Enterprise AI
Gemini Enterprise
Also known as: Google Gemini Enterprise
Gemini Enterprise is Google's platform for bringing Gemini models, AI agents and company data together in a single workplace interface. It lets staff search internal systems, ask questions in natural language and run agents that carry out multi-step tasks, with admin controls over access and governance. It is aimed at organisations that want assistants grounded in their own documents, tickets and records rather than general web knowledge.
What it is
Gemini Enterprise is a managed environment where employees interact with Gemini models through a chat-style front end connected to approved company sources. It combines search across connected repositories, prebuilt and custom agents, and an agent builder that does not require heavy engineering work. Administrators configure connectors, permissions and audit logging centrally.
Why it matters
It changes where internal discovery happens: instead of searching an intranet or a CRM directly, people ask an assistant and receive a synthesised answer with citations. For marketing and content teams, that means internal documentation, brand guidance and product truth need to be as structured and retrievable as public pages. Poorly maintained internal content produces confident but wrong answers in the same way it does in public AI search.
How it works
Teams connect data sources such as Google Workspace, SharePoint, Confluence, Jira, Salesforce or object storage, then define agents for recurring tasks like drafting briefs, summarising research or answering policy questions. Retrieval grounds responses in the connected corpus, and permissions are inherited so users only see answers built from content they are entitled to read. Usage is monitored through admin dashboards and audit trails.
When it applies
It applies when an organisation already runs on Google Cloud or Workspace and wants governed, data-grounded AI assistance for internal teams rather than a public-facing chatbot.
Examples
- A product marketing team asks an agent to summarise the last four quarterly launch retrospectives held in Google Drive before planning the next launch.
- A support operations lead builds an agent that drafts a first-response answer by pulling from the help centre and recent Jira tickets.
- A compliance team restricts a policy agent to a single approved document library so answers never draw on draft material.
How it is measured
- Weekly active users and repeat usage per connected team
- Share of agent answers with citations traced to approved sources
- Deflection rate: internal queries answered by an agent versus escalated to a colleague or helpdesk
- Time to complete recurring tasks before and after agent rollout
Insights on Gemini Enterprise
Related terms in Enterprise AI
- AI agentsAI agents are software systems that use a language model to plan and carry out multi-step tasks, rather than simply returning a block of text. They can call tools, query APIs, browse websites and write to other systems in pursuit of a goal, with varying degrees of human oversight. The term covers everything from a scripted assistant that books a meeting to a research agent that gathers sources and drafts a report.
- AI chipsAI chips are processors designed or optimised to run machine learning workloads, especially the large matrix operations behind training and inference. The category covers GPUs, tensor and neural processing units, and custom ASICs, usually paired with high-bandwidth memory and fast interconnects. They are also called AI accelerators or AI semiconductors.
- AI cost managementAI cost management is the practice of tracking, forecasting and controlling what an organisation spends on AI systems, including model API calls, inference compute, vector storage and the tooling around them. It applies familiar financial discipline to usage that is variable, token based and easy to scale without noticing. The goal is predictable spend at an acceptable level of quality and latency, rather than the lowest possible bill.
- AI drug discoveryAI drug discovery is the use of machine learning and computational models to support the early stages of finding and refining new medicines. Models are applied to tasks such as predicting protein structures, identifying candidate molecules, prioritising targets and estimating properties like toxicity or binding affinity. The aim is to narrow a very large search space before expensive laboratory and clinical work begins.
- AI infrastructureAI infrastructure is the stack of hardware, networking, storage and software needed to train, fine-tune and serve AI models at scale. It spans accelerators such as GPUs, the data centres and power that house them, and the orchestration and serving layers that turn raw compute into working model endpoints. For most marketing teams it is a cost and capacity constraint they consume through APIs rather than something they build.
- AI procurementAI procurement is the process of evaluating, buying and contracting artificial intelligence tools and services, from assistants and content platforms to models accessed through an application programming interface. It covers commercial terms alongside security, data protection, accuracy and integration questions that do not arise with ordinary software. Most organisations run it as a structured review with pilots, stakeholder sign off and defined exit conditions.
Primary research · August 2026
How ChatGPT Shortlists Software Brands
An audit across 10 categories and 60 buying questions. I recorded what ChatGPT reads, throws away and links to when a buyer asks it which software to buy, and what that decides.
- 60
- Questions asked
- 10
- Software markets
- 2,680
- Results read
- 367
- Links shown
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