What Is AI Context?
AI context is all the information an AI tool or agent uses to answer questions and execute tasks. If AI training data is the broader information fed to an AI platform to help it succeed more widely, AI context is the data specific to your organization that makes AI tools more effective for your specific needs. That means AI pulls information from the tasks in your project management tools, the documents in your shared drives, the deals in your sales pipeline, and the decisions scattered throughout all your software tools. You get answers and actions rooted in your organization’s unique situation rather than relying exclusively on broad training data.
The difference between broad data and AI context isn’t abstract. It’s backed up by data. Atlassian’s own Teamwork Graph connects Atlassian apps like Jira and Trello with 100 other tools, giving access to a network of interconnected data to Atlassian’s built-in AI agents. According to Atlassian’s data, Teamwork Graph leads to 44% more accurate results and 48% fewer tokens used.
If you’re using AI, it likely needs better context.
Context isn’t just for AI
AI context allows agents to make better decisions independently and take the right action at the right time. But that context doesn’t just benefit AI. It benefits everyone who uses AI. Project managers who need a better idea of a team’s workload ask an AI hooked up to their project management tool to evaluate how a new project would affect the team. When that AI has access to every project and every task that team is working on, it gives that project manager more accurate information, allowing them to make a better decision.
Even with the most advanced AI agents out there, you’re still relying on human collaborators to get things done. The more context your AI tools have, the more information they can pull from and the better decisions they can make. The more effective your AI tools are, the more efficient the people relying on them can be.
Why your tools are the missing piece
You already have all the data you need. It’s just buried in your tools. Project management tools don’t just have data on the status of existing projects or your team’s workload; they surface trends about how teams complete projects, what keeps blocking them, and how project managers keep things moving. Software development platforms don’t just hold the code to your product or service; they can teach an AI platform how your developers work together. Your sales pipeline reveals the kinds of deals you can regularly attract and close. Your customer support portal surfaces recurring problems and how you fix them.
The same way you’d train a new employee on the platforms they use and the context in them, AI platforms need what’s in your tools.
Why more tools won’t fix the problem
Some teams think they need another tool to centralize data from the rest of their stack, and then AI can pull from that system to get the context it needs. But this approach doesn’t solve the core issue. Here’s why.
You’re adding another system to your stack
The average organization uses over a hundred SaaS apps, and each app adds its own costs that often go unseen. Not only do you need to train people on new tools, but you often need to figure out how they connect with the rest of your stack. Even if adding a new repository for your AI context is meant to centralize data, it can actually cause the problem to compound. A new tool means new processes, and the time it takes to onboard people and load data can delay the gains you get from AI.
Tools aren’t the problem, data flow is
AI context isn’t a matter of adding the right tool. It’s about keeping data flowing between the tools you already have. A database or data lake might allow you to centralize data from throughout the rest of your stack, but that still depends on improving the way that data flows. Tasks from project management tools, deals from your sales pipeline, records from your customer support portal, and more, need to become interchangeable data points for your AI platforms. That’s done with integrations and data management, not adding new tools.
Data will still be outdated
Even a central system won’t have up-to-date data. Data transfers in these systems usually happen in batches, meaning there are windows of time when your AI agents won’t have access to the latest information. Outdated data isn’t much better than no data at all. Your AI tools won’t have the best data to work with, so your centralized system won’t do them much good.
The AI context maturity curve
AI context maturity moves through three stages: silos, connected but out of sync, and fully in sync.
Stage 1: Silos
Tool silos are a default in most organizations. Data from specific tools stays in those tools until someone manually copies and pastes that data out. Even before AI, this problem impacted workflows and projects. With AI, this means agents and chatbots have little to no access to most of your tools. If they’re built into one of the tools you use (like Asana’s AI teammates or Atlassian’s Rovo), then they have access to the data in these tools, but no more. If you’re using a general AI model, like Claude or ChatGPT, then you would only have access to the data available through their built-in connectors. ChatGPT, for example, can connect with tools like Google Calendar, Dropbox, and Notion. Beyond these connectors, your tools would be completely siloed.
Stage 2: Connected but out of sync
Breaking data out of silos usually requires some kind of integration. Simple, one-way automation tools like Zapier can push data out of silos to other platforms AI agents have access to. More advanced platforms like Workato can chain automations across your tool silos to support more complex workflows.
But this stage has its own problems. One-way automations only move data in one direction, unless you chain several of them. This creates additional maintenance and potential troubleshooting, as you need multiple automations to get data moving along your entire workflow. If that chain breaks, your AI agents end up dealing with outdated data, which can be worse than no data at all.
Another issue: data is often sent out in batches. If an AI agent happens to take on a task between batches, it won’t have access to your most recent data.
Stage 3: Fully in-sync
At this stage, your AI agents have access to all your data in real time, or about as close to real time as possible. Data flows back and forth between tools so it’s up-to-date throughout your tool stack. AI tools can pull from contextual data from throughout your organization, whether they’re built into specific tools or are dedicated models. When you ask an AI chatbot a question, you get an answer based on thorough knowledge of your organization. When an AI agent executes on a task, it has the same context as any of your human collaborators.
When you reach this level of AI maturity, you get what Atlassian promised with its Teamwork Graph. More effective AI with fewer resources.
How a two-way sync builds and maintains AI context
There’s no shortage of integration solutions to connect AI tools with the rest of your stack. General AI tools like Claude and ChatGPT have built-in connectors for platforms like Google Drive, Notion, and Slack, which allow them to act independently in these platforms and grab the context they need. One-way automation solutions like Zapier can push data from your stack to AI models, or even allow AI agents to take actions in tools they wouldn’t otherwise have access to. But they’re limited to simple actions and simple updates, which leads to less context for your AI tools.
A two-way sync platform gives you the best method for building AI context. Because it doesn’t connect AI to the rest of your stack; it connects your stack so AI can pull from everywhere at once.
Platforms like Unito create continuous, two-way relationships between work items in project management tools, software development platforms, sales pipelines, and more. These relationships keep all your tools up to date as you work, meaning no matter where your AI agents and chatbots are, they have full access to your organization’s context — just like a human employee switching between tools.
With a two-way sync, you can turn a disparate tool stack into a single, living ecosystem.
FAQ: What is AI context?
FAQ: What is AI context?
What happens to AI agent accuracy when context is missing or stale?
When context is missing or stale, AI agents are less accurate in the tasks they perform and the answers they give you. They might hallucinate more often, double up on work, or stray from processes established in tools they don’t have access to. The main problem is these failures aren’t always visible. The agent will complete an action as normal, but missing context means the output doesn’t actually match your expectations.
Who is responsible for maintaining AI context, IT, ops, or individual teams?
In most organizations, IT owns your software integration stack while individual teams have the responsibility to ensure the data in their tools is accurate. There’s rarely a responsibility that accounts for where integration and data accuracy intersect, meaning no one has oversight on how context happens. You can shift from this disconnect to a model where a single person owns both the connection between two tools and the data accuracy in them.
Does giving AI agents more context create new security or governance risk?
It does, since you’re connecting systems with an autonomous AI tool. Research from Cloud Security Alliance found that 92% of enterprise security leaders lack full visibility into AI agent identities (i.e., their credentials, permissions, and access history) while 71% say AI has access to their core business platforms. That combination is a hotbed for security and governance risk.
How do you know when your AI tools have enough context?
AI context is an ongoing commitment, with no fixed threshold, but here are signs your agents have enough context:
- Changes in a source tool show up in tools agents read from.
- Humans don’t need to double-check outputs as often.
- No one is re-entering data in multiple tools.
Get better context with a two-way sync
Your AI tools are only as powerful as the context they have, but giving them that context shouldn’t require months of specialized training. A two-way sync is the best platform for giving AI the context it needs to do better work.