From Document Storage to AI-Native Knowledge Fabric
iManage is using its ConnectLive 2026 conference to signal a decisive shift in document management AI strategy. Instead of treating the document management system as a static repository, the company is repositioning its platform as an AI-native foundation that understands and orchestrates knowledge. Center stage in this repositioning is the so-called context fabric, an architectural layer that reasons over documents, relationships, and real-time activity. Rather than simply indexing files, the fabric turns institutional work product into a living, governed knowledge layer that can be safely exposed to agentic AI platforms. Governance and security are embedded in the platform core, not bolted on afterwards, allowing organizations to keep permission-aware controls intact even as AI agents traverse matters, clients, and practice areas. This reframing moves document management from a back-office utility to a strategic component of enterprise AI infrastructure.
Context Fabric: The Infrastructure for Agentic AI Platforms
The context fabric is designed to solve a central problem in enterprise AI infrastructure: how to give AI agents rich, contextual access to knowledge without compromising security. iManage describes the fabric as continuously enriched by what people and agents are doing, creating a feedback loop between daily work and AI-driven insights. It functions as an inference layer above governed data, brokering access to matter context, work product, and institutional expertise while honoring granular permissions. This makes it possible to deploy document management AI capabilities that are deeply context-aware, yet tightly controlled. The company’s Model Context Protocol (MCP) Server plays a key role here, providing a secure bridge between external AI tools and the governed iManage corpus. Together, these components aim to turn the DMS into a knowledge broker for agentic AI platforms rather than a passive content warehouse.
Why iManage Compares This Shift to the Cloud Transition
CEO Neil Araujo has explicitly compared this platform overhaul to the industry’s move to cloud, emphasizing its structural impact rather than any single feature. With iManage now used by 83% of the Top Global 100 firms, 79% of the Am Law 100, and 40% of the Fortune 100, a fundamental re-architecture of its platform effectively shifts the baseline for knowledge management evolution across large enterprises. The company reports 90 new customer logos in 2026 and a cloud footprint covering 78% of its global base, underlining that this is not a niche experiment but a mainstream transition. As organizations move from AI experimentation to AI operationalisation, iManage argues that the core question is no longer which large language model to pick but whether the underlying knowledge layer is governed, permission-aware, and ready for AI at scale.
New Governance, Monitoring, and UX Capabilities for AI Workflows
The platform evolution is arriving with a suite of concrete capabilities aimed at putting AI to work safely inside knowledge-intensive environments. iManage has introduced AI-specific controls that let firms define how AI is applied across clients and matters, aligning AI usage with existing risk and compliance policies. Enhanced monitoring and reporting of AI agent activity provide the audit trails many legal and professional services organizations demand, while multi-region search and native OCR strengthen the underlying data fabric that AI workflows depend on. The expansion of the MCP Server gives organizations more options for connecting external AI tools while keeping knowledge governed within iManage. On the front end, the company is rolling out a refreshed user experience, redesigned interfaces, and workflow enhancements co-developed with customers, ensuring that the AI-ready platform still works for human users on a daily basis.
Implications for the Future of Document Management AI
For CIOs and knowledge leaders, the ConnectLive announcements mark a shift from feature-led innovation to platform-level re-architecture. iManage is effectively recasting the document management system as a controlled substrate for agentic AI platforms, turning governed institutional knowledge into a first-class component of enterprise AI infrastructure. This has strategic implications: firms standardizing on iManage gain a ready-made context layer that can power both vendor-native tools like Ask iManage and third-party AI agents connected via MCP. It also raises the benchmark for governance expectations in document management AI, emphasizing permission awareness, activity monitoring, and policy-native design as table stakes. As knowledge work accelerates and AI agents take on more complex tasks, iManage’s context fabric model suggests a future in which DMS platforms actively surface, connect, and mediate knowledge—rather than merely storing it—becoming central to how organizations operationalise AI safely and at scale.