Google I/O wrapped up last week, and as expected, AI dominated the announcements. I’ve spent a few days going through the keynotes, sessions, and documentation. Here’s what matters for enterprise AI planning.

The Gemini Evolution

Google’s Gemini model family got significant updates:

Gemini 2.0. The next generation model is faster, more capable, and more efficient. Google claims parity or better performance versus GPT-4 on most benchmarks, with significantly lower inference costs.

Expanded context windows. Gemini 2.0 Pro handles up to 2 million tokens – enough to process an entire codebase or years of documents. This is a meaningful capability expansion for document-heavy enterprise use cases.

Improved multimodal reasoning. Better performance on tasks combining text, images, audio, and video. Potentially valuable for enterprises with diverse content types.

Lower pricing. Google is competing aggressively on price. Gemini 2.0 Flash is positioned as a cost-effective option for high-volume applications.

My take: The capability gap between Gemini and GPT-4/Claude has largely closed. Model choice is now more about ecosystem and less about fundamental capability.

Vertex AI Updates

Google’s enterprise AI platform got substantial upgrades:

Grounding improvements. Better ability to connect Gemini to enterprise data sources, reducing hallucination on factual queries. This addresses one of the major concerns with LLM deployment.

Agent capabilities. New frameworks for building AI agents that can take actions, similar to what OpenAI and AWS announced. The multi-vendor convergence on agent architectures continues.

Fine-tuning simplification. Easier processes for customising Gemini with organisation-specific data. Still requires technical expertise but less than before.

Security enhancements. More granular controls over data handling, model access, and audit logging. Enterprise procurement teams will appreciate these.

My take: Vertex AI is now a credible alternative to Azure AI and Bedrock. If you’re Google Cloud native, there’s no longer a strong reason to look elsewhere for core AI platform capabilities.

Google Workspace AI Integration

The Duet AI rebrand to “Gemini for Workspace” came with capability expansions:

Improved document generation. Better quality drafts in Docs, more sophisticated spreadsheet analysis in Sheets, and enhanced slide creation in Slides.

Cross-application intelligence. AI that understands context across Docs, Sheets, and Slides rather than operating in silos.

Meeting integration. Better transcription, summarisation, and action item extraction in Google Meet.

Third-party data connections. Ability to ground Workspace AI in external enterprise data sources.

My take: Gemini for Workspace is catching up to Microsoft Copilot. For organisations committed to Google Workspace, it’s now a reasonable productivity AI option. Feature parity isn’t complete, but the gap is manageable.

What’s Missing

Some things I was hoping to see but didn’t:

Australian data residency clarity. Google’s data handling for AI services in Australia remains less clear than Azure’s. Compliance-sensitive organisations still need detailed due diligence.

On-premises options. Everything announced is cloud-first. Organisations with strict data localisation requirements have limited options.

Vertical solutions. Most announcements were horizontal platforms. Industry-specific AI solutions were notably absent compared to Microsoft and AWS.

The Competitive Landscape

After I/O 2025, the enterprise AI platform picture:

Microsoft Azure AI remains the leader in enterprise adoption, driven by Microsoft 365 integration and OpenAI partnership.

Google Vertex AI is now competitive on core capabilities. Strong for Google Cloud customers, harder sell for others.

AWS Bedrock offers model flexibility and integration with AWS services. Good for AWS-native organisations.

OpenAI directly is strong for developer-focused applications but lacks enterprise governance features of cloud platforms.

The honest assessment: platform capabilities have converged. Your cloud relationship and existing infrastructure should drive the decision more than AI features.

What This Means for Enterprise Planning

If you’re Google Cloud native: Evaluate Vertex AI and Gemini seriously. The platform has matured to enterprise-grade.

If you’re multi-cloud: Consider adding Google to your AI evaluation mix. Pricing competition benefits everyone.

If you’re evaluating AI platforms: Don’t default to Microsoft because it’s the incumbent. Google and AWS now offer comparable capabilities.

If you’ve already committed: No need to switch. The platforms are similar enough that your current investment remains valid.

My Recommendations

Based on I/O 2025:

1. Update your AI platform assessment if it’s more than six months old. The landscape has shifted.

2. Test Gemini 2.0 on your specific use cases. Benchmarks are one thing; performance on your data is what matters.

3. Compare pricing carefully. Google is pricing aggressively. Run your expected usage through multiple pricing calculators.

4. Don’t ignore the ecosystem. Platform capability is only part of the picture. Integration, tooling, and support matter too.

5. Watch the data residency story. If you’re in regulated industries, clarify Google’s Australian data handling before committing.

The Bigger Picture

Google I/O 2025 confirms what’s been emerging for months: the AI platform wars are settling into a three-way competition where capability differences are increasingly marginal.

That’s good for enterprise customers. Competition improves offerings and constrains pricing. The worst-case scenario – lock-in to a single dominant platform – isn’t materialising.

The strategic question isn’t “which AI platform is best” anymore. It’s “how do we build AI capabilities that work regardless of which platform we choose?” Platform-agnostic architecture is becoming as important as platform selection.

Plan accordingly.