The Challenge
A global manufacturing leader with 50,000+ employees faced a critical bottleneck: their SAP Center of Excellence was drowning in over 3,000 support tickets monthly. The average resolution time stretched to 18 hours.
Their legacy support system relied on manual triage, extensive documentation searches, and consultant expertise. This approach was expensive and slow, with consulting fees alone exceeding $4M annually.
“Dodge AI didn't just automate our support - it fundamentally changed how we think about SAP operations.”
The Solution
Dodge AI proposed a comprehensive overhaul of the support infrastructure, leveraging our proprietary retrieval language model specifically trained on SAP ecosystems. The solution was designed to work alongside existing teams, not replace them.
The approach centered on three core pillars: intelligent ticket routing, automated knowledge retrieval, and predictive issue detection. Each pillar was designed to address specific pain points identified during the discovery phase.
Discovery Phase
Our retrieval LM conducted a comprehensive health scan, analyzing WRICEFs, custom objects, and 18 months of ticket history to identify patterns. This deep analysis revealed that 40% of tickets were variations of just 50 core issues.
Context Mapping
Context graphs mapped recurring issues across modules, enabling continuous problem management. The system surfaced patterns before they escalated, allowing teams to address root causes rather than symptoms.
Implementation Process
The implementation followed a carefully structured four-phase approach, designed to minimize disruption while maximizing adoption. Each phase built upon the previous, creating a foundation for sustainable success.
Phase one focused on data integration and model training. Our team worked closely with the client's SAP administrators to establish secure data pipelines and ensure compliance with enterprise security policies.
Phase two introduced the pilot program with a select group of power users. This allowed us to gather real-world feedback and fine-tune the system before broader rollout. The pilot group processed over 500 tickets during this period, providing invaluable insights.
Phases three and four covered department-wide rollout and optimization. By the end of month three, the entire COE team was actively using the platform for daily operations.
Real-time analytics dashboard showing ticket resolution patterns
System Integration
One of the critical success factors was seamless integration with the client's existing technology stack. The company relied heavily on ServiceNow for ticket management, Confluence for documentation, and Microsoft Teams for communication.
Dodge AI's platform connected to all three systems through secure APIs, enabling bidirectional data flow without requiring users to change their established workflows. Tickets created in ServiceNow were automatically enriched with relevant context and suggested solutions.
The integration also extended to the SAP system itself, allowing real-time monitoring of system health and automatic correlation between performance metrics and support tickets. This visibility proved invaluable for identifying emerging issues before they impacted users.
Training & Adoption
Technology adoption often fails not because of technical limitations, but because of inadequate change management. We addressed this head-on with a comprehensive training program tailored to different user personas.
L1 support agents received focused training on using AI-suggested responses and when to escalate. L2 specialists learned advanced features for complex troubleshooting. Team leads were trained on analytics dashboards and performance monitoring.
The training program also included certification modules, gamification elements, and ongoing support through dedicated Slack channels. Within six weeks, user adoption reached 94%, far exceeding our initial target of 75%.
"The training program was exceptional. Within two weeks, even our most skeptical team members were advocates for the platform."
The Impact
Within 90 days of deployment, ticket volume dropped by 73%. Teams moved from firefighting to strategic optimization. The transformation was evident across every metric we tracked.
Average resolution time plummeted from 18 hours to just 4.2 hours. First-contact resolution rates improved from 23% to 67%. Customer satisfaction scores for IT support jumped from 3.2 to 4.6 out of 5.
Context graphs revealed systemic configuration issues that had been invisible in the noise. By fixing these root causes, thousands of future tickets were prevented. The proactive identification of issues before they escalated became a game-changer for the operations team.
Return on Investment
The financial impact exceeded initial projections. Annual savings reached $2.4 million, driven by reduced consulting fees, improved team efficiency, and decreased system downtime.
Consulting spend dropped by 60% as internal teams became more self-sufficient. The knowledge captured in the system meant that solutions to complex problems were available instantly, rather than requiring expensive external expertise.
Productivity gains were equally impressive. Support agents reported saving an average of 2.5 hours per day on research and documentation. This time was redirected to higher-value activities like process improvement and user training.
The platform paid for itself within 4 months, with a projected 3-year ROI of 340%. These numbers have since been validated by the client's finance team and presented to their board as a model for future technology investments.
Key Takeaways
Proactive beats reactive: Fixing systemic issues prevented more tickets than resolving individual cases. The shift from reactive to proactive support was the single biggest driver of improvement.
Context matters: Generic AI solutions couldn't match SAP-specific intelligence. Domain expertise built into the model made all the difference.
Change management is critical: The best technology fails without proper adoption strategies. Investing in training and user experience paid dividends.
Integration accelerates value: Connecting to existing tools rather than replacing them reduced friction and accelerated time-to-value.
Future Roadmap
Building on this success, the client has committed to expanding Dodge AI across additional enterprise systems. Phase 2 will extend coverage to their Oracle and Salesforce environments, creating a unified intelligent support layer across the organization.
We're also collaborating on advanced features including predictive maintenance alerts, automated change impact analysis, and natural language querying of system configurations. These capabilities will further reduce manual effort and improve decision-making.
The partnership has evolved from a vendor relationship to a strategic collaboration. Together, we're defining the future of enterprise IT operations - one where AI augments human expertise to deliver exceptional outcomes.
