Building AI Business Cases

12 min evaluate 4 sections
Step 1 of 4

WHY WHY This Matters

AI projects compete for resources with every other initiative in your organization. Without a compelling business case:

  • Great ideas don't get funded
  • Successful pilots don't scale
  • AI becomes a "science project" rather than business tool
  • You can't measure success or course-correct

The operators who can translate AI capabilities into business outcomes lead the transformation.


Step 2 of 4

WHAT WHAT You Need to Know

The AI Value Framework

ROI Calculation

Basic ROI formula:

ROI = (Value Generated - Total Cost) / Total Cost × 100

AI-specific considerations:

Value Component How to Measure
Time saved Hours × hourly rate
Quality improvement Error rate reduction × cost per error
Speed improvement Faster cycle × value of time
Scale achieved Volume increase × marginal value
Innovation New capability value (harder to quantify)
Cost Component What to Include
AI costs API fees, subscriptions, compute
Development Build time, integration work
Training User education, change management
Maintenance Ongoing monitoring, updates
Risk buffer Unexpected issues (15-25% typical)

Building the Business Case

Structure:

1. EXECUTIVE SUMMARY
   └─> One-page overview: problem, solution, ROI, ask

2. CURRENT STATE ANALYSIS
   ├─> Process description
   ├─> Pain points and costs
   ├─> Performance metrics
   └─> Competitive context

3. PROPOSED SOLUTION
   ├─> AI approach
   ├─> How it works
   ├─> Technical requirements
   └─> Implementation plan

4. VALUE ANALYSIS
   ├─> Quantified benefits
   ├─> Qualitative benefits
   ├─> ROI calculation
   └─> Payback period

5. RISKS AND MITIGATION
   ├─> Technical risks
   ├─> Operational risks
   ├─> Financial risks
   └─> Mitigation strategies

6. RECOMMENDATION
   ├─> Investment request
   ├─> Timeline
   ├─> Success metrics
   └─> Decision points

Value Driver Analysis

The Comparison Trap

Don't just compare AI to doing nothing. Compare to alternatives:

Alternative Fair Comparison
Do nothing Value of status quo problems
Hire more people Full cost (salary, benefits, training, management)
Outsource Vendor costs + coordination overhead
Different technology Full implementation and maintenance cost
Process improvement Realistic improvement potential

Key Concepts

Key Concept

ai value

AI creates value through multiple channels:

Cost reduction:

  • Labor hours saved
  • Error reduction
  • Process efficiency
  • Resource optimization

Revenue enhancement:

  • Faster time to market
  • Better customer experience
  • New product capabilities
  • Increased capacity

Risk mitigation:

  • Improved compliance
  • Better decision-making
  • Earlier problem detection
  • Reduced human error

Strategic positioning:

  • Competitive advantage
  • Capability building
  • Market differentiation
  • Future readiness
Key Concept

value drivers

Value drivers are the specific mechanisms through which AI creates business value. Identify and quantify each:

Efficiency drivers:

  • Task completion time reduction
  • Multi-tasking capability (AI works 24/7)
  • Batch processing vs. sequential
  • Reduced context-switching

Quality drivers:

  • Consistency (same approach every time)
  • Completeness (doesn't forget steps)
  • Accuracy (pattern recognition)
  • Compliance (follows rules)

Scale drivers:

  • Handle volume spikes
  • Expand capacity without hiring
  • Serve more customers
  • Process more data
Step 3 of 4

HOW HOW to Apply This

Exercise: Build a Business Case

Business Case Template

AI BUSINESS CASE: [Initiative Name]

EXECUTIVE SUMMARY
Investment: $[X]
Annual Value: $[Y]
ROI: [Z]%
Payback: [N] months

THE OPPORTUNITY
- Current process: [Description]
- Problem: [Pain points]
- Impact: [Cost/scale of problem]

THE SOLUTION
- Approach: [AI implementation]
- How it works: [Brief description]
- Implementation: [Timeline]

VALUE ANALYSIS
| Benefit | Annual Value | Calculation |
|---------|-------------|-------------|
| [Benefit 1] | $[X] | [How calculated] |
| [Benefit 2] | $[Y] | [How calculated] |
| Total | $[Z] | |

| Cost | Investment | Ongoing/Year |
|------|------------|--------------|
| [Cost 1] | $[X] | $[Y] |
| [Cost 2] | $[X] | $[Y] |
| Total | $[X] | $[Y] |

RISK ASSESSMENT
| Risk | Probability | Impact | Mitigation |
|------|-------------|--------|------------|
| [Risk 1] | H/M/L | H/M/L | [Approach] |

RECOMMENDATION
[Clear ask with decision criteria]

Common Business Case Mistakes

Mistake Problem Correction
Only counting savings Misses revenue and strategic value Include all value types
Ignoring hidden costs Underestimates investment Include training, maintenance
Over-promising Destroys credibility Be conservative, then exceed
No comparison baseline Can't prove value Measure before implementing
Vague benefits Not actionable Quantify everything possible

Self-Check


Practice Exercises

You want to implement an AI assistant to help your customer service team handle initial customer inquiries.

Current state:

  • 10 support agents handling 500 tickets/day
  • Average handle time: 15 minutes
  • Customer satisfaction: 75%
  • First response time: 4 hours
  • Agent salary: $50,000/year

AI solution proposal:

  • AI handles 40% of tickets autonomously
  • Reduces handle time by 30% for agent-assisted tickets
  • Implementation cost: $50,000
  • Ongoing cost: $2,000/month

Build the business case:

  1. Calculate current costs:

    • Total labor cost
    • Cost per ticket
  2. Project future state:

    • Tickets handled by AI
    • Tickets handled by humans (faster)
    • New cost structure
  3. Quantify benefits:

    • Direct cost savings
    • Capacity increase
    • Satisfaction improvement potential
  4. Calculate ROI:

    • Year 1 ROI
    • Payback period
    • 3-year projection
  5. Identify risks:

    • What could go wrong?
    • How would you mitigate?
Step 4 of 4

GENERIC Up Next

In Module 4.2: Organizational Change, you'll learn how to navigate the human side of AI adoption—getting buy-in, managing transitions, and building AI-ready culture.

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