How to Forecast IT Workload Growth Before Capacity Becomes a Problem

Forecasting IT workload growth means estimating future demand for IT services, infrastructure, and staffing so your organization can prepare before capacity becomes a bottleneck. Instead of reacting to rising ticket volumes, expanding endpoint fleets, or new business initiatives after they strain your team, workload forecasting helps you anticipate future demand using historical data, business plans, and structured planning.

Whether you're managing internal IT or delivering services as an MSP, a consistent forecasting process makes it easier to plan hiring, budget for infrastructure, and maintain service quality as your environment evolves.

What is IT workload forecasting?

IT workload forecasting is the process of estimating future demand for IT work based on historical operational data, business objectives, and expected organizational changes.

Workload may include:

• Help desk tickets
• Incident response
• Change requests
• Endpoint management
• User onboarding and offboarding
• Software deployments
• Infrastructure maintenance
• Security operations
• Compliance initiatives

The NIST Guide on Workload Forecasting recommends beginning by clearly defining the workload being measured before selecting forecasting methods. Clearly identifying individual workload categories also helps reveal that different services may follow different demand patterns, making forecasts more meaningful and actionable.

Build a reliable historical baseline

Historical data provides the foundation for forecasting.

Useful operational metrics include:

• Monthly ticket volume
• Active endpoints
• User count
• Managed devices
• Change requests
• Infrastructure assets
• Security incidents
• Major project activity

Rather than relying on averages alone, historical data helps identify long-term trends, recurring seasonal patterns, and unusual events that may influence future demand.

The NIST/SEMATECH Engineering Statistics Handbook explains how time series analysis can be used to identify trends, seasonality, and other patterns within historical data. Because forecasting methods rely on historical measurements, maintaining consistent and reliable operational data is an important foundation for producing useful forecasts.

Consider the business drivers behind growth

Historical trends should be combined with planned business changes.

Examples include:

• Hiring initiatives
• Office expansions
• Cloud migrations
• New software rollouts
• Regulatory requirements
• Mergers or acquisitions
• Digital transformation projects

These activities can significantly affect future IT demand before they appear in historical metrics.

The OPM Workforce Planning Guide recommends aligning workforce planning with organizational strategy so organizations can prepare for future requirements instead of reacting only after workloads increase.

Understand the difference between workload forecasting and capacity planning

Workload forecasting estimates how much IT work your organization is likely to face in the future. Capacity planning focuses on whether your existing people, skills, processes, and infrastructure can support that projected demand.

In practice, the two activities complement one another. A forecast may indicate that ticket volume, endpoint growth, or project work will increase over the next year, while capacity planning determines whether current staffing levels, infrastructure resources, and operational processes are sufficient to maintain service quality as demand grows.

Using both approaches together helps organizations make better decisions about hiring, budgeting, infrastructure investments, and operational priorities before capacity constraints begin affecting users.

Build multiple forecasting scenarios

Forecasts should account for uncertainty.

Instead of relying on a single projection, develop several realistic scenarios based on possible business outcomes.

For example:

• Expected business growth
• Faster hiring than planned
• Large infrastructure projects
• Expanded compliance requirements
• Delayed modernization initiatives

The OPM Scenario-Based Workforce Planning guidance recommends evaluating multiple plausible scenarios to better understand how different conditions may affect future workforce requirements.

Scenario planning helps organizations prepare for changing conditions without assuming a single forecast will always prove correct.

Match projected demand with available capacity

Forecasting demand is only one part of effective planning.

Organizations must also evaluate the capacity available to support future work.

Capacity includes:

• Technical staff
• Specialized skills
• Infrastructure resources
• Operational processes
• Available working time

The OPM Workforce Planning Guide recommends assessing future workforce needs alongside organizational objectives, while the GAO report on Human Capital: Key Principles for Effective Strategic Workforce Planning emphasizes identifying future competencies, addressing workforce gaps, and continuously evaluating workforce strategies.

Headcount alone does not represent true capacity. Different roles require different expertise, and planned leave, training, documentation, meetings, and internal improvement efforts all affect the amount of work a team can realistically perform.

Forecast infrastructure growth alongside staffing

Increasing workload affects more than staffing.

Growing organizations often require additional computing resources, storage, network capacity, monitoring, identity services, and cloud resources.

The NIST Cloud Computing Synopsis and Recommendations explains that cloud computing provides elasticity and rapid resource provisioning, allowing organizations to adjust resources as demand changes. Estimating expected resource requirements during planning helps organizations make informed decisions about performance, availability, and cost.

Forecasting infrastructure growth alongside workforce needs provides a more complete view of future operational requirements.

Choose forecasting methods that fit your objectives

Different forecasting methods are appropriate for different situations.

The NIST Guide on Workload Forecasting introduces techniques such as moving averages, exponential smoothing, regression, and time series analysis for estimating future workloads.

For organizations with larger datasets or more complex planning requirements, Forecasting: Principles and Practice discusses forecasting approaches including trend analysis, regression models, exponential smoothing, ARIMA models, forecast intervals, and methods for evaluating forecast accuracy.

The companion review, Forecasting: Theory and Practice, explains that different forecasting approaches are appropriate under different conditions and forecasting objectives, reinforcing the importance of selecting methods that fit the available data and planning goals.

Review forecasts regularly

Forecasting should be an ongoing process rather than a one-time exercise.

Compare forecasts with actual operational results on a regular basis and review questions such as:

• Which assumptions proved accurate?
• Which business events affected demand?
• Which services grew faster than expected?
• Where did forecasts consistently overestimate or underestimate demand?

Regularly comparing forecasts with actual outcomes helps organizations refine planning assumptions and improve future forecasts.

Continual review also aligns with the service management principles established in ISO/IEC 20000-1, while ISO/IEC 20000-2 provides guidance for applying those principles as part of an ongoing service management system.

How to get started with IT workload forecasting

A practical starting point does not require sophisticated forecasting software.

Begin by reviewing the previous 12 months of operational metrics such as ticket volume, endpoint growth, infrastructure changes, and major projects. Compare those trends with upcoming business initiatives, hiring plans, and planned technology investments. Then review the forecast regularly and adjust it as new information becomes available.

As historical data improves, organizations can gradually adopt more advanced forecasting techniques while maintaining a repeatable planning process.

How Level can support workload planning

Accurate forecasting depends on reliable operational visibility.

Platforms like Level provide visibility into managed endpoints, devices, automation, alerts, and day-to-day operational activity. Combined with a structured forecasting process based on historical data, workforce planning, and capacity planning, that operational visibility can help IT teams make more informed decisions about future staffing, infrastructure, and service delivery.

Forecasting cannot eliminate uncertainty, but it gives organizations a structured way to prepare for growth before demand begins affecting service quality.

Frequently Asked Questions

How do you forecast IT workload?

Start by defining the workloads you want to measure, collect historical operational data, identify trends and business drivers, compare projected demand with available capacity, and review forecasts regularly as business conditions change.

What metrics should IT teams track?

Useful metrics include ticket volume, endpoint growth, user count, infrastructure assets, change requests, security incidents, and major project activity. The most appropriate metrics depend on the services your IT organization provides.

What is the difference between workload forecasting and capacity planning?

Workload forecasting estimates future demand for IT services, while capacity planning evaluates whether your people, skills, infrastructure, and operational resources are sufficient to support that demand.

How often should IT workload forecasts be updated?

Forecasts should be reviewed regularly and refined as new operational data and business plans become available. The appropriate review frequency depends on how quickly your organization changes.