Retail & consumer businesses
Customers' agents are starting to do the buying. Brands that agents can't read will lose share.
The lines that matter Gross margin, markdowns, inventory days and cost to serve
Open protocols now let AI agents compare products and complete purchases on a shopper's behalf. Brands that depend on advertising and shelf position lose leverage unless their product data, pricing and fulfilment are clear enough for agents to compare and trust.
Inside the business, agents can improve forecasting, replenishment, pricing and service. The prize shows up in gross margin and inventory, so it should be measured there.
- Margin pressure from promotions and markdowns
- Inventory and write-offs from forecasting errors
- Rising cost to serve online customers
- Visibility in agent-led buying
- Product data not structured for agents to read
- Forecasting spread across spreadsheets and teams
- Pricing and promotion decisions without clear limits
- Customer data across separate channels
Where AI can move the numbers
Opportunities grouped by Agent Value Ledger category. They are possibilities to test, not promised results.
Revenue
On-shelf availability, visibility to buying agents, personalised service
Operating costs
Customer service, order handling, content production
Capital
Inventory days, safety stock, markdown and write-off reduction
Risk
Pricing errors, fraud and returns abuse, product compliance
The lines this sector is judged on
Consumer goods and retail
- Gross margin
- Pricing, promotions and markdowns
- Revenue
- On-shelf availability and visibility to buying agents
- Inventory and write-offs
- Demand forecasting and replenishment
- Cost to serve
- Order handling and customer service
Marketplaces and online retail
- Conversion
- Search, product content and service
- Returns cost
- Better product information and fit guidance
- Cost to serve
- Order and service automation
- Fraud losses
- Payment and returns abuse detection
A demand-planning agent at a consumer goods company
Assumptions: revenue US$500m; gross margin 40%; cost of goods US$300m; 75 days of inventory; sales lost to stock-outs at 3% of revenue; cost of capital 8%.
| Line | How the agent moves it | Measured by | Working | Per year |
|---|---|---|---|---|
| Gross profit | Better forecasts and replenishment reduce stock-outs | Lost sales; on-shelf availability | 0.5% of revenue recovered × US$500m × 40% margin (gross profit, not revenue) | +US$1.00m |
| Capital | Less safety stock for the same service level | Inventory days | 7 days × US$300m ÷ 365 = US$5.8m released once, at 8% | +US$0.46m |
| Operating cost | Fewer emergency shipments | Expedited freight spend | One-fifth of a US$2m expediting budget | +US$0.40m |
| Write-offs | Less obsolete and short-dated stock | Write-offs and markdowns | Not counted until a full seasonal cycle | Excluded |
| Cost of the agent | Model usage, data integration, planner oversight, assurance | Total cost of ownership | Annual run cost | −US$0.80m |
| Net annual value | ≈ +US$1.1m, plus US$5.8m of cash released once | |||
Hypothetical illustration of the method. The figures are assumptions, not a client result or a forecast.
- Limits on automated pricing and promotions
- Clear responsibility for what customer-facing agents promise
- Consumer protection and fair treatment in personalisation
AI Readiness & Value Diagnostics
Start with an agent-readiness review of product data and a margin and inventory diagnostic.
About this serviceSupply Chain, Consumer and Manufacturing in the Age of Agency
Agents now plan, buy, sell and move goods, so the advantage will go to firms that redesign how they delegate decisions, not to those that only add tools.
Read the brief →When customers' agents do the buying
Situation. A consumer goods maker's retail partners have joined agentic commerce, while its own planning agents run without spending limits.
Our approach. An agent-readiness review of product data and pricing, delegation policies for the planning agents, and a deliberate plan for the roles that change.
What good looks like. The company stays visible and trusted in agent-led buying, and its own agents work within clear limits.
Scenarios and worked ledgers are hypothetical illustrations. They do not describe specific clients.
Start with an executive conversation
A confidential discussion of your priorities, and where AI could move the numbers in your business.