With so much data being created on top of what already exists, the ability to sort through it all and extract meaningful insights has become a growing business challenge.
The issue isn’t just volume. Many organizations don’t have a clear view of what they have, where the data lives, or how it should be handled. With unstructured data making up about 90% of enterprise-generated information, per IBM, it’s easy to see why classification and organization can become the largest hurdles for information management systems.
This is a problem of visibility and control. When teams cannot easily find or trust their information, work slows and decisions rely on incomplete context. Data that lacks organizational context becomes far less useful for building business insights.
Artificial intelligence is starting to help organizations better organize, retrieve, and manage information across formats and locations. Its use continues to grow, with McKinsey reporting that 88 percent of organizations now use AI in at least one business function.
Read on to explore what intelligent information management looks like in practice and why the impact AI and machine learning are having on it matters so much right now.
When someone says AI, most of us think of ChatGPT, Copilot or Claude, but in daily business, AI takes many different forms.
AI as we understand it today was driven by the work of the mathematician Alan Turing, and the term AI was eventually coined by John McCarthy, who defined Artificial Intelligence as “the science and engineering of making intelligent machines.”
AI that’s being used in business functions can be categorized in a few different ways:
Viewed generally, AI provides broad capability, machine learning enables pattern recognition and automation, and intelligent information management applies those capabilities to real business challenges.
In this webinar we’ll discuss the records, data, and governance foundations organizations need to address first, and how information management teams can play a more strategic role in AI readiness.
AI is improving how organizations classify, retrieve, govern, and act on information across the lifecycle. Here’s how:
AI can sort, label, and structure content as it enters a system, working far more quickly than manual processes. It analyzes documents, emails, images, and other formats to identify patterns and extract metadata, allowing information to be organized based on its content rather than relying only on user-applied tags. During digitization, tools that combine OCR and machine learning can capture metadata at the point of scanning and use it to create an index, making records easier to find and use from the start.
For many organizations, classification has long been a bottleneck. Teams either spend significant time managing it manually or accept gaps in structure, both of which create downstream challenges. When classification improves, organizations gain a clearer view of what information they have and how it should be handled. Retrieval becomes more reliable, governance policies apply more consistently, and information management efforts rest on a stronger foundation.
AI-enhanced tools can convert physical and semi-structured information into usable digital content. Optical character recognition, transcription, and data extraction technologies can process scanned documents, handwritten notes, and audio files with increasing accuracy.
This expands access to information that often remains underused. Many organizations hold large volumes of archived material that contain useful data but require manual review to unlock. AI reduces that barrier by making content searchable and easier to analyze.
The benefit extends beyond convenience. When organizations can access and work with historical information, they gain a more complete view of operations, decisions, and obligations, supporting better planning and reducing the risk of overlooking important records.
Search has long been a point of friction in information management. Traditional systems rely on exact matches, predefined fields, or user-applied tags. When those inputs fall short, retrieval suffers.
AI improves search by adding context. It can interpret intent, connect related concepts, and surface relevant results even when the query does not match exact terms. It can also improve indexing, which strengthens the overall quality of search results.
Faster retrieval has direct operational impact. Teams spend less time looking for information and more time using it, and decisions move forward with better context.
Organizations must manage their data in line with regulatory, legal, and operational requirements. That includes knowing what to keep, how long to keep it, and when to dispose of it.
AI can support governance efforts by helping organizations apply retention policies, manage review workflows, and prepare for audits. It can identify records that meet certain criteria, flag exceptions, and assist with defensible disposition when paired with clear rules.
Ultimately, it reduces reliance on manual review for routine decisions while preserving the ability to escalate complex cases.
Information management professionals are increasingly being asked to do more with less. Therefore, repetitive, time-consuming tasks that don’t require much expertise, such as sorting, tagging, and initial reviews, are ideal activities to pass along to AI.
AI is like the assistant that never complains about their workload. It can perform first-pass classification, suggest metadata, and identify items that require further attention. This allows professionals to focus on higher-value work such as policy development, exception handling, and strategic planning.
Even though AI has the potential to change the way organizations process, manage, and govern their information, its capabilities are limited. AI only operates within the boundaries we set upon it, so its ability to apply rules correctly relies on clear policies.
AI cannot and should not replace records managers—it should be used to help make them more effective. Organizations benefit when experienced teams can concentrate on decisions that require judgment and context rather than routine processing.
Another way to think about it is that AI works best as an enabler. It supports execution, improves consistency, and reduces manual effort. It does not and should not replace the structures and expertise that define effective information management.
AI is making intelligent information management easier to put into practice at scale. It helps organizations gain better visibility into their information, find what they need faster, and manage it with more consistency.
These improvements show up in everyday work. Teams spend less time searching, policies get applied more reliably, and information supports decisions instead of slowing them down.
Progress does not require a full overhaul. Many organizations start with focused steps like improving classification, strengthening search, or digitizing high-value records, then build from there. Starting small still offers the chance to see value right away.
A practical approach centers on real use cases, clear policies, and steady implementation. AI provides the tools, but results depend on how they are used.
For more insights into how AI is shaping information management practices and how Access is helping organizations think more strategically about AI, take a look at these resources:
Want to talk about how AI fits into your organization’s information management structure?
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