What question did this study set out to answer?

To provide a realistic ROI timeline for enterprise AI projects by addressing hidden costs and integration phases.

February 19, 2026Open Access

The ROI Timeline — Realistic Expectations for Enterprise AI Projects

Key Points

To provide a realistic ROI timeline for enterprise AI projects by addressing hidden costs and integration phases.
Analyzed 52 enterprise AI deployments.
Developed a phase-by-phase ROI model.
Introduced the AI ROI J-Curve framework to map production data.
Identified hidden costs in integration and change management.
Established timeline benchmarks for different AI use cases.
Provided guidance for CFOs in setting board expectations.

Abstract

The single most damaging piece of misinformation in enterprise AI is the promise of rapid return. Drawing on 52 enterprise AI deployments, this article presents a phase-by-phase ROI model accounting for hidden costs of integration, change management, and data remediation. The AI ROI J-Curve framework matches observed production data and provides use-case-specific timeline benchmarks enabling CFOs to set defensible board expectations.

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Oleh Ivchenko

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Odessa National Polytechnic University

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The ROI Timeline — Realistic Expectations for Enterprise AI Projects

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Abstract

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