Building a context layer for AI
Half of the organisations we surveyed have no single source of truth for their work. Our first webinar of the autumn series looked at what that costs, and at how a context layer lets AI reach the systems where your work already lives without handing over control.
Table of contents
51% of organisations have no single source of truth
What a context layer actually is
AI context layer in practice
Data security in the first place
TL;DR
A context layer is a standard way for AI to reach the systems where your work already lives. MCP is that standard. Connect your project management tool, your mailbox and your automation platform, and AI stops giving generic answers and starts doing useful work on your real data.
51% of organisations have no single source of truth
We asked 79 organisations across IT services, manufacturing and regulated industries how they organise, track and report their work. 51% said they have no single source of truth. 27% treat their project management tool as the main source, 13% rely on an ERP or CRM, and 7% on spreadsheets.
Gain more insights based on our research to improve your workflows and eliminate no shared truth, manual reporting and constant context switching. These are the real costs of a fragmented stack.
What a context layer actually is
Most AI chats are blind. If they are not connected to your systems, they do not know your backlog, your project status or your customer history, so they give you generic answers.
A context layer changes that. In practice the standard is MCP, the Model Context Protocol. It sits on the application side, knows the API and exposes a set of tools your AI assistant can call. Your assistant asks, each system answers for its own data, and the assistant assembles the whole picture. More connected sources means more context, and more context means better output.
AI context layer in practice
Easy8 experts showed three ways to reach that context today, from inside Easy8 and from outside it:
- Aura inside Easy8: A context based AI agent in the newest version, currently in beta. In the demo it listed tasks updated today, named the projects it manages and logged an hour of time on each one, which we then verified in the timesheet.
- Claude plus the Easy8 MCP server: Claude picked up a request sitting in Gmail, pulled the project data it needed through the Easy8 MCP connector and drafted a full status report back as an email draft. Around 15 tool calls to Easy8 and three to Gmail, with a draft to review before anything is sent.
- Easy8 Workflow catalogue: A set of n8n based automation templates you install, configure and attach to a button on a task. One click turned a long, unstructured task description into a clean set of suggested action items.
Data security in the first place
Gartner's February 2026 report puts EU sovereign cloud at almost 80 billion dollars this year, 36% growth in a single year, with Europe named the fastest growing region. Organisations outside the US and China want digital independence. Meanwhile, Atlassian has announced end of life for Jira Data Center in 2029, and SAP has set end dates for its on-premises products.
The AI part of your stack does not have to be a black box either. Your context layer can stay in Europe, in your own infrastructure, or inside your own building.
Next in the series: Good AI vs Bad AI, a deeper session on on-premises deployment with our security specialist, and a step by step guide to migrating off Jira Data Center. Register now!
Frequently asked questions
How can I build a context layer for AI?
A context layer for AI is a structured knowledge base or data store that the AI system pulls from to answer questions or complete tasks with current, accurate, or domain-specific information. You build it by identifying what information your AI needs, organizing it into a searchable format (like embeddings, vector databases, or semantic search), and connecting retrieval tools so the AI can access it at runtime rather than relying only on its training data.
How do I automate my business with AI?
- Start by auditing and prioritizing your workflows to identify high-impact, repeatable tasks suitable for AI automation.
- Choose the right mix of technologies—such as RPA, ML models, NLP, or generative AI—and implement them via low-code platforms, APIs, or custom development.
- Orchestrate and monitor your solutions with MLOps practices, ensure stakeholder buy-in and governance, then iterate from pilots to full-scale deployment.
Can Project AI be deployed on-premises
Yes! With Project AI that is a part of Easy AI, you have the unique flexibility to choose between cloud hosting or on-premises deployment for your project management AI.
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