INFO~TECH RESEARCH GROUP

Leverage AI to Improve Information Management

From complexity to clarity: AI-powered information management.

Analyst perspective

Trust your information, power your AI.

AI does not change the foundations of information management – it rapidly amplifies its effectiveness and magnifies its risks.

AI in information management is most usefully thought of as a “layer” that drives speed and precision and offers results in highly complex environments. The “AI layer” is continually improving, but it’s based on foundational AI competencies – the ability to digitize what we see, hear, say, and read; the ability to predict what comes next; the ability to organize, plan, and execute; and the ability to communicate. These foundational AI competencies are recompiled into increasingly advanced commercial offerings that are driving actual value and results at the functional level.

We developed this blueprint to help leaders navigate the changes AI has triggered in the information management space. Your responsibility is twofold:

1. Let go of dichotomous and hierarchal views of information, where structured data and unstructured data are worlds unto themselves, and the hierarchy debate between “what is knowledge,” “what is an insight,” and “what is information” leads to chatter but no outcomes.
2. Harness AI as a partner in radically improving information management practices – AI can help prepare your information for advanced, customer-facing AI use cases – by providing information you trust at scale.

This blueprint walks you through a process of defining a type-agnostic information management framework that enables you to prioritize your activities effectively; demystifies commercially available AI capabilities that significantly improve information management practices; and provides you with steps and tools to identify AI-enabled information management initiatives that are impactful, valuable, and tangible.

Jinit Shah
Research Analyst
Info-Tech Research Group

Jinit Shah
Research Analyst
Info-Tech Research Group

EXECUTIVE BRIEF

Executive summary

Your Challenge

• The proliferation of AI has put increased pressure on IT departments to communicate and defend their organizations’ information management practices. Initial questions are: Can we find our information? Can we trust our information? And is our information secure? These questions underpin the larger questions: Are we ready to move forward with AI? What opportunities can we create with our information? And how do we make sure we are not left behind?
• AI is blurring the lines between structured data and unstructured content and breaking down long-standing barriers to knowledge creation. The shift is real and requires leaders to respond with management frameworks that span information disciplines (data, content, and knowledge) and rapidly uplift their practices both to bolster their defensive posture against AI risks and unlock value creation opportunities presented by exponentially advancing AI.

Common Obstacles

• Navigating the Accelerating Pace of AI

  The rapid advancement of AI steepens the learning curve for leaders, making it difficult to determine where to begin, what to prioritize, and how to identify the most valuable opportunities in their information management landscape.

• Overcoming Fragmentation and Misalignment

  Disconnected management principles across data, content, and knowledge domains – along with inconsistent terminology – make it challenging to form a unified strategy for AI readiness, resulting in noise, misalignment, and missed opportunities for strategic progress.

• Demonstrating Tangible Value

  Organizations struggle to measure and communicate the real benefits and ROI of improved information management, which hinders their ability to justify investment and sustain momentum for change.

Info-Tech’s Approach

Use AI to prepare for AI.

• Rapidly define your prioritization framework – what makes information important to your organization, and your critical dimensions of information management health. This becomes the foundation of your information management practice.
• Use your prioritization framework to uncover distractions, problems, and strengths. Use these insights to halt activities that do not benefit your organization and prioritize turning problems into strengths.
• Use AI as your supercharger – recognize the AI capabilities that rapidly improve information management practices, score the highest value initiatives to your organization, and use AI to turn your problems into strengths and your strengths into strategic fuel that propel your AI journey.

Info-Tech Insight

Use AI to prepare for AI. AI’s biggest impact is that it eradicates the traditional fault lines between data and content and radically disrupts the way we create, capture, and process knowledge, information, and insight. Use these capabilities to your advantage and apply them to your organization’s most important assets by creating a prioritization framework that is content and data agnostic. Additionally, select the AI capabilities that can move the needle on your information management practices, building the base your organization needs to take long-term, strategic advantage of one of its most important assets – its information.

Your challenge

This research is designed for information management leaders committed to using AI strategically to transform how information is managed, ensuring risk is minimized and value is maximized.

• As a leader in information management (IM), keeping pace with emerging trends and technologies can feel like chasing a moving target in a fog. The speed of change – driven by evolving AI capabilities, shifting business priorities, and the sheer complexity of data – is overwhelming. The pressure to anticipate, adapt, and guide others through uncertainty is real, especially when the stakes are high and the answers aren’t obvious.
• That’s why having a stable framework matters. A clear, principle-based approach for prioritizing within your information landscape – and for matching the right AI capabilities to the right problems – gives structure to navigating rapidly evolving landscapes. While the specific tools and technologies will continue to evolve, the framework remains your compass. It helps you focus on what’s most urgent, most impactful, and most aligned with your strategic goals. More importantly, it creates a repeatable way to evaluate new opportunities without reinventing your decision-making process every time. By leading with this discipline, you move beyond chasing trends and start shaping outcomes, guiding your team to adopt AI capabilities with intention.

90% of the world’s data has been generated in the past two years.

90%

(Source: Rivery.io, 2025)

Insight summary

The Dichotomy is Dead

The AI layer bridges what was once a fault line between structured data and unstructured content. This is a significant game changer because it immediately allows for cross-modality insights and automations – LLM, and the metadata and knowledge graphs that inform them, can simultaneously process disparate information types such as images, sounds, and structured data, hypothesize the relationship between them, and produce insights and new information. “Interoperable information” is no longer a stretch goal, it’s a capability that you can buy and refine to your context.

Barriers are Demolished

AI capabilities have demolished the barriers to information creation and capture. Nearly any type of input can be captured, extracted, annotated, categorized, parsed, and prepared for processing. Organizations must look at the information they are not capturing today and immediately put AI capabilities to work – the barriers (time, resources, access) are no longer relevant. Processes, procedures, approaches, records, methods, conversations – the market is flooded with tools to capture and process these. Extend your thinking to the customers and stakeholders your organization serves – what does your organization need them to capture to improve its services to them, and how can you leverage AI to facilitate this?

Recognize the New Risks

The AI layer has had significant impact on enhancing search and supporting the discoverability of information. This has led many to adopt information management frameworks that promote “sharable and accessible information.” But most organizations will find that overly-available “shareable and accessible information” is an operational and reputational risk. Process requirements, division of duties, confidentiality, and the protection of sensitive information will force organizations to lock down critical assets and be deliberate about what is shared and with whom. Focus your efforts on classifying information, clarifying the boundaries of what should be accessible and shareable to whom and under what conditions. This will allow your organization to leverage AI capabilities in search and discoverability on a deliberately architected foundation.

Systems, services, and processes are generating massive amounts of information

• 402.74 million terabytes — data generated daily worldwide, 2025
• 376.4 billion — emails sent and received each day, 2025
• 100 zettabytes — total cloud-stored data by 2025
• 720,000 hours — of video content uploaded to YouTube daily
• 500 million — tweets sent daily on X (formerly Twitter)

50% of world’s data is stored in the cloud by 2025. (Source: TechJury, 2024)

30% of organizational data is regarded as high-quality and trustworthy. (Source: gitNux, 2025)

Knowledge workers lose nearly a third of their working hours just trying to find the information they need.

27%

(Source: RocketSoftware, 2025)

IT core priorities are leaning toward employee effectiveness and productivity.

60% of IT leaders cited “improving employee collaboration and productivity” as a core information management priority.

Integration, interoperability, and lack of automation are top obstacles to productivity.

• 19% TOP CHALLENGE: Information is siloed across applications, making it hard to search, access, and use.
• 24% TOP CHALLENGE: Applications lack integration with each other.
• 20% TOP CHALLENGE: Insufficient automation forces repetitive manual tasks.
• 19% TOP CHALLENGE: Collaboration across different applications is challenging.
(Source: ''IT Turns its Attention to Employee Productivity,” OpenText, 2021)

Information management provides enterprise-wide strategy and governance across data, content, and knowledge

• Information Management

  IM provides enterprise-wide strategy and governance across data, content, and knowledge. It aligns all layers with business goals, designs unified architectures, and drives initiatives like enterprise search and informed decision-making.

• Knowledge Management

  KM transforms information into actionable knowledge by capturing expertise and lessons learned. It enables sharing through collaboration and communities, building on inputs from DM and ECM to add context and foster a knowledge-rich environment.

• Data Management

  DM focuses on organizing structured data like databases, spreadsheets, and quantitative records. It ensures data is accurate, well-defined, and accessible, forming the backbone for analytics and reporting.

• Enterprise Content Management

  ECM manages unstructured content such as documents, emails, images, and web pages. It governs the lifecycle of content, ensuring consistency, compliance, and findability.

Data growth, AI, and compliance put information management in the spotlight.

• Explosion of Data:
  Rapid growth in volume, variety, and velocity of information across organizations.
• Digital Transformation:
  Shift to cloud, remote work, and digital business models increases complexity and distribution of information.
• AI & Analytics Demands:
  Effective AI and automation require high-quality, well-managed information as a foundation.
• Regulatory Pressure:
  Stricter compliance requirements (GDPR, SOX, ISO, etc.) make precise information management essential.
• Risk & Security Concerns:
  Rising risks of data breaches, litigation, and reputational damage.
• Competitive Advantage:
  Organizations recognize information as a strategic asset for agility, resilience, and differentiation.

79% of leaders state that information management will become more important.

(Source: Association for Intelligent Information Management, 2023)

Integrate your information management framework to prioritize effectively across information types

Debate priorities, not hierarchies.

Integrated information management prioritizes overarching principles that guide decision-making and resource allocation, rather than rigidly distinguishing between content, knowledge, and data or debating their hierarchical relationships.

Focus on defining what makes information important, and what “well managed information” means to your organization and use that framework to prioritize your efforts and investments.

“The new world is all about Data AND Content, not Data OR Content.
We’ve operated in the past with a convenient dichotomy between data management and content management.
If this dichotomy ever made sense, it makes less and less as time goes on.” (Source: Acambah, 2025)

Apply AI’s base functions to reshape information management

Foundational AI Capabilities

• LEARNING

  Acquire knowledge from data or experience; improve performance over time using ML.

  IMPACT

  • Automates data classification, tagging, and anomaly detection. Improves predictive analytics by learning from historical data.

• PERCEPTION

  Interpret sensory input like images, audio, and signals (e.g. computer vision, speech recognition).

  IMPACT

  • Facilitates automated document scanning, image-based data extraction, and voice-driven data entry. Improves accuracy in capturing unstructured data from diverse sources.

• REASONING/ PROBLEM SOLVING

  Apply logic and algorithms to infer, conclude, and solve complex problems.

  IMPACT

  • Enables intelligent query resolution and decision support systems. Helps enterprises optimize workflows and resource allocation by analyzing complex scenarios.

• NATURAL LANGUAGE PROCESSING

  Understand, interpret, and generate human language for text or speech tasks.

  IMPACT

  • Transforms how employees interact with data – natural language queries replace complex SQL. Powers chatbots and virtual assistants for knowledge retrieval and customer support.

• KNOWLEDGE REPRESENTATION

  Encode facts, relationships, and context so AI can reason and answer questions.

  IMPACT

  • Creates structured knowledge graphs from scattered enterprise data. Enhances semantic search and contextual recommendations, making information more discoverable.

• PLANNING/DECISION-MAKING

  Set goals, plan steps, and choose optimal actions based on predicted outcomes.

  IMPACT

  • Supports dynamic data-driven strategies by simulating outcomes and recommending optimal actions. Critical for supply chain, risk management, and project planning.

• GENERATIVE CAPABILITIES

  Create new content such as text, images, or music using advanced generative AI models.

  IMPACT

  • Automates and facilitates content creation – reports, summaries, and presentations – saving time and improving consistency. Enables personalized communication at scale.

Leverage AI to Improve Information Management

Enterprise AI

AI applied internally to improve how the organization operates and manages information. It focuses on efficiency, productivity, and decision-making.

Example Use Cases:

• Intelligent Search & Retrieval — Quickly find relevant documents across silos.
• Data Classification & Governance — Automate tagging, compliance checks, and retention policies.
• Workflow Automation — Streamline repetitive tasks like approvals or data entry.
• Predictive Analytics for Operations — Forecast resource needs or detect anomalies in processes.
• Employee Productivity Tools — AI assistants for summarizing reports, drafting emails, or answering queries.

GOAL

Boost workforce productivity and maintain well-managed, protected, and easily accessible information

Product AI

AI embedded into the products or services you deliver to customers, creating new features, smarter experiences, and competitive differentiation.

Example Use Cases:

• Personalization Engines — Tailor recommendations for e-commerce or media platforms.
• Smart Features in SaaS Apps — Automated insights, predictive suggestions, or natural language interfaces.
• AI-Powered Analytics for Clients — Deliver dashboards that predict trends or optimize performance.
• Conversational Interfaces — Chatbots or virtual assistants integrated into customer-facing products.
• Fraud Detection or Risk Scoring — Embedded intelligence for financial or security products.

GOAL

Enhance customer value, differentiate offerings, and create new revenue streams

Info-Tech’s Methodology to Leverage AI to Improve Information Management

Blueprint deliverables

Key deliverable:

Leverage AI for Information Management C-Suite Presentation Template

A presentation template that enables easy customization and executive-facing content.

Each step of this blueprint is accompanied by supporting deliverables to help you accomplish your goals:

Information Management Principles

Leverage a library of principles to create an information management framework that is appropriate for your organization. (Part of the Leverage AI for Information Management Workbook.)

Prioritization Matrix

Prioritize information assets and define your next steps. (Part of the Leverage AI for Information Management Workbook.)

ROI and Business Case Tool

Leverage a library of AI capabilities and benefits to build a business case that is appropriate for your organization. (Part of the Leverage AI for Information Management Workbook.)

Measure the value of this blueprint

Leverage this blueprint to ensure your AI-powered information management approach delivers measurable business value and aligns with your organization’s strategic priorities.

Critical inputs required for leveraging AI to improve information management

To complete this blueprint, you will need to have the following items:

• Organizational Priorities

  Organizational priorities will drive the scope of your analysis and action plan.

• Information Domain Lists or Catalogues

  Having ready-made lists of information domains or catalogues streamlines the prioritization process.

Info-Tech offers various levels of support to best suit your needs

Guided Implementation

A Guided Implementation (GI) is a series of calls with an Info-Tech analyst to help implement our best practices in your organization.

A typical GI is 8 to 12 calls over the course of 2 to 3 months.

What does a typical GI on this topic look like?