AI agents in the enterprise sound like the future. As of last week, they are reality. Google has officially launched Gemini Enterprise Agents. With their own workspace, email address, calendar, and audit trail.
Not a copilot that makes suggestions. Not a chatbot that answers questions. An agent that acts independently: answering emails, retrieving data from BigQuery, Snowflake, or Salesforce, creating reports, and setting up tasks in Jira. Without a human having to approve every step. The opportunities are vast. How quickly companies benefit depends on their preparation.
What is an AI agent in a business context?
An AI agent is an AI system that independently performs multi-step tasks without requiring human approval for each step. It receives a goal, plans the necessary steps, accesses tools and data sources, and delivers a result. The difference from a traditional AI assistant: the agent acts proactively, not reactively.
Google’s Gemini Enterprise Agent takes it a step further: it is given a corporate identity: its own email address, a calendar, a Workspace account. It knows the team structure, the time zones, the approval hierarchies. It acts on behalf of the company, not just on a user’s instructions.
CEO Sundar Pichai put it this way at the announcement: “From an assistant that waits for instructions to an agent that works independently on the most difficult problems. With security, scale, and performance.”
Why is this relevant for controlling and finance teams?
AI agents in the enterprise have so far had a fundamental problem: they don’t know the context. Who is responsible? Which policy applies? What budget is still available?
Gemini Enterprise Agents solve this problem through context awareness. The agent knows:
- Team structure and responsibilities within the company
- Approval workflows and signing authorizations
- Calendars and availability of the relevant people
- Current data from connected systems (BigQuery, Databricks, Postgres, Snowflake)
- Documents from Google Workspace, Microsoft 365, Confluence, and Jira
For finance teams, this means that routine tasks that currently take hours can be delegated. Not to an assistant that keeps asking questions, but to an agent that delivers.
Thomas Kurian, CEO of Google Cloud, described the principle this way: “We’re shifting from instructions to goals.” You give the agent a goal. It decides how to achieve it.
Is your system landscape agent-ready?
We’ll work together to analyze what already fits and what is still missing in a structured assessment.
Which systems does Gemini Enterprise Agent integrate with?
The strength of the Gemini Agent lies in its deep integration. Supported at launch:
For controlling teams, the combination of BigQuery/Snowflake and Google Workspace is particularly interesting: the agent can retrieve data directly from the data warehouse, consolidate it in Sheets, and send it as a report by email, without any manual intermediate steps.
What does the audit trail look like, and what does compliance need to know?
That’s the question every finance department should ask first. And Google has an answer:
Every action of the Gemini Enterprise Agent is logged: who had which agent perform what, what data was retrieved, what was changed or sent. The audit trail is a core feature, not an afterthought.
What this doesn’t solve: The question of whether an agent may act autonomously is a governance decision, not a technical one. Companies must define which tasks AI agents may handle without individual approval, and which they may not.
Our recommendation: Initially deploy AI agents in a clearly defined area that does not touch sensitive financial data. Clarify audit trail configuration and data classification before the first productive use.
Which tasks may an agent complete autonomously?
We help you draw a clear line — before compliance rules emerge reactively.
Which tasks can an AI agent take on in finance and controlling?
Five tasks that an AI agent in finance already handles independently today:
- Data queries and status reports: The agent retrieves actual figures from the data warehouse daily, compares them with the plan, and creates a short status report, without a manual Excel export
- Reporting automation: Standard monthly reports are built, checked, and emailed to the defined distribution list
- Exceptions and escalations: The agent detects deviations above a threshold and automatically creates an escalation draft for the person responsible
- Data integration: Sources from different systems (CRM, ERP, BI) are consolidated for ad hoc analyses without an analyst having to connect each step manually
- Meeting preparation: Before the monthly management review, the agent summarizes relevant KPIs, deviations, and comments
What AI agents can’t do: Replace professional judgment. The decision as to whether a deviation is a real problem or can be explained by one-off effects remains with humans. The agent provides the foundation. Not the judgment.
How BI2run puts AI agent development into perspective for you
Implementing AI agents in finance practice doesn’t mean choosing a new AI vendor. It means making your own data foundation agent-ready. That is the part that determines success or failure.
If an agent encounters incomplete, poorly structured, or non-harmonized data, it delivers incorrect results, without warning. Our approach: first clean up the data foundation, then put the AI on top. IBM Planning Analytics and TM1 are a strong foundation for this because dimensions, hierarchies, and consolidation rules are already semantically well defined.
If, after an assessment, we tell you that your company isn’t ready yet, that’s honest advice. Not bad news, but a clear roadmap.
AI Agents · Finance & Controlling
First the data foundation. Then the AI.
We’ll work out together whether your company is agent-ready, or what is still missing. Not a sales pitch, but an honest assessment with a clear roadmap.
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Glossary
FAQ
How does Gemini Enterprise Agent differ from an AI copilot like Microsoft Copilot?
A copilot is reactive: it responds to requests and makes suggestions. An agent is proactive: it receives a goal, plans the steps on its own, and executes them: with real tools and data sources, not just text generation. Gemini Enterprise Agent also has a corporate identity: it acts on behalf of the company, not as an add-on for an individual user.
How reliable are AI agents for financial analysis?
That depends directly on the quality of the data foundation. An agent working with clean, semantically well-defined data delivers reliable results. An agent running on unstructured, poorly harmonized data makes mistakes without warning. That’s why data preparation is the critical factor, not the choice of AI.
Do I need Google Workspace to use Gemini Enterprise Agents?
No. Gemini Enterprise Agents also natively integrate with Microsoft 365, Slack, Jira, and common databases. Google Workspace is an advantage, but not a requirement.
How do I deal with GDPR requirements for AI agents?
AI agents that process personal data are subject to the same GDPR requirements as other systems. Important: data minimization (the agent should only access the data it needs), purpose limitation, and clear responsibilities. Before production use, we recommend a data protection impact assessment for the specific use cases.
Which tasks should AI agents in finance not perform autonomously?
Anything that requires individual approvals, has regulatory relevance, or has irreversible consequences still belongs in a human approval workflow. Payment approvals, contractual commitments, credit decisions: here, an agent makes sense as a preparer, but not yet as the sole decision-maker.
When should a company start using AI agents in finance?
Now, but with a clearly defined pilot. A reporting agent that creates and sends standard reports without personal data is a good place to start. It demonstrates the value without taking on regulatory risk. The experience from the pilot then informs the rollout strategy.
