Nasuni’s Solutions to Bridge the AI Data Readiness Gap

Enterprises worldwide are investing in AI but struggle to measure returns due to fragmented data strategies, according to research from Nasuni, a hybrid cloud storage provider.

The company's ‘The Era of Hybrid Cloud Storage 2025’ report finds that while nearly half of surveyed organisations cite AI as their top spending priority for the next 18 months, only 20% consider their data structured, accessible and ready for AI implementation.

On top of this, only 27% of AI projects deliver accurate return on investment metrics.

The research, which surveyed 1,000 purchasing decision makers across the US, UK, France and German-speaking European regions, sheds light on the disconnect between AI aspirations and data readiness that affects performance outcomes.

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The consequences of data migration implementation barriers

Data migration emerges as a central challenge in the report, with 96% of respondents reporting difficulties moving their file data across systems — a prerequisite for effective AI deployment.

Nasuni identifies unstructured data, which lacks a predefined data model and is often stored in disparate systems, as a primary obstacle to extracting value from AI applications.

“Organisations are making AI a top priority and significantly ramping up their investments, but what we are finding is they are not always taking the critical steps necessary to ensure success when it comes to data management,” says David Grant, President of Nasuni.

“A modern approach that unifies, organises and makes unstructured data accessible is needed to reliably and securely harness AI, enabling teams to navigate the complexities of AI deployment confidently.”

Nasuni's response to these challenges is its ‘Fit for AI’ framework, which aims to consolidate fragmented datasets into a unified information repository that can serve as a consistent reference source for AI systems.

The framework addresses the need for organisations to prepare their data infrastructure before expecting returns from AI investments.

The problem of data security as a barrier to AI adoption 

The report further finds that data security poses another barrier to AI adoption, with 34% of respondents citing concerns about data protection and privacy as challenges to implementing AI initiatives.

However, Nasuni suggests that hybrid cloud storage – which combines on-premises infrastructure with cloud-based services – offers a solution to both data management and security issues.

By leveraging hybrid cloud models, organisations can maintain control over sensitive information while benefiting from the scalability and processing capabilities of cloud platforms.

Nasuni surveyed decision makers to understand attitudes towards data storage
infrastructure, security protocols and AI integration strategies – and the research helps contextualise how enterprises approach these interconnected technological domains.

Yet investment alignment remains an issue, with only a third of respondents planning to direct resources toward cloud data management systems necessary to support their AI objectives.

This gap between stated priorities and implementation plans suggests companies may continue to face challenges in realising value from AI without addressing fundamental data architecture requirements.

Hybrid cloud storage as a security solution for AI-driven enterprises

The Nasuni report emphasises that organisations need to consider both the front-end applications of AI and the back-end data infrastructure that supports these systems.

“By adopting a hybrid cloud storage model, businesses put forward a strong risk mitigation strategy,” it says, highlighting the security advantages of this approach in addition to its operational benefits.

This means that for enterprises seeking to improve their AI outcomes while managing cybersecurity risks, Nasuni recommends adopting unified file data management systems that bring scattered information into coordinated repositories.

This approach transforms unstructured data from a hindrance into a resource that can power AI applications effectively.

“A modern approach that unifies, organises and makes unstructured data accessible is needed to reliably and securely harness AI, enabling teams to navigate the complexities of AI deployment confidently,” David concludes.

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