Five steps to AI-ready unstructured data

Unstructured data is a goldmine for AI, but most organizations aren’t ready to use it. In fact, our recent whitepaper discovered that while 96% of organizations believe unstructured data will become a core pillar of AI strategies, only 23% routinely use it today.

Without preparation, governance, and the right infrastructure, unstructured data can create more risk than insight. The key to success lies in turning scattered information into a foundation that AI can trust. With the right approach, organizations can transform unstructured data into a competitive advantage for AI in just five simple steps.

Step 1: Recognize the strategic value of unstructured data

Unstructured data often comes in formats such as physical documents, images, audio, and video. This type of information is difficult for AI to process, leaving much of its potential locked away. When harnessed effectively, unstructured data can unlock new insights, drive innovation, and create a strong competitive advantage.

Organizations are beginning to realize this potential value: 56% say it will be very important to their success in the next two years, and 96% believe it will become a core pillar of their AI strategies. These numbers reflect a growing awareness that unstructured data is increasingly central to competitive differentiation and strategic decision-making.

Recognizing unstructured data as a strategic asset is the first step towards AI readiness. Without this, organizations risk leaving valuable information untapped and falling behind competitors that have put in the work. Investing time to understand and prioritize unstructured data sets the stage for effective AI initiatives later.

Step 2: Structure scattered data to fuel AI

AI systems can’t extract value from unstructured data if it remains scattered and unorganized. To be usable, this data must be structured, tagged, and centralized to make it easier for AI to interpret. This process often starts by consolidating content from scattered repositories into a central hub, then applying consistent metadata to create order and make assets searchable. From there, automation can be introduced to classify and update data at scale, ensuring the system stays accurate as data grows.

Once organized, a combination of structured and unstructured data can fuel advanced AI capabilities such as natural language processing, computer vision, and agentic and generative AI. These capabilities empower organizations with the ability to analyze customer sentiments in real time, summarize large volumes of information, and generate predictive and prescriptive insights to support better decision-making.

Step 3: Make data high-quality and accessible

Structured and centralized data isn’t enough to be considered usable. For meaningful outcomes, data needs to also be clean, complete, and consistent. As AI models are only as strong as the data they learn from, poor-quality data can introduce risks that undermine both compliance and decision-making.

Nearly every organization recognizes this as a priority: 97% agree they must improve the trustworthiness, quality, and accessibility of their unstructured data. By ensuring AI consumes only accurate and reliable information, enterprises can train models that produce insights stakeholders can trust and confidently act on.

Platforms like Iron Mountain InSight^® Digital Experience Platform (DXP) streamline this process at scale by centralizing and structuring content while also enriching it with consistent metadata, classification, and tagging. With cross-platform search and discovery, teams can access information wherever it resides, while advanced analytics help to flag redundant, obsolete, and trivial (ROT) data. This ensures unstructured data stays clean, reliable, and ready for AI.

Step 4: Build trust with strong data governance

With optimized data in place, governance provides the guardrails that make AI trustworthy. Strong governance ensures that information is accurate, secure, and used responsibly, protecting both compliance and stakeholder confidence. Yet, only 27% of organizations consider themselves highly effective at the governance of unstructured data for AI applications.

Without proper governance, unreliable data can slip through the cracks and undermine AI effectiveness. With this, organizations risk introducing hidden bias, amplifying inaccuracies, and exposing sensitive information. These issues can erode trust in AI and slow adoption across the business. By implementing strong governance measures, organizations ensure their AI models only act on reliable data, forming the foundation for scalable, confident decision-making.

Step 5: Unify strategy and management for scale

Even well-structured, high-quality data can lose value if it’s managed in silos. Without a unified strategy, scaling AI initiatives becomes difficult, leading to operational complexities and lower confidence in AI-driven insights.

Incorporating a unified asset strategy and management platform has become a necessity for organizations looking to fully leverage their unstructured data for AI. In fact, 98% of decision-makers view these frameworks as critical to being AI-ready, giving enterprises a foundation for sustainable, data-driven success.

Using platforms such as InSight DXP, organizations can easily bring their unified asset strategy to life. Connecting governance, accessibility, and lifecycle management in a single platform eliminates friction, allowing teams to spend less time managing data and more time innovating with it. InSight DXP’s digital asset management foundation keeps data secure and structured, while agentic AI-driven automation handles repetitive tasks, enabling teams to focus on higher-value work. Together, these capabilities ensure unstructured data stays reliable and ready to power AI at scale.

Turn your data chaos into AI confidence

Success with AI starts long before models are trained—it begins with clean, organized, and accessible unstructured data. Through these five easy steps, organizations can unlock the full potential of their unstructured data, accelerate AI adoption, and build lasting confidence among stakeholders.

Iron Mountain InSight^® Digital Experience Platform can make this transformation straightforward. Learn how to organize your unstructured data into an actionable foundation for AI.