by Shana Burg, AI Trainer
For years, Maura Thomas has argued that you cannot solve a distraction problem with a time management solution. The real issue is attention, and the solution is learning how to manage it.
Now, artificial intelligence is changing something even more fundamental: how we make decisions at work.
This shift isn’t just about generating content faster. Context aware AI is grounded in your work environment. It can pull from your emails, documents, meetings, and conversations to identify what’s relevant and what actually matters.
Instead of gathering all the pieces yourself, the AI connects them for you. This shifts how AI is being used in the workplace. No longer is it just for content generation.
Used well, this advancement in AI can give us back something that modern work has stolen: the space to think.
From Information Overload to Decision Readiness
Most knowledge workers do not lack information. They are overwhelmed by it.
Meet Maya, Director of Operations at a financial technology company.
She starts her day preparing for a leadership meeting. Her inbox is full. A Slack channel has been active overnight. Several documents relate to an ongoing project, but none clearly show what has changed.
Before AI, Maya spent the first hour of her day piecing this together. She searched email, scanned messages, opened documents, and tried to reconstruct what mattered.
Now, she starts with a different question: What do I need to know to make good decisions today?
Context aware AI that’s grounded in your everyday work makes this question easier to answer.
The Real Breakthrough: Context, Not Just Intelligence
AI’s value is no longer defined by how well it responds to a prompt in isolation. It depends on how well it understands the situation in which the prompt exists.
Before:
Workers manually connect the dots across fragmented systems.
After:
Systems connect the dots, grounding AI in ongoing work, so workers can focus on interpreting and deciding.
This reduces a core cost of modern work: the constant switching, searching, and reconstructing of context before meaningful work can begin.
Without a change in how this is handled, the problem grows. As information increases, so does the cognitive load required to process and manage it. Maura refers to it as “cutting through the ‘noise’ to find the ‘signal.’ ”
From Context to Action: How AI Is Used in the Workplace
This shift is already visible across the tools people use every day. Here are three examples of how AI is used in the workplace that show how grounding AI in the context of your everyday work can be transformative.
1. Microsoft Copilot: From Processing Email to Approving Decisions
Copilot is evolving from a tool that helps people generate new work to one that helps move work forward.
Before:
Maya begins her day in her inbox, sorting, reading, deciding, and responding one message at a time.
After:
Copilot reviews her inbox, groups related emails, and recommends actions, what to archive, what to respond to, and what requires attention.
Instead of processing email, Maya is approving recommendations about what priorities she should attend to for the day.
Why this matters for your attention:
The system handles triage and organization, reducing friction so attention can shift to priorities, judgment, and higher-value work.
2. Slack: From Interrupting Others to an Always-On Teammate
Onboarding a new team member often means pulling information from conversations, documents, and people.
Before:
When Maya brings someone new onto her team, onboarding depends on repeated interruptions.
New hires ask:
● Where should I start?
● What matters most?
● Who should I talk to?
Answers are scattered, and progress depends on finding the right person at the right time.
After:
Maya can show the new hire how to use Slackbot, Slack’s built-in AI assistant, which is designed to answer questions and surface information from across your workspace.
Slackbot acts as an always-on teammate, grounded in the team’s existing conversations and knowledge. The new team member can simply prompt Slackbot like this: “I just joined. Where should I start?”
They’ll immediately receive recommended channels, key resources, and relevant contacts. Slackbot can also summarize large channels or find experts on specific topics.
Instead of relying on repeated explanations, onboarding becomes self-service instead of interruption-driven.
For a walkthrough of this use case, see this demo from Slack.
Why this matters for your attention:
Instead of answering the same questions repeatedly, Maya and her team can focus on higher-value work. They can shift their attention from accommodating interruptions to using their time more intentionally.
3. Claude and Emerging Work Agents: From Insight to Execution
A new class of tools, often described as AI agents or digital coworkers, is pushing this shift further. These systems don’t just surface context. They help carry work forward.
Before:
Maya is overwhelmed by the constant flow of employee feedback coming from surveys, emails, and team conversations. She collects and reviews this information manually, trying to spot patterns and identify issues. By the time she pulls insights together and shares them with leadership, the information is already dated.
After:
Maya uses an AI agent that continuously monitors operational signals and team feedback across surveys, internal communications, and the systems her teams use every day. The agent identifies patterns, flags emerging risks, and maintains an up-to-date summary of key themes. It drafts a weekly briefing for Maya, recommends next steps aligned to business priorities, and notifies her when action is needed.
Why this matters for your attention:
Maya no longer spends her time gathering, sorting, and synthesizing feedback. That work is handled for her. She can focus her attention on interpreting what matters, making decisions, and guiding leaders on the right response at the right time.
The Limits and the Responsibility
Grounding AI in the context of your work is powerful, but it is not infallible.
It can surface incomplete information, miss nuance, or misinterpret what matters most. Human involvement and judgment remains essential.
As Maura Thomas has long emphasized, we can improve our performance at work by reducing the friction that hijacks our attention and gets in the way of unleashing our genius. When systems handle the work of gathering and organizing information, they free up humans to do higher-level strategic thinking in an undistracted way.
For Maya, the shift is simple: less time gathering context, more time making decisions. But the deeper change is about attention. Instead of reacting to inputs, she can focus on making meaningful progress toward her most significant results.
Empowered Productivity with Copilot training can help your team learn how to use AI tools to free your cognitive load, so you can focus on what you do best — using your human judgment, decision-making power, analysis, creativity and strategic thinking.
Key Takeaways
● AI doesn’t have to just speed up work. It can improve how decisions get made.
● The biggest shift is from gathering information to acting on it.
● Context aware systems are redefining how AI is used in the workplace—shifting from generating content to supporting decisions.
● The most effective use of AI is not replacing thinking, but freeing up space for better thinking.
Frequently Asked Questions
What does it mean for AI to be “grounded in context”?
It means the system is connected to your actual work environment, so it responds based on your emails, documents, meetings, and conversations, not just general knowledge.
How is this different from earlier AI tools?
Earlier tools required you to provide all the context in a prompt. Now, AI can pull relevant information from across your work systems, reducing the need to gather it manually.
Is AI replacing human decision-making?
We shouldn’t let it. A better use of AI is to support decisions by surfacing information and suggesting actions, but human involvement remains essential. Leaders should emphasize that team members are responsible for what AI creates. We can talk to it like an expert, but we must supervise it like an intern.
What skills are important as AI becomes more context aware?
Human value shifts toward:
- asking better questions
- interpreting results
- making strategic decisions
What is the biggest risk with context aware AI?
Over-reliance. Context can still be incomplete or misinterpreted, so outputs need to be reviewed and validated by humans.
