Google tests an AI productivity agent that lives in your inbox

Google is testing a new AI-powered productivity agent that operates through the inbox, signaling a potential shift in how enterprise workers may interact with calendars, documents, and daily tasks.

The experimental tool, called CC, delivers a daily briefing to users’ inboxes and can draft emails and suggest next actions by pulling information from Gmail, Google Calendar, and Google Drive, pointing to Google’s broader effort to make AI agents more proactive rather than reactive.

“This briefing synthesizes your schedule, key tasks, and updates into one clear summary, so you know what needs to be done next, whether it’s paying a bill or preparing for an appointment,” Google said in a blog post. “CC also prepares email drafts and calendar links when needed to help you take action quickly.”

Users can also guide how CC works by communicating with it directly through email, using messages to give specific instructions.

CC is launching as a consumer-focused experiment, but analysts say its inbox-first design highlights patterns that could later translate into enterprise workflows.

“Google understands, like others, that AI has far greater potential on the enterprise productivity side, and that everyone is exploring how to capture that opportunity,” said Faisal Kawoosa, founder and chief analyst at Techarc. “Microsoft has Copilot, which is now integrated across Office and is effectively the default for many enterprise users.”

Inbox as a control layer

Analysts say an email-centric AI agent is not a productivity tool in the conventional sense. It functions as a behavioral layer, shaping what employees see first each day and what they interpret as important.

“Email is the first and last thing most of us check in enterprises,” Kawoosa said. “Putting the AI agent directly in the inbox makes sense because it does not force users to detour from their normal workflow.”

Neil Shah, VP for research at Counterpoint Research, pointed out that on average, email-related workflow accounts for a significant 25–30% of the daily worker productivity. This makes it the lowest-hanging opportunity for AI to automate related workflows and boost productivity and work efficiency.

This design choice would matter especially in large organizations, where adoption often stalls when tools require users to change habits.

“The gains would show up fastest in coordination-heavy roles,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. “Executives, managers, sales leaders, operations teams, and program owners spend most of their day reconciling signals from different threads. A daily operating brief reduces decision drag, not workload. It shortens the gap between knowing and acting. It also reduces the number of meetings created purely to regain shared context.”

At the same time, analysts warn that the very compression that makes these tools attractive can introduce subtle risks. Summaries inevitably strip away nuance, and prioritization logic encodes assumptions that may not align with how decisions are actually made inside an organization.

Trust and control risks

The primary governance risk is not access to data, but the combination of implied authority and lasting records. Email is where informal approvals happen, intent is inferred, and context often matters more than exact wording.

When an AI agent operates in this environment, its outputs can turn fleeting signals into durable artifacts, influencing decisions and creating records that extend beyond the original exchange.“CIOs have to be careful about how these AI agents access, process, personalize, and share information,” Shah said. “If an employee switches roles or leaves, how is the knowledge secured with all privacy protocols in place?”

What often catches enterprises off guard, Gogia said, is that AI does not simply process information but creates new artefacts. In many cases, those summaries, extracted actions, inferred priorities, and generated content can persist beyond the moment they are created.

“Those artefacts become discoverable records whether teams plan for them or not,” Gogia said. “If IT cannot clearly account for them, the organization is creating legal exposure at machine speed.”

This means that governance must be built in from the outset. Read-only access should be the default, with explicit human approval for any actions. Equally important, all AI activity needs clear logging and ownership, while retention and deletion policies must explicitly include AI-generated content. Data residency and boundary controls should be defined before enterprise rollout, not retrofitted after a compliance or legal incident.Google tests an AI productivity agent that lives in your inbox – ComputerworldRead More