The Impact of AI on Call Center Workforce Management (WFM)

    The Impact of AI on Call Center Workforce Management (WFM)

    Navigating a maze of complex internal and external challenges can make managing your contact center seem impossible. From handling inbound call volumes to managing agent schedules, ensuring compliance, and maintaining high service quality, call center workforce management is full of hurdles that can compromise productivity and customer satisfaction.

    But it's incredibly important to get it right, and managing your workforce effectively requires time, dedication, and the right tools for the job.

    Effective workforce management can lead to a more efficient and productive contact center, which in turn leads to happier customers and higher profits. But how do you get there? What are the key elements of a successful workforce management strategy? And what tools can help you achieve your goals?

    This is one of the key reasons we're beginning to see the integration of AI into workforce management. AI can help contact centers manage their workforce more effectively by automating many manual tasks. This frees up time for managers to focus on strategic initiatives around customer service and cost reduction.

    In this article, we'll cover the key components of call center workforce management (WFM), how AI can improve it, and why you should consider integrating it into your call center.

    The key components of call center workforce management (and how AI can help)


    What is forecasting?

    Forecasting within call centers involves predicting the volume of calls, emails, chats, and other interactions in a given time period and using that information to estimate the number of agents needed to handle them properly without over or understaffing.

    To do this, managers use historical data on seasonality and trends in volume, as well as projections based on year-over-year changes, and more.

    However, this isn't a foolproof approach. It can be difficult to spot trends in this data, especially if you’re not sure what to look for—or if you’re simply not collecting the right data in the first place.

    How does AI help with forecasting?

    Data analysis tools that leverage the power of artificial intelligence (AI) and machine learning (ML) offer far more accurate, detailed analyses compared to standard human-driven platforms. They’re more intelligent and efficient and offer more precise forecasting because they can ingest more data than humans.

    ML algorithms can analyze historical data to identify patterns based on specific windows of time (weekly, monthly, quarterly, seasonal, custom intervals, or any other time frame), creating more accurate insights. And as more data is analyzed, the algorithm continues to learn and adjust these insights to reflect new information.


    What is scheduling?

    The next step after forecasting is scheduling, which is essentially assigning the right number of agents with the right skills at the right times to accommodate demand without over or understaffing.

    It helps determine shifts, hours of coverage, and any other staffing needs to optimize service levels and manage interaction volumes. To do it properly, contact centers must factor in attributes like agent skills, preferences, availability, calendar conflicts, contract terms, and more.

    How does AI help with scheduling?

    AI call center workforce management platforms can automate scheduling, including all of the factors we mentioned before. In fact, research has found that supervisors save nearly two hours per week using AI tools for scheduling and capacity training.

    These WFM solutions can create and balance shift templates and break rules, and use algorithms to generate multiple scheduling scenarios to find the most efficient and cost-effective one.

    They can even adjust staffing and projections on the fly in response to real-time data to accommodate for sudden surges in activity or changing business needs.

    Agent assignment

    What is agent assignment?

    Unlike scheduling, where managers try to staff enough agents to satisfy demand, agent assignment involves allocating agents based on their skills, experience, and availability to handle specific tasks and customer interactions.

    It needs to consider aspects like expertise, length of service, performance levels, and individual skills to properly select and staff agents.

    This can be difficult to balance, as management needs to keep agents engaged and their morale high while also ensuring they stay productive. They must also consider the risk of agent burnout and stress from being overworked or understaffed.

    How does AI help with agent assignment?

    AI call center workforce management tools can consider your agent’s preferences and availability, as well as other factors like shift changes and rules or time off requests. They use this information to adequately staff agents daily and ensure fair assignment.

    These tools can even distribute your agents equally across more and less desirable shifts so everyone can work their fair share and none of them end up stuck repeatedly working shifts they don’t enjoy. Night shifts or holidays, for example.

    Managing intraday activity

    What’s involved in managing intraday activity?

    You can spend weeks planning for a specific day, but there’s never a guarantee that day will follow your plan down to the letter. There’s always a chance that something can affect it, which makes real-time tracking of metrics like call volumes and agent performance essential.

    Supervisors and managers need to stay flexible and roll with the punches on a day-to-day basis.

    Issues like sick/unavailable agents, emergencies, sudden surges in call volume, and others can quickly turn a well-planned day into a nightmare, meaning they’ll need to adapt by changing schedules and reassigning agents.

    How does AI help with managing intraday activity?

    Adopting an AI contact center workforce management software can support intraday management by ingesting real-time data and comparing actual behavior to the originally scheduled activities.

    It can continuously analyze performance data to identify trends, anomalies, and areas for improvement, all much faster than a human could. Then, it provides real-time feedback to help fix issues before they escalate and become major problems.

    Performance analysis

    What is performance analysis?

    Performance analysis evaluates individual agent performance and overall call center operations against key metrics like service level, customer satisfaction, and response time. It also helps highlight areas for improvement and agent skill gaps, and can identify coaching and training opportunities for your agents.

    While this can fall under the purview of your QA team, it’s not uncommon to pair performance analysis with workforce management. That way, both your QA and operations teams can grasp your call center's everyday activity.

    How does AI help with performance analysis?

    AI contact center workforce management platforms can continuously analyze 100% of your interactions, monitoring, catching, and resolving issues in real-time, as well as conducting sentiment analysis and root cause analysis.

    Then, they use this data to build custom reports and populate personalized dashboards. You can even drill down to categorize data by agent, team, group, or department to track their performance across various KPIs.

    Using AI to automatically score interactions can also speed up your evaluation process, improving the consistency of your agent’s performance and the objectivity of your evaluation process. Plus, it can reduce evaluator workloads and offer real-time feedback for management to make data-driven decisions on the fly.


    What is call center compliance?

    Compliance within call centers is about adhering to industry standards and regulations regarding customer data, privacy, consumer rights, safety, ethical business practices, and more. It can include a strong focus on cybersecurity, vulnerability management, access control measures, and processes for handling sensitive information.

    All businesses need to consider compliance whenever they collect, exchange, or review sensitive data, including when speaking directly to customers. Reducing the chance of breaches and fraud is essential to keeping data safe, which often means beefing up security standards and following important regulations such as PCI, HIPAA, and GDPR.

    How does AI help with compliance and WFM?

    AI can help monitor and audit 100% of your interactions, including both speech and text conversations, to identify any instances of noncompliance and alert the relevant persons. It helps ensure your agents adhere to scripts and disclose information correctly when needed.

    An AI call center workforce management tool can also help businesses with labor laws. For example, it can monitor breaks and accommodate for sick days and overtime.

    Plus, it helps proactively identify and address security risks within your contact center, offering you the chance to fix them before a data breach occurs.

    4 big benefits of using AI in call center workforce management

    #1. Budget and resource allocation

    Money talks, and running under (or over) your budget is easy if you don’t manage your agents and staff effectively.

    AI algorithms can predict peak times and forecast interaction volumes, helping managers to eliminate unnecessary labor costs resulting from overstaffing, or avoid a dip in customer satisfaction (CSAT) scores caused by understaffing.

    This can significantly impact your business, as over 95% of costs associated with a call center are related to staffing.

    ML models can optimize the use of your available resources, ensuring that your call center is effectively deploying its human and technological assets, increasing your ROI.

    Additionally, automating workflows and workforce management can help reduce labor costs and operational spending.

    #2. Customer experience

    Improving CX is key to increasing retention and strengthening brand loyalty. 93% of customers are likely to make repeat purchases after receiving excellent customer service.

    By effectively implementing AI call center workforce management, you can offer shorter wait times, faster resolutions, and more personalized service. These AI tools can integrate with your CRM and be trained on your existing knowledge base (and even update it on the fly).

    AI can analyze customer data to provide a tailored experience, suggesting offers and solutions based on individual customer preferences and behaviors. It also helps boost your first contact resolution (FCR) rate, which 52% of customers cite as the most important factor in their customer experience.

    You can even use AI to power virtual agents and chatbots and let them handle routine tasks and queries, freeing up your agents to tackle more complex issues that require a human touch.

    #3. Agent performance and engagement

    Keeping your agents performing at their best and remaining engaged with their work is vital for maintaining a high-quality customer service environment.

    Implementing an AI call center workforce management tool can help monitor individual agents and offer on-the-spot training and recommendations, which allows them to improve their own skills and their customer interactions.

    AI-driven analytics can even help identify the signs of agent disengagement or burnout, enabling timely interventions like training sessions or schedule adjustments.

    In addition, you can use AI to analyze massive data sets and draw vital information on KPIs, productivity metrics, feedback, and evaluations to quickly build detailed performance reports and dashboards. And because it can do all of this in real-time, it enables better feedback for your coaches and agents, as it can be delivered while interactions are fresh in the mind.

    #4. Decision-making and operational efficiency

    Effective decision-making within call center workforce management involves leveraging data to make informed choices about your overall strategy and operations. With AI, your teams are capable of handling much larger and more complex data sets than before.

    And adding that data alongside AI tools with access to real-time data means you not only get better insights more often, but you can make data-driven decisions to push your business forward.

    AI is able to simulate different scenarios within your call center, such as sudden changes in call volumes or introducing new products, to help your team identify weak spots or prepare for the future.

    On top of all that, the addition of automation within your workforce management for scheduling, tracking, and resource allocation means your teams will save more time and boost efficiency by removing tedious manual tasks.

    Why you should use AI in call center workforce management training

    Integrating AI into call center workforce management training is a practical way to transform how training programs are designed and delivered.

    Here's why using AI in your call center's training modules can help enhance agent performance and boost customer satisfaction:

    • AI enables more targeted training: By understanding each agent's unique strengths and weaknesses, AI tailors the training content to meet individual needs, ensuring more effective learning outcomes.
    • Automatic analysis of data points: Through assessing metrics such as schedule adherence, agent productivity, and customer sentiment, AI identifies skills gaps and trends. This allows for the implementation of personalized training and coaching sessions that directly address these areas.
    • Building out your internal knowledge base: AI aids in creating and expanding an easily accessible repository of information. This serves as a vital resource for agents seeking quick answers or wanting to deepen their understanding of complex subjects.
    • Improvement of agent-customer interactions: By equipping agents with the right tools and knowledge, AI significantly enhances the quality of interactions between agents and customers, leading to higher satisfaction rates and more positive outcomes.


    Call center workforce management is vital to keeping your operation running smoothly and efficiently. But running a contact center isn’t as simple as staffing your agents and calling it a day—interaction volumes continue to grow, as do customer demands.

    Using AI call center workforce management tools can make a dramatic impact in a variety of ways. From more efficient forecasting and scheduling to improved analytics and compliance standards, AI helps teams do more with less and improve both their workflows and the customer experience.

    Interested in getting a jumpstart on your call center workforce management training? Check out Scorebuddy AI, and see how artificial intelligence can bring automation, efficiency, and insights to your organization. Sign up for a free trial today.


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      What does workforce management do in a call center?

      Workforce management in a call center optimizes staffing by forecasting interaction volumes and scheduling agents accordingly. It ensures that the right number of agents with appropriate skills are available at the right times to meet customer demand efficiently, enhancing service quality and agent utilization.

      Additionally, it manages real-time adjustments, monitors performance, and ensures compliance with labor laws and company policies, all while aiming to improve customer satisfaction and operational efficiency.

      What is call center workforce management training?

      Call center workforce management training equips staff with skills to forecast demand, schedule resources, monitor agent performance, and manage day-to-day operations effectively.

      It focuses on optimizing efficiency and service quality while ensuring compliance with relevant standards and improving overall customer satisfaction.

      How can artificial intelligence (AI) be used in call center workforce management (WFM)?

      AI in call center WFM optimizes scheduling, forecasts demand, enhances real-time decision-making, automates routine tasks, and provides performance analytics.
      It improves efficiency, agent engagement, and customer satisfaction by analyzing data and making predictive adjustments to workforce deployment.