Gemini 3 Just Launched. Here's What It Means for Your AI Strategy

Google announced Gemini 3 yesterday, and the benchmarks are impressive. Multimodal reasoning improvements, new developer capabilities, and performance gains across the board.

If you're a Google Workspace customer running Gemini Enterprise, this is excellent news.

But if you are a ChatGPT, Claude or Copilot customer and your first thought was "should we switch providers?" you have a different problem.

Model launches shouldn't trigger platform evaluations.

The question shouldn't be "which model has the best benchmarks this week?"

It should be "are our teams using our chosen provider well enough that model-hopping isn't a simple option?"

Applications, Not Models

Every few months, a new model launch dominates headlines. GPT-5.1, Claude 4.5, Gemini 3.

The pattern is predictable: impressive benchmarks, excited tweets, and companies wondering if they should switch.

Here's my challenge: enterprise value in employee-led use cases doesn't come from model capabilities. It comes from the applications, primitives, and collaboration features built on top of them.

What matters for your business is where work gets done:

• ChatGPT Shared Projects that contain your team's institutional knowledge
• Custom GPTs that automate repeatable workflows
• Canvas for collaborative document editing
• Gemini's Google Workspace integration for simple sharing and collaboration
• Claude Skills for training the model how to act
• Connectors to your data systems (Google Drive, SharePoint, Linear)

My point is not a ChatGPT v Gemini v Copilot one - its that whatever tool you have the value comes from using it as a team, not as individuals.

But companies never get there because their employees never go deep enough to discover them.

The T-Shaped Approach: Broad Awareness, Deep Expertise

The best AI strategies I see follow a T-shaped model:

Horizontal bar (breadth): Employees have awareness of multiple AI tools. They know ChatGPT exists, they've tried Claude, they've seen Gemini demos. They understand the landscape.

Vertical bar (depth): Employees across the company have genuine expertise on ONE platform and collaborate with their colleagues in that platform.

They know how to create Projects, build custom GPTs, set up team workflows, integrate with company data, and teach others.

Companies sometimes get the horizontal bar right. They give employees access to multiple tools, encourage experimentation, and celebrate exploration.

But they fail to build the vertical bar. No one goes deep enough to unlock collaboration features that require setup, configuration, and behavior change.

The result? Everyone uses AI like a search engine. FAQ-level queries. "Summarize this." "Write me a draft." "Explain this concept." Useful, but nowhere near transformative.

What Model-Hopping Actually Tells You

When employees constantly switch between AI platforms chasing the latest model release, that's not curiosity. That's a symptom.

It means your change program hasn't delivered.

Here's why: If your teams were deep on ChatGPT Enterprise, they'd have Projects containing months of team context, custom GPTs automating critical workflows, and Canvas documents integrated into their process. When GPT-6 launches, they'd immediately apply it to those existing workflows and see compounding returns.

Same for Gemini Enterprise customers. If they've built AI Studio workflows, created team-shared Gems, and integrated Gemini with Google Workspace, a new model version makes those investments more powerful, not obsolete.

But if employees are model-hopping, they're not invested in any platform's advanced features. They're using AI like a better Google search. When a new model launches with better benchmarks, of course they want to try it. They have nothing to lose.

Model-hopping is a diagnostic. It tells you no one has gone deep enough to unlock real value.

Tools Everywhere, Depth Nowhere

I spoke with a COO last week whose company is experiencing this pattern. Their team uses:

• ChatGPT (some on free, some on Plus, a few on a Business plan)
• Claude (individual subscriptions including Claude Code)
• Gemini (comes with Google Workspace)
• Plus an AI module that comes with each SaaS product.

It is easy to celebrate this as "AI-forward culture." Employees are experimenting! Multiple tools means optionality! No vendor lock-in!

But when I ask people in this situation about collaboration features, the picture changes:

• Few/zero custom GPTs built for repeatable workflows
• Few/zero Shared Projects containing team knowledge bases
• Few/zero connectors to company data systems
• Lack of team training beyond "here's how to ask questions"

Every employee is using AI alone, for basic tasks, switching tools when they hit limitations or heard about a new model.

The diversity of tools now isn't a strength. It is preventing depth.

No one had invested enough in any single platform to discover Projects, build custom GPTs, or set up team workflows. And because no one had made that investment, AI remained a personal productivity tool, not a team capability.

What "Going Deep" Looks Like

Deep platform usage isn't about memorizing features. It's about behavioral integration that creates compounding returns.

For ChatGPT Enterprise teams:

• Projects contain sales call transcripts, product specs, and customer research that inform every new conversation
• Custom GPTs automate proposal generation, code review, and customer onboarding workflows
• Canvas enables real-time collaboration on strategy documents and pitch decks
• Data connectors pull from Google Drive and Github so AI understands company context

For Google Gemini Enterprise teams:

• AI Studio is where product teams test prompts and refine workflows before rolling them out
• Gemini in Docs/Sheets/Slides becomes the default co-author, not an afterthought
• NotebookLM Bring research, documents and videos to life with AI generated podcasts and video summaries
• Gems share prompts, templates, and best practices

For Claude Enterprise customers:

• Projects with shared team context and uploaded documentation
• Artifacts for generating and iterating on code, documents, and analysis
• Skills package up your way of working and executing tasks

These aren't advanced features reserved for power users. They're the core value proposition of enterprise AI platforms. But they require setup, training, and sustained usage to deliver returns.

Action Plan: Platform Consolidation + Training Investment

If Gemini 3's launch has you questioning your AI strategy, here's what to do:

1. Audit Current Usage Patterns

Ask three questions:

• How many AI platforms do employees actively use? (Not licenses purchased—actual usage)
• What percentage of employees use collaboration features? (Projects, custom GPTs, team workspaces, connectors)
• Can you name three repeatable workflows that run on AI? (Not individual tasks—team workflows)

If the answers are "many platforms," "almost no one," and "no," you have a depth problem, not a model selection problem.

2. Pick Your Platform (And Commit)

Choose based on your existing infrastructure:

• Google Workspace customers: Gemini Enterprise (native integration advantage)
• Microsoft 365 customers: Copilot or ChatGPT Enterprise (both integrate well)
• Platform-agnostic teams: ChatGPT Enterprise (broadest feature set, largest ecosystem), or Claude Enterprise for companies with large engineering teams.

The specific choice matters less than the commitment to depth. Pick one. Invest in training. Build workflows. Measure adoption of advanced features, not just chat volume.

3. Design Training for Depth, Not Awareness

Most AI training programs teach "how to ask questions." That creates FAQ users who model-hop when they hit limitations.

Effective training teaches:

• How to create Projects that accumulate team knowledge
• How to build custom GPTs that automate repeatable workflows
• How to use connectors to pull company data into AI context
• How to collaborate using Canvas, shared workspaces, or team features

Training should produce measurable behavior change: Projects created, GPTs deployed, workflows automated. Not just "employees who've tried AI."

4. Celebrate Depth, Not Breadth

When Gemini 4 launches (and GPT-6, and Claude 5), your teams should react with excitement about applying new capabilities to existing workflows, not excitement about switching platforms.

That shift only happens when you've built real depth. When employees have invested in Projects, custom GPTs, and team workflows that would be costly to abandon.

Model launches should trigger celebration from deep users, not platform evaluations from surface users.

Drive Real AI Adoption Across Your Organization

Model launches are exciting. But enterprise transformation requires depth, not just access. If you're ready to move your teams from FAQ usage to workflow automation, we can help.

Book a demo to see how Kowalah's platform and training programs build the depth that turns AI from a toy into a business advantage.