Case Study

AI-Assisted Triage & Defect Resolution Across the Salesforce SDLC

An embedded AI Lab transformed defect management from a reactive, manual process into a more proactive model—connecting context across workstreams, accelerating triage and resolution, and helping teams maintain delivery momentum throughout a complex Salesforce modernization.

RESULTS

Faster Defect Resolution

Lower Stakeholder Burden

Improved Delivery Velocity

OVERVIEW

Project

Salesforce-centered modernization ecosystem

Industry

California State Government​

Client

Large state department​

The Challenge

Fragmented Defect Context Slowed Root-Cause Analysis and Increased Schedule Risk

Defect management required coordination among development, testing, business, data, and project-management teams. Important context was often distributed across defect records, requirements, test results, logs, business processes, and individual workstreams.

Teams spent valuable time gathering information, determining the source of failures, identifying dependencies, and routing issues to the correct resources.

During a high-pressure modernization effort, this additional investigation could slow defect resolution and increase schedule risk.

Data conversion and validation required significant investigation, comparison, and reconciliation before teams could confidently use information for development, SIT, UAT, and production-readiness activities. This created additional risk to an already compressed schedule.

Our Solution

AI-Assisted Triage Connected Defect Context, Accelerated Investigation, and Automated Repeatable Workflows

The embedded AI Lab created automated and AI-assisted workflows around defect processing and triage. The team helped:

  • Analyze defect and execution information.
  • Collect relevant context from multiple workstreams.
  • Surface potential causes and dependencies.
  • Support routing and prioritization.
  • Connect defects back to requirements, testing, and business processes.
  • Automate recurring triage and reporting workflows.
  • Provide live support for urgent project issues.

Our team followed a human-in-the-loop model for critical decisions while using AI to accelerate investigation and synthesis.

A key principle was “use AI, but don’t depend on AI.” When AI helped identify a repeatable process, the team converted that knowledge into conventional scripts, applications, or automated workflows whenever practical. This reduced unnecessary token consumption and kept long-term operating costs lower.

Results

Faster Defect Resolution, Less Manual Coordination, and Stronger Delivery Velocity

Defect information became easier to understand and act on, helping teams resolve issues with less manual coordination.

The embedded model also reduced the burden on customer teams. Instead of requiring extensive meetings or detailed explanations, the AI Lab could work from short problem statements, investigate the surrounding context, produce an initial solution, and allow stakeholders to review and refine the result.

This created a more proactive operating model: issues were surfaced earlier, workstreams were better connected, and project teams could maintain delivery velocity while managing a complex Salesforce modernization ecosystem.

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