Our method
From the shop floor to the decision.
AI4-Retail connects operational reality, data and quantified objectives to measure the value of change in food and non-food retail.
01 — Our position
Start with the role. Make value explicit.
AI4-Retail starts neither with a catalogue of tools nor with technology to deploy. We start with an operational situation, an expected outcome and the data that can make change observable.
Technology is not an end in itself. It matters when it helps people decide better, act more effectively and measure a concrete outcome for retail.
02 — The measurement chain
Connect every project through function points.
Each function point connects a shop-floor reality to data, a decision and an indicator. The project can then be managed and adjusted throughout deployment.
- 01
Situation
Describe the issue experienced in the field precisely.
- 02
Function point
Isolate the business function whose effect should improve.
- 03
Data
Identify observable facts already available or still to be produced.
- 04
Objective
Set a quantified outcome and a baseline.
- 05
Action
Implement the useful change at the right level.
- 06
Measurement
Compare the effects achieved with expected results.
- 07
Adjustment
Correct, reinforce or stop on the basis of evidence.
03 — A measurable approach
Three levels of evidence.
The value of change is not limited to going live. It is verified in use, in performance and in its ability to scale.
Performance
Is the quantified objective being met?Scale
Can the value be reproduced?04 — Use cases to challenge
Eleven areas, one measurement framework.
The approach covers the full retail value chain, in food and non-food. Choose a role to open its method case.
01Direction and management
+
Example to challenge
Prioritise action across a store network.
Combine commercial results, operational gaps and field signals to focus management attention where a decision can create the most value.
- Value chain
- Network signals → evidenced priority → action plan → verified progress.
- Possible evidence
- Speed of correction, fewer gaps between stores, target achievement and management time saved.
02Purchasing and supply
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Example to challenge
Align orders with real demand.
Connect forecasts, sales, stock, supplier lead times and local events to reduce stock-outs, overstock and waste at the same time.
- Value chain
- Anticipated need → adjusted order → available stock → fulfilled sale.
- Possible evidence
- Stock-out rate, stock cover, rotation, shrinkage and forecast reliability.
03Sales, reception and customer service
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Example to challenge
Help teams respond more precisely to customer needs.
Analyse flows, recurring requests, waiting times and customer feedback to adapt sales-floor coverage and resolve friction points faster.
- Value chain
- Need detected → resource mobilised → response delivered → satisfaction measured.
- Possible evidence
- Waiting time, conversion rate, first-contact resolution, satisfaction and average basket.
04Marketing, communication and loyalty
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Example to challenge
Measure the real impact of a commercial activation.
Connect audience, exposure, engagement, purchase behaviour and loyalty to distinguish campaigns that create lasting value from those that merely shift sales.
- Value chain
- Audience → message → activation → behaviour → customer value.
- Possible evidence
- Incremental revenue, repeat-purchase rate, acquisition cost, margin and customer value.
05Fresh food production & service counters
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Example to challenge
Match production to demand while keeping risk under control.
Combine sales, forecasts, schedules, production records, temperatures and dates to prepare the right quantities, limit waste and strengthen food-safety control.
- Value chain
- Forecast demand → adjusted production → critical-point control → sale or corrective action.
- Possible evidence
- Waste rate, availability, HACCP compliance, response time and department margin.
06Merchandising
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Example to challenge
Verify how a layout affects availability and sales.
Observe shelf execution, apparent stock-outs, product visibility and sales to separate the effect of a merchandising plan from stock or execution issues.
- Value chain
- Target layout → observed execution → real availability → measured performance.
- Possible evidence
- Layout compliance, shelf availability, sales per linear metre and incremental margin.
07Logistics, goods receiving & inventory
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Example to challenge
Make the transition from theoretical to physical stock more reliable.
Connect orders, deliveries, checks, movements, inventories and anomalies to spot discrepancies earlier and direct the right action.
- Value chain
- Expected goods → controlled receipt → reliable stock → availability assured.
- Possible evidence
- Stock accuracy, receiving time, supplier disputes, stock-outs and inventory discrepancies.
08Finance and administration
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Example to challenge
Automate variance checks without losing control.
Compare orders, receipts, invoices, contracts and payments to isolate exceptions requiring human review and speed up compliant processing.
- Value chain
- Document received → matching → qualified exception → validation or correction.
- Possible evidence
- Processing time, exception rate, amount recovered, administrative cost and closing quality.
09HR and training
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Example to challenge
Adapt training to the situations teams actually face.
Connect expected skills, observed practices, incidents, results and team feedback to target useful learning and measure its transfer to the workplace.
- Value chain
- Skills gap → targeted pathway → applied practice → operational effect.
- Possible evidence
- Time to competence, adoption of practices, fewer errors, safety and engagement.
10Maintenance, energy and security
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Example to challenge
Act before a drift becomes a breakdown or a risk.
Identify abnormal equipment behaviour or consumption, qualify its business impact and prioritise interventions by criticality.
- Value chain
- Observable drift → contextualised risk → intervention → return to baseline.
- Possible evidence
- Energy used, breakdowns prevented, equipment availability, response time and safety incidents.
11Data, IT and Compliance
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Example to challenge
Turn data quality into decision-making and evidence.
Identify critical data, its sources, discrepancies and uses to make decisions more reliable, secure processing and document compliance.
- Value chain
- Critical data → quality control → secure use → traceable decision or evidence.
- Possible evidence
- Reliable-data rate, incidents prevented, time to information, traceability and processing compliance.
05 — From HACCP to function-point management
A concrete framework for explaining a general method.
In food retail, HACCP shows how critical points, limits and corrective actions can keep a risk under control. AI4-Retail applies this logic of control and measurement to other retail functions; it is not presented as an IoT integrator or a HACCP solution.
The HACCP frameworkThe AI4-Retail method
Identify a hazardIdentify a priority business issue
Define a critical pointDefine the function point to improve
Set an observable limitAssociate data and a quantified objective
Monitor and documentMeasure use and the effect achieved
Trigger corrective actionAdjust before scaling
06 — Assess before deploying
Choose the cases where the approach is genuinely relevant.
Not every issue calls for more data, new technology or artificial intelligence. The assessment begins by checking whether the situation can be observed, whether action is possible and whether its effect can be measured.
- 01
Business pain point
Is the issue frequent, costly or risky?
- 02
Observability
Are reliable facts available to understand the situation?
- 03
Ability to act
Can a decision or intervention change the outcome?
- 04
Evidence of value
Can a baseline and a quantified objective be set?
- 05
Ability to scale
Can the conditions for success be reproduced?
07 — People committed to transformation
An approach grounded in shop-floor experience.
Huberise — Be an active participant in your own modernisation
Huberise supports business transformation through AI and data, enabling companies to build their own modernisation hub and lead the change themselves rather than be disrupted by it.
Gildas — Research, experiment, measure
An entrepreneur with hands-on experience, Gildas works across very different environments, from food-supplement distribution to food retail and mass retail. He researches and tests the most relevant solutions to help companies benefit concretely from data, AI and new technologies.
This range of experience underpins a pragmatic approach: start from operational realities, identify technology that is genuinely useful, measure its effects and support implementation.
Start with a real situation and measurable evidence
Identify a use case, set objectives and decide what comes next.
Define the function points, quantified objectives and an experiment short enough to decide the next step.
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