Enterprise Intelligence Group

Retail & CPG resources

Practical retail and CPG guides, checklists, articles and frameworks for business decisions, technology delivery and useful AI.

Practical questions, working tools and clear explanations for the decisions your team needs to make. Explore merchandising, planning, technology selection, delivery and AI. Start with a useful resource, then follow the related service if you need help applying it.

Start with the decision you need to make

Use a practical guide or working tool to clarify the problem, examine the evidence and identify the support you need. You do not need to start an AI programme or replace your software to use these resources.

Define requirements before choosing software

Turn an operating need into requirements, demonstration scenarios and a decision record before committing to a vendor.

• Explore requirements and vendor selection
• Use the pricing software evaluation scorecard

Improve forecasting and replenishment

Separate demand evidence, planning decisions and ordering rules before deciding what needs to change.

• Use the five-check planning diagnostic
• Explore planning and replenishment support

Review assortment and space decisions

Examine demand-transfer assumptions, range changes and store constraints together.

• Use the assortment evaluation guide
• Review space-planning requirements

Evaluate pricing and promotion performance

Test the evidence behind price comparisons and promotional results, then connect the findings to the next decision.

• Evaluate pricing intelligence
• Review promotion performance

Resolve data and supplier handoffs

Clarify definitions, mappings and acceptance checks when records arrive but the business process still fails.

• Use the supplier onboarding checklist
• Review planning-data readiness

Prepare implementation and modernization

Establish responsibilities, readiness and acceptance evidence for the change your business needs to deliver.

• Explore implementation and programme delivery
• Discuss your project

AI frameworks & agentic strategy

Explore specialist resources on enterprise knowledge, AI decision support and controlled adoption. These complement EIG’s business consulting and technology-delivery services; they are not prerequisites for working with us.

Working tools & guides: practical resources for merchandising, planning, technology and delivery

• Retail technology demo scorecard: Five scenarios to test requirements, evidence and decision ownership before accepting a software demonstration.
• Retail demand forecasting: five checks before replacing your planning system: Before replacing retail planning software, inspect demand, overrides, replenishment rules, supply constraints and shelf execution.
• Retail Pricing Software: Requirements, Vendor Evaluation and Implementation Readiness: Evaluate retail pricing software with practical requirements, demo scenarios and a scorecard covering data, integration, adoption and measurable business value.
• Private-label demand transfer: will the category grow, or will demand move?: A practical private-label review connecting product attributes, demand sources, cannibalization and shelf space.
• Retail supplier onboarding: seven checks before calling it complete: Seven checks for supplier and item onboarding: ownership, data definitions, validation and exception handling.
• Buying supplier onboarding software? Test the retail workflow before the feature list: Evaluate supplier onboarding software against real retail workflows, data ownership, integration responsibilities and business acceptance.
• CPG demand forecasting: a larger order is not automatically stronger consumer demand: Before changing a CPG forecast, separate consumer sales, customer orders, shipments and network inventory.
• Retail promotion performance: sales uplift is not the verdict: Review retail and CPG promotions using a credible baseline, funding, costs, demand shifts and execution.
• EIG delivery capability sheet: Defined work packages for requirements, planning-data readiness and business/technology workstream leadership.
• Retail & CPG Consulting Capability Overview: EIG consulting capabilities across retail planning, technology selection, programme delivery, data and practical AI, with defined work packages and clear ownership.
• Assortment optimization: what to test before buying or changing the software: Evaluate assortment planning software using product mix, demand transfer, NPI, private label, space and data requirements.
• Retail demand planning software: requirements to test before selection: Evaluate retail demand planning software with requirements for forecast testing, replenishment, promotions, data, exceptions and operational acceptance.
• Micro and macro space planning: what to test in retail software: Evaluate micro and macro retail space planning software across planograms, category footprints, store clustering, data and execution.
• Retail master data readiness: can your planning decisions trust the inputs?: Test retail master data and planning inputs across products, locations, units, history and ownership.
• Pricing & Promotion Optimization: Define pricing and promotion requirements, support implementation and review adoption and value after go-live.
• Turn retail data into accountable action: Connect a shelf, sales or inventory exception to the evidence, owner and approved next action before investing in more software.
• Phantom inventory in retail: what to check before buying software: Investigate stock shown in your system but unavailable to sell: check the balance definition, trace the movements and review the replenishment decision separately.
• Business requirements document: retail & CPG example: Turn a business need into a requirement that stakeholders can review, own and test.

AI Agent Tips: eleven working sheets, written for the person who makes the call (free, no sign-up; PDF)

• Your AI agent is only as smart as your knowledge layer: Retailer-CPG-distributor coordination. The knowledge that runs a retail business moves between merchants, planners, suppliers and stores, and where it is not captured, it leaks.
• Pick your first AI agent in one afternoon: One decision before one platform. You don't need a roadmap or a vendor shortlist to start. You need one consequential decision, one owner, one measure and one guardrail.
• Four tests for your first AI-agent decision: One decision before one platform. Repeats weekly, costs real money but not the company, your best operator can explain it, one person owns it. Boring, frequent, explainable, owned.
• How to train your AI agent in three simple steps: Operator-led intelligence capture. Training an AI agent is not a data exercise. It is an operator exercise. Capture the reasoning, structure it, govern how it is applied.
• Eight questions that surface the reasoning behind a decision: Operator-led intelligence capture. Eight questions that pull the reasoning out of an operator's head before an agent is asked to act on it.
• Five things your product master can't tell your AI agent: Retail commercial decision memory. Demand transfer, override reasons, constraint context, deal history, reversals: the decision memory no product master carries.
• The 4-line override log your forecast is missing: Retail commercial decision memory. What changed, why, how long it holds, who owns it, all written at the moment of the override. An override without a reason is noise; with one, it is training data.
• How much should your AI agent decide alone?: Earned autonomy. Five levels of agent authority, from retrieve to bounded action, each stating what the agent may do and what a person still does. The same agent can sit at level five on reorders and level two on markdowns.
• Write the handoff rule before the permissions: Earned autonomy. Define when the agent must stop: confidence floor, human-only categories, value threshold, what gets logged. Then let it earn everything above that line.
• Four supplier exceptions your data feed will never carry: Retailer-CPG-distributor coordination. Optimistic lead times, promo allocation overrides, shopper-vs-buyer substitutions, deal terms that flip the answer, and the 30-minute review that captures them.
• 8 questions for your SaaS provider before you buy their AI: The enterprise intelligence layer. Can my agent call your system? Where does the judgment live? Who owns the decision log? Eight questions to structure a vendor evaluation; treat incomplete answers as unresolved requirements.

Board Briefs: nine briefs on the Retail AI Operating Model, written for the leadership table (free, no sign-up; PDF)

• The C-suite AI readiness map for retail: One decision before one platform. Not every executive has to be technical. Every executive has to understand how agents change the decisions their function owns, and what they are accountable for. One question per seat.
• From pilot sprawl to coordinated enterprise intelligence: The enterprise intelligence layer. More pilots do not equal more maturity. Coordination determines whether value compounds or cancels. Four recognisable states, each earned by fixing what the previous one lacks.
• Your enterprise strategy is missing from the prompt: The enterprise intelligence layer. The board approved the priorities. The merchandising, supply, pricing, space and marketing agents have never read them, because nobody translated the priorities into rules and trade-offs an agent can use.
• When two AI agents disagree, who wins?: The enterprise intelligence layer. The forecasting agent recommends more inventory. The margin agent recommends less. Both are technically correct. Without a rule, the answer is whichever system ran last, and a planner reconciling by hand on Friday afternoon.
• Governance should create value, not just control risk: Earned autonomy. Compliance-only governance asks whether an agent is permitted to act. Performance governance asks whether it understands the decision it is influencing, the strategy it serves and the conditions under which it must stop.
• Every agent needs a decision contract: Earned autonomy. A technical specification explains how an agent works. A decision contract explains how it operates responsibly inside your business: five fields, one page, signed by the human accountable for the outcome.
• The five levels of enterprise agent authority: Earned autonomy. Autonomy is not a property of the agent. It is a property of the decision. Set the level by the cost of a wrong call, and let one agent hold a different level on each decision it touches.
• Measure agent outcomes, not just agent accuracy: One decision before one platform. A technically accurate agent can create no operational value at all: the recommendation was right and nobody acted on it. Accuracy is the vendor's metric. Five measures for the scorecard, baselined before the agent goes live.
• The Executive AI Value Review: seven questions per agent: One decision before one platform. Run every agent, live or proposed, through the same seven questions at the same table. The output is a verdict per agent: expand, redesign or stop. On operational evidence, not enthusiasm.

Free to read

• Building Enterprise Knowledge: The practical guide to building enterprise knowledge your people, and your AI, can actually trust: what belongs, who owns it, how it stays current, and where to start.
• The Decision Interview Question Bank: Eight sets of questions for buyers, planners, category and supply chain leads, plus a 45-minute interview protocol and a one-page capture template.
• Retail Intelligence: The Foundation of Modern Retail AI: The institutional knowledge, commercial experience and business rules that shape how retailers decide, and why operationalizing them is the real AI advantage.
• Why Most Retail AI Projects Fail Before the First Prompt: The problem usually isn't the AI. It's the intelligence behind it. Why retail AI scales inconsistency without a governed knowledge foundation, and the sequence that works.
• Your Best Employee Can't Scale. Your Best Knowledge Can.: Experienced people are a real advantage, and so is keeping their knowledge available to the organization when they are busy, move on or retire. How recording decision knowledge can reduce that dependency.
• Why the Most Valuable Data in Your Business Isn't in Your Systems: Understanding a business decision requires more than locating its data. Explore how observation, interviews and validation can make operational knowledge more usable.
• The Most Dangerous Knowledge in Your Business Is the Knowledge Nobody Owns: Everyone knows the undocumented critical knowledge exists. Nobody owns it. Why unowned knowledge is hidden risk, and how the Enterprise Intelligence Register makes it a governed asset.
• AI Doesn't Fail Because of Technology. It Fails Because Nobody Agrees on the Truth.: Ask five departments the same commercial question and get five answers. Why AI scales disagreement without authoritative knowledge, and how Enterprise Intelligence fixes it first.
• The Companies Winning with AI Didn't Start with AI: Capable AI models are widely available. What remains unique is your business. Why we recommend investing in Enterprise Intelligence before technology, and how to start.
• Enterprise Knowledge Accelerator: A governed, AI-ready knowledge foundation built around one high-value business decision, discovered, structured and applied inside 90 days.

Request a copy

• Intelligence Before Automation™: Intelligence Before Automation is the discipline that makes AI safe to rely on: understand and govern the knowledge before you automate the work.
• Retail Intelligence Framework™: AI does not create competitive advantage. Retail Intelligence does.
• Enterprise Knowledge Foundation: The governed foundation required before AI can answer, recommend or act with confidence.
• Retail Intelligence Maturity Model™: Six AI-readiness discussion prompts covering outcomes, knowledge, data, process, governance and technology. No score is calculated.
• Decision Intelligence Framework™: Connect trusted knowledge to the specific commercial decisions that move margin.

In preparation: not yet available

• Executive Guide to RAG: Retrieval-augmented generation for leaders: what it is, why it matters, and how to do it responsibly.
• The Future of Retail Intelligence: Where retail, knowledge and AI are heading, and how to be ready.
• Building Trusted AI: Governance, controls and human accountability for AI you can actually trust in production.
• Grocery: Retail Intelligence for grocery: where knowledge and AI move margin and availability.
• CPG: Retail Intelligence for CPG: stronger accounts, joint business planning and category advisory.
• Specialty Retail: Retail Intelligence for specialty retail: assortment, experience and margin in focused categories.
• Distribution: Retail Intelligence for distribution: pricing, margin and preserving the knowledge your reps carry.
• Enterprise Memory: Preserving institutional knowledge at scale, so expertise compounds instead of walking out the door.
• Decision Intelligence: How trusted knowledge connected to decisions changes commercial outcomes: the evidence and the method.

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