Why Your Capacity Planning Strategy Is Holding Your Dev Team Back

Leaders of successful businesses know how to adapt. Whether in times of sudden economic lows or highs, these individuals understand what their teams are capable of doing and where they can make necessary changes. They are able to balance investments in the present and future to guide their business through uncertain times without losing sight of strategic goals. Part of this involves understanding the capacity their teams have for current and future tasks.

Unfortunately, even the smartest strategies can cause harm to development teams and lead to an incorrect estimation of capacity. Likewise, without proper adjustments, companies may unknowingly undermine the teams' capacity and make less ambitious plans based on incorrect data.

In software development, capacity planning is a framework managers use to understand future demands and what resources they will need to meet them. These could include developers, infrastructure, and tools. Proper capacity planning ensures teams can deliver features, fix bugs, and maintain performance without over- or under-utilizing resources. It is similar to organizing a dinner party. The host will expect to have 10 hungry guests (demand), so they will look in the refrigerator to assess their available ingredients (capacity) and begin planning what they will or will not need to buy.

There are three capacity planning strategies leaders use in software development. They match different priorities and resources that a business may or may not have at a given time.

1. Lead strategy: This is the process of hiring or scaling infrastructure ahead of projected demand to ensure readiness through an increase in initial costs.
2. Lag strategy: Waiting to see how demand has increased and then expanding teams or resources in an effort to minimize costs, although with a risk of delays.
3. Match strategy: Assessing demand in increments and adjusting resources based on real-time project workload to balance cost and efficiency.

The right strategy for a company will depend on factors like development velocity, budget, and product growth expectations. Leaders will also need to consider the risks associated with each strategy when selecting the best approach: can they afford to invest in preparations that may not be needed or only react to changes as they come?

Two terms can often be confused. These are capacity planning and resource planning. While they both help organizations allocate resources efficiently, there are differences in their focus and scope. Capacity planning is a high-level, long-term strategy that determines whether an organization has enough overall capability (team size or infrastructure) to meet future demand. It revolves around the question: Do we have enough capacity to handle upcoming projects?

Resource planning, on the other hand, is focused on details and short- to mid-term timelines related to the specific allocation of available resources. It ensures that individuals are assigned to tasks effectively and have the necessary tools to complete their work. For example, in software development, capacity planning might involve the decision to hire more developers or upgrade cloud infrastructure. In contrast, resource planning involves assigning developers to sprints or balancing workload across teams. In sum, capacity planning ensures resources exist, while resource planning optimizes their use.

The capacity planning strategies above provide a framework leaders and companies can use to understand what their teams can accomplish in a specified period of time. However, even the best structure will only yield the same quality of results as the input it receives. Below are 7 mistakes companies make during their capacity planning process that hold their development teams back from achieving more.

Evaluating capacity requires managers to use metrics that measure the productivity and performance of their engineers. Given the abundance of data today, it is not difficult to gather this information; however, there is a temptation to see metrics as the final evidence of certain behaviors. For example, a short cycle time indicates that a team is working efficiently, or a long one shows that engineers are not performing as well as they could. This may not be true, though.

Metrics are only signals that highlight situations. The cycle times are indications of how well processes are working, but managers will need to look under the hood of their development teams to understand the details of how they work. When managers accept the signal approach, it can lead to more fruitful conversations with engineers that reveal more information than a report.

When managers review their current and future capacity, they may fall victim to their habits, even inefficient ones. One such example is regular synchronous meetings. If these are the norm for a team, its members will sacrifice valuable time and resources that could be directed to more important tasks. This is closely connected to how information is shared within teams and across a company. Searching for necessary updates and data can also take time away from productive tasks. That means that inefficient communication and information-sharing practices will skew capacity predictions.

To save time and ensure engineers and team leaders have the information they need, companies can introduce several changes to their workflows. For example, instead of synchronous stand-up meetings at a given time, asynchronous text stand-ups allow team members to submit their reports when they are able. Moreover, the text reports serve as artifacts that everyone can view for quick project updates. Another helpful addition is a culture of recording what work has been done and the time spent on a task with worklogs. AI tools can analyze this data to create summaries that managers can use to calculate capacity more accurately.

Time is a critical factor in capacity planning that many teams overlook. Demand fluctuates throughout the year due to holidays, market events, and industry cycles, while workforce availability varies across globally distributed teams with different regional calendars. Without accounting for these seasonal patterns, capacity plans become unrealistic, leading to overworked teams during peak periods and underutilized resources during quieter times.

The solution requires both cultural and technical approaches. For remote teams, implementing flexible time-off policies allows employees to accommodate regional holidays while maintaining consistent collective capacity. Simultaneously, AI-powered forecasting tools can analyze historical demand patterns, providing data-driven insights that enable teams to anticipate seasonal variations and adjust their roadmaps accordingly. This balanced approach ensures development resources align with actual demand throughout the year, preventing both burnout and waste.

In some cases, data becomes irrelevant or out-of-date as soon as it leaves a team and takes its place in a report. Predictions that rely on this information risk assuming the current state will remain the same. An experienced engineer may leave a team or reduce their hours. A project may require more resources than originally planned. These and other issues can occur at unexpected moments, which is why the use of real-time data helps managers correct their capacity planning to include new changes. Moreover, when a company has made changes to its workflows and processes, the effects may appear over time, and real-time data can help account for improvements (or the lack thereof).

Of course, managers cannot always observe and record the day-to-day information that teams produce in their activities. Fortunately, AI tools can do this in the background 24/7 and provide alerts to planners or stakeholders when conditions change. Furthermore, AI requires as much data as possible to build an understanding of what is happening. Worklogs again serve as an excellent source of information that keeps everyone and AI agents informed about team and individual activity.

Planning for future tasks should include more than a quick look at the surface-level skills of each individual engineer or team. If a project requires Kubernetes experience, but the company lacks someone with this expertise, that does not mean they will have to hire someone from outside. Skills matrices are helpful in finding engineers who may lack a specific skill but are able to acquire it quickly based on their current knowledge and ability.

Accurate and up-to-date Scope of Work dashboards keep this information organized in a single location for managers and planners to refer to when planning for future capacity. AI tools can make suggestions on which employees match best with specific projects and tasks based on their current availability.

Similar to inefficient communication practices, capacity planning may suffer from mismanagement of developer expertise within a team. This may manifest itself in situations where experienced engineers spend their time on tasks that do not require their level of expertise. If this happens on a regular basis, then their performance could drop due to a lack of interest in the work. Likewise, if junior engineers are given more complex tasks without proper support, the number will show they are underperforming despite the fact that their working conditions prevented them from completing the task. Capacity planning that uses such workflows to make predictions will encourage them and not offer improvement insights.

To avoid inaccurate capacity calculations, organize opportunities that account for levels of expertise. Senior team members can benefit from challenging tasks and mentorship responsibilities, while junior members will appreciate trust that they can perform complex assignments with the proper support.

The presence of tech debt is nothing new for any software team or business; however, proper or improper management of the debt will affect how they calculate their capacity. This is especially true in companies that lack clear communication on their debt. Planners who are not aware of how their teams make these decisions will lack the information they need to account for the ability to meet future demand.

Two solutions can help ensure that technical debt becomes a part of capacity planning and does not hinder it. First, teams can establish workflows that encourage a regular return to issues and features that require updates or fixing. Another is to implement AI that can assess the state of software and equipment to provide a clear picture of when to schedule technical debt into future plans.

The solutions mentioned in the list above all connect with advancements in AI technology that assist managers and planners in better understanding their teams' capacity. When there is an excess of data on performance, the time required to gather and analyze it can be overwhelming without the help of AI tools. Likewise, AI monitors performance 24/7 to provide real-time updates. With more time to focus on strategy, managers can develop their resources to assess capacity and make adjustments that support their predictions accurately.

The Enji platform offers several AI features that improve capacity planning and resource allocation. Whether managers need access to real-time information on their employees and projects or to know what employees are available, Enji can help by keeping a finger on the pulse of a company.

These and other features gather data from the work teams produce. This includes task status changes, comments to tasks, worklogs, and stand-ups. The data these features collect around the clock provides managers with accurate information and predictions that reflect the actual state of projects and teams. Furthermore, Enji takes engineering data and turns it into actionable business insights that bridge the gap between development and business.

However, Enji's benefits extend beyond data collection and analysis. At the heart of Enji is a commitment to building effective and responsible software engineering processes that deliver consistent high performance. Transparent work and information fuel such workflows and ensure quality data returns to the system to boost planning capabilities. Enji creates and maintains this cycle that improves a company from C-level management to junior engineers.

Capacity planning can return useful insights and accurate results if planners consider all the factors involved in performance, reap the benefits of adjustments to workplace culture, and introduce AI tools. By putting AI to work, businesses can maintain a 24/7 watch on the pulse of their company, from individuals to teams and departments, to ensure they always know as much as they can about current and future capacity.