Case Study
In 8 weeks, fewer repeat queries (~20–30%) and faster first responses (~10–15%).
Executive Summary
A team wanted to reduce repeat questions and provide consistent answers.
Scope: Contact center pilot (≈25 agents), retail banking policies; email + chat channels.
Key Results
The Business Case
The Challenge
The CX team faced a team wanted to reduce repeat questions and provide consistent answers.
Business Impact: Senior agents spent 30% of their time answering policy questions, new agents felt overwhelmed, and response quality varied significantly across the team.
The Solution
An AI-powered Q&A assistant that instantly searches company policies and provides accurate, contextual answers with source references—like having an expert policy specialist available 24/7.
Our Approach: short assessment and a controlled pilot with clear success measures and escalation path.
The Transformation
Reduction in repeat queries, faster response time, and clearer policy gaps to fix.
Cultural Impact: The CX team evolved from gatekeepers of information to strategic problem solvers, with dramatically improved job satisfaction.
Implementation Timeline
Discovery & Assessment
We embedded with the CX team to understand their daily reality. Sarah, the team lead, initially expressed skepticism: "We've tried tech solutions before." Through stakeholder interviews and observing real customer interactions, we identified the core challenge wasn't just speed—it was confidence.
Key Activities: Process mapping with CX team, policy document audit, stakeholder alignment with Legal and Compliance
Deliverable: Technical requirements document and pilot plan
Challenge: Initial resistance from senior agents who saw this as potential job threat
Week 1-2 | 40 hours
Pilot Development & Deployment
We selected three CX agents—including skeptical Sarah—for the pilot. The first week was rough: 70% accuracy, forgotten logins, and frustrated users. But we stayed the course, making daily refinements based on their feedback. By week 5, Sarah admitted: "I actually reached for it before asking my colleague."
Key Activities: System build, daily user feedback sessions, iterative improvements
Deliverable: Production-ready AI assistant with 92% accuracy
Challenge: Users initially forgot to use the system under pressure—required workflow coaching
Week 3-6 | 120 hours
Optimization & Scale Planning
Analyzed pilot results, refined the system based on user feedback, and developed scaling strategy. Prepared documentation and training materials for broader organizational rollout.
Key Activities: Performance analysis, system refinement, scale planning
Deliverable: Production-ready system and scaling roadmap
Week 7-8 | 60 hours
Investment & Return Analysis
Project Investment
Time Savings
Additional Benefits
• Improved response consistency
• Reduced training overhead
• Better customer satisfaction
• Knowledge gap identification
ROI Calculation
Based on average team member cost and time savings, this implementation paid for itself within 3 months and continues to deliver value equivalent to adding 0.6 FTE to the team without the associated overhead.
Human Impact & Team Transformation
Sarah - Team Lead
Before: Spent 30% of time answering repeat questions, felt like a human FAQ system
After: "I can finally focus on complex customer issues and strategic improvements. The AI handles the routine stuff better than I ever could."
Outcome: 40% more time on strategic work
Mark - New Hire
Before: Anxious about policy questions, relied heavily on colleagues
After: "I feel confident handling any policy question. The system gives me instant expertise without the awkwardness of constantly asking for help."
Outcome: 60% faster onboarding to full productivity
Cultural Shift
Knowledge democratization
Reduced hierarchy barriers
Enhanced job satisfaction
Team satisfaction score: +45%
Employee retention: +20%
Executive Insights & Lessons Learned
Critical Success Factors
Choose pilot users carefully: Include both supporters and skeptics for balanced feedback
Expect initial resistance: Focus on workflow integration, not just technology performance
Measure behavior change: Track usage patterns, not just efficiency metrics
Unexpected Challenges
Change management: Required more coaching than anticipated—technology was easy, behavior change was hard
Success paradox: Early wins created demand from other teams before system was fully scaled
Quality control: Needed stronger governance as usage expanded beyond pilot group
Risk Management & Governance
Controls Implemented
PII redaction and scoped access
Ensures compliance and maintains system reliability
Audit logging and weekly review
Ensures compliance and maintains system reliability
QA sampling for sensitive topics
Ensures compliance and maintains system reliability
Governance Framework
Oversight Committee
Cross-functional team monitoring performance and compliance
Monthly Reviews
Regular assessment of system performance and user feedback
Continuous Improvement
Ongoing refinement based on usage patterns and business needs
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