Cut turnover in half and scaled quality monitoring from 1% to 100% of calls

Overview

Our contact center operates in a complex, multi-client environment supporting numerous programs and lines of business. As call volumes, quality expectations, and operational demands increased, we recognized that isolated AI experiments would not be sufficient. We needed a structured, scalable, and responsible approach to applied AI.

As AVP of the Contact Center, I defined and led a multi-year AI strategy focused on measurable business outcomes, responsible governance, and workforce empowerment. Over the past year, we implemented AI across coaching, quality, analytics, forecasting, and leadership decision-making, transforming how our 120-seat, $10M annual revenue operation runs.

Achievements

Operational Performance

- Increased quality monitoring coverage from auditing 1% of calls to auditing 100% of calls through AI-enabled QA, allowing earlier identification of behaviors and faster coaching interventions.

- Achieved annual call-volume forecasting accuracy within 2% of actuals, enabling precise staffing, budgeting, and revenue forecasting.

- Contributed to customer satisfaction performance of over 94%, significantly exceeding the industry average of approximately 76%.

Financial Impact

- Reduced turnover by half year-over-year, contributing approximately $425,000 in savings across recruiting, HR, and support functions.

- Supported stable revenue performance in a $10M annual operation with a 37% net EBITDA margin through improved forecasting, planning, and operational discipline.

Strategic and Organizational Impact

- Developed and presented a three-year AI roadmap that clarified priorities, sequencing, and investment decisions for leadership.

- Established guiding principles emphasizing AI as a tool to enhance human roles rather than replace them, shaping adoption and governance decisions.

- Influenced executive adoption and alignment by presenting KPI outcomes, strategy, and long-term vision.

- Led multiple AI implementations and coached leaders and frontline employees on practical usage and responsible practices.

Tools and Technologies

- Generative AI models used for coaching, workflow support, and decision preparation

- AI-enabled quality and analytics platforms supporting full-coverage QA and performance insights

- Forecasting and financial modeling tools supported by AI-assisted analysis and scenario planning

- Custom GPT configurations supporting leadership workflows, planning, and operational execution

- Structured knowledge bases and governance processes to ensure accuracy and responsible usage

Methodology

1. Define Strategy Before Tools

2. Rather than adopting tools opportunistically, we developed a multi-year AI roadmap outlining vision, guiding principles, yearly goals, and evaluation criteria. This created alignment and prevented fragmented adoption.

3. Focus on High-Impact Use Cases

4. We prioritized areas with clear operational and financial leverage:

- Coaching and quality improvement

- Forecasting and financial planning

- Performance analytics

- Hiring and workforce management

1. Implement, Measure, and Iterate

2. Each implementation was evaluated using pre- and post-KPIs. In some cases, such as hiring automation, results were mixed, reinforcing the importance of disciplined evaluation rather than assuming positive outcomes. This strengthened our governance and decision-making framework.

3. Build Governance and Responsible AI Practices

4. We established governance mechanisms, knowledge-base standards, and feedback loops to identify inaccuracies, risks, and improvement opportunities. Guiding principles ensured that AI adoption remained ethical, transparent, and workforce-supportive.

5. Empower People to Use AI

6. More than 100 employees were trained in prompt engineering and practical AI usage. Leaders were coached on integrating AI into daily workflows, enabling sustained adoption rather than short-term experimentation.

7. Align Leadership and Communicate Results

8. KPI outcomes, roadmap progress, and strategic priorities were regularly shared with senior leadership, ensuring continued sponsorship and alignment.

Learnings

- Applied AI delivers the greatest value when tied to measurable operational metrics rather than experimental pilots.

- Governance and knowledge management are essential to maintaining accuracy and trust at scale.

- Not every implementation produces immediate gains; disciplined measurement and willingness to adjust are critical to long-term success.

- Training frontline employees in AI skills accelerates adoption and builds lasting organizational capability.

- A clear roadmap and guiding principles reduce fragmentation and increase executive confidence in AI investments.

Recognition and Validation

Our applied AI initiatives have received external recognition, including multiple Stevie Award wins and industry acknowledgment for execution speed, governance, and measurable results.

Press release:

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