Understanding Enterprise GenAI Architecture (Beginner-Friendly)
Everyone talks about Enterprise AI…
Very few explain how it actually works behind the scenes.
So here’s the simplest way to understand GenAI Architecture in a business context, no engineering needed, and also a task for you to come up with a more straightforward way to explain it, and share your explanation via responding to this email or article...
The 4-Layer Enterprise GenAI Architecture:
Download the Draw.io file from here. Genai_architecture-simplified.drawio.zip
1: Data Layer (Your Sources of Truth)
This includes all structured and unstructured data repositories across the organization:
Structured data systems:
- Enterprise applications (ERP, CRM, HCM, Finance, Supply Chain)
- Databases (Oracle, MySQL, Postgres, SQL Server)
- Data warehouses & marts (Oracle Data Warehouse, Snowflake, BigQuery, Redshift)
- Analytics systems
Unstructured / semi-structured systems:
- Documents (PDF, Word, PPTX, Contracts, Policies)
- Email archives
- Support & ITSM systems (tickets, chats, case notes)
- Collaboration platforms (Slack, SharePoint, Confluence, Drive)
- File systems & storage buckets (OCI Object Storage, S3, locally storage)
Think of this layer as:
“Every system where your organization’s knowledge is stored.”
To organize the raw information an AI system needs for reasoning, summarization, extraction, and decision support.
2: Integration & Retrieval Layer, Connecting AI to the Enterprise (How AI Connects to Your Business)
This layer is responsible for securely accessing, transforming, and delivering data to the AI model.
Data Access & Movement
- APIs (REST/SOAP/OData)
- ETL/ELT pipelines (Fivetran, Informatica, Data Factory)
- Integration platforms (Oracle Integration Cloud, MuleSoft)
- Event streams (Kafka, OCI Streaming)
Retrieval & Search Technologies
- Vector databases (Oracle Vector, Pinecone, Weaviate)
- Retrieval Augmented Generation (RAG) pipelines
- Indexing & embeddings
- Document chunking & preprocessing
Security & Governance
- IAM / OAuth / SSO
- Data masking
- Access control/audit trails
Think of it as: “Pipes that deliver information to the brain.”
To ensure AI receives the correct data, in the proper format, at the right time, securely and reliably.
3: Intelligence Layer - AI Models & Reasoning Engine
This is the “brain” of the system, but it’s more than just the LLM.
Large Language Models (LLMs)
- OpenAI GPT-4/5
- OCI Generative AI (Cohere / Llama models)
- Anthropic Claude
- Google Gemini
- Mistral / Llama 3 / Mixtral
Supporting Components
- Embeddings models
- Domain-tuned models (fine-tuning, adapters, LoRA)
- Tool-calling / function-calling
- Agents & multi-step reasoning
- Business rules engines (hybrid AI)
Core Capabilities
- Summarization & extraction
- Classification & analysis
- Contextual Q&A
- Decision support
- Forecasting
- Multi-turn reasoning
- Code generation & automation
Think of it as: “Your digital analyst.”
To transform raw enterprise data into insights, narratives, decisions, or actions using AI reasoning capabilities.
4: Application Layer, Business Interfaces & User Experience
This is where business users experience the value of AI.
AI-Powered Interfaces
- Chatbots/assistants
- Knowledge bots
- Enterprise copilots (Finance Copilot, HR Copilot)
- Workflow automation tools
- Email assistants
- Document AI Assistants
Business Applications Enhanced by AI
- ERP extensions (invoice extraction, supplier analysis)
- CRM augmentations (lead scoring, email drafting, call summaries)
- PMO & ITSM copilots (ticket summaries, project health reports)
- Finance AI Assistants (narratives, variance analysis, commentary)
Delivery Channels
- Web apps
- MS Teams / Slack plugins
- Browser extensions
- Mobile apps
- Embedded AI in SaaS platforms
This is: “AI that people can actually use.”
To deliver AI outputs in a usable, actionable form that fits naturally into business workflows.
Example: Month-End Financial Commentary Generator for Finance Team
This is one of the most practical and commonly implemented enterprise GenAI workflows.
1-Data Layer: Sources of Truth
The workflow pulls from structured and unstructured finance data:
Structured Inputs
- ERP General Ledger (GL)
- Accounts Payable / Accounts Receivable
- Budget vs Actuals
- Cost center data
- Financial KPIs from Data Warehouse
Unstructured Inputs
- CFO notes
- Finance team emails
- PDF / Excel reports
- Supporting documents for variance explanations
Finance data is fragmented.
AI only works when it can see both numerical data and narrative context.
2-Integration & Retrieval Layer, Connecting to Data Securely
The workflow retrieves data via:
Data Access
- API calls to ERP (Oracle Fusion ERP, SAP, NetSuite)
- SQL queries from the data warehouse (Oracle Data Warehouse, Snowflake, Redshift)
- Secure upload of Excel/CSV exports
- Reading & document parsing for PDFs
Retrieval & Processing
- Convert tables into structured JSON
- Chunk narrative inputs (CFO notes, emails)
- Embed documents into an Oracle vector DB for context recall
- Apply RAG (Retrieval Augmented Generation)
Governance
- Role-based access control
- Financial data masking
- Audit logging
Finance requires tight access control and traceability.
3-Intelligence Layer, AI Reasoning & Decision Support
AI reads the numbers and narratives, and generates:
Core AI Tasks
- Summarize GL movements
- Explain variances (revenue, cost, margin)
- Identify anomalies or suspicious entries
- Detect trends and risks
- Suggest narrative commentary
- Propose next steps for the CFO
Tech Behind the Scenes
- LLM for narrative generation
- Embedding model for contextual retrieval
- Business-rule layer to ensure compliance (“don’t hallucinate numbers”)
- Optional fine-tuning for finance terminology
AI-generated text is not enough; Finance needs structured, explainable, and accurate outputs.
4-Application Layer, User Experience & Output
The business user (Finance Analyst / FP&A team) receives:
Outputs
- A CFO-ready month-end narrative
- Variance explanations (Revenue/Cost/EBITDA)
- Risk hotspots (cost overruns, revenue dips)
- Tables & bullets for board decks
- Suggested commentary for management reports
- A short 150-word TL;DR summary
Delivery Channels
- Email
- MS Teams / Slack
- FP&A dashboard widget
- Embedded in ERP Analytics
- Notion / Confluence summary page
Finance users don’t care about the architecture…They care about a clean, accurate narrative that saves 4–6 hours per cycle.
Why This Matters
You don’t need to know every detail of AI.
But understanding these four layers helps you:
- Design better use cases
- Pick the right tools
- Communicate clearly with IT
- avoid unrealistic expectations
- build a roadmap the business can follow
This is the foundation of Enterprise Generative AI maturity.
