Unlocking unstructured data for AI readiness

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AI is no longer a future goal for organisations. Instead, it’s a driving force behind how organisations manage, govern, and extract value from their data. Turning that potential into performance requires rethinking the foundations of information management strategy.

5 September 20257 mins

Swami Jayaraman, Iron Mountain’s Enterprise Chief Technology Officer, recently joined AIIM On Air to share practical, future-focused insights to help organisations navigate the complexity of AI adoption in information-rich environments.

From overcoming data fragmentation to building trust in AI outcomes, Swami offers a clear perspective on what it takes to prepare your information infrastructure for what’s next.

Why AI struggles in the enterprise

According to AIIM, 77% of enterprises have at least experimented with AI. However, many of these organisations face significant challenges and barriers to adoption. This AI implementation maturity gap suggests that the issue lies not in adoption, but in data readiness and a clear strategy.

While organisational inertia plays a big role in stalled AI progress, many enterprises find the real barrier to be disparate data systems. With unstructured data scattered across upstream and downstream systems, many organisations lack the unified platform strategy needed for AI to make a real difference. While helpful, even technologies such as data lakes and data warehouses are typically designed only for structured data. Without a clear plan to integrate and access both types of data, AI struggles to deliver meaningful insights.

This fragmentation also raises the stakes for security and compliance. When AI is applied across silos and interacts with sensitive information, information governance becomes even more critical—especially in regulated industries.

Still, the payoff for overcoming these barriers is significant. Organisations that overcome these hurdles are already seeing AI deliver measurable results such as increased productivity, operational agility, and increasingly personalised customer experiences.

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The value of unstructured data in the AI era

MIT estimates that 80 to 90% of the world’s data is unstructured—hidden away in emails, audio files, social media posts, and physical documents. With this data being challenging to organise and analyse, it has become a huge untapped resource for many organisations.

However, machine learning and natural language processing (NLP) have changed the game. Historically, extracting value from unstructured sources was slow, manual, and difficult to scale. With the introduction of machine learning and NLP, organisations can now automate this analysis, gaining real-time access to insights, context, and customer intent. Since unstructured data is unique to each organisation, these insights can become your proprietary edge, fuelling greater innovation in product development and creating more personalised customer experiences.

As AI becomes foundational to how businesses operate and compete, tapping into unstructured data is essential to staying ahead. Organisations that can harness this opportunity unlock actionable intelligence, and with it comes greater operational efficiency and the ability to deep dive into customer sentiments and create new revenue streams.

Don’t go for the moonshot, take baby steps.

Artificial intelligence person typing concept

How AI drives smarter operations

AI isn’t a silver bullet. But in the right context, it can be a powerful accelerator for managing and extracting value from unstructured data. Organisations are using it to automate manual tasks, uncover hidden insights, and free up teams to focus on higher-value work. The most effective applications aren’t about replacing people. Instead, they help your team work smarter, faster, and more securely.

Some of the most transformative use cases include:

Intelligent document processing:

Extract, classify, and validate information, cutting turnaround times and reducing human error.

Predictive information governance:

Apply data retention and disposal policies at scale, minimising the risk of regulatory breaches amid evolving regulations.

Risk and compliance management:

Monitor anomalies, flag potential security threats, and automate compliance cheques before issues arise.

These aren’t theoretical benefits—they’re real, achievable outcomes. But they only work if your AI has access to the right data, at the right time, in the right context. For this, you need a platform that connects your data, understands your workflows, and evolves with your business.

That’s where Iron Mountain InSight® Digital Experience Platform (DXP) comes in.

This scalable, end-to-end solution brings structure to data by adding metadata and enforcing governance while integrating seamlessly with your existing systems. InSight DXP first reduces clutter by analysing your physical and digital information to identify Redundant, Obsolete, and Trivial (ROT) data. It then applies governance policies consistently, providing recommendations on which records to retain, destroy, or digitise so you stay in control of your information. By creating a clean, connected dataset, InSight DXP gives AI the foundation it needs to drive automation, surface insights, and reduce risk without introducing complexity.

Yet the true value lies beyond data. By eliminating repetitive tasks and increasing confidence in information, InSight DXP empowers teams to focus on high-impact strategies and better business outcomes.

Preparing your enterprise for the next wave of AI

While AI today is already a powerful tool, it is expected to become even more embedded across enterprise systems. The widespread democratisation of AI capabilities has come in the form of AI agents, which are expected to evolve to make autonomous decisions and execute complex tasks with minimal human input.

As these systems take on more responsibility, governance will be more important than ever. With AI systems accessing and acting on growing volumes of enterprise data, organisations will need to rethink how they manage security, compliance, and accountability.

To support this, organisations must implement information infrastructure that ensures data is AI-ready. Structured content, standardised metadata, and seamless integration across platforms are essential to ensure AI surfaces insights accurately.

Ultimately, AI will become a foundational infrastructure for organisations. Information strategy and AI strategy will converge into a single imperative: delivering value from data, faster and with greater precision.

Take the lead on AI readiness

AI is already fundamentally changing how organisations manage information. It has become deeply embedded across enterprise systems and is transforming how information is accessed, decisions are made, and services are delivered.

Organisations that lay the groundwork for AI now will be best positioned to unlock its full potential in the years ahead. Building the right content infrastructure today—one that supports structure, governance, and integration—ensures you can fully capitalise on what AI will make possible tomorrow. Organisations that act early won’t just keep up. They’ll set the pace.

Learn more about how Iron Mountain InSight® ensures secure, compliant AI adoption or explore best practises for integrating privacy and cybersecurity into every stage of AI.

To learn more about Information Governance Advisory Services, visit here.

Listen to Swami Jayaraman on AIIM On Air here.