The Seven Patterns for Scalable GenAI in Document-Centric Processes

Enterprises are adopting GenAI, but most deployments remain disconnected and brittle. Teams build document summarizers, contract analyzers, or chat interfaces as isolated experiments. These efforts don’t scale. They don’t integrate. They can’t be governed.

The solution isn’t a better model. It’s a better system structure.

This article introduces seven architectural patterns that define how GenAI should operate inside business processes—especially those built around documents, forms, and structured decisions. These patterns are modular, repeatable, and designed for scale.

Most GenAI architectures today are model-centric or prompt-centric. Teams chain prompts, fine-tune models, or connect APIs to downstream tools. These implementations may produce results in isolation but lack system-level structure. Patterns define execution structure—how a GenAI task runs, how it connects, and how it scales.

Why GenAI Needs Structure

In most organizations, GenAI tools are built as one-offs. A claims team runs a pilot for document extraction. A legal team builds a clause classifier. An HR team uses RAG for policy lookup. These efforts may work technically, but they fail architecturally. There’s no common trigger model, no shared logging, no integration path, and no control surface.

Patterns solve this. Each one defines a complete execution structure: the input format, the model interaction, the routing logic, and the system boundary. Patterns give GenAI implementations a defined shape that can be repeated, governed, and scaled.

What a Pattern Is

Each GenAI pattern is a deployment unit. It describes how a GenAI-enabled task behaves inside a system—not just which model is used, but how the task operates from trigger to output.

Each pattern defines:

• Trigger – What event or input activates the pattern
• Input – What kind of content it receives (PDFs, forms, emails, chats)
• Model Task – What the model does (classify, extract, generate, score)
• Routing Logic – How the result is handled (thresholds, branches, handoffs)
• Output – What is produced and where it goes (structured data, queues, downstream systems)
• Integration Surface – Where the pattern connects to BPM, RPA, case management, or data layers

Some patterns apply existing techniques but assign them specific execution roles within a system.

The Seven Patterns

These are the seven core patterns used in enterprise document workflows.

1. Triage (Confidence-Gated Input Routing)
   Routes low-confidence or incomplete inputs to human queues, while allowing high-confidence documents to continue automatically.
2. Inline (In-Workflow Classification or Generation)
   Runs classification or extraction directly inside a structured workflow step or case task, providing outputs to drive routing or logic.
3. Assembly (Multi-Document Normalization and Structuring)
   Combines multiple related documents into a single structured object, mapping inputs into a standard schema for downstream use.
4. Chain (Sequenced Task Orchestration with Hand-offs)
   Executes a sequence of model tasks in a fixed order. Each step depends on outputs from the previous one. Chain includes fallback logic, logging, and routing. It differs from general-purpose orchestration tools by enforcing traceable transitions and structured handoffs between tasks.
5. Retrieve (Grounded Content Retrieval for Controlled Generation)
   Answers questions using only approved internal content. Uses a retrieval step to select relevant text, then generates an answer grounded in those inputs. The output includes citations and remains confined to the source corpus.
6. Extract (Unstructured-to-Structured High-Volume Processing)
   Processes large volumes of unstructured documents to produce structured outputs for analytics, compliance, or reporting.
7. Scope (Tenant- or Domain-Isolated Execution Control)
   Enforces separation across teams, tenants, or jurisdictions. Routes requests through isolation layers with specific prompts, logging rules, and access controls.

Examples of GenAI Patterns in Real Processes

Here are three example showing how a distinct GenAI pattern is used in a business context. They describe where the pattern fits, what it handles, and how it integrates with surrounding systems.

1. Use Case: Claims Intake (Insurance)
   Pattern: Triage
   Claims arrive as emails, uploads, or scanned forms. The system classifies the document type and checks for required fields. If confidence is low or data is missing, the item is routed to a human queue. High-confidence documents continue automatically. Triage controls intake quality.
2. Use Case: NDA Review (Legal)
   Pattern: Chain
   Incoming NDAs are segmented, classified, and scored for risk using a defined sequence of model tasks. Chain manages the task order, fallback logic, and escalation to legal review. The result is consistent, auditable contract handling.
3. Use Case: HR Policy Lookup (HR)
   Pattern: Retrieve
   Employees submit HR questions through chat or forms. Retrieve selects relevant passages from internal policies and generates a response using only that content. The system includes citations. Output remains grounded in approved materials.

What This Enables

The pattern framework allows GenAI to be structured into repeatable units that can be deployed, observed, and governed. Patterns describe how each unit behaves—what it receives, what it produces, and how it connects to systems around it.

Each pattern defines a boundary. It specifies what kinds of inputs are handled, how the model behaves, what routing logic applies, and where the outputs go. This allows reuse across functions, departments, and industries without redesigning the architecture each time.

Patterns support governance by making the system's structure observable and consistent. Because the shape of execution is known—model inputs, task type, thresholds, escalation paths—it’s possible to enforce policies, log decisions, and audit outcomes.

While cloud platforms like Azure OpenAI, Amazon Bedrock, and Workato offer orchestration and monitoring tools, they don’t define execution shape. The pattern framework complements these ecosystems by providing a consistent system structure that can plug into—rather than depend on—any specific vendor’s tools.

This shifts GenAI from isolated tools to systems that can be deployed and managed at scale.

What to Do Next

Start by mapping your current GenAI efforts to the seven patterns. Identify where tasks like document classification, clause extraction, or policy lookup already exist—but lack clear structure, governance, or reusability. These are the candidates for conversion.

Next, evaluate whether each implementation has a defined trigger, known input format, routing logic, and integration boundary. If not, the system isn’t pattern-based—it’s ad hoc. Replace it or refactor it using the correct pattern.

Finally, use the patterns to structure your roadmap. Instead of prioritizing features or vendor tools, prioritize pattern coverage. Ask: which patterns do we need to support across our core document-centric processes? Which patterns need to be governed across business units or regions?

This establishes a structure for GenAI work that can grow with the business.