Executive Summary: State of RN Product Development Group

The Reinvention Navigator (RN) product team has undergone significant organizational transformation in recent months, including a major shift of engineering teams to India/Philippines. Despite these changes, the team has achieved notable successes including two on-time releases and successful onboarding of their first client (The Church).

The product is at a critical juncture, with Release 1.4 in UAT phase targeting end-of-January delivery, while simultaneously planning Release 1.5 for April. The development group, consisting of approximately 70 people across engineering and QA, operates in multiple geographical locations but primarily in India with some presence in Philippines.

The current operating model reflects a transition state, with engineering teams organized into separate functional streams (Backend, Frontend, DWH, AI) while product management has moved to a pod-based structure aligned to three product areas. This misalignment between product and engineering structures creates coordination challenges.

Quality assurance is a significant concern, with the current UAT phase revealing numerous defects that should have been caught earlier. The QA team is notably understaffed and lacks automated testing capabilities, creating a bottleneck for releases.

While basic agile ceremonies and processes exist, they are inconsistently applied across teams. Engineering capacity management remains a challenge, with multiple concurrent workstreams (production support, 1.4 UAT fixes, 1.5 development) competing for resources.

The group has demonstrated resilience through recent transitions but requires structural improvements to achieve predictable delivery and sustainable development practices.

Thank you, I'll now outline the Top Challenges facing the RN Product Development Group.

Top Challenges

1. Release Management & Quality

• Large volume of defects being discovered late in UAT phase, indicating systemic quality issues
• Lack of clear release ownership and acceptance criteria
• No standardized UAT process or criteria for defect severity classification
• Inadequate regression testing coverage with less than 50% completion rate

2. Structural & Organizational

• Misalignment between product pods and engineering team structure causing coordination issues
• Multiple functional silos (BE, FE, DWH, AI) operating independently with limited cross-team collaboration
• Severely understaffed QA team relative to development capacity
• Unclear accountability for end-to-end delivery of features

3. Capacity & Resource Management

• Competing demands across production support, defect fixes, and new development
• Limited visibility into true engineering capacity across teams
• No dedicated production support team, causing disruption to release activities
• Resource strain from managing multiple parallel releases (1.4, 1.4.1, 1.5)

4. Process & Planning

• Inconsistent estimation practices across teams
• Late engagement of engineering in feature planning and scoping
• Multiple handoffs creating communication overhead
• No standardized metrics or data-driven decision making
• Lack of automated testing creating bottlenecks

5. Communication & Collaboration

• Geographical and timezone challenges impacting team coordination
• Unclear escalation paths and decision-making processes
• Information silos between product, engineering and design teams
• Inconsistent documentation and requirements management practices

Top Engineering-Specific Challenges

1. Team Structure & Organization

• Engineering teams remain siloed in functional groups (BE, FE, DWH, AI) rather than aligned to product value streams
• Lack of senior technical leadership to provide architectural vision and guidance across teams
• No dedicated production support team, causing disruption to development work
• Heavy dependency on few key technical leads creating bottlenecks in decision making

2. Technical Quality & Practices

• Significant technical debt accumulating from rushed releases
• Absence of automated testing infrastructure
• No standardized code review or quality practices across teams
• Limited test environment management causing data quality issues
• Inadequate QA staffing (ratio significantly below industry standards)

3. Delivery & Engineering Process

• No clear engineering ownership of end-to-end feature delivery
• Reactive rather than proactive approach to technical planning
• Late discovery of cross-team dependencies causing delays
• Inconsistent development practices across teams
• Poor visibility into team capacity and velocity

4. Technical Leadership & Strategy

• Lack of strategic technical voice in product planning
• No clear technical roadmap for platform evolution
• Limited proactive identification of technical risks
• Absence of engineering metrics and quality indicators
• No formal architecture review process

5. Engineering Culture & Capability

• Heavy reliance on direction rather than engineering autonomy
• Limited knowledge sharing across engineering teams
• Varying skill levels across offshore teams without clear upskilling plan
• Low engineering confidence in estimation and commitment
• Insufficient focus on engineering excellence and innovation

Recommendations & Action Plan

Immediate Actions (First 30 Days)

1. Establish Technical Leadership Structure

• Appoint Solution Architect to own technical vision and cross-team architecture
• Create dedicated production support team staffed with senior engineers
• Implement daily technical lead syncs across BE, FE, DWH, and AI teams
• Define clear escalation paths and decision-making framework

2. Address Critical Quality Issues

• Increase QA staffing immediately through internal transfers/external hiring
• Define standardized testing requirements for all features
• Implement basic automated regression test suite
• Create consistent defect management process

3. Improve Engineering Processes

• Establish engineering estimation framework
• Create standard Definition of Done across teams
• Implement consistent code review practices
• Set up basic engineering metrics collection

Medium Term (60-90 Days)

1. Restructure Engineering Teams

• Reorganize into cross-functional pods aligned with product structure
• Create platform engineering team for shared services
• Establish clear ownership and accountability model
• Implement Scrum of Scrums for cross-team coordination

2. Build Technical Foundation

• Create technical roadmap aligned with product vision
• Establish architecture review board
• Implement automated CI/CD pipeline
• Create test automation framework

3. Enhance Engineering Practices

• Roll out standardized development practices
• Implement proper environment management
• Create engineering career development framework
• Establish regular technical knowledge sharing sessions

Longer Term (90-180 Days)

1. Drive Engineering Excellence

• Implement engineering metrics dashboard
• Create innovation time for technical improvements
• Establish centers of excellence for key technologies
• Build engineering capability development program

2. Scale Engineering Operations

• Implement portfolio-level capacity planning
• Create self-organizing team structure
• Establish technical debt management process
• Build predictable delivery engine

Success Metrics

• Release predictability (on-time delivery rate)
• Quality metrics (defect density, test coverage)
• Team velocity and capacity utilization
• Technical debt reduction
• Engineering satisfaction scores

Would you like me to expand on any particular aspect of this plan?