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The next era of information management is about making organizational knowledge usable—not simply storing files. AI may help people find context and connect information across repositories, but its value depends on trustworthy data, governance, and workable processes. Infrastructure choices, from cloud and edge systems to compute access, also shape what organizations can do.
What is changing in information management?
Information management increasingly means organizing information so people and systems can find, understand, protect, and use it across the organization. That is broader than document storage: it can include content, business processes, data exchange, security, and digital services.
OpenText frames the field through five areas—Content Services, Business Network, Cyber Resilience, Digital Experience, and Advanced Technologies—and five guiding principles: Cloud, Edge, Security, API Services, and Data and AI. These are the vendor’s categories, not a universal industry standard. In its 2021 white paper, OpenText summarizes its view this way: “Making data available is at the heart of Information Management.” OpenText, Are You Ready for the Great Rethink? Exploring the Future of Business (2021).
How could AI change the way organizations use information?
M-Files predicts that AI could help contextualize unstructured material and synthesize information across repositories. In its 2026 outlook, the vendor points to longstanding research, project documentation, customer interactions, and intellectual property as material that may become easier to use when connected to context. This is a forecast, not proof that AI can reliably understand an organization’s entire knowledge base today.
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For an organization considering AI-enabled discovery, the practical question is whether a system can connect its answers to relevant source information and metadata, while respecting permissions. A fluent response alone does not establish that it is complete, accurate, or authorized for a given user.
What foundations does effective information management need?
AI is not a substitute for sound information practices. M-Files argues that outcomes depend on data quality, governance, change management, and scalable operating processes as well as the AI technology itself. People need clear responsibilities and workflows for maintaining information, deciding who can access it, and handling changes over time.
- Data quality: Information must be sufficiently accurate, current, and consistently described for its intended use.
- Governance and security: Define oversight, permissions, and lifecycle controls appropriate to the organization’s information and obligations.
- Change management: Help staff understand new workflows and when to rely on, verify, or escalate AI-generated results.
- Skills and operations: Plan for ongoing support and the capacity to maintain systems and processes as needs change.
The reviewed sources do not establish one governance framework or show that any particular platform solves these challenges automatically.
Why do compute, cloud, and edge infrastructure matter?
Information systems depend on the infrastructure that stores, moves, and processes data. Cloud, edge, security, APIs, and data capabilities are among the principles OpenText emphasizes in its own framework; organizations should treat these as design considerations rather than a prescribed architecture.
Infrastructure is also a public-policy issue. The UK Government Office for Science’s 2023 Independent Review of The Future of Compute links compute access to research, innovation, AI, and the wider digital economy. It recommends long-term planning and coordination, access to public systems, investment in hardware and software, and development of skills and data capabilities. These recommendations concern UK policy and should not be read as requirements for organizations in every country. The report also notes that some businesses cited data-management and governance costs as barriers to using more substantial computing resources; that observation does not establish that every business faces the same barrier. UK Government, Independent Review of The Future of Compute: Final report and recommendations (2023).
How should an organization assess its next steps?
There is no single information-management setup established as right for every organization. Use these questions to assess whether a proposed approach fits the information, workflows, and infrastructure involved:
- Coverage: Which repositories, file formats, and workflows will be included—and which will remain outside the system?
- Governance and security: How will permissions, oversight, and information lifecycles be handled?
- AI grounding and context: Can users trace answers to source information and relevant metadata, and are access controls respected?
- Deployment and access: Do cloud, edge, hybrid, and compute-access needs match the organization’s work and constraints?
- Operational readiness: Are data quality, staff skills, change management, and ongoing support accounted for?
- Cost and sustainability: What are the infrastructure, governance, and operating costs over time? The sources cited here do not provide a neutral comparative cost dataset.
These are practical evaluation questions, not a published scoring rubric. A sound decision starts with the organization’s information and uses—not with an assumption that a new platform or AI feature will make existing knowledge useful by itself.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What is established—and what remains uncertain?
The direction described by vendors is toward making information across repositories more usable, with AI as a possible aid to discovery and synthesis. M-Files’ 2026 outlook is a vendor forecast, while OpenText’s framework is its own strategic model, published in 2021. Neither establishes a universal outcome or neutral standard. The UK Government review offers a separate, geographically specific perspective on the compute foundations that can enable information-intensive work.
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The sources reviewed do not validate a broadly applicable statistic for future information-management adoption, outcomes, or market size. Organizations should therefore assess capabilities and readiness against their own needs rather than assume a market-wide result.
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