Most enterprise AI journeys follow a familiar arc. Early excitement. Promising pilots. A handful of use cases that work in isolation. Then reality sets in, and leaders start asking harder questions about trust, scale, and risk.
What’s becoming clear is that real AI progress isn’t about speed, it’s about readiness. Ready to deliver insight reliably. Ready to protect information. Ready to let AI operate in context, at scale.
As organizations look toward 2026, AI readiness is emerging as the defining factor, driven by how content is managed, connected, and governed.
Here are five predictions shaping content management in 2026, informed by analyst insight and what we’re seeing firsthand with customers scaling AI.
AI-ready content becomes a board-level requirement, not an IT project
What changes in 2026: Enterprises stop treating GenAI as a tool problem and start treating it as a content foundation problem: trusted, governed, permission-aware information with consistent metadata. The gap remains significant: only 10% of organizations consider themselves fully prepared for AI1, and just 12% claim their data is AI-ready2.
Implication for content management: Content platforms matter most when they provide accuracy, explainability, and control through lifecycle management, access controls, and structured metadata.
Recommended reading: 4 practical aspects to automate AI readiness
AI assistants move from search to execution
What changes in 2026: AI assistants shift from search tools to active contributors in everyday work. Instead of simply retrieving documents, they help draft responses, extract insights, and move work forward inside business applications.
Implication for content management: Assistants only deliver value when grounded in AI-ready content. Content platforms that provide governed, contextual access enable AI to operate accurately where work happens.
Recommended reading: Breaking language barriers in global mining operations
Agentic AI scales, but only with guardrails
What changes in 2026: Enterprises move beyond chat-based copilots toward agentic AI that can initiate actions and orchestrate workflows across systems. Analysts are loud on both sides: Gartner is highlighting multi-agent systems as a top strategic trend directionally for 20263, while also warning that 40%+ of agentic AI projects will be canceled by end of 2027 due to cost, unclear value, and risk controls4.
Implication for content management: Agentic AI scales only when AI-to-AI integrations are built on a strong content management foundation. Agents need access to trusted business content and clear enterprise guardrails to act with confidence. When content management connects with orchestration tools such as Salesforce Agentforce, SAP Joule, and Microsoft Copilot, agents can retrieve, summarize, translate, and act on information in context. With governance and auditability enforced at the content layer, AI can move from insight to action across the technology ecosystem.
Recommended reading: From Hype to Practical Enterprise AI
Multi-cloud architectures force zero-copy, sovereignty-first design
What changes in 2026: Regulatory pressure, data residency requirements, and risk management prevent organizations from centralizing content in a single cloud. At the same time, AI increases demand for broader real-time access to information. IDC predicts that by 2027, 80% of agentic AI use cases will require real-time, contextual, and ubiquitous access to data, accelerating the shift toward distributed, multi-cloud environments.5
Implication for content management: Content platforms provide a unified governance and access layer across multi-cloud environments, allowing AI to work with trusted information wherever it resides. This approach reduces unnecessary data movement, strengthens compliance, and enables AI to operate reliably at enterprise scale.
Recommended reading: Explore the limitless enterprise with keynotes from OpenText World Nashville
Intelligent document processing (IDP) becomes foundational to AI readiness
What changes in 2026: Document-heavy processes remain a major source of friction in modern work. Invoices, claims, onboarding packets, emails, and forms continue to slow operations and introduce risk. By 2026, expectations shift. Organizations no longer talk about “using IDP.” They simply expect documents to be understood, classified, and acted on automatically as part of everyday workflows.
Implication for content management: Embedded intelligent document processing turns content from a bottleneck into a trigger. Information is extracted, validated, and routed directly into the right process, improving accuracy, cycle times, and data quality. IDP becomes a critical input to AI readiness by converting unstructured documents into trusted, structured information that feeds automation, analytics, and AI. This makes document intelligence a prerequisite for enterprise-scale AI.
Recommended reading:
Five things to look for from an IDP solution
See why IDC named OpenText a Leader in the IDC MarketScape for Intelligent Document Processing
The bottom line for 2026
Organizations that succeed in 2026 will treat content as a strategic system, not a storage layer. They will build AI-ready foundations, deliver assistants that complete work, adopt agents with guardrails, enable multi-cloud governance without copying data, and accelerate outcomes through targeted solutions.
Because in the end, AI is only as smart as the content it’s built on.
[1] Harvard Business Review Analytics Services, Pulse Survey: Data Readiness for the AI Revolution, January 2024.
[2] Drexel LeBow and Precisely, 2025 Outlook: Data Integrity Trends and Insights Report, September 2024.
[3]Gartner Top 10 Strategic Technology Trends for 2026 https://www.gartner.com/en/articles/top-technology-trends-2026
[4] Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027 https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027
[5] IDC FutureScape: Worldwide Data and Analytics 2026 Predictions
