Pillar 2: Ecosystem Architecture

The Technical Bedrock of Trustworthy AI: Build a cloud city that scales securely with every new AI workload.

Executive Snapshot

EOA promotes data-first thinking, preventing silo creep.

Time-to-value shortens when data is already consolidated, indexed & governed.

Core services, data, apps & AI must live in a composable cloud ‘city’.

Why Ecosystem Architecture Matters

Ecosystem-oriented architecture (EOA) inverts the old ‘one-app/one-server’ mindset.

Architects instead build a living cloud ecosystem - a collection of reusable, highly scalable services that evolve to meet today’s needs and embrace future innovation.

EOA speeds AI deployment because data is already consolidated, indexed, governed and secured, with application-lifecycle management in place.

Undertaking a transition to EOA also reduces technical debt, retires legacy licensing, and lowers data-governance risk.

Core platform services

Core Platform Services include infrastructure, security, governance, management and monitoring services used across a cloud ecosystem - largely synonymous with a ‘cloud landing zone.’ Examples include Microsoft Purview, Entra ID, Key Vault, Azure Monitor, Azure DevOps, Security Center, Sentinel, Application Insights and Managed Environments.

Purview plays a vital role in data governance - securing, cleansing, establishing lineage and compliance so your data produces quality AI responses.

The key question: Have we built a broadly-based landing zone that governs Azure, Fabric and Power Platform workloads while providing ALM for traditional and AI solutions?

Data distribution

Data Distribution provides for consolidation in OneLake and for ‘downstream’ distribution such as search, APIs, warehousing, analytics and AI workloads.

Azure AI Search is increasingly the principal ‘front door’ through which AI uses enterprise data, making a vast index extraordinarily useful to RAG and other patterns.

Pre-requisite to AI is data landing in stores accessible to AI services. Most organisations still harbour vast unconsolidated stores - from OneDrive and spreadsheets to point-solution databases.

Data consolidation happens on a spectrum—from ‘epic mess’ to ‘single source of truth.’ Each organisation must find its realistic middle ground.

Integration

An Integration Neighbourhood - also called an integration platform - can be architecturally complex. The strategic focus is to normalise its use across the ecosystem to avoid spaghetti-web integrations that are costly to maintain and the antithesis of scalable AI.

Representative services include OneLake shortcuts, Dataverse virtual tables, Event Grid, Service Bus, API Management, Logic Apps, Azure Functions, Azure Data Factory and Master Data Management.

Business Applications

Modern business applications are crucial because they are (a) the primary user touch-point and (b) often the principal generator of data upon which AI relies.

Core Business Systems include ERP, CRM and HRMS solutions (e.g., SAP, Dynamics 365, Workday) and bespoke apps on Azure SQL and Power Platform.

Application Portfolio covers hundreds of Tier 2-3 apps that may hold valuable -or risky—data for AI, especially legacy SharePoint-based apps.

Power Platform plus Azure AI services and Microsoft Fabric form a compelling trio for modernising these apps and eliminating shadow IT.

AI Development Tools

As of late 2024 most organisations were not training their own LLMs, but metaprompting, fine-tuning or extending existing models via Azure AI Studio, Copilot Studio and similar platforms.

Three tool classes

  1. LLMs & foundation models provided by vendors.

  2. Productivity copilots such as GitHub Copilot or Power Apps Copilot.

  3. AI workload tools like Azure AI Studio & Copilot Studio that accelerate agent/solution build-out.

Better to learn to play the piano before composing a symphony: focus on leveraging vendor models effectively before attempting to train your own.

Success Checklist

✔ Core Platform is a mature landing-zone with Purview, Entra ID & monitoring.

✔ Data Distribution consolidates & indexes data in OneLake + AI Search.

✔ Integration platform avoids point-to-point “spaghetti.”

✔ Business Apps modernised on Dataverse/Power Platform.

✔ AI Dev Tools (GitHub Copilot, Azure AI Studio, Copilot Studio) deployed & adopted.

FAQs

  • Point solutions create siloed data and brittle integrations. EOA prevents that by consolidating data first and exposing it via reusable services.

  • Almost all firms succeed faster by extending vendor-provided models - training your own only makes sense after you reach a “Proactive” maturity score across the framework.