RCG and Client
Executive Summary
Client, a leading digital banking platform serving regional and community financial institutions, engaged to accelerate case resolution, reduce support costs, and scale self-service through an AI-first operating model. Within six months, Client transformed its global support function—moving from reactive ticket handling to a predictive, AI-enabled ecosystem that combined automation, knowledge, and intelligent triage. The results were measurable:
- 42% reduction in average time-to-resolution (TTR)
- 51% increase in self-service deflection rate
- 27% lower cost-per-case (CPC)
- CSAT improvement from 2 → 4.7
This transformation positioned Client’s support organization as a proactive enabler of customer success rather than a reactive responder.
Client Context and Challenge
As Client expanded its footprint across more than 200 financial institutions, its client support operation faced increasing case volume and complexity. While customers valued Client’s secure, cloud-native platform, many encountered recurring “how-to” and configuration inquiries that strained Tier 1 capacity. The existing support model was functional but rigid:
- Tier 1 handled all inbound cases manually via Salesforce Service Cloud.
- Knowledge articles were static and underutilized.
- Escalations occurred prematurely due to incomplete context capture.
- Agents relied on tribal knowledge instead of structured, searchable solutions.
By late 2023, Client’s leadership recognized a widening gap between case growth (up 28% YoY) and agent capacity (flat). Support efficiency and customer satisfaction were at risk. RaSee Consulting was engaged to design and implement a scalable AI-first model that would transform how customers and agents resolved issues.
Diagnostic and Root Cause Analysis
RCG began with a six-week diagnostic assessment across Client’s support ecosystem—reviewing case data, workflows, knowledge content, and agent behavior. Key insights included:
- High case redundancy – ~35% of incoming tickets repeated previous issues with known resolutions not surfaced automatically.
- Low knowledge adoption – fewer than 15% of agents referenced knowledge articles during resolution.
- Siloed data systems – customer history, sentiment, and product metadata weren’t unified, leading to context loss between handoffs.
- Reactive triage – no intelligent routing or AI-based deflection; all tickets entered the same queue regardless of complexity.
The analysis confirmed that Client’s growth required an operational redesign—embedding AI and knowledge as the foundation for scalability.
RaSee’s AI-First Strategy
RCG designed a three-phase transformation roadmap grounded in its proprietary AI-First Support Framework: Phase 1 – Foundation & Enablement
- Unified Client’s knowledge base (Service Cloud + internal Confluence) into a single searchable corpus.
- Implemented RaSee’s Knowledge Loop methodology to tag, validate, and feed knowledge back into AI models.
- Introduced new KPIs for knowledge quality, reuse, and self-service adoption.
Phase 2 – AI & Automation Deployment
- Integrated Salesforce Einstein and Forethought Solve to power AI-driven knowledge surfacing and contextual case suggestions.
- Deployed a conversational AI chatbot on Client’s support portal for Tier 0/1 inquiries.
- Embedded AI-assisted case summarization and suggested responses within the agent console.
Phase 3 – Continuous Optimization
- Established an AI governance board (led by RCG and Client’s VP of Support) to monitor precision, deflection, and CSAT trends.
- Introduced A/B testing for article relevance and chatbot dialogue paths.
- Automated deflection reporting and case-type clustering for weekly optimization.
Implementation and Change Management
The transformation succeeded because it aligned people, process, and technology. People: RCG led enablement sessions for all 60 Client support agents, focusing on AI literacy, prompt feedback, and knowledge authoring. “AI Tool Owner” and “Knowledge Curator” roles were introduced to ensure continuous model training and governance. Process: New standard operating procedures were introduced for:
- AI case triage and routing workflows.
- Real-time knowledge article updates post-resolution.
- Agent feedback loops to flag model inaccuracies.
Technology: Integration between Service Cloud, Forethought, and Confluence allowed AI to retrieve and summarize answers in milliseconds, enabling Tier 0 self-resolution.
Outcomes and Quantified Impact
Six months post-launch, Client realized substantial operational and experiential gains:
| Metric | Before | After (6 Months) | Change |
| Average Time-to-Resolution (TTR) | 14.8 hrs | 8.6 hrs | ↓ 42% |
| Self-Service Deflection | 29% | 80% | ↑ 51% |
| Cost per Case | $18.20 | $13.25 | ↓ 27% |
| CSAT | 4.2 | 4.7 | ↑ 0.5 |
| FCR (First Contact Resolution) | 54% | 72% | ↑ 18 pts |
Qualitative Outcomes:
- AI portal handled ~60% of after-hours inquiries automatically.
- Agents reported higher engagement and lower burnout due to reduced low-value workload.
- Leadership gained real-time visibility into deflection and customer sentiment trends.
Lessons Learned & Best Practices
- Start with clean data: Unified knowledge and structured tagging were critical to AI accuracy.
- Governance drives trust: Continuous tuning and transparent model oversight sustained adoption.
- Measure relentlessly: Deflection, CSAT, and model precision became weekly review metrics.
- Human + AI synergy: Agents who collaborated with AI assistants achieved 30% faster resolutions.
Conclusion
Through RCG’s AI-First Support Transformation, Client evolved its support model from transactional to intelligent—one where AI empowers both customers and agents. The engagement established a scalable foundation for proactive support, cost optimization, and a superior customer experience, positioning Client as a benchmark in AI-enabled digital banking support.