Merchandising & Buying
Open-to-buy exception alerting
Monitor committed intake against open-to-buy budgets and flag lines where forward commitments are at risk of breaching targets. Alerts merchandisers before issues become entrenched.
30–50%
reduction in OTB overcommitment incidents
Opportunity assessment
Moderate impact. Meaningful cost or revenue improvement, likely noticeable at function level but modest relative to total business scale.
Moderate effort. Requires configuration, prompt engineering, and testing. A capable team can get there but expect several months.
Demanding data requirements. Relies on clean, integrated data across multiple systems. Significant data preparation work typically needed.
Low risk. Limited external exposure. A human reviews output before it reaches anyone outside the team.
Moderate people impact. Part of someone's working day changes. Requires training and some adjustment time, but roles remain broadly the same.
Tooling required
Things to consider
Depends on having a reliable, up-to-date view of committed orders against OTB budgets — if this data is manually maintained or lagged, the alerts will be inaccurate.
The alert logic must be agreed with the trading team: what threshold triggers a flag, what action is required, and who owns resolution.
This is largely a workflow automation and data integration problem. The AI adds value in interpreting the exception and suggesting options; the flagging itself can often be done with simpler tooling.
The real value is speed of escalation — catching a commitment breach three weeks earlier gives the team options (hold, cancel, negotiate) that aren't available if it surfaces in a monthly review.
Experiment starter: Export the last 12 months of OTB tracking data and identify instances where budgets were exceeded. Replay those periods through a simple alert rule (flag when commitments exceed 95% of OTB) and measure how many weeks earlier the breach would have been visible. Use this as the business case for automation.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — RAG and Knowledge Systems: Unlocking Proprietary DataThis is an decision support opportunity — see the relevant playbook section for how to approach it.More in Merchandising & Buying
Product description writing
Generate first-draft product descriptions from attributes, imagery, and brand guidelines. Buying team reviews, edits, and approves before publish.
Supplier spec sheet extraction
Extract structured product data from unstructured supplier spec sheets, PDFs, and email attachments. Outputs clean records ready for system upload.
Range review analysis
Summarise sales performance, markdown rates, and return data by category to surface underperformers and inform ranging decisions.
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