signals arrive without a consistent view of affected locations and customer promises
- Allocation and replenishment decisions are recorded separately from the evidence used
After
Exception ready for review
- The exception combines current stock, demand, supply, lead time, location, and policy context
- AI prepares affected SKUs, open confirmations, comparable situations, and a review-ready option set
- The planner approves quantity, allocation, transfer, supplier escalation, and the system update
Inventory exceptions reach accountable planners with current evidence attached, while allocation and replenishment remain human decisions.
Faster planner review
Define the exception and decision owner before connecting commerce systems.
Name the trigger, approved sources, decision owner, update authority, escalation path, and customer promise at risk. Those choices define the pilot.
Assess the first exceptionWhy ZenAI
Built around retail exceptions, not a generic chat experience.
ZenAI combines commerce-system integrations, review queues, and explicit commercial decision points so one real exception can run in daily operations.
Exception-workflow-first
The build starts with one repeated retail exception, its accountable owner, current systems, and controlled outcome.
Current retail context
Prepared packets use approved product, inventory, order, store, customer, and policy context with sources and timestamps visible.
Commercial decisions stay human
AI can organize evidence and options; authorized teams retain pricing, allocation, refund, assortment, promise, and policy decisions.
Integration without replacement
ZenAI works around POS, PIM, OMS, WMS, ERP, CRM, and task systems that already hold records and transaction authority.
Production rollout and monitoring
A focused pilot defines ownership, source coverage, review latency, fallback behavior, support, and release criteria for daily use.
Retail-network adaptation
Workflows reflect channel, banner, region, fulfillment model, store role, policy, and escalation differences rather than a generic retail model.
FAQ
What retail and commerce teams ask before a pilot.
Retail AI pilots raise questions about system replacement, commercial authority, and where to start. Here is what we typically see.
Do we need to replace POS, OMS, WMS, ERP, or CRM?
No. ZenAI connects approved records and events from the systems already responsible for products, transactions, inventory, orders, customers, and tasks. Reviewed updates return through existing interfaces, permissions, and transaction controls.
Can AI make pricing, assortment, allocation, or refund decisions?
AI can structure evidence, compare policy and operating context, and prepare options. Authorized merchandisers, planners, store leaders, finance owners, and service teams retain commercial decisions and customer commitments.
Which retail workflow should start first?
Choose a repeated exception with accessible source data, a named owner, a clear review or approval gate, and an outcome already recorded in a connected system. Inventory, order promise, returns, or store-task exceptions are common starting points.
Assessment
Start your retail roadmap with one repeatable exception.
Choose a recurring exception with accessible source data, a named retail owner, and an existing update path. The three-step assessment narrows it into a practical pilot.
retail_workflow_boundary.py
def prepare_retail_exception(model, request):
context = fetch_from(["PIM", "OMS", "WMS", "ERP", "CRM"])
if not (context.retail_owner and context.commercial_authority == 'human'):
raise ReviewBoundaryError("Named retail owner required")
return model.prepare_next_step(context)
> The workflow advances only when context, ownership, and customer-promise boundaries are explicit.
01 / 03 — Operations priority
Which retail exception should the team solve first?
Step 1 of 3
Ready for Transformation?
Choose the first retail exception to put intoproduction.
Bring one exception queue, its approved product and operating sources, and the people authorized to resolve it. We will define the integration, decision points, handoffs, and pilot around that work.