What is your AI investment actually worth?
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Your AI value at a glance
35% adoption
AI productivity value being generated today
—
Annual incremental value from your current AI users across all roles.
AI cost today
—
Annual run-rate for licenses, infrastructure, and metered token usage at current adoption. One-time training and ramp-up costs appear in first-year ROI below.
Unrealised value — the adoption gap
—
Value sitting idle — the gap between now and 80% adoption.
first year
Return on your AI investment
—
—
Total first-year investment
—
Net first-year value
—
Payback period
Value generated today vs licenses, metered token usage, training, and a temporary productivity dip while teams ramp up on AI. Token costs are estimates — Worklytics AI Cost Tracking measures your actual spend by tool.
Outcome metrics
Engineering — quality-adjusted view
Gross output value
—
PRs shipped, unverified
Quality-adjusted value
—
PRs kept in production
⚠ Quality risk —
Raw output overstates AI value when reversion rates rise. Worklytics shows both so you know which number to trust.
Sales — quota attainment outcome
—
more likely to hit quota as an AI power user vs a non-AI rep
AI power users punch above their weight — not more emails, but better prepared and focused on the right accounts. This assumes a conservative 20% uplift — adjust it under Outcome adjustments if your team sees more.
Finance, Legal & Ops — capacity created
—
Your G&A team is doing the work of — more people
without adding headcount
—
AI-active G&A employees
—
hours reclaimed per year
—
value of that capacity
Based on 3 hrs/week saved per AI-active employee on email, documents, research, and data tasks.
Your organisation
Headcount
Workforce covered:
50%
Loaded annual salary by role (USD)
Engineering
Sales / GTM
G&A / Support
G&A salary also applies to Support / CS roles.
% of AI-enabled knowledge workers actively using AI 35%
Your AI investment
Annual cost per AI-enabled worker (USD)
Metered token usage (USD)
≈ Annual total, active users
—
Usage-based costs for agentic tools like Claude Code or Codex scale with actual use, not seats — often the most underestimated AI cost. Heavy engineering users can run 5–10x this figure.
Adoption ramp — the J-curve
Temporary productivity dip for active AI users while they learn new workflows — the "J-curve" from DORA's research.
Role mix
Engineering
Sales / GTM
Support / CS
Finance, Legal & Ops
✓ Total: 100%
Outcome adjustments
📊 Conservative assumptions — informed by comparative insights from Worklytics' work with large organizations
⚙️
Engineering code quality
% of AI output reaching production
High reversion ←→ Clean output85%
📈
Sales attainment lift
how much more likely AI power users are to hit quota
1.2x default (+20%) ←→ go higher1.2x
⏱
G&A hours saved
per AI-active employee per week
Minimal ←→ High automation3 hrs
About these estimates: This calculator applies deliberately conservative productivity-lift assumptions by role, informed by comparative insights Worklytics has developed across its work with large organizations. The results are directional estimates, not a measurement of your organization. The adoption gap is calculated against an 80% adoption ceiling. The engineering view separates gross output from quality-adjusted output, because raw volume overstates value when AI-generated work doesn't survive review. The sales attainment lift is adjustable and defaults to a conservative 1.2x. G&A capacity converts hours saved into FTE equivalents (2,080 hours/year) and represents reinvestment potential, not headcount reduction. First-year ROI weighs this value against licenses, training, metered token usage for active users, and a temporary adoption-ramp productivity dip — the "J-curve" described in DORA's research.

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