Why many organisations are closer to AI success than they realise.

The race to adopt Artificial Intelligence is well underway.

Boards are asking about AI strategies. Executives are exploring productivity gains. Business units are experimenting with new AI-powered tools. Vendors are embedding AI into almost every technology platform they sell.

Yet for many organisations, the biggest barrier to unlocking value from AI isn’t choosing the right model.

It’s finding and preparing the right data.

The Hidden Challenge of AI Adoption

When organisations think about AI, they often focus on large language models, copilots, chatbots, machine learning platforms and analytics tools.

These technologies are important, but they are only one piece of the equation.

AI is only as valuable as the data it can access.

Most organisations have spent decades building vast repositories of information:

• Documents and records
• Research and intellectual property
• Customer information
• Project files
• Images and media
• Operational data
• Corporate knowledge

The challenge is that much of this information sits locked away in traditional file systems that were never designed with AI and advanced analytics in mind.

As a result, many AI projects begin with the same costly and time-consuming exercise:

• Identify the data
• Copy the data
• Move the data
• Transform the data
• Build pipelines to keep the data synchronised
• Repeat whenever requirements change

Before any AI model delivers value, significant effort has often been invested simply making data accessible.

A Different Way to Think About AI Readiness

This creates what many organisations are now experiencing as an AI readiness gap.

The irony is that the data often already exists.

Most organisations have spent years curating, organising, classifying and protecting highly valuable information. The challenge is not a lack of data. The challenge is making that data accessible to modern AI and analytics platforms without introducing unnecessary risk, duplication or complexity.

Historically, many AI initiatives began with a familiar process:

• Identify the data
• Copy the data
• Move the data
• Transform the data
• Build pipelines to keep everything synchronised
• Repeat whenever requirements change

Every new copy of a dataset creates additional governance questions:

• Which copy is authoritative?
• Are permissions consistent?
• Is the data current?
• Who owns and governs it?
• Have we increased our attack surface?

These are not simply technical considerations. They are governance, compliance, risk and cost management concerns.

As a result, organisations should increasingly be asking:

“How do we allow AI to securely access the data we already have?”

rather than:

“How do we move our data to AI?”

This shift is driving interest in modern data platforms that can present the same information using multiple access methods simultaneously. Technologies such as NetApp’s Data Duality capability, along with similar approaches from other enterprise storage vendors, allow traditional file-based data to be accessed by modern AI and analytics platforms without requiring wholesale migration or duplication.

In practical terms, users and applications can continue to access information through familiar file structures while AI and analytics platforms consume the same data using S3-compatible object interfaces. The data remains in place, existing workflows remain unchanged, and organisations can potentially avoid creating another large-scale data migration project simply to support AI initiatives.

What This Means in Practice

Imagine a research institution that holds decades of valuable data on network file shares.

Researchers and users access that information through familiar file structures.

Historically, if the organisation wanted to use modern AI tooling, they may have needed to create a separate object storage environment and replicate significant portions of that data before advanced analytics tools could consume it.

Modern multiprotocol data platforms are beginning to eliminate that requirement by allowing the same dataset to be presented both as traditional files and as S3-compatible object data.

That means:

• Existing user workflows remain unchanged
• Existing data management processes remain intact
• Existing security controls continue to apply
• AI and analytics platforms gain access to the information they need
• Large-scale data movement can potentially be reduced or eliminated

The result is faster access to the organisation’s knowledge assets and a more direct path towards AI experimentation and innovation.

Why This Matters for Governance

The conversation around AI readiness is often framed as a technology challenge.

In reality, it is just as much a governance challenge.

The organisations likely to succeed with AI over the next few years will not necessarily be those deploying the largest models.

They will be the organisations that can answer questions such as:

• What data do we have?
• Where is it stored?
• Who owns it?
• Who can access it?
• Is it trustworthy?
• Can we provide controlled access to AI systems without losing control of it?

Technologies that reduce data sprawl and minimise unnecessary duplication can play an important role in answering those questions.

Less copying often means less complexity.

Less complexity often means stronger governance.

And stronger governance ultimately creates greater confidence in AI outcomes.

Not Unique, But Increasingly Important

The concept of allowing data to be accessed through both traditional file protocols and modern object-based interfaces is not exclusive to any single vendor. Similar capabilities are emerging across several enterprise storage platforms.

What is becoming increasingly important, however, is the strategic outcome these capabilities enable.

For many organisations, the most valuable data already exists.

The challenge is unlocking that value safely and efficiently.

The organisations that solve this problem will be able to accelerate AI initiatives without embarking on large-scale data migration programs every time a new analytics, machine learning or AI opportunity presents itself.

Bridging the Gap Between Traditional Data and Modern AI

This challenge is one of the reasons we are seeing increased focus on technologies such as NetApp’s Data Duality capability. Rather than treating file storage and object storage as separate worlds, these platforms aim to make existing enterprise data accessible to both traditional business applications and modern AI services simultaneously. The value isn’t simply technical elegance. It’s the ability to accelerate AI initiatives without first embarking on lengthy projects to relocate, reformat or replicate large volumes of data.

The Real AI Opportunity

The AI conversation often centres on models.

The real opportunity lies in data.

Before investing heavily in new AI platforms, organisations should take stock of the information they already possess and ask a simple question:

If our most valuable data could be securely accessed by modern AI and analytics platforms tomorrow, what new opportunities would become possible?

The answer may reveal that the journey to AI readiness is shorter than expected.

Because in many cases, the data is already there.

The challenge is simply making it available.

And increasingly, technologies such as NetApp’s Data Duality are demonstrating that enabling AI access to existing enterprise data may not require organisations to start from scratch. It may simply require a smarter way of connecting modern AI platforms to the information they already trust and manage every day.

Author’s Note: AI readiness should not be viewed solely as a technology initiative. Successful adoption requires equal consideration of governance, security, compliance, data ownership, and user education – not to mention, cost. Organisations that approach AI through this broader lens are far more likely to realise sustainable business value from their investments.

By Chris “Cyril” Starsmeare