Artificial intelligence (AI) continues to change the game in almost every way we work, and workforce management (WFM) is no exception. From virtual agents handling more interactions to changing patterns in employee availability and turnover, AI introduces new dynamics into staffing and capacity planning. And as these technologies become more deeply embedded in operations, organizations must adopt more agile planning practices.

Navigating increasingly competitive environments means that businesses must evaluate decisions that can have major implications on their success. Now organizations can make informed decisions with capacity planning that’s natively built with other Genesys Workforce Management components.

This means that related functions, such as forecasting and scheduling, are automatically fueled by long-range capacity planning. Additionally, the latest data from forecasting and scheduling feeds back into capacity planning for higher forecast accuracy. This unified approach helps you right-size your workforce based on data – not guesswork.

Solving Efficiency Problems with Better Capacity Planning

Most organizations, by necessity, will have established practices for generating forecasts and schedules for individual agents. Fewer organizations will have mature practices for long-term strategic planning for staffing, or capacity planning. Capacity planning can answer the question of how many people we need, in total, in six months or in a year.

Capacity planning has always played a vital role in preparing for seasonal volume shifts, new product introductions and long-term growth. But traditional methods, which often use disconnected data or manual spreadsheets, can’t keep pace with today’s business requirements.

Relying on siloed systems or spreadsheets for capacity planning often means that the data is pulled from various sources and quickly becomes outdated. This increases the risk of incorrect staffing.

Ultimately, that can hamper strategic planning decisions, making it difficult or impossible for organizations to adequately project time-consuming projects like hiring, training and other operational changes.

AI-powered capacity planning can alleviate these legacy issues. With capacity planning that’s unified into workforce management solutions that are built on AI, strategic planning becomes a more efficient practice based on current actual operational data.

This can give customer experience (CX) and customer service managers more confidence in the results. And it allows organizations to make strategic decisions earlier on.

For example, HR team members can learn months in advance that they will need to hire 235 new employees to service customers during a critical peak season. With automated planning, HR can work back from those dates to help ensure employees are fully onboarded when needed.

Let AI Do the Heavy Lifting in Workforce Capacity Planning

With native predictive and prescriptive AI built on proprietary AI models, you can more easily align staffing plans with long-term demand by understanding attrition percentage, or shrinkage, along with full-time equivalency expectations. These insights are essential to developing right-sized hiring plans for the next year, next season, event or launch.

By using accurate, data-driven plans, teams can be prepared earlier through improved coordination between operations, HR and leadership — without the slowdowns of disjointed or siloed systems.

Every organization does some form of capacity planning. But when it’s AI-driven and natively embedded in your WFM tools and analyzes future needs, it can become a strategic advantage. It’s time to move beyond “the way we’ve always done it” and start planning for what’s next.

AI doesn’t just assess what you need today — it helps analyze future requirements so you can take action early. With its AI-driven accuracy, you can plan in reverse: knowing when to start onboarding, when training should begin and how many agents you’ll need.

You can account for attrition, shrinkage and full-time equivalencies. It empowers teams to plan and hire ahead of holiday surges, product launches or seasonal events with more confidence and precision.

Key Concepts in AI-Powered Capacity Planning: Terms Every Workforce Leader Should Know

To fully harness the power of AI-driven capacity planning, it’s important to understand the key concepts that underpin this strategic function. These are especially useful as more teams from HR, operations and IT collaborate on long-term staffing strategies.

1. Capacity Planning

At its core, capacity planning is the process of determining the workforce resources needed to meet forecasted demand over a longer time horizon, typically months or quarters in advance. Unlike short-term scheduling, capacity planning focuses on strategic decisions like hiring, training pipelines and infrastructure investments.

In an AI-powered environment, this process is enhanced by data integration, continuous updates and scenario modeling, making long-term planning more accurate and responsive to change.

2. Forecasting

Forecasting in resource management refers to the estimation of future workload — including interaction volume, timing (average handle time) and types of customer interactions. AI-driven forecasting uses historical data, trends and machine learning algorithms to generate more precise predictions.

These forecasting methods inform capacity plans, helping determine how many agents will be needed — and when — to better meet customer demands.

3. Shrinkage

Shrinkage represents the portion of time that staff is unavailable to handle customer interactions. This includes factors like breaks, vacations, training, meetings, sick leave and unplanned absences. Accurately modeling shrinkage is vital to avoid underestimating staffing needs.

AI models can learn from historical shrinkage patterns, apply seasonality and dynamically adjust for more precise planning.

4. Attrition

Attrition, or employee turnover, refers to the rate at which employees leave an organization. In workforce planning, this is a critical variable, especially in contact centers and service roles where attrition can be high.

With AI-powered workforce planning, organizations can better predict attrition patterns using signals like tenure, performance metrics, engagement scores and other key metrics. This allows for proactive hiring and retention strategies.

5. Scenario Modeling

A powerful aspect of AI-enhanced planning is scenario modeling. This capability allows teams to simulate different business conditions, such as launching a new product, entering a new market or responding to a downturn. They can then see how those scenarios affect staffing needs.

This “what-if” analysis allows leaders to better stress-test their plans and create contingencies well in advance.

6. Unified Data Model

AI-powered capacity planning is most effective when it operates on a unified data model and flow. This means that all components of workforce management (contact center forecasting, scheduling, capacity and strategic workforce planning, performance and employee data) are seamlessly integrated and constantly updated.

With a unified model, AI can deliver real-time data insights based on the most accurate and current information available, helping to remove the blind spots and lags caused by siloed systems.

Meet Future CX Needs with AI-Powered Capacity Planning

With the Capacity Planning offering in Genesys Cloud Workforce Management, organizations can address long-term, strategic workforce questions on [y]our platform. Built natively into Genesys Cloud Workforce Management, it’s the smarter way to forecast, hire and operate.

Learn more about AI-powered capacity planning and all Genesys Cloud Workforce Management capabilities here. And use this checklist to help you choose the best workforce management solution for your business.