Contact Center Staffing

Why AI Could Change How Contact Center Staffing Is Calculated

Welcome to the future of contact center staffing! Gone are the days when staffing was purely a numbers game. Today, the magic lies in blending traditional methods with the power of artificial intelligence (AI). This isn’t just about having more hands on deck; it’s about working smarter, not harder. So, how can you determine the perfect number of staff for an efficient and effective contact center? Let’s explore the journey from traditional staffing techniques to AI-driven insights that are changing the game for modern call centers.

Read More: 6 Powerful Examples of AI in Contact Center

Traditional Factors in Contact Center Staffing

1. Estimating Call Volume

When planning contact center staffing, estimating call volume is your first step. Historically, this involves analyzing past call data to understand the average number of calls received daily. Knowing your call volume is crucial as it provides a baseline for staffing needs.

However, call volume isn’t static. Certain times of the day or year can see spikes in call activity. Anticipating these peak times ensures you’re not caught off guard. Predictive analytics can be invaluable here, offering forecasts based on patterns from historical data.

Accurate call volume estimation also means considering future trends. Predictive models can help anticipate changes due to marketing campaigns, product launches, or seasonal demands. This foresight is key to maintaining a balance in staffing levels without over or under-staffing.

2. Calculating Average Handle Time (AHT)

Average Handle Time (AHT) measures the average duration an agent spends on a call, including talking, holding, and follow-up time. Understanding AHT is vital for effective contact center staffing because it influences how many agents you need at any given time.

To calculate AHT, gather total talk, hold, and follow-up times and divide by the number of calls handled. This gives you an average time per call. Remember, AHT can vary by channel—phone, chat, or email—each may have different handling times.

Optimizing AHT involves analyzing factors that extend call duration, such as complex inquiries or inefficient systems. Training agents and improving processes can help reduce AHT, making it a critical metric for staffing efficiency.

3. Determining Calls Per Hour and Staffing Levels

Once you know your call volume and AHT, you can calculate the number of calls an agent can handle per hour. This metric helps in determining staffing levels for different times of the day or week.

The Erlang C formula is a traditional method used for these calculations. It models traffic, considering call volume, length, and required service levels to estimate the number of agents needed. This approach helps manage call queues and ensure adequate staffing to meet service standards.

AI tools can augment this process by providing real-time data and adjustments, making staffing more dynamic and responsive. For instance, AI can predict when call volumes will rise, allowing you to adjust staffing levels accordingly.

How AI Enhances Contact Center Staffing

1. Improving Call Volume Predictions

AI transforms call volume prediction by analyzing vast amounts of data and detecting patterns that might be missed by traditional methods. This allows for more accurate and real-time adjustments to staffing levels.

With AI, you can implement dynamic staffing strategies that adapt to changes in call volume almost instantly. This flexibility reduces the need for overstaffing and helps in managing costs more effectively. AI’s predictive capabilities also mean better preparation for unexpected surges in call volume, improving overall efficiency.

Benefits include:

  • Enhanced accuracy in call volume prediction.
  • Real-time adjustments to staffing levels.
  • Reduced need for overstaffing, leading to cost savings.

2. Optimizing Average Handle Time (AHT)

AI tools can significantly reduce AHT by providing agents with instant, accurate information during calls. Instead of manually searching for answers, agents receive AI-suggested responses based on the customer’s query, speeding up the resolution process.

AI can also analyze call recordings to identify common issues and suggest training opportunities to improve handling times. This leads to faster, more efficient interactions and higher customer satisfaction.

In effect:

  • AI provides quick, accurate responses to reduce AHT.
  • It identifies training needs through call analysis.
  • Faster resolutions enhance customer satisfaction and agent productivity.

3. Streamlining Staffing Requirements

AI doesn’t just help in predicting call volumes; it also enhances the actual staffing process. By analyzing performance metrics, AI can suggest the optimal number of agents needed at different times. It takes into account factors like expected call volume, AHT, and agent performance.

Moreover, AI tools can schedule agents based on predicted busy periods, ensuring you always have the right number of staff. This leads to better resource allocation and reduces the chances of under or overstaffing.

Practical benefits:

  • AI suggests optimal staffing levels based on performance data.
  • Automated scheduling aligns staff availability with predicted demand.
  • Better resource allocation reduces staffing inefficiencies.

AI Tools for Contact Center Staffing

The rapid adoption of AI in contact centers has led to the development of various tools that enhance staffing efficiency and effectiveness. Each tool offers unique features tailored to specific aspects of contact center operations. Here, we provide an extensive review of some leading AI solutions, highlighting their capabilities, advantages, and potential use cases.

1. Bigly Sales

Key Features

Bigly Sales stands out for its comprehensive approach to contact center management, combining AI-driven analytics with intelligent automation to streamline operations and enhance efficiency.

  • AI-Driven Analytics: Analyzes call data in real-time to provide actionable insights on staffing needs, performance metrics, and customer trends.
  • Intelligent Automation: Automates routine tasks such as call routing, data entry, and follow-up actions, freeing up agents to focus on complex issues.
  • Seamless Integration: Integrates smoothly with existing contact center systems and platforms, ensuring a unified and cohesive workflow.


Bigly Sales excels in improving operational efficiency by automating routine tasks and providing real-time analytics. This leads to better resource allocation, enhanced agent productivity, and improved customer satisfaction.

  • Operational Efficiency: Automates mundane tasks, reducing workload and allowing agents to handle more complex interactions.
  • Real-Time Insights: Provides immediate feedback on performance and customer trends, enabling quick adjustments to staffing and operations.
  • Enhanced Integration: Works seamlessly with existing systems, reducing implementation time and ensuring smooth transitions.

Use Cases

Bigly Sales is ideal for contact centers looking to enhance operational efficiency and improve the quality of customer interactions through advanced AI-driven automation and analytics.

  • E-commerce Contact Centers: Manages high call volumes and automates order tracking and customer inquiries.
  • Customer Support: Automates ticketing and follow-ups, allowing agents to focus on resolving complex issues.

Best For

  • Automation: Perfect for contact centers aiming to reduce manual workloads through intelligent automation.
  • Real-Time Analytics: Suitable for centers requiring real-time insights to adjust operations dynamically.

2. Level AI

Key Features

Level AI is distinguished by its advanced real-time analytics and predictive staffing capabilities. It continuously analyzes call data to provide dynamic adjustments to staffing levels, ensuring optimal resource allocation.

  • Real-Time Data Analysis: Monitors and analyzes call volumes, agent performance, and customer interactions in real-time.
  • Predictive Staffing: Uses machine learning to forecast staffing needs based on historical and real-time data.
  • Dynamic Adjustments: Automatically adjusts staffing levels to match current call volumes and predicted trends.


Level AI excels in its ability to adapt to real-time changes in call volume, minimizing the risk of under or over-staffing. This leads to improved operational efficiency and cost savings.

  • Increased Efficiency: By dynamically adjusting staffing, Level AI ensures that resources are allocated where they are needed most, reducing idle time and overstaffing.
  • Cost Savings: Reduces unnecessary staffing costs by aligning staff levels with actual demand.
  • Enhanced Customer Experience: Ensures that enough agents are available during peak times, reducing wait times and improving service quality.

Use Cases

Level AI is particularly beneficial for contact centers experiencing fluctuating call volumes or those looking to enhance their real-time responsiveness. It is ideal for large contact centers with complex staffing needs that require constant adjustment.

  • Retail Contact Centers: Handles seasonal spikes in call volumes effectively.
  • Tech Support: Adapts to unpredictable support call patterns.

Best For

  • Dynamic Staffing Adjustments: Ideal for contact centers needing real-time, data-driven staffing decisions.
  • Large Operations: Suitable for large centers with complex staffing needs.

3. AgentGPT

Key Features

AgentGPT focuses on reducing the burden of training by providing real-time, AI-driven knowledge management. It supplies agents with immediate answers and information, minimizing the need for extensive training.

  • Knowledge Base Integration: Seamlessly integrates with existing knowledge bases to provide instant information during calls.
  • Learning Capabilities: Continuously learns from agent interactions and feedback to improve its response accuracy.
  • Training Reduction: Diminishes the time agents spend in training by equipping them with the information they need in real-time.


AgentGPT’s real-time knowledge support significantly cuts down on training times, allowing agents to become productive more quickly. This enhances the overall efficiency of the contact center.

  • Reduced Training Costs: By minimizing the need for extensive initial training, AgentGPT reduces the costs associated with onboarding new agents.
  • Improved Agent Efficiency: Provides agents with quick access to information, speeding up call handling and reducing AHT.
  • Continuous Learning: Adapts and improves its responses based on new data and feedback, ensuring up-to-date information.

Use Cases

AgentGPT is ideal for contact centers looking to reduce training time and costs, especially those dealing with complex product or service inquiries that require extensive agent knowledge.

  • Technical Support: Helps agents quickly access technical documentation and troubleshooting steps.
  • Customer Service: Provides instant answers to frequently asked questions, improving call resolution times.

Best For

  • Reducing Training Times: Perfect for contact centers aiming to minimize training durations and costs.
  • Complex Knowledge Management: Suitable for centers handling complex queries that require detailed information.

4. Genesys AI

Key Features

Genesys AI focuses on delivering superior customer experiences through predictive engagement and smart routing. It utilizes AI to predict customer needs and route calls to the most appropriate agents or resources.

  • Predictive Engagement: Analyzes customer data to predict needs and proactively offer solutions.
  • Smart Routing: Directs calls to the best-suited agents based on their skills, availability, and previous interactions.
  • Personalized Interactions: Tailors responses and interactions based on customer history and preferences.


Genesys AI excels in enhancing customer satisfaction by ensuring that calls are handled by the most suitable agents and that interactions are personalized and effective.

  • Improved First Call Resolution: Increases the likelihood of resolving issues on the first call by routing to the best-suited agent.
  • Enhanced Customer Satisfaction: Provides personalized interactions, making customers feel valued and understood.
  • Efficient Resource Utilization: Optimizes agent utilization by matching their skills with customer needs.

Use Cases

Genesys AI is ideal for contact centers prioritizing customer experience and looking to leverage AI for smarter call routing and interaction personalization.

  • Financial Services: Personalizes interactions based on customer account history and preferences.
  • Healthcare: Routes calls to specialists based on patient needs and history.

Best For

  • Customer Experience Enhancement: Ideal for centers aiming to deliver personalized and effective customer interactions.
  • Predictive Engagement: Suitable for organizations that want to proactively address customer needs.

5. NICE inContact


NICE inContact offers a full suite of AI tools for workforce management, including advanced forecasting, scheduling, and real-time adherence monitoring. It provides a holistic view of staffing needs and agent performance.

  • Advanced Forecasting: Predicts staffing needs based on historical data and future trends.
  • Intelligent Scheduling: Automatically creates schedules that align with predicted call volumes and agent availability.
  • Real-Time Adherence: Monitors agent adherence to schedules and provides real-time updates.


NICE inContact provides comprehensive workforce management solutions, making it easy to plan, schedule, and monitor staffing effectively. Its advanced features help maintain optimal staffing levels and enhance overall contact center performance.

  • Accurate Forecasting: Delivers precise staffing forecasts, reducing the risks of under or over-staffing.
  • Efficient Scheduling: Creates intelligent schedules that align with actual call volumes and agent preferences.
  • Real-Time Monitoring: Offers real-time insights into agent performance and adherence, enabling quick adjustments.

Use Cases

NICE inContact is ideal for contact centers looking for a comprehensive solution that integrates forecasting, scheduling, and real-time management in a single platform.

  • Large Contact Centers: Manages complex scheduling and adherence needs effectively.
  • Outsourcing Firms: Handles varying staffing requirements for multiple clients.

Best For

  • Workforce Management: Ideal for contact centers needing an all-in-one workforce management solution.
  • Large and Complex Operations: Suitable for large centers with diverse staffing requirements.

Best Practices for AI-Augmented Contact Center Staffing

Choosing the Right AI Tools

When selecting AI tools for your contact center, look for features like real-time analytics, predictive modeling, and easy integration with existing systems. It’s also important to choose tools that are user-friendly and offer good support and training.


  • Real-time analytics and predictive capabilities.
  • Ease of integration with existing systems.
  • User-friendliness and quality of support.

Training and Onboarding

Training your staff to work with AI tools is crucial. Provide comprehensive training sessions that cover the functionalities and benefits of the AI systems. Ensure that agents understand how AI can assist them in their roles and make their work more efficient.

Training tips:

  • Offer detailed training on AI functionalities.
  • Emphasize how AI assists in daily tasks.
  • Provide continuous support and refresher sessions.

Continuous Improvement

AI tools should be monitored regularly to assess their performance and impact on staffing. Use this data to make improvements and adjustments. Engage with your staff to gather feedback on the tools and identify areas for enhancement.

Steps for improvement:

  • Regularly assess AI tool performance.
  • Make adjustments based on data and feedback.
  • Engage staff in the evaluation process.


Combining traditional staffing metrics with AI tools creates a powerful approach to contact center staffing. AI’s ability to predict call volumes, optimize AHT, and streamline staffing requirements leads to more efficient operations and happier customers. As AI continues to evolve, the future of contact center staffing looks promising, offering a harmonious blend of human insight and technological prowess.


How does AI impact contact center staffing needs?

AI enhances staffing by providing accurate call volume predictions, optimizing AHT, and suggesting optimal staffing levels based on real-time data.

What is the Erlang C formula, and how is it used in contact center staffing?

Erlang C is a traditional formula used to calculate the number of agents needed in a call center, considering call volume, length, and service levels.

Can AI tools completely replace human agents in contact centers?

While AI significantly enhances efficiency, human agents are still essential for handling complex inquiries and providing personalized service.

How do predictive analytics help in staffing contact centers?

Predictive analytics use historical data and patterns to forecast future call volumes and staffing needs, leading to better resource allocation and efficiency.

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