Google Launches Gemini Enterprise to Bring AI Into Workflows

Google on Oct. 9 launched Gemini Enterprise, a new business offering that embeds the Gemini model family into workplace workflows. The platform ships with pre-built Google agents, a no-code agent workbench and connectors to core business systems — positioning Google squarely against Microsoft, OpenAI and Anthropic in the enterprise AI market. 

What Gemini Enterprise offers

Gemini Enterprise packages Google’s most advanced Gemini models into a conversational platform where employees can query company data, documents and applications. The product includes a library of pre-built agents for tasks such as deep research and data insights, plus a no-code/low-code workbench that lets teams build and orchestrate custom agents and multi-step workflows. Google says the aim is to let non-technical users automate routine processes while giving engineers tools to compose more complex agent logic.

Early adopters and integrations

Google has already signed a set of enterprise customers from retail to fintech and is positioning Gemini Enterprise to work across common enterprise stacks. Early customers named in Google’s launch include Gap, Figma and Klarna, and the platform ties into Google Workspace while offering connectors to other business systems, enabling contextual responses that reference corporate files and apps.

How Gemini fits the enterprise AI race

Big cloud and AI vendors are converging on the same playbook: model access + agent tools + enterprise-grade controls. Gemini Enterprise mirrors rival offerings by bundling pre-built agents and developer tooling while pushing Google’s strengths — large-scale models, search and data indexing — as differentiators. Analysts see the launch as another step in the broader “AI agents for work” battleground where Microsoft, OpenAI and Anthropic are also racing to win enterprise mindshare. 
To the CIOs and risk teams, Gemini Enterprise will be subject to trade-offs:

• Data governance: The fact that agents can access live corporate data increases the importance of having tight access control, provenance, and an audit trail.
  
• Precision & delusion: Ready-made agents can accelerate processes, yet companies have to check the results and incorporate human-in-the-loop controls in important decision-making.
• Complexity of integration: It is not a case of zero effort incorporation into ERPs and CRMs and custom applications; real-world integration work requires provision of secure, reliable connectors.
• Vendor lock-in & portability: Building business processes around a single cloud-provider agent ecosystem raises long-term portability questions.

These are practical considerations that separate pilot projects from production deployments.

Quick CIO checklist before rollout

• Define clear use cases with measurable KPIs (time saved, error reduction, TTV).
• Audit data access: map which documents and systems agents will touch.
• Require immutable logs and explainability for decision-critical agents.
• Run red-team tests for hallucinations and adversarial prompts.
• Plan phased adoption (sandbox → controlled rollouts → enterprise scale).

Gemini Enterprise brings Google’s model stack and ecosystem play to the heart of the workplace. For enterprises, the value will come from pragmatic use-case selection, robust governance, and integration discipline — not from hype. If Google can balance usability with enterprise controls, Gemini Enterprise could become a practical way to fold AI into everyday workflows; if not, the platform risks becoming another costly pilot in the enterprise AI graveyard.

Pricing and Language Support

Gemini Enterprise has two price levels, Standard and Plus, which begin with the price of $30 per user per month, and the Gemini Business plan costs $21 per user per month.The platform can be found in India and every other market that Google Cloud has penetrated, with the Indian pricing not announced yet.Gemini Enterprise also facilitates various Indian languages such as Hindi and Gujarati.