Enterprise AI 2.0: Building a Comprehensive AI Backbone

Our Enterprise AI 2.0 lays out a company-wide AI operating model. Building on our EAIS framework that maps existing tools, we create a comprehensive AI strategy that can yield integrated, durable and value-generating contributions. Enterprise AI 2.0 is designed to be the comprehensive AI backbone of organizations, vendor-agnostic and capable of operating across all deliverables.

This framework presents a vision for the future where AI can make a comprehensive, coherent, lasting, controlled and trackable contribution to organizations. It's designed to be vendor-agnostic, allowing organizations to build the AI backbone they need without being tied to any specific vendor's ecosystem.

The Enterprise AI 2.0 Architecture

Enterprise AI 2.0 connects documents, data, and systems into a strategic asset organizations can actually manage. The architecture consists of several key components working together:

Key Components Explained

🎛️ Central Server (Control Plane)

The central server acts as the control plane for the entire AI infrastructure. It manages routing, security, logging, and access control across all AI interactions. This is where governance and compliance policies are enforced.

🧠 Large Language Model (Reasoning Engine)

The LLM serves as the core reasoning engine, providing the intelligence that drives all AI interactions. This can be any vendor's model: OpenAI, Anthropic, Google, or self-hosted, integrated through the control plane.

💡 Vector DB (Index by Meaning)

Vector databases enable semantic search, allowing AI to find information based on meaning rather than just keywords. This enables more intelligent retrieval of relevant context from organizational knowledge bases.

🎓 Knowledge DAG (Global Knowledge Graph)

The Knowledge DAG connects documents, data, code files, and functions in a structured way, enabling AI to understand relationships and provide context-aware responses. This creates a comprehensive map of organizational knowledge.

🔍 Keywords (Traditional Search)

Traditional keyword search complements semantic search, providing fast, precise lookups for specific terms and phrases. This multi-modal indexing approach ensures comprehensive knowledge retrieval.

💻 Desktop & 📱 Mobile Clients

Human interfaces that connect users to the AI backbone. These clients provide access to AI capabilities across devices, ensuring consistent experiences whether users are at their desk or on the go.

🤖 Autonomous Agent (AI Worker)

AI agents that can work autonomously on behalf of the organization, performing tasks, making decisions within defined parameters, and executing workflows without constant human intervention.

🔗 Model Context Protocol (Tool Calling)

MCP enables AI to interact with existing tools, workflows, and systems. This allows AI to take action, not just provide information, by calling APIs, executing scripts, and integrating with business systems.

🔄 GitOps (Everything as Text)

GitOps treats documents, infrastructure, and processes as manageable code. This enables AI to understand, summarize, track, and review everything systematically, creating a complete audit trail of changes and decisions.

Core Principles

Avoiding Dangling, Untethered AI Conversations

In Enterprise AI 2.0, we avoid dangling, untethered AI conversations. Every AI interaction is connected to the knowledge base, includes relevant context, and is tied to a specific business objective with clear ownership.

This means:

• Context-Rich Interactions: Every conversation has access to relevant organizational knowledge, not just generic responses.
• Business Alignment: Every AI interaction serves a specific business purpose with measurable outcomes.
• Ownership & Accountability: Clear ownership ensures AI interactions are managed, reviewed, and optimized over time.
• Auditability: All interactions are logged, tracked, and can be reviewed for compliance and optimization.

Vendor Agnosticism

Like EAIS, Enterprise AI 2.0 is completely vendor-agnostic. Organizations can utilize existing AI solutions, purchase dedicated AI solutions that align with the vision, or build custom solutions. This flexibility ensures:

• No Vendor Lock-in: Organizations are not tied to any single vendor's ecosystem or pricing model.
• Best-of-Breed Solutions: Choose the best tool for each specific need, regardless of vendor.
• Future-Proofing: As new AI technologies emerge, organizations can integrate them without rebuilding the entire infrastructure.
• Cost Optimization: Use the most cost-effective solutions for each use case, rather than being locked into a single vendor's pricing.

Building on EAIS

Enterprise AI 2.0 builds on the EAIS (Enterprise AI Stack) framework. While EAIS provides the structured, layered approach for mapping and governing existing AI tools, Enterprise AI 2.0 takes the next step by creating a comprehensive, company-wide AI operating model.

Together, these frameworks provide:

• EAIS: A way to map, understand, and govern existing AI landscapes
• Enterprise AI 2.0: A vision and architecture for building comprehensive AI backbones

This combination provides both the tactical framework to manage what organizations have today and the strategic vision to build what they need for tomorrow.

Getting Started

Building an Enterprise AI 2.0 backbone starts with understanding the current state. Our approach begins by mapping existing AI landscapes using the EAIS framework, then designing Enterprise AI 2.0 architectures to connect everything into a coherent, manageable system.

Whether starting from scratch or integrating existing AI tools, Enterprise AI 2.0 provides the framework to build a comprehensive AI backbone that delivers lasting strategic value.

For more on the foundational framework, see our article on the EAIS framework. To learn about our approach to building Enterprise AI 2.0, visit our approach page.