Computer Vision for Business

Session 2: Business Applications & Use Cases

From Capabilities to Value

Mapping CV to business opportunities across industries

Session 2 Overview

Mapping CV capabilities to business value (3 hours)

1

Industries

1h30

2

Frameworks

1h

3

Workshop

30min

Learning Objectives

Map CV capabilities to specific business functions
Analyze real-world case studies across industries
Evaluate CV opportunities with ROI frameworks

How CV Creates Business Value

Four pathways to measurable impact

1 Cost Reduction

Automate manual inspection, reduce labor, minimize waste

2 Revenue Increase

Better CX, new products, higher conversion rates

3 Risk Mitigation

Safety compliance, defect detection, fraud prevention

4 Strategic Edge

Data insights, competitive differentiation, innovation

Success criterion: Measurable business impact, not just technical accuracy

CV Across Industries

Computer vision is transforming every sector

Retail & E-Commerce

Visual search, virtual try-on, inventory management

Manufacturing

Defect detection, assembly verification, safety monitoring

Healthcare

Radiology, pathology, diagnostics support

Agriculture

Crop health, yield prediction, livestock monitoring

Retail & E-Commerce

CV applications throughout the customer journey

1

Discovery

Visual search

2

Experience

Virtual try-on

3

Operations

Inventory

4

Checkout

Cashierless

Stage	Application	Value Proposition
Discovery	Visual Search	Find products from photos
Experience	Virtual Try-On	AR visualization
Operations	Shelf Monitoring	Real-time inventory
Checkout	Self-Service	Frictionless payment

Visual Search

Find products that look like this photo

1

Photo

Customer

2

Extract

Features

3

Match

Similarity

4

Rank

Results

How It Works

Extract CNN embeddings
Compare against catalog
Rank by visual similarity

Business Impact

+30% conversion rate
+25% time on site
2x engagement vs text

Virtual Try-On

AR-powered product visualization

1

Camera

Input

2

Detect

Body/Face

3

Pose

Estimate

4

Overlay

Product

5

Render

Real-time

Eyewear

Face tracking + 3D overlay

Makeup

Facial landmarks + color mapping

Clothing

Body segmentation + texture

Jewelry

Wrist/neck detection + rendering

Key metric: -25% return rate when customers use virtual try-on

Retail CV: Impact Metrics

Application	Metric	Typical Improvement
Visual Search	Conversion Rate	+30%
Virtual Try-On	Return Rate	-25%
Shelf Monitoring	Out-of-Stock	-40%
Automated Checkout	Wait Time	-60%
Customer Flow Analysis	Store Layout ROI	+15%

Source: Industry benchmarks from major retail implementations (2024-2025)

Case Study: Amazon Go

"Just Walk Out" shopping experience

Concept: Grab items and leave - automatic charging via app

Technology Stack

Hundreds of cameras
Weight sensors
CV + Deep Learning
Sensor fusion

Customer Flow

Scan app to enter
Pick/return items
Walk out
Auto-charge

Amazon Go: Key Lessons

What Works

Frictionless customer experience
Sensor fusion for reliability
Data collection at scale
Premium convenience positioning

Challenges

High infrastructure cost
Complex edge cases
Limited scalability
Privacy concerns

Key insight: CV alone isn't enough - requires multi-modal sensing for production reliability

Manufacturing & Quality Control

CV in the industrial production line

1 Quality Control

Defect detection, assembly verification, dimensional measurement

2 Safety

PPE detection, hazard monitoring, zone compliance

3 Process

Workflow analysis, predictive maintenance, efficiency optimization

Visual Defect Detection

Automated quality inspection at production speed

1

Camera

High-speed

2

Analyze

Image

3

Detect

Defect?

4

Reject

Or pass

Advantages over manual inspection:

24/7 consistency - No fatigue or distraction
Speed - 100s of items per minute

Precision - Detects microscopic defects
Traceability - Every inspection logged

Types of Detectable Defects

Surface Defects

Scratches
Dents
Discoloration
Contamination

Structural Defects

Cracks
Warping
Missing parts
Misalignment

Dimensional

Size variation
Shape deviation
Position error
Tolerance issues

Case Study: BMW Visual QC

AI-powered quality control in automotive manufacturing

Aspect	Details
Implementation	AI cameras at 8 locations per production line
Training Data	Historical defect images from manual QC
Detection Rate	99.7% (vs 95% manual)
Speed	5x faster than manual inspection
ROI	Payback period under 18 months

Transformation

Before: 5 inspectors, 95% detection
After: AI + 1 operator, 99.7% detection

Healthcare & Life Sciences

Medical imaging analysis at scale

Medical Imaging

Radiology (X-ray, CT, MRI)
Pathology (Tissue analysis)
Ophthalmology (Retinal scans)
Pulmonology (Lung analysis)

AI Output

Detection of abnormalities
Measurement of features
Risk score calculation
Treatment recommendations

CV in Healthcare: Value Proposition

Clinical Benefits

Earlier disease detection
Reduced diagnostic errors
Consistent interpretation
Faster turnaround

Operational Benefits

Radiologist productivity +30%
Triage urgent cases
Scale to underserved areas
24/7 availability

1

AI Screen

All cases

2

Abnormal?

Detect

3

Priority

Specialist

4

Standard

Queue

Regulatory Considerations

Medical AI has unique requirements

Regulatory Bodies

FDA - USA
CE Mark - Europe
PMDA - Japan

Key Requirements

Clinical validation
Explainability
Continuous monitoring
Human oversight

Key: Most medical AI is approved as "decision support" not autonomous diagnosis

Case Study: DeepMind Diabetic Retinopathy

AI-powered eye disease screening

Aspect	Details
Task	Detect diabetic retinopathy from retinal scans
Performance	Matches specialist ophthalmologists
Scale	Can screen millions in underserved areas
Impact	Early detection prevents blindness

Key insight: CV can democratize access to specialist-level diagnostics, especially in areas with physician shortages

Agriculture & Environment

Precision farming through visual AI

1

Data

Satellite/Drone

2

Analyze

CV

3

Insights

Health/Yield

4

Action

Treatment

Crop Health

Disease detection, stress monitoring

Pest Detection

Early intervention, targeted treatment

Yield Estimation

Harvest planning, pricing optimization

Livestock

Health tracking, behavior monitoring

Precision Agriculture Benefits

Application	Benefit
Weed Detection	30-50% reduction in herbicide use
Disease Detection	Early intervention, reduced crop loss
Yield Prediction	Better harvest planning and pricing
Livestock Monitoring	Health tracking, reduced vet costs

Sustainability angle: CV enables precision application of water, fertilizer, and pesticides - reducing environmental impact while maintaining yields

Part 2: Evaluating CV Opportunities

Framework for assessing potential CV projects

1

Identify

Opportunity

2

Assess

Suitability

3

Evaluate

Data

4

Build

Business case

5

Check

Feasibility

6

Go/No-Go

Decision

1. Task Suitability

Is this task a good fit for CV?

Key Questions

Is the task visually based?
Can a human do it from visual information alone?
Is it repetitive and high-volume?
Are the success criteria clear and measurable?

Good Candidates

Defect inspection
Product categorization
Document verification

Poor Candidates

Requires non-visual context
Subjective criteria
Low volume / one-off tasks

2. Data Availability

Do you have what's needed to train/deploy?

Question	Why It Matters
Do labeled examples exist?	Training requires examples
Is data diverse enough?	Edge cases cause failures
Volume available?	Deep learning needs 1000s+
Privacy/consent issues?	Legal and ethical requirements
Can you collect more?	Continuous improvement needs data

3. Business Case

Does the ROI justify investment?

Costs

Development
Infrastructure
Operations
Maintenance

Benefits

Labor savings
Error reduction
Speed increase
New revenue

Decision criterion: ROI > 1 within acceptable timeframe (typically 12-24 months)

4. Technical Feasibility

Can it actually be built?

Factor	Consideration
Proven tech?	Is similar CV already working elsewhere?
Accuracy needed?	What error rate is acceptable?
Latency?	Real-time vs batch processing?
Infrastructure?	Edge device vs cloud deployment?
Integration?	How does it fit existing systems?

Red Flags to Watch For

Warning signs that indicate project risk

Expecting 100% accuracy

No CV system is perfect

Underestimating edge cases

Rare scenarios cause failures

Ignoring environmental variations

Lighting, weather affect performance

Insufficient training data

Quality over quantity

No clear success metrics

Define before you build

Ignoring human workflow

Technology serves people

Reality check: Every CV system makes mistakes. Plan for errors, not perfection.

ROI Analysis Framework

Development (One-time)

Data collection & labeling
Model development or API licensing
Integration & testing
Infrastructure setup

Operational (Ongoing)

Compute resources
Model monitoring
Retraining & updates
Support & maintenance

Quantifying CV Benefits

Category	Examples	How to Measure
Cost Reduction	Labor savings, reduced waste	Direct cost comparison
Revenue Increase	Higher conversion, new products	A/B testing, sales data
Risk Mitigation	Fewer recalls, compliance	Incident reduction, fines avoided
Strategic Value	Data insights, competitive edge	Harder to quantify directly

ROI Example: Visual Quality Inspection

Current State

5 Inspectors: €200K/year
2% Miss Rate: €150K recalls/year

CV Solution

Development: €100K one-time
Operations: €30K/year
0.3% Miss Rate: €22.5K recalls

Year 1 Savings: €197.5K

ROI Calculation Details

Item	Current	With CV	Difference
Labor Cost	€200,000	€0 (1 operator incl.)	+€200,000
Recall Costs	€150,000	€22,500	+€127,500
Development	€0	€100,000	-€100,000
Operations	€0	€30,000	-€30,000
Year 1 Net	-	-	+€197,500

ROI: €197,500 / €130,000 = 152% first-year return

Build vs Buy Decision

Decision tree for CV implementation approach

Key Questions

Is this a standard use case?
What's your processing volume?
Do you have ML expertise in-house?

Cloud API

Standard + Low/Medium volume

Transfer Learning

Custom + High volume + ML expertise

AutoML

Custom + High volume + No ML expertise

Custom Dev

Highly specialized requirements

Workshop: Case Analysis

Group exercise: Evaluate these scenarios

A Insurance

Damage assessment from photos

B Fashion

Style matching for recommendations

C Construction

Safety monitoring and PPE compliance

For your scenario, answer:

What CV tasks are required?
What training data would you need?
What are the main risks?
Rough ROI estimate?
Build, buy, or API?

Scenario A: Insurance Claims

Automate vehicle damage assessment from photos

1

Photos

Damage

2

Analyze

CV

3

Report

Damage

4

Estimate

Cost

Volume	10,000 claims/month
Current process	Manual adjuster review (15 min each)
Goal	Automate 70% of straightforward claims
Challenge	Accuracy requirements, fraud detection

Scenario B: Fashion Retail

Visual style matching for personalized recommendations

1

Browse

History

2

Analyze

Style

3

Match

Similar

4

Convert

Higher

Catalog size	50,000 products
Challenge	Subjective "style" is hard to define
Data	Product images + purchase history
Success metric	Click-through rate, conversion

Scenario C: Construction Safety

Real-time PPE compliance monitoring

1

Cameras

Site

2

Detect

PPE

3

Check

Compliant?

4

Alert

Or log

Scope	50 cameras across construction site
PPE types	Hard hat, vest, goggles, gloves
Environment	Outdoor, variable lighting, weather
Real-time	< 2 second alert latency

Self-Practice Assignment 2

Duration: 1 hour | Deadline: Before Session 3

Task: Business Case Development

Choose an industry and develop a CV business case

1 Opportunity (20 min)

Identify process, pain points, success criteria

2 Technical (20 min)

CV task type, data needs, approach

3 Business Case (20 min)

Benefits, risks, ROI estimate

Deliverable: Business case document (2-3 pages)

Session 2 Summary

1 Industries

Retail, Manufacturing, Healthcare, Agriculture

2 Evaluation

Task suitability, Data availability, Business case

3 ROI

Cost reduction, Revenue increase, Risk mitigation

4 Decisions

Build vs Buy, Cloud vs Edge, API vs Custom

Next session: Hands-on with Cloud Vision APIs

Next Session Preview

Session 3: Hands-on with Cloud Vision APIs

1

Google

Vision API

2

AWS

Rekognition

3

Azure

Computer Vision

4

Claude

Vision

What we'll build:

Document OCR processing
Product image analysis
Scene understanding
Complete expense tracker application

Questions?

Let's discuss Business Applications

Next: Session 3 - Hands-on Cloud Vision APIs