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IJONIS
Enterprise AI

AI ROI

AI ROI (Return on Investment) measures the economic return of an AI investment relative to total costs. It considers direct savings (labor time, error reduction), indirect benefits (faster decisions, scalability), and implementation costs (development, infrastructure, change management) over a defined period.

Why does this matter?

Without clear ROI calculation, AI projects remain a matter of faith. CEOs and board members need solid numbers to prioritize AI investments over other projects. A clean ROI analysis shows which use cases have the greatest leverage — and which are not economically worthwhile despite technical feasibility.

How IJONIS uses this

We create a transparent ROI projection before every AI project: process cost analysis of the current state, savings potential through automation, implementation costs, and ongoing operating costs. After launch, we measure actual ROI against the projection — for continuous optimization and honest accountability.

Frequently Asked Questions

When does an AI project typically pay for itself?
For automation projects with clear process connections, typical payback time is 3-12 months. Quick wins like document automation often pay back in under 6 months. Transformative projects like enterprise AI platforms have longer horizons of 12-24 months but offer exponential benefits.
How do I calculate ROI when the benefit is hard to quantify?
Beyond direct savings (work hours x hourly rate), we measure proxy metrics: processing time per transaction, error rate, customer satisfaction, time-to-decision. For strategic advantages (scalability, competitive position), we use scenario analyses with best/worst case considerations.
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