How much is AI worth to your company?
Build the business case for Artificial Intelligence by quantifying the ROI of human-AI synergy.
Quantifying the value of AI requires a focus on augmentation ROI rather than just task automation. The Stages of Augmentation framework provides back-of-the-envelope calculations for the business case of human-AI synergy. This AI-Native & People-First approach demonstrates how teams in the loop can drive significant productivity gains and high-velocity problem-solving.
Back-of-the-envelope math for a 1,000-person knowledge-work company
1. Baseline assumptions
- 1,000 employees at $100,000 average salary
- Fully loaded cost: 1.4× salary = $140,000 per person (includes taxes, benefits, equipment, overhead) [1]
- Total labor cost: $140,000,000 annually
- Effective rate: $70/hr × 2,000 hrs/year
2. Business case: AI automation
- Scope: ~20% of tasks are high-potential for automation (data entry, reporting, scheduling)
- Displacement: AI successfully automates 50% of those tasks
- Calculation: 1,000 × 20% × 50% = 100 FTEs reclaimed
- Bottom line: 100 FTEs × $140k = $14,000,000 in annual cost avoidance
- Payback: 2-4+ years (only 13% of the most successful projects within 12 months) [2]
3. Business case: AI augmentation
- Productivity gain: daily AI users save 4+ hrs/week = 200 hrs/person/year [3] [4]
- Output value: each hour is worth 2 (conservative est.) × the loaded rate — $70/hr cost → $140/hr value
- Calculation: 1,000 × 200 hrs × $140 = $28,000,000 in reclaimed capacity
- Adjusted impact: less tooling (~$2,800/seat) = $25,200,000 net, scaling to $36,400,000 at full power-user adoption
- Payback: 3-5+ years [2]
Blockbuster's regret wasn't failing to automate the back office faster — it was failing to reinvent the business. The same choice sits in front of every company today.
All figures are illustrative estimates. The 2× value multiplier assumes knowledge workers generate ~2× their loaded cost in output (a conservative estimate).
Sources [1] BLS, Employer Costs Dec. 2025 · [2] Deloitte, State of AI Jan. 2026 · [3] Bick et al., St. Louis Fed Feb. 2025 · [4] Dillon et al., Microsoft Research Apr. 2025
About the author
Lindsay McGregor
Meet Lindsay McGregor, the best-selling co-author of Primed to Perform, and co-founder of Factor.ai and Vega Factor. She's on a mission to build organizations that are AI Native & People First, because, let's be honest, who wouldn't want a world where every company thrives and everyone genuinely loves their career?
Lindsay is a hard-working nerd at heart. She holds an MBA from Harvard Business School and an undergraduate degree from Princeton University. A former McKinsey & Company consultant, she's also a New York City Library cardholder and a science fiction enthusiast.