Architecture Work

These examples reflect enterprise architecture decisions made across large systems, where business processes, application systems, data flows, and technology platforms have to evolve together.

Enterprise CRM and Platform Architecture

Problem Context

CRM capabilities had grown across multiple patterns, making integration behavior harder to predict and change increasingly expensive.

Architecture Approach

Established shared platform standards, clearer application boundaries, and a roadmap that aligned business processes, shared customer data, and core technology platforms without expanding the estate unnecessarily.

Key Tradeoffs

  • Balanced delivery speed against the need for reusable platform standards.
  • Reduced local customization freedom to improve integration predictability and data consistency.

Impact

In many large platforms, this level of alignment improves release predictability while supporting very high user and transaction volume.

Provider Network Modernization

Problem Context

A healthcare provider platform relied on aging systems where workflows and integrations were tightly coupled and difficult to evolve.

Architecture Approach

Introduced a cloud architecture on Salesforce Health Cloud, separated provider workflows from partner-facing application integrations, and clarified how operational data should move between business capabilities and supporting platforms.

Key Tradeoffs

  • Modernized critical workflows while limiting disruption to external partner connections.
  • Accepted phased migration complexity to gain clearer system ownership and cleaner data flows.

Impact

Workflow modernization became more manageable, and the platform gained better operational visibility without sacrificing delivery stability.

Global Contract Management Transformation

Problem Context

Contract lifecycle capabilities were distributed across systems and teams, creating orchestration friction in a business-critical process.

Architecture Approach

Used Salesforce, cloud services, and a service layer to coordinate contract business processes, clarify application ownership, and establish a more coherent data model across the supporting technology stack.

Key Tradeoffs

  • Balanced centralized orchestration against team autonomy across distributed systems.
  • Invested in a service layer to reduce future integration friction and reporting inconsistency.

Impact

Delivery coordination improved across distributed teams, and the platform became easier to adapt as requirements evolved.

Frontline Workforce Transformation Platform

Problem Context

Frontline teams were operating with fragmented tools, which made field collaboration and operational consistency difficult at scale.

Architecture Approach

Designed a mobile-first Salesforce platform that matched frontline business processes, kept field applications simple, and phased supporting data flows and platform integrations around real operating constraints.

Key Tradeoffs

  • Prioritized frontline usability and phased delivery over a single big-bang transformation.
  • Allowed some operational variation to keep the platform practical for distributed teams.

Impact

The platform supported more than 25,000 frontline users and improved day-to-day collaboration across distributed operations.

Global Pharmaceutical Commercial Platform Transformation

Problem Context

Regional commercial platforms had diverged over time, making global reporting, integration, and governance increasingly difficult.

Architecture Approach

Unified regional systems into a Salesforce-centered operating model with shared commercial data, clearer application and integration boundaries, and controlled regional variation across the supporting platform landscape.

Key Tradeoffs

  • Standardized core capabilities while preserving limited regional flexibility where it mattered operationally.
  • Balanced global reporting and governance needs against local market requirements and delivery speed.

Impact

Commercial users moved toward a more consistent operating platform, and the architecture set a stronger foundation for analytics and AI capabilities.