Roi Calculator

AI Implementation ROI Calculator

Gather inputs about current operations and produce a comprehensive roi-analysis.md with data-backed financial insights and a clear recommendation.

Contents

• references/inputs.md — required and optional inputs, with defaults
• references/methodology.md — all calculation formulas and sensitivity/comparison logic
• references/output-template.md — full roi-analysis.md structure to fill in
• references/rules-and-protocol.md — calculation rules, interaction protocol, quality checklist

Workflow

1. Collect inputs. Gather every required cost, time, and labor input. If any required input is missing, ask for it in one organized message grouped by category. Do not guess. See references/inputs.md for the full input list and defaults. 2. Apply defaults. For optional parameters the user omits, use the conservative defaults in references/inputs.md and note that defaults were applied. 3. Calculate metrics. Compute time savings, cost savings, net monthly savings, ramp-up adjustment, payback period, 12-month ROI, NPV, and productivity gain using the formulas in references/methodology.md. 4. Run sensitivity and comparison scenarios. Produce conservative/base/optimistic cases and at least three comparison scenarios per references/methodology.md. 5. Generate the report. Write roi-analysis.md to the current working directory following references/output-template.md. Apply the formatting and calculation rules in references/rules-and-protocol.md. 6. Verify and summarize. Run the quality checklist in references/rules-and-protocol.md, then report the top 3 findings and the saved file path.

Required and Optional Inputs

Collect these inputs before calculating. If any required input is missing, ask for it explicitly. Do not guess or assume values.

Cost Inputs (Required)

• Current monthly software/tool costs: What the organization currently pays for tools the AI will replace or augment (legacy software licenses, SaaS subscriptions, outsourced services).
• AI solution cost: Monthly or annual cost of the proposed AI solution (licensing, API costs, infrastructure).
• Implementation cost: One-time costs for setup, integration, training, migration, and consulting.
• Ongoing maintenance cost: Monthly cost for support, updates, monitoring, and fine-tuning.

Time and Labor Inputs (Required)

• Team size: Number of employees affected by the AI implementation.
• Average hourly rate: Fully loaded cost per hour per employee (salary + benefits + overhead). If the user gives salary only, multiply by 1.3 to estimate the fully loaded rate.
• Hours per week on manual processes: Average hours each team member spends on tasks the AI will automate or accelerate.
• Expected time reduction percentage: How much of that manual time the AI is expected to eliminate. Use conservative defaults when the user is unsure: 40% for augmentation, 70% for full automation.

Optional Inputs (Apply Defaults If Not Provided)

• Ramp-up period: Months to reach full productivity with the AI (default: 3 months).
• Annual salary increase rate: For projecting future savings (default: 3%).
• Discount rate: For NPV calculations (default: 10%).
• Error/rework reduction: Percentage reduction in errors from AI (default: 50%).
• Current error rate cost: Monthly cost of errors, rework, and quality issues (default: 0 if unknown).
• Revenue impact: Expected revenue increase from faster throughput or better quality (default: 0 if unknown).
• Analysis period: Number of months to project (default: 12 months; can extend to 24 or 36).

Calculation Methodology

Compute every metric below. Round currency to the nearest dollar, percentages to one decimal place, and hours to one decimal place. Use commas in numbers over 999.

1. Monthly Time Savings

weekly_hours_saved_per_person = hours_per_week_manual * time_reduction_percentage
monthly_hours_saved_per_person = weekly_hours_saved_per_person * 4.33
total_monthly_hours_saved = monthly_hours_saved_per_person * team_size

2. Monthly Labor Cost Savings

monthly_labor_savings = total_monthly_hours_saved * hourly_rate

3. Monthly Error Reduction Savings

monthly_error_savings = current_error_rate_cost * error_reduction_percentage

4. Total Monthly Savings (Gross)

total_monthly_savings = monthly_labor_savings + monthly_error_savings + monthly_revenue_impact

5. Net Monthly Savings

net_monthly_savings = total_monthly_savings - ai_solution_monthly_cost - ongoing_maintenance_cost
net_monthly_savings += current_tool_costs_eliminated

6. Ramp-Up Adjustment

Reduce savings linearly during the ramp-up period:

month_1_savings = net_monthly_savings * (1 / ramp_months)
month_2_savings = net_monthly_savings * (2 / ramp_months)
...
month_N_savings = net_monthly_savings * (N / ramp_months) [until N >= ramp_months]

After ramp-up, apply full net_monthly_savings.

7. Payback Period

cumulative_savings = sum of ramp-adjusted monthly savings over time
payback_month = first month where cumulative_savings >= implementation_cost

If payback never occurs within the analysis period, state this clearly.

8. 12-Month ROI

total_12_month_savings = sum of ramp-adjusted monthly savings for months 1-12
total_12_month_cost = implementation_cost + (ai_monthly_cost * 12) + (maintenance_cost * 12)
total_12_month_benefit = total_12_month_savings + (current_tool_costs_eliminated * 12)
roi_percentage = ((total_12_month_benefit - total_12_month_cost) / total_12_month_cost) * 100

9. Net Present Value (NPV)

monthly_discount_rate = (1 + annual_discount_rate)^(1/12) - 1
npv = -implementation_cost + sum( net_monthly_savings_month_i / (1 + monthly_discount_rate)^i ) for i=1 to N

10. Productivity Gain Percentage

current_productive_hours = (40 - hours_per_week_manual) * team_size
new_productive_hours = (40 - hours_per_week_manual + weekly_hours_saved_per_person) * team_size
productivity_gain = (new_productive_hours - current_productive_hours) / current_productive_hours * 100

11. Sensitivity Analysis

Run calculations across three scenarios:

12. Comparison Scenarios

Generate at minimum three comparison scenarios:

1. Do Nothing: Project costs of maintaining the status quo over the analysis period, including salary inflation, growing error costs, and opportunity cost of manual work. 2. Partial Implementation: Implement AI for only the highest-value use case (50% of team, highest-impact process only). 3. Full Implementation: The proposed full rollout. 4. Phased Rollout (include only if team_size > 10): Stagger implementation across departments over 6 months.

Output Template

Generate a file called roi-analysis.md in the current working directory using the structure below. Use proper Markdown tables. Include dollar signs and commas on all currency values and the % symbol on all percentages. No emoji anywhere in the output.

# AI Implementation ROI Analysis

**Prepared**: [Current Date]
**Analysis Period**: [N] Months
**Organization**: [Company name if provided, otherwise "Your Organization"]

---

## Executive Summary

[3-5 sentence summary of the key findings. Lead with the headline ROI number. State the payback period. Mention the most significant benefit. Include a clear recommendation: Proceed, Proceed with Caution, or Do Not Proceed.]

### Key Metrics at a Glance

| Metric | Value |
|--------|-------|
| 12-Month ROI | [X]% |
| Payback Period | [X] months |
| Monthly Net Savings | $[X] |
| Annual Net Savings | $[X] |
| Total Hours Saved (Annual) | [X] hours |
| Net Present Value (12-month) | $[X] |
| Productivity Gain | [X]% |

---

## 1. Input Parameters

### Current State

| Parameter | Value |
|-----------|-------|
| Team Size | [X] employees |
| Average Hourly Rate (Fully Loaded) | $[X]/hr |
| Hours/Week on Manual Processes | [X] hrs/person |
| Current Monthly Tool Costs | $[X] |
| Current Monthly Error/Rework Cost | $[X] |

### Proposed AI Solution

| Parameter | Value |
|-----------|-------|
| AI Solution Monthly Cost | $[X] |
| One-Time Implementation Cost | $[X] |
| Monthly Maintenance Cost | $[X] |
| Expected Time Reduction | [X]% |
| Expected Error Reduction | [X]% |
| Ramp-Up Period | [X] months |

---

## 2. Cost-Benefit Analysis

### Monthly Savings Breakdown

| Category | Monthly Savings |
|----------|----------------|
| Labor Cost Savings | $[X] |
| Error/Rework Reduction | $[X] |
| Tool Cost Elimination | $[X] |
| Revenue Impact | $[X] |
| **Gross Monthly Savings** | **$[X]** |
| Less: AI Solution Cost | ($[X]) |
| Less: Maintenance Cost | ($[X]) |
| **Net Monthly Savings** | **$[X]** |

### Annual Cost Comparison

| Cost Category | Without AI (Annual) | With AI (Annual) | Difference |
|--------------|--------------------:|------------------:|-----------:|
| Labor (manual processes) | $[X] | $[X] | $[X] |
| Software/Tools | $[X] | $[X] | $[X] |
| Error/Rework | $[X] | $[X] | $[X] |
| AI Solution | $0 | $[X] | ($[X]) |
| Maintenance | $0 | $[X] | ($[X]) |
| **Total** | **$[X]** | **$[X]** | **$[X]** |

---

## 3. Monthly Projection

[Table showing month-by-month for the full analysis period]

| Month | Monthly Savings | Cumulative Savings | Cumulative vs. Implementation Cost |
|------:|----------------:|-------------------:|-----------------------------------:|
| 1 | $[X] | $[X] | ($[X]) or $[X] |
| 2 | $[X] | $[X] | ($[X]) or $[X] |
| ... | ... | ... | ... |
| 12 | $[X] | $[X] | $[X] |

[Mark the payback month clearly with bold formatting.]

---

## 4. Break-Even Timeline

**Break-even point: Month [X]**

[2-3 sentences explaining the break-even analysis. If break-even is not reached within the analysis period, state this clearly and explain what would need to change.]

### Cumulative Cash Flow

[Text-based chart showing cumulative cash flow over time]

... N | [bar representation] $X <-- Break-even ... 12 | [bar representation] $X

---

## 5. Sensitivity Analysis

### Scenario Comparison

| Metric | Conservative | Base Case | Optimistic |
|--------|------------:|----------:|-----------:|
| Monthly Net Savings | $[X] | $[X] | $[X] |
| Annual Net Savings | $[X] | $[X] | $[X] |
| Payback Period | [X] mo | [X] mo | [X] mo |
| 12-Month ROI | [X]% | [X]% | [X]% |
| NPV (12-month) | $[X] | $[X] | $[X] |

### Variable Impact Analysis

[Show how changing each key variable by +/-20% affects the 12-month ROI]

| Variable | -20% Change | Base | +20% Change | Impact Rating |
|----------|------------:|-----:|------------:|:-------------:|
| Time Reduction % | [X]% ROI | [X]% ROI | [X]% ROI | [High/Med/Low] |
| Team Size | [X]% ROI | [X]% ROI | [X]% ROI | [High/Med/Low] |
| Hourly Rate | [X]% ROI | [X]% ROI | [X]% ROI | [High/Med/Low] |
| AI Solution Cost | [X]% ROI | [X]% ROI | [X]% ROI | [High/Med/Low] |
| Ramp-Up Period | [X]% ROI | [X]% ROI | [X]% ROI | [High/Med/Low] |

---

## 6. Comparison Scenarios

### Scenario 1: Do Nothing (Status Quo)

| Metric | Year 1 | Year 2 | Year 3 |
|--------|-------:|-------:|-------:|
| Manual Labor Cost | $[X] | $[X] | $[X] |
| Tool Costs | $[X] | $[X] | $[X] |
| Error/Rework Cost | $[X] | $[X] | $[X] |
| **Total Cost** | **$[X]** | **$[X]** | **$[X]** |

[2-3 sentences on the risk of inaction: growing costs, competitive disadvantage, scaling limitations]

### Scenario 2: Partial Implementation

[Assume 50% of team, primary use case only]

| Metric | Value |
|--------|------:|
| Implementation Cost | $[X] |
| Monthly Net Savings | $[X] |
| Payback Period | [X] months |
| 12-Month ROI | [X]% |

[When partial implementation makes sense vs. full rollout]

### Scenario 3: Full Implementation (Recommended)

| Metric | Value |
|--------|------:|
| Implementation Cost | $[X] |
| Monthly Net Savings | $[X] |
| Payback Period | [X] months |
| 12-Month ROI | [X]% |

[Why full implementation is or is not recommended]

### Scenario 4: Phased Rollout

[Only include if team_size > 10. Show 3-phase approach.]

| Phase | Team | Timeline | Cumulative Savings |
|-------|-----:|:--------:|-----------------:|
| Phase 1: Pilot | [X] people | Months 1-3 | $[X] |
| Phase 2: Expansion | [X] people | Months 4-6 | $[X] |
| Phase 3: Full Rollout | [X] people | Months 7+ | $[X] |

---

## 7. Risk Factors and Assumptions

### Key Assumptions

1. [List each major assumption made in the analysis]
2. [Time reduction percentages are estimates and may vary]
3. [Hourly rates include overhead at standard 1.3x multiplier if estimated]
4. [Ramp-up follows linear progression]
5. [No major organizational changes during implementation]

### Risk Factors

| Risk | Probability | Impact | Mitigation |
|------|:-----------:|:------:|:-----------|
| Adoption resistance | [H/M/L] | [H/M/L] | [Strategy] |
| Integration complexity | [H/M/L] | [H/M/L] | [Strategy] |
| Actual savings below estimate | [H/M/L] | [H/M/L] | [Strategy] |
| Vendor reliability | [H/M/L] | [H/M/L] | [Strategy] |
| Data quality issues | [H/M/L] | [H/M/L] | [Strategy] |
| Scope creep | [H/M/L] | [H/M/L] | [Strategy] |

### What Could Go Wrong

[Honest assessment of 2-3 scenarios where the investment underperforms, and what the financial impact would be in each case]

---

## 8. Recommendations

### Verdict: [PROCEED / PROCEED WITH CAUTION / DO NOT PROCEED]

[3-5 sentences with the final recommendation, supported by the numbers above]

### Recommended Next Steps

1. [Specific action item with timeline]
2. [Specific action item with timeline]
3. [Specific action item with timeline]
4. [Specific action item with timeline]
5. [Specific action item with timeline]

### Success Metrics to Track

| Metric | Baseline | Target (Month 3) | Target (Month 6) | Target (Month 12) |
|--------|:--------:|:-----------------:|:-----------------:|:------------------:|
| Hours on manual tasks/week | [X] | [X] | [X] | [X] |
| Error rate | [X] | [X] | [X] | [X] |
| Monthly cost | $[X] | $[X] | $[X] | $[X] |
| Team satisfaction | Baseline | +[X]% | +[X]% | +[X]% |

---

## Appendix: Calculation Details

### Formulas Used

- **Monthly Labor Savings**: (hours_saved_per_person * 4.33 * team_size) * hourly_rate
- **Net Monthly Savings**: gross_savings - ai_cost - maintenance + tool_cost_elimination
- **Payback Period**: implementation_cost / average_monthly_net_savings (adjusted for ramp)
- **12-Month ROI**: ((total_benefits - total_costs) / total_costs) * 100
- **NPV**: -implementation_cost + SUM(monthly_savings / (1 + r)^month) where r = monthly discount rate
- **Productivity Gain**: (hours_reclaimed / previous_productive_hours) * 100

### Raw Input Values

[List every input value used, including defaults, so the analysis is fully reproducible]

Calculation Rules, Interaction Protocol, and Quality Checklist

Calculation Rules

1. Never inflate numbers. Use the user's inputs as-is. If inputs seem unrealistic, note this in the Risk Factors section but still calculate based on what was provided. 2. Always show the work. The Appendix must contain enough detail to reproduce every number. 3. Round currency to the nearest dollar. Round percentages to one decimal place. Round hours to one decimal place. 4. Use commas in numbers over 999 (for example, $1,000 not $1000). 5. Default to conservative. When the user does not specify a value and a default is required, use the conservative end of the range and note this. 6. Flag unrealistic inputs. If the user provides inputs that seem too optimistic (for example, 95% time reduction or $0 implementation cost), add a warning in the Executive Summary. 7. Treat negative ROI as valid. If the numbers do not justify the investment, say so clearly. Do not spin a negative ROI as positive. 8. Account for opportunity cost. The time saved has value only if the team can redeploy that time productively. Note this assumption.

Interaction Protocol

1. If the user provides all inputs in their message: Proceed directly to calculation and generate the full roi-analysis.md. 2. If inputs are missing: Ask for the missing required inputs in a single organized message. Group questions by category (Cost, Time/Labor). Provide examples to help the user estimate. 3. If the user says "use defaults" or "estimate": Use conservative defaults for optional parameters. For required parameters (team size, hourly rate, manual hours, AI cost, implementation cost), ask explicitly. These cannot be defaulted because they vary too widely. 4. After generating the report: Summarize the top 3 findings in the response message and mention the file path where the report was saved.

Quality Checklist

Before delivering the report, verify:

• [ ] All tables render correctly in Markdown
• [ ] All numbers are internally consistent (monthly * 12 = annual, and so on)
• [ ] Payback period matches the monthly projection table
• [ ] Sensitivity analysis shows materially different outcomes across scenarios
• [ ] At least 3 comparison scenarios are included
• [ ] Risk factors are honest and include mitigation strategies
• [ ] Executive summary matches the detailed findings
• [ ] Recommendation is clear and defensible based on the numbers
• [ ] No emojis anywhere in the output
• [ ] All currency values have $ signs and commas where appropriate