IM BLOG: Unlocking Business Value from Data: AI Use Cases for Information Management

The most valuable insights your organisation needs might already exist - hiding in plain sight within your existing data systems. The challenge isn't gathering more data or implementing more sophisticated technology. It's asking the right questions and providing the context that transforms information into actionable intelligence.

This is the paradox facing information management professionals today: we're drowning in data while starving for insights. But artificial intelligence is changing the equation, offering powerful new ways to unlock value from the information assets we already manage. The key is understanding how to prepare your data, frame your questions and leverage AI agents to work alongside your teams.

Your Data Is Already Primed for Possibility

Here's a startling reality: it could be said that 90% of organisational insights are already captured in existing systems, waiting to be discovered through strategic prompting. The answers you seek exist within your data. The power lies in asking the right questions.

This represents a fundamental shift in how we think about information management. Traditionally, we've focused on storage, classification, retention, and retrieval - the mechanics of managing information assets. But AI invites us to think differently about three critical elements:

Prompts lead the way - the right questions can unlock more value than the most sophisticated algorithm. Instead of building complex search queries or spending hours filtering through results, focus on what you need to know. AI can sort it out from there.

Context creates clarity - data without context is noise. The same dataset can answer completely different questions depending on how you frame your inquiry. This is where information management professionals have a distinct advantage, we understand the business context, the regulatory requirements and the organisational structures that give data meaning.

Framing matters - how you approach your data determines what insights emerge. AI doesn't just retrieve information; it interprets it based on the context you provide.

Many Environments Are Already AI-Ready

If your organisation uses SharePoint or similar modern content management platforms, you may already have the foundation for intelligent, conversational data access. These systems increasingly include three critical capabilities:

• Vector Search - semantic understanding enables natural language queries to find contextually relevant content, not just keyword matches. Instead of searching for exact terms, you can ask questions the way you'd ask a colleague.
• Built-in Security - confidence in your security model ensures answers are relevant and trimmed based on permissions. AI respects your information governance framework, only surfacing content users are authorised to access.
• Grounding - AI responses are anchored to your organisational data, ensuring accuracy and eliminating hallucinations. This is crucial - grounding means AI pulls from your verified content rather than generating speculative answers.

For information management professionals, this represents a significant opportunity. The systems you've been managing and governing are evolving into intelligent platforms that can engage in conversation, understand context and surface insights rather than just documents.

What If Your System Isn't AI-Ready?

Not every organisation is working with AI-enabled platforms and many critical systems weren't designed with modern AI capabilities in mind. But this doesn't mean you're locked out of AI's benefits. Information enhancement strategies can bridge the gap by enriching your existing data assets with AI-derived metadata and context.

Three key enhancement approaches can transform traditional systems:

Classification - use AI to classify your data into meaningful sets. Knowing the purpose of your documents or data goes a long way in understanding when and where to apply prompts. Instead of manually categorising thousands of documents, AI can analyse content and apply consistent classification based on business rules and examples you provide.

Perspective - data has different meanings at different times. Putting several perspectives against content lets us look at data with a different lens depending on our needs. A contract might be viewed from legal, financial, operational or risk perspectives - AI can tag content with these multiple viewpoints automatically.

Data Extraction - picking select metadata out of content and making it into fields accelerates your ability to differentiate one item from the next. AI can identify and extract key data points (dates, names, values, obligations) turning unstructured content into structured, query-able information.

The Scale Challenge: Why Content Alone Isn't Enough

Here's where many AI implementations hit a wall: grounding (also known as RAG - Retrieval Augmented Generation) is effective when the data source is refined and contained, but it breaks down at scale:

• 20 source documents - excellent, high-quality answers
• 200 source documents - start to miss important details
• 2,000 source documents - can't trust the answers are complete or accurate

When we move beyond human scale, AI searching starts to break down without proper enhancement. This is where pre-conditioning your grounding data using more than just its content becomes essential. By enriching data with business context, extracted values, categories, perspectives and sensitivity markers, you can achieve 2-5x better precision and recall in AI responses.

This enhancement approach provides two critical filtering capabilities:

• Factual Filtering - focus on extracted data (specific dates, amounts, parties or requirements)
• Conceptual Filtering - focus on perspectives (legal implications, financial impacts, operational requirements)

The result? Grounded datasets with semantic richness, less ambiguity and better query alignment - even at enterprise scale.

Enter AI Agents: From Retrieval to Automation

AI agents represent the evolution from simple search and retrieval to automated execution of business processes. These agents can work alongside or on behalf of a person, team or organisation, with complexity and capabilities that vary depending on your needs - from simple retrieval tasks to advanced autonomous operations.

For information management professionals, agents offer practical applications that directly address daily challenges:

Auto-Classification Agents - connect content management systems to AI engines to automatically classify records based on content, context and business rules. This addresses one of the most time-consuming and error-prone aspects of information management.

Data Inquiry Agents - provide accessible interfaces for content supporting FOI/GIPA requests and general access. Instead of manually searching through systems to respond to information requests, agents can identify, compile and present relevant content while respecting security and privacy requirements.

Retention and Disposal Accelerators - apply disposal based on configured classification and disposal rules. This tackles one of the most challenging aspects of information governance, ensuring records are disposed of appropriately and defensibly.

Monitoring and Compliance Dashboards - extract metadata for compliance dashboards and retention analytics, providing real-time visibility into information governance health.

The Information Management Advantage

What makes AI particularly powerful for information management professionals is that it leverages the expertise you already have. You understand:

• Business context and how information flows through organisational processes
• Regulatory requirements and compliance obligations
• Classification schemes and taxonomies
• Retention requirements and disposal authorities
• Security and privacy considerations
• Quality and accuracy standards

AI doesn't replace this expertise, it scales it. An agent can apply your classification logic to thousands of documents. It can monitor retention schedules across your entire information estate. It can surface compliance risks based on the governance rules you've defined.

The key is recognising that successful AI implementation for information management isn't about replacing professionals with technology. It's about empowering information management professionals to work at a scale and speed that manual processes simply can't achieve.

Making AI Work in Your Information Management Practice

The pathway to leveraging AI effectively starts with understanding what you already have and what questions you need to answer:

Assess your current state

• What platforms and systems are you managing?
• Do they include AI-ready capabilities like vector search and grounding?
• What enhancement opportunities exist to enrich your data with context?

Identify high-value use cases:

• Where do manual classification and categorisation consume the most time?
• What information requests or inquiries are repetitive and time-consuming?
• Which compliance or governance processes would benefit from automation?

Start with contained datasets

• Don't try to AI-enable your entire information estate at once
• Focus on specific document types, business processes or compliance requirements
• Build confidence and understanding with manageable pilots

Enhance before deploying

• Invest in classification, perspective-tagging, and data extraction
• Ensure your grounding data is rich with business context
• Build the semantic foundation that makes AI responses accurate and useful

The organisations seeing the greatest return from AI in information management aren't necessarily those with the most advanced technology. They're the ones asking the right questions, providing proper context and leveraging their information management expertise to guide AI implementation.

Your data is already brimming with possibilities. The insights exist. The value is there. Now it's time to unlock it by combining the power of AI with the irreplaceable expertise of information management professionals who understand what questions matter, what context is essential, and what outcomes serve the business.

The future of information management isn't about being replaced by AI - it's about being empowered by it.

This article is based on contents of the RIMPA Live 2025 presentation Unlocking Business Value from Data: AI use cases to empower Information Management professionals by Roger Hogg and Andrew Ly.

Meet your blog authors:

 

Roger Hogg has over 20 years of expertise in information and records management, Roger brings a unique blend of traditional knowledge and cutting-edge innovation to INFORMOTION. Following a five-year tenure as a senior platform engineer at an AI and data enhancement startup, Roger has returned with fresh insights into how artificial intelligence and emerging technologies can transform real-world applications. His experience bridges the gap between established information systems and modern AI capabilities, enabling solutions that previously required extensive development resources. Roger is passionate about leveraging these advanced technologies to deliver practical benefits and streamline complex information challenges for clients.

 

 

Andrew Ly has 24 years of Microsoft expertise spanning diverse enterprise solutions. With a decade focused on healthcare, he led teams supporting clinical research trials, hospital systems, and health insurance. He was recognized as an 8 x Microsoft MVP (Most Valuable Professional) and 5 x Microsoft FastTrack Recognised Solution Architect (FTRSA) and is an active blogger and contributor of open-source projects, particularly for Power Platform. He has been extensively involved in establishing user group communities such as the Dynamics 365 User Group Australia. Presently, he works as the Principal Solution Architect of Microsoft Cloud and AI at INFORMOTION Pty Ltd in Australia.