As organisations look ahead to 2026, competitive advantage is no longer defined by whether a business has adopted AI, but by how effectively it connects people, processes and information into a single, intelligent operating model.
Enterprises drowning in disconnected documents, siloed systems and manual workflows are finding that AI alone does not eliminate friction. Instead, performance gains are emerging where information flows in context, supporting faster decisions and more resilient operations.
M-Files positions this shift as the move toward context-first document management, an approach that treats information as a living asset rather than static files stored in folders. The company recently delved into five trends about the future of context-first work.
One of the defining developments shaping this future is the rise of unified AI experiences. For many enterprises, Microsoft has become the backbone of digital work, with Copilot, Purview and the broader Microsoft 365 ecosystem increasingly acting as the default enterprise AI layer. As knowledge graphs and generative AI mature, users expect secure, compliant and seamless experiences regardless of where they start their work. Deeper integration between AI assistants and enterprise content platforms is helping remove friction, allowing users to access trusted insights across tools such as Teams, Outlook and document management systems without constant context switching.
Alongside this, AI itself is becoming less visible and more useful, M-Files noted. Rather than being experienced as a separate feature, AI is increasingly embedded directly into everyday workflows. In a context-first environment, governance, compliance and information controls are applied automatically in the background. As a result, compliance evolves from a manual obligation into a natural by-product of how work gets done.
Another major trend is the move toward job-specific AI experiences. Generic, horizontal AI tools are proving insufficient for complex industries with specialised workflows. Organisations are now demanding AI experiences tailored to the roles their employees perform, particularly in sectors such as financial services, manufacturing, professional services and life sciences. By aligning content, processes and analytics around specific use cases, context-first platforms help accelerate decision-making and reduce cognitive overload, enabling users to focus on what matters most in their daily work.
Product-led growth is also reshaping expectations for enterprise systems. Businesses are increasingly unwilling to invest months in training programmes and change management initiatives. Instead, modern platforms are expected to guide users intuitively, embedding onboarding, tutorials and in-product intelligence directly into the experience.
Underlying all of these trends is trust, which is emerging as the central battleground for enterprise AI. As organisations move beyond experimentation, they are demanding clear data boundaries, verifiable provenance and explainable AI decisions.
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