Two years into Copilot, the individual productivity gains are no longer in doubt. Drafting is faster. Summaries arrive in seconds. Questions that used to require three colleagues now require one prompt. For most knowledge workers, AI has become a genuine part of the daily workflow.
But spend time inside teams that depend on this technology, and a second pattern surfaces. The individual is faster. The team is not yet that fast.
That gap is the most interesting frontier in enterprise AI right now.
The shift we're already in the middle of
AI in the workplace started as features. Smart Compose. Transcript summaries. Autocomplete in the IDE. Those features became systems: Copilot in Microsoft 365, GitHub Copilot, agents that plan and act across tools. Each step moved the unit of value up a level.
The next step moves it up again — from the individual to the team. Features helped a person finish a task. Systems helped a person do their whole job better. Team intelligence helps a group of people operate as a single, aligned whole.
Why individual AI works so well
The wins at the individual level share a common shape. The user has the context. The task is bounded. The output goes back to the same person who asked for it. Drafting, summarization, retrieval, prototyping — AI fits this loop perfectly: clear input, clear output, one mind in charge of integrating the result.
Teams don't work that way.
Where teams are different
Teams aren't collections of individuals. They run on something more elusive: shared understanding.
Take a product team deciding whether to ship. The context lives across Teams chats, SharePoint docs, a backlog ticket, last week's email thread, and a half-remembered hallway conversation. Decisions shift. Risks come and go. The current state of the team's thinking exists, partially and in different forms, inside everyone's heads.
Today's AI gives each person a faster path through information. But it's invoked one prompt at a time, by one user, against whatever slice of data they happen to have open. The next prompt starts from scratch. That works for individual tasks. It's not yet how teams stay aligned.
The clearest comparison: engineering vs. knowledge work
The cleanest way to explain this is to compare knowledge work to engineering.
Engineering has spent decades building shared infrastructure for collaboration. The codebase is the source of truth. Version control captures every change. Pull requests make decisions explicit. Tests encode shared expectations. State is observable, and the system can tell you exactly where things stand.
Knowledge work has some of that, but information is unstructured and scattered across tools that were never designed to share state. Decisions surface in a chat thread that scrolls out of view. The "state" of a project lives in slide decks that are already stale and meeting notes that captured some of what was said and almost none of what was decided.
This isn't a failure of knowledge workers. The substrate is genuinely harder. And it's exactly where AI has the most room to add a new kind of value.
What "team intelligence" actually means
Team intelligence, as I think about it, is concrete:
A shared, continuously updated picture of what a team is working on, what's been decided, what's at risk, and what comes next — available to everyone, in the form each person needs.
It's the difference between asking "can you summarize this thread?" and asking "what's the current position on the launch date, and what changed it last week?" One is retrieval. The other is continuity.
What this means for AI systems
A few things have to come together to get there.
AI systems need to work across tools and data sources, not within one at a time. The signal that a project is at risk rarely lives in one document; it lives in the gap between a Teams message, a delayed task, and a doc comment no one followed up on. Shared context has to become a first-class concept, not a side effect of one user's prompt history. And continuity has to span days and conversations, not reset with every prompt.
The pattern that keeps emerging is a combination of two strengths: deterministic, structured systems that reliably track entities, decisions, and state, paired with AI's ability to extract meaning from unstructured information and reason over it. Neither is sufficient alone. Together, they extend what Copilot already does well into the shared layer above the individual user.
What you can do today
You don't have to wait for the next generation of tools to start moving in this direction. Most of what makes team intelligence possible is groundwork, and the teams that get the most out of AI tomorrow will be the ones doing that groundwork now.
Start with where your team's context lives. Decisions buried in chat threads that scroll away are invisible to any system trying to help you. Decisions captured in a doc, a recap, or a structured channel post are not. The small habits compound quickly: writing decisions down where Copilot can see them, keeping project information in shared spaces instead of personal drives, and treating meeting notes as a deliverable rather than a side effect.
The same logic applies to how teams use Copilot. The biggest gains aren't from individuals discovering clever prompts in isolation. They come from teams agreeing on a few shared ways of working: how status gets summarized, how meetings get prepped, how context gets handed off. AI amplifies whatever pattern a team already has. Investing in that pattern is the highest-leverage thing you can do today.
None of this requires waiting. And it positions teams well for the richer, more connected experiences ahead.
If you want to go deeper
This is what I'll be unpacking in my session at ECS 2026 — what's working with Copilot at the team level today, patterns I'm seeing across real team workflows, and concrete steps for setting your teams up for the next shift. If this resonates, I'd love to see you there.
The question is no longer whether AI can make individuals faster. It clearly can. The question is what we build on top, so teams can think and act with the same clarity their members are starting to enjoy.
That's the shift worth watching. And it's the one I'm most optimistic about.