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Cibi Creative / AI Readiness Tools
AI Cost Reality Calculator
What does skipping readiness validation actually cost?
March 2026
US SMBs · 1–25 employees
Vendors will show you a license fee. That's 15–30% of what you'll actually spend. This tool models the full 3-year cost of an AI adoption — including data prep, training, productivity dip, maintenance, and regulatory overhead — so you can decide whether the investment makes sense before you're already committed to it.
If any of the five readiness gates below aren't green before you start, your real cost goes up. That's the point.
1 — Your company
Salary + benefits + overhead. Use $65k–$90k for most SMB roles.
2 — Planned AI scope
People whose daily work will change.
3 — Cost assumptions (adjust to model your scenario)
Defaults are conservative multipliers derived from 2024–2026 implementation studies. They represent typical, not worst-case, outcomes for SMBs at the "first serious project" stage.
× annual license cost, year one
× annual license cost, year one
On impacted work, after full adoption
Results
Calculating...
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3-year total cost
—
license as % of total
—
3-year net value
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3-year ROI
Cost component
Year 1
Year 2
Year 3
3-year total
Licenses
Data & integration
Training & change management
Maintenance & regulatory
Productivity dip (opportunity cost)
Total costs
Efficiency benefits (labor savings)
Net value (benefits − costs)
ROI
Readiness gate check — before you proceed
⬜
Process gate
Is the target workflow documented, trainable, and consistent across everyone who runs it? If the answer is no, AI will magnify the inconsistency — not fix it.
⬜
Data gate
Is the data accessible, clean enough to trust, and owned by someone accountable? 89% of failed projects skipped this assessment. Budget 3–6 months for cleanup if the answer is uncertain.
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People gate
Is there a real adoption plan, a training budget, and a named owner for ongoing quality review? Change management gets less than 15% of budget in most failed projects.
⬜
Risk gate
What happens when it's wrong? Do you have error-handling, logging, and a human review layer for the 10% failure case? If that 10% touches billing, legal, or customer trust — you're not "90% good."
⬜
Money gate
Does the TCO above change your decision? Do you have budget for data prep and training, not just the license? What does success look like in six months, and how will you measure it?