Why are so many enterprise AI initiatives failing to show ROI?
IBM's 2025 CEO Study reported that 61% of CEOs are actively adopting AI agents and preparing to scale them, while only 25% of AI initiatives delivered expected ROI over the last few years. A common failure mode is 'accurate and ignored': the AI deliverable works but never enters the operating rhythm.
The short answer.
IBM's 2025 CEO Study reported that 61% of CEOs are actively adopting AI agents and preparing to scale them, while only 25% of AI initiatives delivered expected ROI over the last few years. A common failure mode is 'accurate and ignored': the AI deliverable works but never enters the operating rhythm.
This is a question Aegis hears regularly during discovery. Here is the practical way to frame it.
Source for the market statistic above.
- IBM Institute for Business Value 2025 CEO Study via PR Newswire: Source for the 61% AI-agent adoption and 25% expected-ROI statistic.
How Aegis approaches this.
Aegis Boardroom's answer is shaped by three frameworks. Truth Architecture: recommendations are designed to be source-traced. Confidence Contract: recommendations are mapped to the canonical Aegis confidence states (I Know / I Think / I'm Inferring / I Don't Know). Life Integrity Engine: recommendations that may increase irreversible-harm risk are flagged for refusal or human review, not softened.
The fastest path is the AI Readiness Assessment: it returns a confidence-mapped band for your specific situation. From there, the Quick Win Plan or a deeper engagement scopes the right paid Aegis next step.
Frequently asked questions.
If the AI works, why does it not deliver ROI?
The common failure mode is 'accurate and ignored': the deliverable is correct but never enters the operating rhythm. ROI comes from adoption and workflow change, not from the model being right.
What share of AI initiatives actually pay back?
IBM's 2025 CEO Study found only about 25% of AI initiatives delivered the expected ROI, even as about 61% of CEOs are actively adopting AI agents.
How do you avoid the 'accurate and ignored' trap?
Design the operating model around the AI before deploying it: who acts on the output, when, and in which existing workflow. That sequencing is the core of the Aegis approach.