Today, customer service departments face multiple challenges: increasing volumes of interactions, demands for faster responses, greater case complexity, and ever-higher customer expectations. In this context, human agents remain essential for managing sensitive cases, building empathy, and fostering loyalty. However, they are often overwhelmed by repetitive tasks, scattered information, and the constant pressure of operational indicators.
Deploying generative AI in customer service operations provides decisive support. It acts as an ally to the human agent, assisting in real time with suggested responses, help in retrieving relevant information, interaction summaries, intent or sentiment detection, and proactive recommendations. This enables agents to focus on the human aspects of service: empathy, complex resolution, and strengthening the customer relationship.
Quantitative Evidence of Improvements in CX, Efficiency, and Profitability
Recent research demonstrates measurable benefits. The study “Generative AI at Work” (Brynjolfsson, Li & Raymond, 2023), for instance, analyzed the introduction of a generative AI–based conversational assistant in customer support operations involving over 5,000 agents. The result was an average productivity increase of 15% — measured as cases resolved per hour — with more pronounced improvements among less experienced agents.
Key Metrics (KPIs) to Measure Success
To ensure that the implementation of generative AI agents delivers real value, it is essential to define and monitor clear metrics. Some of the most relevant include:
- CSAT (Customer Satisfaction): measures customer satisfaction after the interaction. With AI support, an increase of 15–25% compared to pre-AI levels is expected.
- First Contact Resolution (FCR): indicates the percentage of cases resolved in the first contact. With AI, this can increase by 20–40%, thanks to more precise answers and better informational support.
- Average Handle Time (AHT): measures the average duration of each interaction, including post-call time. With AI, this can be reduced by 20–40% through automation of routine responses and faster information searches.
- Cost per Contact: reflects the average cost of handling each interaction. Savings of 30–50% can be expected by reducing handling time and repetitive human workload.
- Escalation Rate: measures the percentage of cases escalated to higher levels. With AI, this rate decreases significantly, reducing costs and improving the customer experience.
- Response Accuracy / Error Rate: evaluates the quality and accuracy of the agent + AI responses. A significant increase in accuracy and a reduction in errors are expected, leading to greater customer trust.
- Human Agent Utilization: measures how the agent’s time is distributed among high-value, repetitive, or administrative tasks. The goal is to free up a significant portion of time for high-value interactions and complex cases.
Risks, Challenges, and How to Mitigate Them
Implementing a generative AI system also involves certain risks that must be properly managed:
- Model misalignment with real knowledge: if training data is outdated or fails to reflect real customer cases, responses will be inaccurate. Mitigation: use proprietary datasets, perform continuous updates, and collect human agent feedback.
- Agent overload due to irrelevant suggestions: AI assistance must be semantically relevant and non-distracting. The key lies in smooth integration with clear interfaces.
- Security and compliance: in regulated sectors such as energy or finance, data security is critical. Private cloud solutions with audits and encryption ensure regulatory compliance.
- Human agent adoption: resistance to change or lack of training can hinder adoption. Training, pilot programs, and visible performance metrics are essential to demonstrate value.
Recommended Minimum Success Metrics for a Pilot Project
During a pilot phase, it is advisable to measure:
- A CSAT improvement of at least 15%.
- An FCR increase of 20–30%.
- An AHT reduction of 25–35%.
- Response accuracy equal to or greater than 90%.
- A reduction in the escalation rate of 20% or more.
- A decrease in average cost per contact of 30–50%.
- Reallocation of at least 30% of agent time to higher-value cases.
Competitive Advantages of the RunBots Agent Approach
For Customer Service Directors who value innovation, security, and customization, RunBots offers a differentiated proposal:
- Dedicated Private Cloud: the solution runs in private environments or client-dedicated infrastructures, ensuring total control over data, compliance with standards such as GDPR and ISO, audits, and enhanced privacy.
- Client-Specific Models: bots are trained with each client’s own data — knowledge base, products, services, communication style, and interaction history — resulting in higher accuracy and fewer misinterpretations.
- Top-Level Security: includes encryption in transit and at rest, robust authentication, and access policies aligned with the highest standards.
- Controlled Flexibility and Scalability: although private cloud may have higher initial costs, it offers predictable operational expenses, lower failure risks, and better control over latency and availability.
- Dedicated Technical Team: models are constantly recalibrated to incorporate the latest service information and prevent errors in agent responses.
How Generative AI Agents Integrate with Customer Service Teams
RunBots integrates as a real-time assistant for human agents. Its main features include:
- Automatic suggestions of responses or text fragments based on the context of the customer, product, or history.
- Automatic summaries of previous interactions and presentation of relevant information during the call.
- Detection of intent, sentiment, or risk signals (e.g., dissatisfied customers or potential escalations).
- Real-time training capabilities to adapt to new cases as they arise.
- Recommendations for the best possible solution for each customer based on the conversation and their characteristics.
- Reduction of the agent’s effort in administrative or repetitive tasks.
Expected Returns
Based on data from implemented projects, a well-designed pilot can achieve:
- A 25–40% reduction in AHT.
- A 20–40% improvement in FCR.
- A 15–25% increase in CSAT.
- Fewer errors and inconsistencies in responses.
- A 30–50% reduction in cost per contact, depending on automation levels and volume of routine interactions.
- Increased efficiency and better allocation of personnel toward strategic or premium customer service tasks.
Why Include RunBots?
RunBots is not a generic chatbot. It is a solution designed to work alongside human agents through generative AI, operating in private cloud environments and using client-specific models. Its adoption allows organizations to:
- Improve customer experience through faster and more accurate resolutions.
- Free human agents from redundant tasks, increasing their satisfaction and reducing turnover.
- Generate significant operational savings that can be reinvested in innovation or quality.
- Ensure compliance with key metrics such as CSAT, FCR, AHT, escalation rate, accuracy, utilization, and cost per contact.
For a Customer Service Director, generative AI agents supported by humans represent not only an operational improvement but a true strategic lever for customer experience, profitability, and competitiveness. RunBots, operating in secure and customized environments, offers a reliable path to deploy this technology with clear metrics and tangible returns: greater satisfaction, lower costs, more motivated agents, and more loyal customers.
When properly implemented, investment in RunBots becomes a sustainable competitive advantage — driving operational efficiency and delivering a truly distinctive customer experience.
