---
# === IDENTITY ===
id: consulting/recipes/retail-ai-diagnostic-engagement-playbook/2026
canonical_question: "How do you run a 2-3 week Retail AI Readiness diagnostic engagement?"
aliases:
  - "Retail AI readiness assessment playbook"
  - "How to scope and deliver a retail AI diagnostic"
  - "Retail digital transformation diagnostic engagement lifecycle"
entity_type: execution_recipe
domain: consulting > recipes > Retail AI Diagnostic Engagement Playbook
region: global
jurisdiction: global
temporal_scope: 2026-2027

# === VERIFICATION ===
last_verified: 2026-03-30
confidence: 0.85
version: 1.0
first_published: 2026-03-30

# === TEMPORAL VALIDITY ===
temporal_validity:
  status: evolving
  last_breaking_change: "Initial release — Retail AI Diagnostic methodology v1.0"
  next_review: 2026-09-26
  change_sensitivity: high

# === CONSTRAINTS ===
constraints:
  - "NDA required before stakeholder interviews — retailers share competitive data"
  - "POS data access requires IT security sign-off and data processing agreement"
  - "Minimum engagement: 2 weeks — shorter timelines miss adoption psychology signals"
  - "Informal leader identification requires network analysis across at least 3 store locations"
  - "Compliance review must cover state-level AI regulation variance (CCPA, Colorado AI Act, NYC Local Law 144)"

# === SKIP CONDITIONS ===
skip_this_unit_if:
  - condition: "User needs only the data infrastructure audit, not the full diagnostic"
    use_instead: "consulting/recipes/retail-data-infrastructure-audit/2026"
  - condition: "User needs retail AI strategy, not a diagnostic engagement"
    use_instead: "Search knowledgelib.io for retail AI strategy — no dedicated unit yet"
  - condition: "User needs pricing model for retail consulting, not delivery process"
    use_instead: "Search knowledgelib.io for consulting engagement pricing — no dedicated unit yet"

# === AGENT HINTS ===
inputs_needed:
  - key: retailer_type
    question: "What type of retailer is the client?"
    type: choice
    options: ["grocery/supermarket", "department store", "specialty retail", "e-commerce native with stores", "warehouse/club", "not yet defined"]
  - key: store_count
    question: "How many store locations are in scope?"
    type: choice
    options: ["1-10", "10-50", "50-200", "200+"]
  - key: current_ai_maturity
    question: "What is the client's current AI maturity level?"
    type: choice
    options: ["no AI (manual processes)", "basic analytics (BI dashboards)", "some ML models (demand forecasting)", "AI-integrated operations", "not yet assessed"]
  - key: budget
    question: "What is the engagement budget?"
    type: choice
    options: ["$15K-$20K (focused)", "$20K-$30K (comprehensive)", "$30K+ (enterprise multi-region)", "not yet defined"]

# === EXECUTION METADATA ===
execution:
  required_inputs:
    - name: "Signed scope document"
      source: "client/engagement-lead"
      format: "document"
    - name: "Store location list with org chart per location"
      source: "client/operations"
      format: "spreadsheet"
    - name: "Current technology stack inventory"
      source: "client/IT-department"
      format: "spreadsheet"
    - name: "POS system access credentials or data extract"
      source: "client/IT-department"
      format: "API credentials or CSV export"

  outputs:
    - name: "6-Dimension AI Readiness Scorecard"
      format: "PDF + structured JSON"
      description: "Composite scorecard covering data infrastructure, process automation, workforce readiness, adoption psychology, compliance risk, and AI commerce capability — each scored 1-5 with gap analysis"
    - name: "Implementation Roadmap"
      format: "PDF + Gantt chart"
      description: "Phased 12-month roadmap with quick wins (0-3mo), foundation (3-6mo), and transformation (6-12mo) tracks"
    - name: "Retainer Proposal"
      format: "document"
      description: "Ongoing advisory scope: monthly check-ins, quarterly re-scoring, implementation support"

  tools_required:
    - name: "Video conferencing platform"
      purpose: "Stakeholder interviews (C-suite, store ops, IT, merchandising)"
      tier: "free"
      cost: "$0 (Zoom/Teams)"
      alternatives: ["Google Meet", "Microsoft Teams"]
    - name: "Survey platform"
      purpose: "Workforce readiness assessment and fear inventory across store staff"
      tier: "free"
      cost: "$0-$50"
      alternatives: ["Typeform", "Google Forms", "SurveyMonkey"]
    - name: "Network analysis tool"
      purpose: "Informal leader identification via communication and influence mapping"
      tier: "paid"
      cost: "$500-$2K/engagement"
      alternatives: ["OrgMapper", "NetworkX + custom scripts", "Microsoft Viva Insights"]
    - name: "AI evaluation framework"
      purpose: "Structured scoring of AI commerce capabilities and retrieval readiness"
      tier: "free"
      cost: "$0 (proprietary framework)"
      alternatives: ["Gartner AI Maturity Model", "MIT CISR Digital Maturity"]

  credentials_needed:
    - service: "POS system"
      type: "Read-only API access or data export"
      where_to_get: "Client IT department"
      free_tier_limits: "N/A — requires client authorization"
    - service: "Survey platform"
      type: "Account with distribution capability"
      where_to_get: "https://www.typeform.com or equivalent"
      free_tier_limits: "10 responses/month (Typeform free), unlimited (Google Forms)"

  estimated_duration: "2-3 weeks"
  estimated_cost: "$20K per engagement (consultant cost)"

# === DISTRIBUTION ===
canonical_source: "https://knowledgelib.io/consulting/recipes/retail-ai-diagnostic-engagement-playbook/2026"
suggested_citation: "Source: knowledgelib.io — AI Knowledge Library (verified 2026-03-30)"

# === RELATED UNITS ===
related_kos:
  depends_on:
    - id: "consulting/recipes/retail-data-infrastructure-audit/2026"
      label: "Data infrastructure audit sub-recipe (Dimension 1)"
  related_to:
    - id: "business/retail-transformation/retail-digital-maturity-assessment/2026"
      label: "Retail digital maturity assessment benchmark"

# === SOURCES ===
sources:
  - id: src1
    title: "The AI-First Retailer: Competing in the Age of Intelligence"
    author: McKinsey & Company
    url: https://www.mckinsey.com/industries/retail/our-insights/the-next-frontier-of-retail
    type: industry_report
    published: 2025-06-15
    reliability: authoritative
  - id: src2
    title: "Diffusion of Innovations"
    author: Rogers, E.M.
    url: https://www.simonandschuster.com/books/Diffusion-of-Innovations-5th-Edition/Everett-M-Rogers/9780743222099
    type: academic_paper
    published: 2003-08-16
    reliability: authoritative
  - id: src3
    title: "Leading Change"
    author: Kotter, J.P.
    url: https://www.kotterinc.com/methodology/8-steps/
    type: academic_paper
    published: 1996-01-01
    reliability: authoritative
  - id: src4
    title: "Retail AI Adoption: Barriers, Enablers, and the Human Factor"
    author: Deloitte Digital
    url: https://www2.deloitte.com/us/en/insights/industry/retail-distribution/artificial-intelligence-in-retail.html
    type: industry_report
    published: 2025-03-20
    reliability: high
  - id: src5
    title: "State of AI in Retail 2025"
    author: NVIDIA
    url: https://www.nvidia.com/en-us/industries/retail/
    type: industry_report
    published: 2025-01-10
    reliability: high
---

# Retail AI Diagnostic Engagement Playbook

## Purpose

This recipe executes a full Retail AI Readiness diagnostic engagement over 2-3 weeks. It produces a 6-dimension scorecard (data infrastructure, process automation, workforce readiness, adoption psychology, compliance risk, AI commerce capability), a gap analysis, a phased implementation roadmap, and a retainer proposal — transforming a $20K diagnostic into an ongoing implementation advisory pipeline. [src1, src4]

## Prerequisites
<!-- Agents: verify ALL prerequisites before executing. Missing prerequisites = failed execution. -->

- [ ] **Executive sponsor** identified — VP-level or above with authority over store operations and IT
- [ ] **Signed NDA and scope document** — retailers share competitive POS and margin data
- [ ] **Store location list** with org charts per location from operations team
- [ ] **Technology stack inventory** — POS vendor, ERP, CRM, e-commerce platform, analytics tools
- [ ] **POS data access** — read-only API credentials or trailing 90-day data export (transaction-level)
- [ ] **IT security sign-off** on data processing agreement for POS and customer analytics data

## Constraints
<!-- Hard rules. Agents: enforce throughout execution. Violating these = broken output or legal risk. -->

- NDA must be signed before any stakeholder interviews — retailers share margin, supplier, and pricing data that is competitively sensitive. [src1]
- POS data access requires IT security sign-off and a data processing agreement specifying read-only access, anonymization of customer PII, and 30-day retention post-engagement.
- Minimum engagement duration: 2 weeks. Shorter timelines miss adoption psychology signals that only surface in follow-up conversations. [src2]
- Informal leader identification requires communication analysis across at least 3 store locations to establish statistical significance. [src3]
- Compliance review must account for state-level AI regulation variance — CCPA (California), Colorado AI Act, NYC Local Law 144 (automated hiring). Federal fragmentation means no single compliance checklist works nationally. [src4]

## Tool Selection Decision

```
Which path?
├── Retailer has modern cloud POS (Shopify POS, Square, Lightspeed)
│   └── PATH A: API-First — real-time data pull, automated latency measurement
├── Retailer has legacy POS (Oracle MICROS, NCR, Toshiba)
│   └── PATH B: Export + Analysis — CSV/flat file export, manual pipeline measurement
├── Retailer has hybrid (cloud e-commerce + legacy in-store)
│   └── PATH C: Dual-Track — API for e-commerce, export for in-store, reconciliation audit
└── Retailer has no centralized POS data (franchise model)
    └── PATH D: Sample-Based — audit 3-5 representative locations, extrapolate
```

| Path | Tools | Cost | Speed | Output Quality |
|------|-------|------|-------|---------------|
| A: API-First | POS API, Python analytics, network analysis | $0-$500 | 2 weeks | Excellent — automated, comprehensive |
| B: Export + Analysis | CSV exports, Python/Excel, manual profiling | $0-$200 | 2.5 weeks | Good — thorough but manual |
| C: Dual-Track | API + CSV, reconciliation scripts | $0-$500 | 3 weeks | Good — captures full picture |
| D: Sample-Based | Manual audit, surveys, interviews | $0-$200 | 2 weeks | Adequate — extrapolated from sample |

## Execution Flow

### Step 1: Stakeholder Interviews (C-Suite + Department Leads)

**Duration**: 3-4 days
**Tool**: Video conferencing + structured interview guide

Conduct 6-8 structured interviews across four stakeholder groups: (1) C-suite (CEO/COO/CTO — strategic intent, budget appetite, risk tolerance), (2) store operations (district/regional managers — daily friction points, workaround inventory), (3) IT/engineering (CTO/VP Eng — infrastructure constraints, integration debt, security posture), (4) merchandising/buying (category managers — demand signal usage, assortment planning pain points).

Each interview follows a standardized 45-minute protocol:
- 10 minutes: current state walkthrough (what tools, what works, what fails)
- 15 minutes: pain point deep-dive with specific examples
- 10 minutes: AI exposure and sentiment (what they have tried, what they fear)
- 10 minutes: aspirational state (what would change their daily work most)

**Verify**: All 4 stakeholder groups interviewed — minimum 6 interviews completed with notes transcribed. Cross-reference answers for consistency.
**If failed**: If a stakeholder group is unavailable, schedule within 2 business days. Do not proceed to scoring without all 4 groups represented.

### Step 2: Data Infrastructure Audit (Dimension 1)

**Duration**: 3-5 days
**Tool**: Data profiling tools, POS analytics, knowledge graph tools

Execute the full data infrastructure audit. See [Retail Data Infrastructure Audit](/consulting/recipes/retail-data-infrastructure-audit/2026) for detailed sub-recipe.

Inventory all demand signal sources and refresh rates. Measure POS-to-analytics pipeline latency. Assess supply chain data integration completeness. Evaluate product knowledge graph maturity and semantic embedding coverage. Score AI retrieval readiness (GEO audit). Calculate real-time vs batch decision-making ratio. Benchmark against maturity levels 1-5. [src1, src5]

**Verify**: Data infrastructure scorecard completed — maturity level assigned (1-5) with evidence for each sub-dimension.
**If failed**: If POS data access is delayed, proceed with other dimensions and return to this step when access is granted. Document data gaps as findings.

### Step 3: Process Automation Mapping (Dimension 2)

**Duration**: 2-3 days
**Tool**: Process mapping tool + interview data

Map current automation state across 8 core retail processes: (1) demand forecasting, (2) inventory replenishment, (3) pricing/markdown optimization, (4) assortment planning, (5) store labor scheduling, (6) customer service/chat, (7) loss prevention, (8) supply chain logistics.

For each process, document:
- Current automation level (manual / rule-based / ML-assisted / AI-autonomous)
- Decision latency (time from data to action)
- Error rate and cost of errors
- AI readiness score (data availability + process standardization + outcome measurability)

**Verify**: All 8 processes mapped with current state and AI readiness score per process.
**If failed**: If process owners are unavailable for mapping, use IT system logs and stakeholder interview data to estimate. Flag estimates vs confirmed data.

### Step 4: Adoption Psychology Assessment (Dimension 3)

**Duration**: 2-3 days
**Tool**: Survey platform + network analysis tool

Execute two parallel workstreams:

**Workstream A — Fear Inventory**: Deploy anonymous survey to store associates and middle management (target: 60%+ response rate). Measure 5 fear dimensions: (1) job displacement anxiety, (2) skill obsolescence worry, (3) surveillance/monitoring concern, (4) decision authority erosion, (5) technology overwhelm. Score each 1-5 per respondent. [src2, src4]

**Workstream B — Informal Leader Identification**: Analyze communication patterns (email, Slack/Teams, in-store scheduling systems) to identify informal leaders — individuals with disproportionate influence on peer behavior regardless of title. These people determine adoption velocity more than any executive mandate. Map the top 3-5 informal leaders per location. [src3]

**Verify**: Fear inventory scores aggregated by role level and location. Informal leader map produced for at least 3 locations. Cross-reference: do informal leaders show high or low AI anxiety?
**If failed**: If survey response rate is below 60%, have store managers send personal messages and extend deadline by 3 days. If network analysis is blocked, fall back to manager nomination of influential employees.

### Step 5: Compliance and Multi-Agent Risk Review (Dimension 4)

**Duration**: 1-2 days
**Tool**: Compliance checklist + legal framework analysis

Audit AI compliance exposure across three layers:

**Layer 1 — Customer-Facing AI**: Chatbots, personalization engines, dynamic pricing. Check: disclosure requirements (FTC), bias testing (NYC LL144 if applicable), CCPA opt-out mechanisms, children's privacy (COPPA if toy/children's retail).

**Layer 2 — Workforce AI**: Scheduling algorithms, performance monitoring, hiring tools. Check: NYC LL144 compliance (automated employment decisions), ADA accommodation in AI scheduling, EEOC adverse impact testing.

**Layer 3 — Multi-Agent Systems**: If deploying agentic AI (autonomous ordering, dynamic markdown, customer service agents): check authority boundaries, human-in-the-loop requirements, audit trail completeness, liability allocation for autonomous decisions.

**Verify**: Compliance risk matrix produced — each AI use case rated red/yellow/green across applicable regulations.
**If failed**: If legal counsel is unavailable for review, flag all workforce AI and multi-agent use cases as yellow (requires legal review before implementation).

### Step 6: AI Commerce Capability Evaluation (Dimension 5)

**Duration**: 1-2 days
**Tool**: AI evaluation framework

Evaluate the retailer's readiness across 5 AI commerce capabilities:

1. **Generative search and discovery** — Can product catalog support semantic search? Is there structured product data (attributes, taxonomy) for embedding?
2. **Conversational commerce** — Does the retailer have chat infrastructure? Knowledge base for FAQ? Product recommendation engine?
3. **GEO (Generative Engine Optimization)** — How well does the retailer's product content surface in AI-generated answers? Run test queries through ChatGPT, Perplexity, Google AI Overviews.
4. **Autonomous merchandising** — Data foundations for AI-driven assortment, pricing, markdown. Decision authority mapping.
5. **Predictive operations** — Demand sensing, inventory optimization, labor forecasting model readiness. [src5]

**Verify**: Each capability scored 1-5 with specific evidence and gap description.
**If failed**: If e-commerce platform access is limited, score based on public-facing site audit and stakeholder interview data.

### Step 7: Workforce Readiness Assessment (Dimension 6)

**Duration**: 1-2 days
**Tool**: Survey data + interview synthesis

Synthesize findings from Steps 1 and 4 into a workforce readiness score:

- **Digital literacy baseline**: What percentage of store staff can use current digital tools without assistance?
- **Training infrastructure**: Does the retailer have an LMS? Mobile training capability? Time allocated for upskilling?
- **Change capacity**: How many technology changes has the workforce absorbed in the last 12 months? What is their change fatigue level?
- **Champion network**: Map identified informal leaders to AI champion potential — willingness and capability to lead peer adoption. [src2, src3]

**Verify**: Workforce readiness dimension scored 1-5. Champion network identified with specific names and locations.
**If failed**: If individual-level data is insufficient, aggregate to location level and note confidence reduction.

### Step 8: Scorecard Generation and Gap Analysis

**Duration**: 1-2 days
**Tool**: Scorecard template + analysis synthesis

Produce the 6-Dimension AI Readiness Scorecard:

| Dimension | Score (1-5) | Critical Gaps | Quick Win Opportunity |
|-----------|-------------|---------------|----------------------|
| Data Infrastructure | {score} | {gaps} | {quick win} |
| Process Automation | {score} | {gaps} | {quick win} |
| Adoption Psychology | {score} | {gaps} | {quick win} |
| Compliance Risk | {score} | {gaps} | {quick win} |
| AI Commerce Capability | {score} | {gaps} | {quick win} |
| Workforce Readiness | {score} | {gaps} | {quick win} |

Calculate composite score (weighted average — data infrastructure 25%, process automation 20%, adoption psychology 15%, compliance 15%, AI commerce 15%, workforce 10%). Generate gap analysis: rank gaps by impact (revenue potential x feasibility) and urgency (competitive risk x regulatory deadline).

**Verify**: All 6 dimensions scored with evidence. Gap analysis ranked. Composite score calculated.
**If failed**: If any dimension has insufficient data, score it as "incomplete" with a confidence flag — do not fabricate scores.

### Step 9: Implementation Roadmap Presentation

**Duration**: 1 day
**Tool**: Presentation + structured report (PDF + JSON)

Present findings to executive sponsor and leadership team:

- 6-dimension scorecard with composite score and peer benchmarking
- Top 5 gaps ranked by impact and urgency
- 3-track implementation roadmap: Quick Wins (0-3 months, low cost, high visibility), Foundation (3-6 months, infrastructure and training), Transformation (6-12 months, AI-native processes)
- ROI projections per track based on industry benchmarks [src1, src4]

**Verify**: Client accepts findings and scorecard. Leadership team agrees on top 3 priorities from roadmap.
**If failed**: If client disputes scores, offer to conduct additional interviews or extend data collection. Adjust scores with documented rationale.

### Step 10: Retainer Proposal

**Duration**: 0.5 days
**Tool**: Proposal document

Deliver retainer proposal covering:
- Monthly advisory check-ins (2 hours) — progress tracking, blocker resolution
- Quarterly re-scoring — re-run scorecard dimensions to measure improvement
- Implementation support — on-call consulting for roadmap execution
- Pricing: $3K-$5K/month depending on scope

**Verify**: Retainer proposal delivered. Follow-up meeting scheduled within 5 business days.
**If failed**: If client declines retainer, offer project-based implementation support for top-priority quick wins as an alternative entry point.

## Output Schema

```json
{
  "output_type": "retail_ai_readiness_scorecard",
  "format": "PDF + JSON",
  "sections": [
    {"name": "composite_score", "type": "number", "description": "Weighted composite AI readiness score 1-5", "required": true},
    {"name": "dimension_scores", "type": "array", "description": "6 dimension scores with evidence and gaps per dimension", "required": true},
    {"name": "gap_analysis", "type": "array", "description": "Ranked gaps by impact x urgency with remediation recommendations", "required": true},
    {"name": "implementation_roadmap", "type": "object", "description": "3-track phased roadmap: quick wins, foundation, transformation", "required": true},
    {"name": "informal_leader_map", "type": "array", "description": "Identified informal leaders by location with AI champion potential score", "required": true},
    {"name": "compliance_risk_matrix", "type": "object", "description": "AI use cases rated red/yellow/green per applicable regulation", "required": true}
  ],
  "expected_sections": "6",
  "sort_order": "gap impact descending within each section"
}
```

## Quality Benchmarks

| Quality Metric | Minimum Acceptable | Good | Excellent |
|---------------|-------------------|------|-----------|
| Stakeholder interview coverage (all 4 groups) | 4/4 groups, 6 interviews | 4/4 groups, 8 interviews | 4/4 groups, 10+ interviews |
| Survey response rate (fear inventory) | > 50% | > 65% | > 80% |
| Data infrastructure sub-dimensions scored | 5/7 sub-dimensions | 6/7 | 7/7 |
| Process mapping completeness (8 core processes) | 6/8 mapped | 7/8 | 8/8 |
| Informal leaders identified per location | > 2 | > 3 | > 5 |
| Client satisfaction (post-engagement survey) | > 3.5/5 | > 4.0/5 | > 4.5/5 |

**If below minimum**: Extend engagement by 3-5 days. Add interview slots or expand survey distribution. Prioritize dimensions with lowest coverage for additional data collection.

## Error Handling

| Error | Likely Cause | Recovery Action |
|-------|-------------|----------------|
| POS data access delayed beyond Day 5 | IT security review backlog | Proceed with other dimensions, schedule data audit for Week 2-3, escalate to executive sponsor |
| Survey response rate below 50% | Store managers did not distribute or staff lacks email access | Have executive sponsor send personal video message, extend deadline, add tablet-based kiosk survey option in break rooms |
| Stakeholder no-shows for interviews | Calendar conflicts or engagement skepticism | Reschedule within 48 hours, offer asynchronous structured questionnaire as fallback |
| Conflicting information across stakeholders | Departmental silos or political dynamics | Document discrepancies as findings — they reveal organizational alignment gaps |
| Client disputes AI readiness score | Score challenges leadership assumptions | Present raw evidence for each dimension, offer to re-score with additional data points |
| Compliance review identifies high-risk AI use already deployed | Retroactive compliance exposure | Escalate immediately to legal counsel, add remediation to Quick Wins track |

## Cost Breakdown

| Component | Focused ($15K-$20K) | Comprehensive ($20K-$30K) | Enterprise ($30K+) |
|-----------|---------------------|---------------------------|-------------------|
| Stakeholder interviews | $3K-$4K | $4K-$6K | $6K-$8K |
| Data infrastructure audit | $3K-$4K | $4K-$6K | $6K-$8K |
| Process automation mapping | $2K-$3K | $3K-$4K | $4K-$6K |
| Adoption psychology assessment | $2K-$3K | $3K-$4K | $4K-$6K |
| Compliance + AI commerce evaluation | $2K-$3K | $3K-$5K | $5K-$7K |
| Scorecard + roadmap + presentation | $3K-$4K | $4K-$6K | $6K-$8K |
| **Total engagement** | **$15K-$20K** | **$20K-$30K** | **$30K-$45K** |
| **Monthly retainer** | **$3K/month** | **$4K/month** | **$5K+/month** |

## Anti-Patterns

### Wrong: Skipping the adoption psychology assessment
Jumping straight from data audit to implementation roadmap without assessing workforce fears and informal influence networks. Result: technically sound roadmap that dies on the store floor because associates sabotage tools they fear will replace them. [src2]

### Correct: Front-load the human factor
Conduct fear inventory and informal leader mapping before building the roadmap. Design implementation sequence around adoption psychology — start with tools that reduce associate pain rather than tools that optimize management metrics.

### Wrong: Treating all store locations as identical
Applying a single readiness score across 50+ locations. Result: implementation plan fails in locations with different demographics, tech infrastructure, or management culture. [src4]

### Correct: Score per location cluster, implement in waves
Group locations by similarity (urban/suburban/rural, age of infrastructure, employee demographics). Score each cluster separately. Pilot in the most-ready cluster, learn, then expand.

### Wrong: Presenting compliance risk as a blocker
Listing every possible AI regulation and creating a fear-based compliance matrix that makes leadership abandon AI initiatives entirely. [src1]

### Correct: Present compliance as a competitive advantage
Frame compliance readiness as a differentiator — retailers who get compliance right first can move faster on AI deployment while competitors are paralyzed. Rank compliance risks by probability and severity, not just existence.

## When This Matters

Use when an agent needs to plan or execute a full retail AI readiness diagnostic engagement. This is the master recipe for the 2-3 week diagnostic — it orchestrates stakeholder interviews, data audits, adoption psychology assessment, compliance review, and scorecard generation into a cohesive $20K engagement that feeds an implementation advisory pipeline. Requires executive sponsor access and POS data credentials as prerequisites.

## Related Units

- [Retail Data Infrastructure Audit](/consulting/recipes/retail-data-infrastructure-audit/2026)
- [Retail Digital Maturity Assessment](/business/retail-transformation/retail-digital-maturity-assessment/2026)
- [Retail AI Strategy](/consulting/strategy/retail-ai-strategy/2026)