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Markdown edition

# Google survey reports technology project constraints

> What the survey does and does not establish.

By BIGCHANGE.AI Editorial

Published: 2026-11-10T06:14:38.574Z
Updated: 2026-11-10T06:14:38.574Z
Canonical: https://bigchange.ai/blog/google-survey-on-premises-ai-modernization

![A hand holds a clipboard with a blank heading and three empty circles.](https://bigchange.ai/api/media/file/questionnaire-concept.png)
AI-generated illustration for BIG CHANGE

Google Cloud’s [November 9 announcement](https://cloud.google.com/blog/products/ai-machine-learning/new-survey-finds-enterprises-trapped-between-legacy-software-and-ai-modernization) links on-premises software to delayed, reduced, paused or cancelled AI or other technology initiatives, citing 49% of respondents. That is not an AI-only figure.

## An unresolved question

The [report](https://services.google.com/fh/files/misc/the_innovation_paradox2026.pdf) contains a documentation problem: its appendix pairs these project outcomes with a question about maintenance time. The precise questionnaire item and denominator behind 49% remain unverified.

Google and Hypothesis Group surveyed 1,250 U.S. enterprise decision-makers online in August. Participants were managers or above, influenced AI decisions and worked at organizations already using cloud and enterprise AI. Eligibility also required a significant cloud or AI decision within the previous two years. The report does not disclose recruitment or weighting; these responses cannot establish population-wide prevalence.

## Definitions affect the decision

Here, “legacy” means locally hosted software, not measured age or condition. Google sells cloud services and advocates modernization. This survey neither tests migration nor establishes that changing providers resolves project delays.

In separate [AWS guidance](https://aws.amazon.com/blogs/migration-and-modernization/why-legacy-infrastructure-blocks-ai-success/), the definition instead concerns architecture and operating assumptions. AWS says those constraints can persist in cloud, on-premises or hybrid environments. This is another cloud vendor’s guidance, not independent corroboration of Google’s percentage.

AWS describes integration work such as connecting model endpoints to applications and feeding real-time data to inference systems. Its proposed assessment examines compute readiness, data accessibility, application architecture, model choice and organizational readiness. That framework offers questions for investigating a particular workload; it is not evidence that every organization needs the same migration.

For a project review, separate a delayed deadline from reduced functionality or an abandoned initiative. Identify the specific dependency, document the observed effect, and compare proposed remedies against that problem. A survey headline alone cannot choose the remedy.

## Sources

- [Google Cloud survey announcement](https://cloud.google.com/blog/products/ai-machine-learning/new-survey-finds-enterprises-trapped-between-legacy-software-and-ai-modernization) — Google's summary.
- [The Innovation Paradox report](https://services.google.com/fh/files/misc/the_innovation_paradox2026.pdf) — Methods and findings.
- [AWS on legacy infrastructure](https://aws.amazon.com/blogs/migration-and-modernization/why-legacy-infrastructure-blocks-ai-success/) — A separate cloud provider explains its broader definition.