A target data and AI architecture that integrates your core systems so AI scales once, then reuses everywhere.
Build the data and AI platform your business can grow on.
An Enterprise AI Architecture blueprint that defines how data, models, tools and BI fit together. We map the current landscape, design the target data platform, AI services, integration patterns and security model, then build a migration roadmap with phases, quick wins and major milestones. Outputs are vendor-aware but not vendor-locked.
An enterprise AI architecture is a blueprint that defines how data, models, tools and business intelligence connect across the organization. Enterprises need one because AI value depends on integrating fundamentally different systems: operational platforms, ERP (finance and procurement), CRM (customers and pipeline), data warehouses and unstructured content (documents, emails, reports). Without an architecture, every AI use case solves its own data integration problem from scratch, making AI expensive to build, fragile to maintain and impossible to scale.
Integration follows three layers. First, a data ingestion layer: APIs, file-based connectors and streaming feeds bring raw data from your operational platforms, ERP, CRM and other systems into a central data platform. Second, a semantic layer: a data model maps entities across systems to resolve naming conflicts and create a unified graph. Third, an AI services layer: models, RAG pipelines and agents access unified data through governed APIs rather than connecting directly to source systems.
An enterprise AI platform typically runs on a major cloud provider (Azure, AWS, and GCP are all viable depending on your existing footprint). The minimum infrastructure includes: a data lakehouse for raw and curated data storage; a compute layer for model training and inference; a model registry and deployment platform; a monitoring and observability stack; and network and security controls aligned with ISO 27001 or SOC 2. Most enterprise AI workloads do not require GPU clusters continuously; serverless and managed inference endpoints reduce cost significantly for intermittent workloads.
Operational and digital twin data spans multiple source systems structured for different purposes rather than AI inference. The architecture pattern we recommend connects operational data to AI through a semantic data layer: a data model that maps entities across source systems so they can be joined, queried and analyzed consistently. This unified semantic layer is what allows an AI agent to answer cross-system questions by joining records from multiple data sources.
Scaling AI across multiple projects and business units requires: a federated data model (a common entity schema that each project or business unit follows, even if their source systems differ); a curated data quality layer (automated checks that flag data quality issues before they reach AI models); a metadata and lineage catalog (so every AI output can be traced back to its source data); and a security model that enforces project-level and role-level data access across all AI services. Organizations that invest in these four foundations consistently scale AI faster than those that build each use case on bespoke data pipelines.
Multi-division AI architecture requires two design choices that single-unit implementations often skip: data isolation (ensuring that data, models and outputs from one division cannot leak into another, which matters for competitive and regulatory reasons); and cross-division learning (enabling models trained on patterns across the full portfolio to inform division-level decisions). We design architectures with a logical separation between unit-specific inference and enterprise-level model training, with appropriate data governance bridging both layers.
AI in enterprise architecture transforms technology strategy in three fundamental ways. First, it elevates data to a first-class infrastructure concern: AI systems are only as good as the data they access, so enterprise architecture must now design for data quality, consistency and governance as primary outcomes, not afterthoughts. Second, it introduces a new service layer between source systems and business applications: AI services (models, agents, RAG pipelines) sit above the data platform and below user-facing applications, and the enterprise architecture must define how this layer is governed, monitored and updated. Third, it changes the economics of integration: a data-model-backed AI architecture reduces the marginal cost of each new use case, while a fragmented architecture makes every use case expensive. Our enterprise AI architecture design addresses all three dimensions.