
“Looking ahead, however, there is a broad consensus around future readiness. By 2028, 86% of respondents expect their organizations will be prepared to support AI at scale, with alignment between both businesses and technical stakeholders,” the report reads.
Another hurdle to AI success is data. The Riverbed study asked respondents to rate their data and its relative readiness for AI projects. While 88% of respondents agree that high-quality data is essential to AI success, the percentage of respondents who feel confident in their data varies. Fewer than half of organizations rate their data as excellent in the following areas:
- Relevance and suitability: 34%
- Consistency and standardization: 35%
- Security and protection: 37%
- Quality and completeness: 43%
- Accuracy and integrity: 46%
- Accessibility and usability: 49%
The report also revealed that network performance has emerged as a requirement of AI success. More than 90% of organizations stated that the moving and sharing of data is critical (33%) or very important (58%) to their AI strategy. Three-quarters of those polled said they plan to establish a dedicated AI data repository strategy by 2028, and 88% of enterprises are deploying OpenTelemetry to increase their AI readiness. And 94% of respondents said that OpenTelemetry will “underpin future initiatives such as AI-driven automation.”
“OpenTelemetry is fast becoming the backbone of AI readiness,” Donatelli added. “It provides the visibility and data standardization enterprises need to move from experimentation to execution. At Riverbed, we’re helping organizations bridge this readiness gap with observability, performance, and secure data acceleration so they can unlock AI’s full potential.”