Frontline team built AI coaching system used 100+ times per hour in 90 days

In less than 90 days, a team of frontline contact center employees built and deployed an AI coaching system now used over 100 times per hour across our operation.

Overview

In a complex contact center environment, agents must support more than ten lines of business, each with multiple programs, policies, and brand standards. This level of cognitive load makes consistency, accuracy, and coaching scalability extremely challenging.

To address this, I led the conceptualization, development, and deployment of an AI-powered coaching and guidance assistant called Loyalty Coach. More importantly, I focused on empowering our team to build, improve, and sustain the solution themselves—transforming AI from a leadership initiative into a team capability.

Within 90 days, the solution moved from concept to production and is now used by over 100 employees multiple times per day as part of their normal workflow.

Achievements

- Loyalty Coach is actively used by over 100 employees in their daily work, often 2–4 times per call.

- The tool reduced quality auto-fails by 50%, improving agent success and performance consistency.

- Turnover decreased by 50% year over year, creating an estimated $425,000 in savings across recruiting, HR, and support functions.

- Customer satisfaction reached 94.56%, significantly above the industry average of 76%.

- The solution was conceptualized, built, and launched into production within 90 days.

- The team successfully deployed the solution across multiple clients and lines of business in a highly complex operational environment.

- Loyalty Coach and related initiatives received external recognition through the Stevie Awards in multiple AI categories.

- Over 100 employees were trained in prompt engineering and practical AI usage, equipping hourly teammates with future-ready skills.

Beyond operational results, this initiative helped democratize AI skills. Employees earning hourly wages gained hands-on experience with prompt engineering, governance practices, and responsible AI usage—skills that will benefit them throughout their careers.

Tools

- Generative AI models for real-time guidance and coaching support

- Custom GPT configurations and structured prompting frameworks

- Internal knowledge bases curated for accuracy and sustainability

- Governance workflows for ticketing, issue tracking, and enhancement requests

- Cost-optimization architecture using layered “boss bot” and “baby bot” structures to reduce operational costs

Methodology

1. Problem Identification

2. Agents and supervisors faced increasing complexity due to multiple clients, programs, and quality expectations. Coaching preparation was time-intensive, and agents struggled to quickly locate accurate guidance during live interactions.

3. Vision and Strategy

4. After completing advanced AI training, I worked with the team to define a multi-year AI vision, strategy, yearly goals, and guiding principles for AI usage in the contact center. These were shared openly to align teams around outcomes and responsible adoption.

5. Collaborative Development

6. Rather than building the solution centrally, I coached hourly employees, supervisors, and leaders to help design, refine, and maintain the AI agent. This created ownership, accelerated learning, and built internal expertise.

7. Knowledge Architecture and Governance

8. We prioritized building a sustainable knowledge base to ensure accurate outputs and reduce hallucinations. Governance processes were established so supervisors and agents could submit tickets for errors, improvements, or enhancements.

9. Training and Enablement

10. More than 100 employees were trained on prompt engineering, troubleshooting, and best practices. Clear guiding principles, setup frameworks, and knowledge management standards were documented and shared.

11. Integration into Daily Work

12. Loyalty Coach was embedded into daily workflows rather than treated as an experimental tool. Adoption grew organically because employees experienced immediate value.

13. Executive Alignment and Scaling

14. Results, KPIs, and the strategic roadmap were shared with senior leadership to ensure continued investment and alignment. The team also prepared and submitted award applications, reinforcing pride and ownership.

Learnings

- Empowerment drives adoption faster than top-down implementation.

- Governance and knowledge management are critical to responsible and scalable AI deployment.

- Teaching frontline employees to use AI creates long-term organizational capability, not just short-term productivity gains.

- Starting with a high-impact use case accelerates cultural acceptance of AI.

- Sustainable cost structures and architecture decisions are essential for scaling AI solutions in production environments.

Recognition and Validation

External judges from the Stevie Awards noted:

- “Great use of new AI technology and understanding the importance of the governance layer. The ability to execute new AI technology within the customer service space with speed and solid results is impressive.”

- “Kobie’s greater than 17.9% increase in post-call spend alone is a brilliant marker that establishes a direct correlation between the quality of service and revenue.”

- “Kobie’s achievement in compressing a multi-year AI roadmap into a rapid production rollout across multiple clients is an exceptional example of technical agility and strategic foresight.”

Refernce Links:

https://kobie.com/kobie-sets-the-benchmark-for-ai-in-loyalty-technology/

https://sales.stevieawards.com/awards/sascs-winners/ai-digital-transformation-customer-service-category-winners/