Framework
The GenAI WorkstreamsFramework
A structured methodology for enterprise AI-era modernization built on two core components: the CWR+ Maturity Model and AI Clean Core Principles.
CWR+ Maturity Model
A four-stage maturity framework — Crawl, Walk, Run, Plus — that guides organizations from operational stabilization to enterprise intelligence.
AI Clean Core
A ten-layer architecture stack defining the foundational dependencies that must be addressed before AI can be deployed sustainably at enterprise scale.
Methodology Flow
A structured process from Value Streams through Workstreams, Day-in-the-Life scenarios, Business Processes, Applications, Data, and AI Opportunities.
CWR+ Model
Crawl · Walk · Run · Plus
A practical maturity model for enterprise AI readiness. Each stage builds on the previous, ensuring organizations do not skip foundational work.
The Crawl stage is characterized by organizations that have accumulated significant technical debt, rely heavily on manual processes, and lack the visibility needed to make informed improvement decisions. The primary objective at this stage is stabilization — not transformation.
Characteristics
- Legacy platforms and point solutions with limited integration
- Predominantly manual activities and spreadsheet-dependent reporting
- Undocumented or inconsistently documented business processes
- Limited cross-functional visibility into operational performance
- Reactive rather than proactive operational posture
Key Activities
- Process discovery and documentation
- System inventory and dependency mapping
- Data quality assessment
- Governance baseline establishment
- Stakeholder alignment on modernization priorities
Stage Outcome
Operational stabilization and documented baseline for modernization planning.
Readiness Indicators
AI Clean Core
Ten-Layer Architecture Stack
The AI Clean Core defines the layered dependencies organizations must address before deploying AI at scale. Layers are ordered from foundational (1) to outcome (10). Select any layer to explore.
Select a Layer
Choose any layer from the stack to explore its purpose, dependencies, and enterprise considerations.
Methodology
From Business Understanding to Enterprise Intelligence
The GenAI Workstreams methodology follows a structured progression from business understanding through operational design to continuous improvement.
Identify the primary value streams within the organization — the end-to-end sequences of activities that deliver value to customers and stakeholders.
Decompose value streams into discrete workstreams — coordinated sets of activities, roles, systems, and data that accomplish a specific operational objective.
Map Day-in-the-Life scenarios for key roles within each workstream to understand actual operational experience, pain points, and decision patterns.
Document edge cases, exceptions, and process variations that represent significant operational complexity or risk within each workstream.
Define the underlying business processes that comprise each workstream, including inputs, outputs, decision points, and system interactions.
Map the enterprise applications that support each business process, identifying integration gaps, redundancies, and modernization opportunities.
Identify the data entities, flows, and quality requirements associated with each process and application within the workstream.
Assess where AI capabilities may support process improvement, decision quality, or operational efficiency within each workstream context.
Evaluate digital twin readiness for priority processes — identifying where operational replicas may support simulation, planning, and continuous improvement.
Establish feedback loops, measurement frameworks, and improvement cycles that enable ongoing modernization beyond initial implementation.