agent-business-value

name	agent-business-value
description	Use when calculating business value, ROI, or cost justification for AI agents. Three dimensions: (1) Time Savings, (2) Customer Churn Reduction, (3) Sales Increase. Works standalone or alongside bedrock-pricing/agentcore-pricing estimates. Do NOT use for model pricing (load bedrock-pricing), AgentCore costs (load agentcore-pricing), or capacity planning (load bedrock-capacity).

Agent Business Value

Critical Rules

• ALWAYS use calculate_business_value(). Never implement business value formulas manually.
• ALWAYS present assumptions to user and confirm before running the calculation.
• Agent cost is deducted once from grand total, not per dimension.
• Never fabricate customer data. If lookup fails, say so and ask the user.
• All values in code examples are illustrative only. Always use user-specified values when provided.
• If user asks for detailed explanation, read the report file at result["file_path"]. Present the information as-is, then explain as needed. Do NOT recompute or manually derive calculations.

Business Value Facts (always apply)

• Dim 1a (Productivity Increase) frames time savings as revenue uplift — how much more revenue can employees generate with freed time.
• Dim 1b (Cost Savings) frames the same time savings as labor cost reduction — how many FTE-equivalents are freed.
• Dims 1a and 1b are mutually exclusive. Same time savings, different framing. NEVER add them together.
• Dims 2 and 3 are optional add-ons. Additive with Dim 1 (different value streams). Only for customer-facing agents.
• Three tiers (Conservative/Moderate/Optimistic) are always computed for Dim 1 — present all three to give stakeholders a range.

Quick Reference

# §Load Script
import sys, os
sys.argv = ['bedrock_pricing.py']
script = ("tco_bva_capacity_skills/skills/bedrock-pricing/scripts/bedrock_pricing.py"
          if os.environ.get("USE_IN_KIRO") or os.environ.get("USE_IN_CLAUDE_CODE")
          else os.path.expanduser("~/.quickwork/skills/bedrock-pricing/scripts/bedrock_pricing.py"))
exec(open(script).read())

# 1. Create session directory
session_dir = create_report_session(label="roi-analysis", volume=10000)

# 2. Get agent cost (from prior pricing run or user-provided)
agent_cost_monthly = bedrock_cost["monthly_total"] + agentcore_cost["total_monthly"]

# 3. Calculate business value
result = calculate_business_value(
    sessions_per_month=10000,
    agent_cost_monthly=agent_cost_monthly,
    output_dir=session_dir,
)
# Returns: grand_total_annual, roi_pct, payback_days, net_value_annual

Workflow

1. Present Dimension Menu

Ask user which dimensions apply:

#	Dimension	Default	Notes
1a	Productivity Increase (revenue uplift)	Selected	Mutually exclusive with 1b
1b	Cost Savings (labor cost reduction)	Available	Mutually exclusive with 1a
2	Customer Churn Reduction	Unselected	Customer-facing agents only
3	Sales Increase from Better CX	Unselected	Customer-facing agents only

• If user asks for Dims 2 or 3 without a customer name, ask for it.
• Do NOT auto-include Dims 2/3.

2. Customer Data Lookup (if customer name provided)

Search for: annual revenue, employees, customer base, churn rate, industry.

Search: "{customer_name} annual revenue employees headcount 2024 2025"
revenue_per_hour = annual_revenue / employees / 2000

• Be transparent — share what you found, note gaps, let user decide.
• For Dims 2/3: also search for total customers, churn rate, annual sales revenue.

3. Present Assumptions

Show all parameters and values. Ask user to confirm before calculating:

• Sessions per month, agent cost
• Time without AI vs. with AI
• Agent effectiveness % and efficiency factor %
• Human cost/hr and revenue/hr
• Dim 2: total customers, churn rates, revenue per customer
• Dim 3: annual sales revenue, sales increase %

Only proceed after user confirms or adjusts values.

4. Calculate

session_dir = create_report_session(label="roi-analysis", volume=1000000)

result = calculate_business_value(
    sessions_per_month=1_000_000,
    agent_cost_monthly=44497,
    # Dim 1
    time_without_ai_min=20,
    time_with_ai_min=10,
    human_cost_per_hour=75,
    revenue_per_hour=300,
    # Dim 2 (set total_customers=0 to skip)
    total_customers=100_000,
    churn_without_ai_pct=2.0,
    churn_with_ai_pct=1.0,
    revenue_per_customer_year=1000,
    # Dim 3 (set annual_sales_revenue=0 to skip)
    annual_sales_revenue=100_000_000,
    sales_increase_pct=10.0,
    # Report
    output_dir=session_dir,
)

5. Present Results

The function writes a detailed report and returns:

{
    "file_path": "~/bedrock_reports/.../business-value.md",
    "grand_total_annual": 5400000.00,
    "net_value_annual": 4866000.00,
    "roi_pct": 912,
    "payback_days": 36,
    "dim1_moderate_annual": 4680000.00,
    "dim2_annual": 500000.00,
    "dim3_annual": 220000.00,
    "agent_cost_annual": 534000.00,
}

Present:

1. Key metrics — ROI, payback, net value
2. Grand total — combined annual value, net of agent cost
3. File reference — point user to report for detailed breakdown

• Show annual projections (monthly looks small to stakeholders)
• Always show all 3 tiers for Dim 1 (Conservative/Moderate/Optimistic)
• If _file_write_failed: True → full result is inline, format all dimensions

6. Completeness Check (MANDATORY)

#	Check	Condition	Action
1	Assumptions confirmed	Always	Present all parameters to user before calculating
2	Agent cost sourced correctly	Always	From pricing result or user-provided — never guessed
3	Reports in session directory	Multiple calculations	Use create_report_session() + output_dir
4	All 3 tiers shown for Dim 1	Always	Conservative/Moderate/Optimistic range

7. Offer Follow-ups

• Add/change dimensions
• Adjust assumptions
• Different customer
• Switch between 1a and 1b

Related Skills

Skill	When to load
bedrock-pricing	Need model cost to feed agent_cost_monthly
agentcore-pricing	Need infrastructure cost to feed agent_cost_monthly
bedrock-capacity	Verify workload fits before building business case