About This Case Study

RetailFlow Inc. is a fictional mid-market retail company that will serve as the basis for the Dragon’s Den investment simulation exercises.

Your Challenge

You will be asked to: - Analyse RetailFlow’s strategic position - Evaluate AI investment proposals - Make funding recommendations - Present to an Investment Committee

How to Use This Document

Before the Exercise: - Read the complete company overview carefully - Understand RetailFlow’s strategic context and challenges - Note financial metrics and market position

During Dragon’s Den: - Reference specific data points in your analysis - Use the frameworks to evaluate proposals - Consider RetailFlow’s constraints and opportunities - Think like an investment committee member

Key Information: - This is a realistic but fictional company - Financial data is designed for learning purposes - Apply insights to your own organisation

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Document Structure

Section 1: Company Profile - Basic information and financials - Market position and competitive context

Section 2: Strategic Context - CEO’s vision and priorities - Current AI initiatives underway

Section 3: Technology & Data - Current tech stack and capabilities - Data infrastructure maturity

Section 4: Financial Context - Investment budget and constraints - Strategic priorities for AI spending

Section 5: Organisational Readiness - Team capabilities - Change readiness

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Reading time: 10 minutes

📧 Questions during exercise: Ask your facilitator

🌐 Digital version: https://exec-ed.github.io/ai-business-innovation/

Company Profile

Basic Information

Company: RetailFlow Inc.
Industry: Omnichannel Retail (Apparel & Home Goods)
Founded: 2008
Headquarters: Portland, Oregon
Employees: 2,400
Stores: 85 locations across Western US
E-commerce: 40% of sales (growing 25% annually)

Financial Snapshot (FY2024)

• Revenue: $450M
• Operating Margin: 8.2%
• E-commerce Revenue: $180M
• Physical Store Revenue: $270M
• Year-over-year growth: 6% (below industry average of 9%)

Market Position

Strengths: - Strong brand recognition in Western US - Loyal customer base (60% repeat purchase rate) - Successful omnichannel integration - Well-managed supply chain

Challenges: - Increasing competition from online-first retailers - Pressure on margins from price-matching policies - Aging customer base (median age 45, industry median 38) - Inconsistent customer experience across channels

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Strategic Context

CEO’s Vision

Sarah Martinez, CEO (joined 2023 from Amazon):

“We’re at an inflection point. RetailFlow has strong fundamentals, but we’re facing existential threats from AI-powered competitors. We need to move from asking ‘Should we use AI?’ to ‘Where should we invest in AI?’ I’ve secured $2M for AI initiatives this year. Our job is to invest it strategically.”

Competitive Landscape

Traditional Competitors (Regional Chains): - Mostly reactive to AI - Focused on cost reduction (inventory, staffing) - Limited innovation

Online-First Competitors: - Heavy AI investment in personalisation - Dynamic pricing algorithms - Automated customer service - Superior recommendation engines

Big Box Retailers: - AI-powered supply chain optimization - Sophisticated demand forecasting - Investment in autonomous delivery

The threat: Online-first retailers are opening physical stores. Big box retailers are improving online experience. The middle is getting squeezed.

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Current Technology Landscape

Systems in Place

Core Infrastructure: - ERP: SAP (implemented 2019) - E-commerce: Shopify Plus - POS: Square Retail - CRM: Salesforce - Marketing: HubSpot - Analytics: Google Analytics + Tableau

Data Maturity: - Good data collection (sales, inventory, customers) - Some integration challenges (siloed systems) - Limited predictive analytics - No AI/ML in production

Technology Team: - CTO + 15-person IT team - 3-person data analytics team - No AI/ML specialists - Heavy reliance on vendors/consultants

Current “AI” Initiatives

What they call AI (but isn’t really): - Basic product recommendations (rules-based) - Email segmentation (simple demographic targeting) - Inventory alerts (threshold-based)

Reality: No true machine learning in production. High potential, low current capability.

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The $2M AI Investment Budget

Budget Context

Total IT Budget: $12M/year - $8M operations/maintenance - $2M infrastructure improvements - $2M NEW: AI initiatives

CEO’s directive: “This $2M is separate from BAU. I want strategic bets, not just incremental improvements. Show me how AI can transform RetailFlow, not just optimize it.”

Strategic Objectives (from Board)

Priority 1: Revenue Growth - Increase average order value - Improve customer retention - Attract younger demographics

Priority 2: Margin Improvement - Optimize pricing strategy - Reduce inventory carrying costs - Improve operational efficiency

Priority 3: Competitive Positioning - Match online-first retailers’ capabilities - Create differentiated customer experience - Build data-driven decision-making culture

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Four Competing AI Initiatives

The CTO has commissioned business cases for four AI initiatives. You’ll evaluate these in the Dragon’s Den exercise.

Initiative 1: AI Customer Service Chatbot

Concept: 24/7 intelligent chatbot for customer service
Budget request: $450K
Timeframe: 6 months to launch
Expected impact: Reduce customer service costs, improve response time

Current pain point: - Customer service costs $2.8M/year - Average response time: 18 hours (email), 12 minutes (phone) - Customer satisfaction: 72% (below industry benchmark of 82%)

Initiative 2: Dynamic Pricing Optimization

Concept: AI-powered real-time pricing based on demand, competition, inventory
Budget request: $850K
Timeframe: 9 months to launch
Expected impact: Increase revenue and margins through optimized pricing

Current pain point: - Static pricing rules (weekly manual updates) - Frequent stockouts of popular items (sold at regular price) - Deep discounting of slow-movers (margin erosion) - Losing sales to competitors with better prices

Initiative 3: AI Inventory Optimization

Concept: Predictive analytics for inventory planning and allocation
Budget request: $1.1M
Timeframe: 12 months to launch
Expected impact: Reduce inventory costs, improve in-stock rates

Current pain point: - $45M tied up in inventory - 15% of SKUs out of stock at any time - 25% of inventory older than 90 days - Frequent emergency shipments between stores

Initiative 4: Fraud Detection System

Concept: ML-based fraud detection for e-commerce and returns
Budget request: $650K
Timeframe: 8 months to launch
Expected impact: Reduce fraud losses, improve return policy enforcement

Current pain point: - $3.2M/year in suspected fraud losses - 18% return rate (industry average 12%) - Manual review process is slow and inconsistent - Friendly fraud (wardrobing) is increasing

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The Strategic Dilemma

The Math Problem

Total budget: $2M
Total requests: $2.95M ($450K + $850K + $1.1M + $650K)

You cannot fund everything.

The Strategic Questions

1. Which initiatives align with strategic objectives?
2. Which build capabilities for the future?
3. Which provide quick wins to build momentum?
4. Which address competitive threats?
5. What’s the right portfolio balance?

Complicating Factors

CEO wants: Transformational impact
CFO wants: Clear ROI in 12 months
Board wants: Competitive parity with online-first retailers
Customers want: Better experience
Employees want: Tools that make their jobs easier

Your job: Balance these competing demands with limited resources.

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Key Stakeholders

Sarah Martinez - CEO

Background: 15 years at Amazon, VP of Retail Operations
Priority: Transform RetailFlow before competitors do
Risk tolerance: High (within reason)
Hot button: Losing market share to online-first competitors

David Kim - CFO

Background: Former Big 4 consultant, CFO for 3 years
Priority: Improve margins and ROI
Risk tolerance: Moderate
Hot button: Projects that don’t deliver measurable financial results

Jennifer Wu - CTO

Background: 8 years at RetailFlow, promoted to CTO 2 years ago
Priority: Build modern technology capabilities
Risk tolerance: Moderate to high on technology
Hot button: Vendor lock-in and technical debt

Marcus Johnson - CMO

Background: Digital marketing expert, joined 1 year ago
Priority: Customer experience and engagement
Risk tolerance: High on customer-facing initiatives
Hot button: Technology that doesn’t improve customer experience

Board of Directors

Composition: 7 members (mix of investors and industry experts)
Priority: Long-term growth and market position
Risk tolerance: Moderate (want innovation, but not reckless)
Hot button: Falling behind competitors technologically

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What You’ll Do in the Masterclass

Exercise 3: Dragon’s Den (90 minutes)

You’ll be split into two groups:

Investment Committee (half the class): - Represent CEO, CFO, CTO, CMO, Board - Evaluate four AI proposals - Allocate $2M budget strategically - Use AI Investment Model framework

Project Teams (half the class): - Team A: Chatbot - Team B: Dynamic Pricing - Team C: Inventory Optimization - (Fraud Detection is pre-prepared pitch)

Your challenge: - Teams pitch their initiatives (10 min each) - Committee questions and evaluates (5 min each) - Committee deliberates and allocates budget (20 min) - Debrief: What did we learn? (15 min)

Learning Objectives

1. Apply AI Investment Model to real scenarios
2. Practice portfolio thinking with constrained resources
3. Balance competing priorities from stakeholders
4. Make strategic trade-offs under uncertainty
5. Articulate investment rationale clearly

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Questions to Consider as You Read

1. If you were CEO, which initiative would you fund? Why?
2. What additional information would you need to make the decision?
3. How would you balance quick wins vs. long-term transformation?
4. What risks concern you most about each initiative?
5. How does each initiative fit into the Three Horizons model?
6. Could you fund 2-3 initiatives with a different approach? How?

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A Note on Realism

RetailFlow is fictional, but the challenges are real:

• The financial metrics are typical for mid-market retail
• The AI initiatives mirror real proposals we’ve seen
• The stakeholder tensions are authentic
• The resource constraints are realistic
• The strategic dilemmas are what you’ll face

Use RetailFlow as a safe space to practice decision-making frameworks you’ll apply to your real organisation.

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How This Connects to Your Organisation

As you work through the RetailFlow case, keep asking yourself:

• “How is this similar to my company’s situation?”
• “What challenges does RetailFlow face that we also face?”
• “Would my executive team make the same trade-offs?”
• “What would I do differently?”

The goal isn’t to become an expert on RetailFlow—it’s to use RetailFlow as a lens for thinking more strategically about your own AI investment decisions.

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Now let’s make some tough investment decisions about RetailFlow’s AI future.