Google Duet AI Renamed to Gemini: What Changed (And What Didn't)

Google renamed Duet AI to Gemini for Workspace. The rebrand doesn't change the fundamental reality: Google's AI feels like a stranger to your organization.

It can write emails. It can summarize documents. It can help create presentations.

It doesn't know your business.

Ask it about your strategic priorities. It doesn't know them. Ask about a decision made last quarter. It can't find it. Ask it to connect information across your company's work. It doesn't understand the connections.

This isn't a bug. It's the architecture.

What Gemini for Workspace Does

Document-Level Assistance

Gemini operates at the document level:

In Gmail: Draft emails, summarize threads, suggest replies In Docs: Write content, summarize documents, help with editing In Sheets: Generate formulas, analyze data in the current sheet In Slides: Create presentations, generate images, suggest layouts In Meet: Take notes, summarize meetings

The pattern: Gemini helps with the document or conversation in front of you. Its context window is what you're currently working on.

Google's documentation describes these capabilities accurately. Within their scope, they work.

The Search Addition

Gemini can search across your Workspace data—emails, documents, calendar. When you ask a question, it retrieves relevant content.

The improvement: You don't have to know exactly where something is. The limitation: Retrieval isn't understanding.

Search finds content containing keywords. It doesn't understand organizational context, relationships between documents, or why things matter.

Where Gemini Falls Short

The Strategic Understanding Gap

Ask Gemini: "Based on our strategic priorities, which project should we focus on this quarter?"

What you'll get: Generic advice about project prioritization, or a request for you to provide the strategic priorities it should consider.

What you need: AI that already knows your strategy, understands your projects, and can reason about priorities with organizational context.

Gemini doesn't know your strategy because your strategy lives in scattered documents, emails, and conversations without coherent structure it can understand.

The Memory Gap

Ask Gemini: "Why did we decide to pivot from B2B to B2C six months ago?"

What you'll get: Search results for emails and documents mentioning those terms. Maybe a document that explains it, if someone wrote one and it's findable.

What you need: AI that remembers organizational decisions, knows the context behind them, and can explain rationale even when it wasn't explicitly documented.

Gemini's "memory" is your documented content. If it wasn't written down and made findable, it doesn't exist to AI.

Research shows 80% of organizational knowledge is tacit—in people's heads, not systems. Gemini can only access the 20% that's documented, and even then, only if search can find it.

The Connection Gap

Ask Gemini: "How does our current marketing campaign connect to our product roadmap?"

What you'll get: Potentially some retrieved documents about marketing and product—if they exist, are findable, and explicitly mention each other.

What you need: AI that understands how different parts of your organization relate, can trace connections between initiatives, and identifies dependencies.

Gemini doesn't understand your organization's structure. It searches content. Structural understanding requires organizational memory architecture, not just document search.

The Learning Gap

Ask Gemini: "Based on what worked in previous launches, what should we do differently this time?"

What you'll get: Generic launch advice, or search results that might contain relevant historical documents.

What you need: AI that has accumulated learning from your organization's history—what worked, what didn't, what patterns repeat.

Gemini doesn't learn your organization. Each interaction starts fresh. There's no cumulative intelligence built from organizational experience.

The Architecture Problem

Why This Happens

Gemini for Workspace is built on top of Google Workspace's existing architecture:

Workspace architecture: Files in folders. Emails in threads. Calendar events. Separate silos.

Gemini addition: AI that can search these silos and help with individual documents.

What's missing: A coherent organizational knowledge layer that connects everything.

The architecture problem predates AI. Google Drive's knowledge graveyard isn't fixed by adding AI search—the underlying chaos remains chaotic.

Document AI vs. Organizational AI

Document AI (what Gemini does):

Helps with the current document
Searches and retrieves content
Generates text based on prompts
Works within single contexts

Organizational AI (what's needed):

Understands organizational structure
Maintains persistent knowledge
Connects information across silos
Learns and accumulates understanding

The gap between these isn't incremental. It's architectural.

The Retrieval Limitation

Gemini uses RAG (Retrieval Augmented Generation)—retrieving relevant content to inform AI responses.

RAG limitations for organizational intelligence:

Must find the right content (search quality matters)
Must interpret correctly (context within content matters)
Can't use knowledge that isn't documented
Can't synthesize across many documents coherently
Can't understand relationships not explicitly stated

RAG is a technique. It's not organizational memory.

The Cost-Benefit Analysis

What You're Paying

Gemini for Workspace is included with some plans or available as an add-on. Pricing varies by plan and changes over time.

What You're Getting

Real value:

Faster email drafting
Document summarization
Meeting notes
Formula help
Presentation assistance

These are legitimate productivity improvements. Gemini handles individual tasks well.

What You're Not Getting

Strategic AI that understands organizational context
Memory that persists across interactions and time
Cross-functional intelligence that connects silos
Cumulative learning that grows with your organization

If your need is task-level assistance, Gemini provides it. If your need is organizational intelligence, Gemini doesn't address it.

The Alternative Architecture

What Organizational AI Requires

The Context Compass framework describes what comprehensive AI memory needs:

Working Memory: Real-time awareness of what's happening now Episodic Memory: Historical knowledge of what happened before Semantic Memory: Structural understanding of how things work Procedural Memory: Operational knowledge of how to do things

Gemini touches Working Memory (current document context) and can retrieve from Semantic Memory (documented policies). It lacks true Episodic Memory (organizational history) and Procedural Memory (learned workflows).

From Assistant to Partner

Task assistant (Gemini level):

"Help me write this email"
"Summarize this document"
"Create a slide about this topic"

Strategic partner (organizational AI level):

"Based on our strategy and this client's history, what should we propose?"
"Given past project patterns, what risks should we watch for?"
"How does this initiative align with our goals and resources?"

The difference isn't marginal improvement—it's fundamental capability change.

Building Organizational Memory

Instead of accepting document-level AI as the ceiling:

Unified platforms: Where work, documents, and decisions exist together with inherent structure AI can understand.

Context engineering: Deliberate architecture that builds organizational memory across all four layers.

Learning systems: AI that grows more capable as organizational history accumulates.

The Decision Framework

When Gemini for Workspace Works

Good fit:

Task-level AI assistance is the goal
Workspace is already the productivity platform
Individual productivity improvement is the priority
Organizational knowledge needs are modest

When Something More Is Needed

Consider alternatives when:

Strategic AI capability matters
Organizational memory is important
Work happens across many tools beyond Workspace
You need AI that learns your organization

The Integration Question

"We're in Google Workspace. Can we add organizational memory?"

Possible approaches:

Third-party tools that layer on top of Workspace
Gradual platform transition for key workflows
Hybrid architecture with specialized systems for organizational knowledge

The honest reality: Workspace's underlying architecture fragments data in ways that make comprehensive organizational memory difficult. True unification may require different foundations.

Experience AI That Remembers

Want to see what AI looks like when it actually knows your organization? Waymaker Commander brings organizational memory to work management—AI that understands your strategy, remembers your decisions, and connects context across work.

The result: Ask strategic questions, get strategic answers. Build on previous interactions. Watch AI capability grow with your organization.

Gemini for Workspace is document-level AI marketed as workplace transformation. It helps with individual tasks well. It doesn't provide organizational memory, strategic understanding, or cumulative learning. Understanding this limitation is essential for making informed decisions about AI investments and organizational capability building. Learn more about our Context Compass framework and explore how context engineering builds the organizational memory AI actually needs.

Stuart Leo has evaluated enterprise AI solutions across major platforms. He's the author of Resolute and founder of Waymaker, designed for organizations that need AI with actual organizational understanding.