AI consulting · Retail
AI consulting for retailers
Native helps retailers put AI agents into store operations, merchandising, customer service and the work around demand planning. Agents answer store teams' questions from your own procedures, read supplier and product data, and resolve routine customer contacts, with managers deciding anything that touches price, stock or a refund over policy. The first agent is usually live within weeks.
Featuring insights fromShaun Devan · Founder & CEOUpdated · 3 min read
Where AI has an impact in retail
Store operations assistant
Store teams ask about procedures, promotions and policy and get an answer cited from your own manuals.
Product setup
Supplier spreadsheets and spec sheets turned into complete item records, ready for a merchandiser's approval.
Customer service
Order, delivery and returns contacts resolved from the order record, in every channel customers use.
Returns and refunds
Return requests checked against policy, with anything outside it prepared for a manager.
Demand planning follow-through
Forecast exceptions explained and purchase order changes drafted for the buyer.
Supplier communication
Late deliveries and short shipments chased and reconciled against open orders.
Store reporting
Daily store performance summarised in plain language for district and regional managers.
Product content
Descriptions, attributes and translations kept consistent across stores, web and marketplaces.
Where retail loses time that AI can give back
Retail margins are thin and payroll is the biggest cost a store controls, yet a large share of that payroll goes to work customers never see. Store managers hunt through policy binders and emails for the answer to a returns question. Merchandisers copy supplier spreadsheets into the product system. Customer service answers the same order and returns questions all day.
None of that needs a new platform. It needs agents that read what the business already has and act inside the systems it already uses, so store and head-office teams spend their hours on customers and on decisions.
What agents do, and what stays with your people
| Work | Agent | Your team |
|---|---|---|
| Store procedure and policy questions | Answers from your own manuals, with the source cited | Owns the procedures |
| New products and supplier data | Reads the supplier file and builds the item record | Approves the range |
| Order, delivery and return questions | Resolves them from the order record | Handles exceptions |
| Refunds and goodwill above policy | Prepares the case | Decides |
| Forecast exceptions | Explains the variance and drafts the adjustment | Approves the buy |
Demand planning: where AI fits and where it does not
Forecasting models have been in retail for years, and most retailers already have one inside their planning system. The time sink is the work around the forecast: chasing why a store's sales jumped, reconciling a promotion calendar, rewriting purchase orders after a supplier misses a date.
Agents take on that work. They read sales, promotions and supplier confirmations, explain each exception in plain language and draft the change for a planner to approve. The model forecasts, the agent does the follow-through, and the buyer keeps the decision.
The systems we work with
Agents connect to the point-of-sale, merchandising and service systems you run, such as Oracle Retail, NetSuite, Microsoft Dynamics 365, Shopify POS, Lightspeed, Zendesk and Gorgias. Store-facing assistants live where your teams already work, in Teams, Slack or the store tablet, and answer only from documents you approve.
What an AI engagement in retail looks like
The first two weeks go to the stores and head office: which questions come up most, which tasks get copied between systems, and which customer contacts repeat. We rank them by hours and by customer impact, then put the first agent into production within weeks.
Store support, product setup and customer service usually come first, with planning work following. Within months each team works with agents as part of the day, everything runs in your own accounts, and your staff can update an agent's knowledge without calling anyone.
How to measure the impact
- Customer contacts resolved without staff
- Time from supplier file to item live in store and online
- Store manager hours spent on admin each week
- Forecast exceptions worked per planner
- Customer satisfaction on resolved contacts
Case study · A specialty retailer with about 60 stores
A specialty retailer gave store teams instant answers and cleared its service backlog.
Native helped a specialty retailer put an assistant in every store and an agent on its customer service inbox. Store managers stopped calling head office for routine answers, and most customer emails are now resolved without staff.
The challenge
Store managers spent hours a week searching for procedures and calling head office, and the customer service team was days behind on email during peak season. Both problems grew with every new store.What we built
- A store assistant that answers procedure, promotion and policy questions from approved documents, citing the source each time.
- A customer service agent that resolves order, delivery and returns emails from the order record and hands exceptions to the team.
- A weekly report showing what each agent handled and which questions it could not answer, so head office could fill the gaps.
The result
Store managers got back time for their floor and their staff, customers stopped waiting days for a reply, and head office could see which procedures confused stores most.Frequently asked questions
What does an AI consultant do for a retailer?
An AI consultant finds the work that eats store and head-office hours, such as policy questions, product setup and repeat customer contacts, and builds agents that handle it inside your systems. At Native the same team builds and runs the agents until your staff take them over.
How is AI used in retail store operations?
The most common use is a store assistant that answers procedure and policy questions from your own documents. Others are shift handovers, store reporting and checking that promotions are set up correctly.
Can AI improve retail demand forecasting?
Your planning system likely already forecasts. AI adds the most around the forecast: explaining exceptions, reconciling promotions and supplier dates, and drafting purchase order changes for a buyer to approve.
Will AI replace retail customer service staff?
It changes what they do. Agents resolve routine order and returns contacts, and your team spends its time on the customers and cases that need a person.
Where should a retailer start with AI?
Usually customer service or a store operations assistant. Both are high volume, rely on documents you already have, and show their impact within weeks.