What is Google Gemini?
Google's family of models and AI assistant, integrated across Workspace, Cloud, Android and Search.
Gemini is Google's family of language and multimodal models, and also the name of the conversational assistant built on top of it. It emerged from the convergence of Google's previous AI research efforts (DeepMind and Google Brain brought under one roof), and was multimodal by design from the start: built to work across text, images, audio and video together, not bolted on as a later feature. The name nods to that very fusion, twins born from two research organizations that had spent years working in parallel with different cultures and priorities, and the family ships in several sizes (lighter versions built for on-device use and low-cost apps, more capable ones for complex reasoning tasks) precisely to cover very different use cases, from quickly drafting an email to analyzing an entire technical document or a long video the user uploads.
What it does
Gemini's distinguishing trait is not just the model, but the distribution: it is built into the Gmail inbox, Workspace documents, Google Search, the Android operating system and Google Cloud consoles. For a company already running on Workspace or Google Cloud as its reference platform, Gemini often arrives without an explicit choice being made: it is already there, inside the tools the team uses every day. It is also available via API for anyone who wants to build their own applications on top of its models, much like the other frontier labs.
How to evaluate it as a buyer
Gemini's most concrete competitive advantage is distribution: if the company already lives in the Google ecosystem, adoption friction is minimal and integration with business data (email, documents, spreadsheets) is native, with all the governance questions that raises (who can query what, with which permissions). Outside that ecosystem, it should be compared on equal footing with the other general-purpose assistants on actual quality for your use case, not brand recognition: no vendor should be chosen by default just because it is the largest. Even the right choice carries a separate risk: getting the project all the way to production, not just to the first pilot, which I cover in the post on why AI projects stay stuck in pilot mode.
Related terms
- LLM · An AI model trained on huge amounts of text that understands and generates language: the engine behind ChatGPT, Claude and Gemini.
- Multimodal AI · Models that understand and produce multiple formats together: text, images, audio, video. Documents get read, not transcribed.
- AI Agents (Agentic AI) · AI systems that go beyond answering: they plan, use tools and take actions autonomously inside your processes.
- OpenAI · The lab behind ChatGPT and the GPT model family: one of the industry's reference points, not the only one.
A term that hits close to home? Let's talk.
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