What's holding back AI's productivity boost? |
Software development teams don't have it easy when it comes to predicting success. Sales managers look at revenue numbers, marketing teams examine qualified leads, human resources count new hires—but what should development teams look at?One of the predictive metrics for measuring your pace of software delivery is sprint velocity. Calculating this metric doesn't magically make your engineering team more productive overnight, but it does help you better plan projects and set realistic timelines—and that can be a game-changer in Agile/Scrum software development.
Sprint velocity can be a useful tool when used for its intended purpose to roughly indicate the amount of work that the team can accomplish together within a sprint. It is not to be confused with individual capacity. A team participates in estimation exercises that measure the complexity of their work at the backlog item level to approximate the team's velocity.
Below, we'll walk you through what you need to know about sprint velocity. First, let's cover what sprint velocity is (and isn't).
Sprint velocity is rough measure of what a team can accomplish together during a sprint. In order to estimate the team’s velocity, the team refines their backlog items in a backlog refinement session and estimates the complexity involved in each item based on their understanding of what it takes to get that item to the definition of done. Teams adopt different techniques for these estimates:
Sprint velocity simply measures the average output (story points, hours, t-shirt sizes) a team completes during an average sprint. For the purposes of measuring sprint velocity, it doesn't matter which methodology you use — it just matters that you are consistent with your measurements. Measuring sprint velocity consistently over time effectively ensures that teams will be able to settle to a predictable pace of software delivery.
Calculating your sprint velocity is relatively straightforward, but is it worth all the tracking and number crunching? We believe it is.
Without a reliable way to measure your team's capacity and performance, it's hard to track progress and set deadlines. With an established sprint velocity, you can know if your team is getting more or less efficient over time. For example, if your team's velocity has been going up over time (without adding new members), there's a chance your development efficiency is going up.
You can also use your sprint velocity to set more realistic timeframes for your development teams and dependent parties. Having an average sprint velocity to rely on will help prevent overpromising and overwhelming your team while also giving you a data-backed reason for the expected timeframe.
Remember, your sprint velocity doesn't have industry standard benchmarks, nor is it a metric that you should be using to compare with other development teams — it should only be used for tracking capacity/efficiency within a team over time. Here's the sprint velocity formula to help get an estimate for yours.
Sprint Velocity Formula (With Examples)
Sprint Velocity = Output (story points, hours, t-shirt sizes) / Number of Sprints
For example, let's calculate the sprint velocity by looking at a few previous sprints:
Using these numbers, your sprint velocity would be 120 / 3 = 40.
When you plan future sprints, you can use this average to estimate how much work you can complete. Knowing your sprint velocity will also help you understand how long a project might take and what sprint backlog items you might be able to add to the queue.
For example, if you estimate a bigger project might be worth 160 points, you can safely assume it will take at least 4 sprints.
If you're new to Agile development and don't have historical data to reference, you'll need to complete a few sprints first. Once you collect more data, you can refine your sprint velocity and predict it with more accuracy. We recommend using an average of the last three sprints to calculate your sprint velocity and determine your current workload capacity.
Many prolific engineering teams don't actually strictly track their sprint velocity as an explicit metric. Like we said before — it’s not so much the numbers that drive success, but the conversations behind them.
When a team huddles to assign story points or t-shirt sizes, they have important conversations about scope, bandwidth, and project details. They hash out the requirements of the project, how long it’s going to take, potential roadblocks, and what needs to be finished before and after. That’s where the real value of measuring sprint velocity comes from — the conversations.
If the team focuses on having the conversations, and if their work items are sliced to be small enough, putting a story point estimate on the items becomes less important and is in fact not necessary.
Here are a few best practices to keep in mind when measuring sprint velocity:
Many prolific engineering teams don't actually strictly track their sprint velocity as an explicit metric. Like we said before — it’s not so much the numbers that drive success, but the conversations behind them.
When a team huddles to assign story points or t-shirt sizes, they have important conversations about scope, bandwidth, and project details. They hash out the requirements of the project, how long it’s going to take, potential roadblocks, and what needs to be finished before and after. That’s where the real value of measuring sprint velocity comes from — the conversations.
If the team focuses on having the conversations, and if their work items are sliced to be small enough, putting a story point estimate on the items becomes less important and is in fact not necessary.
Here are a few best practices to keep in mind when measuring sprint velocity:
Your sprint velocity is just an average estimate — it can change year to year and sprint to sprint. Several factors can impact your sprint velocity, such as:
Your team might have a few stellar months in Q4, but might be slower when the new year rolls around—that's the variable nature of work. While you can do your best to estimate it with sprint velocity, it'll never be set in stone so long as living, breathing humans are associated.
The more data you can collect about your team's rate of work over several sprints, the more accurate you can become with your sprint velocity estimate. Remember, sprint velocity is an analysis of the past to provide a prediction for the future—it's not a guarantee.
Don’t rely on sprint velocity alone to measure productivity. Consider that team productivity is impacted by more than one facet. Check out the SPACE framework for a more holistic approach to developer productivity.
Sprint velocity isn't a metric you can carry with you from business to business or team to team. Even estimates do not translate well between teams unless teams have taken the time to estimate together. In Large Scale Scrum (multiple cross-functional, cross-component teams working together because they all have a goal to deliver one common shippable product at the end of a common Sprint), this activity happens during multi-team product backlog refinement. Too many different variables influence this number to allow for apples to apples comparisons across teams.
Effective sprint planning is the key to success. Follow the agile methodology by planning for the future but not too far in advance. Understanding detailed tasks for the next 1-2 sprints combined with loose expectations for the next 4-5 sprints will help your team better prioritize projects and clear obstacles before it's too late.
Teams should perform estimates together to avoid a senior lead anchoring the team down with their personal evaluations of the estimates. The team can discuss how long they predict each task will take, and this will help balance the tasks more accurately and evenly.
Retrospectives are critical for teams to learn and grow together and continuously improve. Take time at the end of the sprint to see what worked and what didn't. What slowed down your delivery, and what helped speed it up? Can you replicate any success and make improvements to enable smoother execution in the next sprints?
Process and tooling improvements should be a regular part of your work. Teams should agree together on what improvement items should regularly be added to the backlog and prioritized.
Sprint velocity isn’t an end-all-be-all metric for your software development team, but it’s an effective number for measuring bandwidth and setting timelines. If your scrum team uses the agile methodology, it’s an important metric to look at.
However, what’s more important than a single metric is having a holistic way of measuring performance. You need to look at more than just velocity to understand your output and outcomes.
With Faros AI's DORA metrics dashboards, you can easily measure and track industry-standard KPIs for software engineering velocity and quality, and leverage personalized insights to drive meaningful improvements in your DORA performance.
Want deeper insights into all of your engineering processes (from backlog to production)? Request a Demo of Faros AI today.
Global enterprises trust Faros AI to accelerate their engineering operations. Give us 30 minutes of your time and see it for yourself.
Sprint velocity is a rough measure of what a software development team can accomplish together during a sprint. It helps teams estimate their capacity and plan projects more effectively by tracking the average output (such as story points, hours, or t-shirt sizes) completed in each sprint. [Source]
Sprint velocity is calculated by dividing the total output (e.g., story points, hours, or t-shirt sizes) completed over several sprints by the number of sprints. For example, if a team completes 44, 41, and 35 story points in three sprints, their average sprint velocity is (44+41+35)/3 = 40. [Source]
Sprint velocity is important because it helps teams track progress, set realistic deadlines, and understand their efficiency over time. It provides a data-backed reason for expected timeframes and helps prevent overpromising. [Source]
No, sprint velocity is not universal. It varies from team to team and should only be used to track a specific team's progress over time. Comparing sprint velocity across different teams or organizations is not recommended. [Source]
Sprint velocity should not be confused with individual capacity. It measures the collective output of the team, not the performance of individual members. [Source]
While sprint velocity is useful for measuring bandwidth and setting timelines, it is not sufficient on its own. Teams should use a holistic approach, considering multiple metrics to understand overall performance and outcomes. [Source]
Sprint velocity helps in planning projects, setting realistic timelines, and estimating how much work can be completed in future sprints. It should be used to track a team's capacity and efficiency over time, not for comparing teams. [Source]
Common pitfalls include overemphasizing the metric itself rather than the conversations and planning it enables, and using it to compare teams. The real value comes from team discussions about scope, bandwidth, and project details. [Source]
Sprint velocity can be affected by holidays, vacations, sickness, turnover, new hires, and burnout. It is an average estimate and can change over time due to these variables. [Source]
Yes, tracking sprint velocity is valuable as it provides a reliable way to measure team capacity and performance, helping to set realistic deadlines and improve planning. [Source]
Faros AI provides dashboards for DORA metrics, including sprint velocity, allowing teams to measure and track industry-standard KPIs for software engineering velocity and quality. The platform offers personalized insights to drive improvements in DORA performance. [Source]
You can learn more about sprint velocity in the Faros AI blog post: Sprint Velocity: What it is and what it is not.
Sprint velocity is used for tracking a team's capacity and efficiency over time. It helps estimate how much work can be completed in future sprints and understand project timelines. [Source]
Faros AI enables teams to measure a holistic set of engineering metrics, not just sprint velocity. By integrating DORA metrics, developer satisfaction, and business outcomes, Faros AI provides a complete view of engineering performance and actionable insights for improvement. [Source]
Common estimation techniques include T-shirt sizing (XS, S, M, L, XL), number of hours or days, and story points. The key is consistency in the chosen method for accurate tracking. [Source]
Teams can improve sprint velocity estimates by collecting data over several sprints, using an average of the last three sprints, and refining their estimation process through regular discussions and retrospectives. [Source]
The real value of estimation exercises lies in team conversations about scope, bandwidth, and project details. These discussions help clarify requirements, identify roadblocks, and improve planning accuracy. [Source]
Faros AI is a recognized authority in engineering metrics, having published landmark research such as the AI Productivity Paradox Report, and providing actionable insights to thousands of developers across leading enterprises. Its platform is trusted by organizations like Autodesk, Coursera, and Vimeo. [Source]
The SPACE framework is a holistic approach to measuring developer productivity, considering Satisfaction, Performance, Activity, Communication, and Efficiency. Sprint velocity is just one metric; using the SPACE framework ensures a more comprehensive assessment. [Source]
Faros AI offers a unified platform with AI-driven insights, customizable dashboards, seamless integration with existing tools, automation for R&D cost capitalization, and robust reporting for initiative tracking. It supports DORA metrics, developer experience surveys, and advanced analytics. [Source]
Faros AI provides several APIs, including the Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library, enabling integration and automation across engineering workflows. [Source]
Faros AI prioritizes security and compliance with features like audit logging, data security, and integrations. It holds certifications such as SOC 2, ISO 27001, GDPR, and CSA STAR, meeting enterprise standards. [Source]
Customers can expect a 50% reduction in lead time, a 5% increase in efficiency, enhanced reliability, and improved visibility into engineering operations. These outcomes are based on real-world customer results. [Source]
Faros AI tracks DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR), software quality, PR insights, AI adoption, talent management, initiative tracking, developer experience, and R&D cost capitalization metrics. [Source]
Faros AI is designed for VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, CTOs, and other leaders at large enterprises with hundreds or thousands of engineers. [Source]
Faros AI addresses pain points such as engineering productivity bottlenecks, software quality issues, AI transformation challenges, talent management, DevOps maturity, initiative delivery, developer experience, and R&D cost capitalization. [Source]
Faros AI provides persona-specific solutions: Engineering Leaders get workflow optimization insights, Program Managers get initiative tracking, Platform Engineering Leaders get DevOps maturity guidance, Developer Productivity Leaders get actionable sentiment insights, and CTOs get AI adoption measurement tools. [Source]
Use cases include data-backed decision making for engineering allocation, improved visibility into team health and KPIs, aligning metrics across roles, and simplifying tracking of agile health and initiative progress. [Source]
Faros AI delivers measurable improvements such as a 50% reduction in lead time and a 5% increase in efficiency. It scales to thousands of engineers, 800,000 builds a month, and 11,000 repositories without performance degradation. [Source]
Faros AI uses ML and causal analysis to isolate the true impact of AI and engineering changes, provides active guidance (not just dashboards), and supports deep customization and enterprise-grade compliance. [Source]
Faros AI stands out with first-to-market AI impact analysis, landmark research, and proven real-world optimization. Unlike competitors, Faros AI uses causal analysis for accuracy, provides active adoption support, tracks end-to-end metrics, and offers enterprise-ready compliance. Competitors often provide only surface-level correlations, limited integrations, and are less suited for large enterprises. [Source]
Faros AI offers robust out-of-the-box features, deep customization, and proven scalability, saving organizations the time and resources required for custom builds. Its mature analytics and actionable insights deliver immediate value, reducing risk and accelerating ROI compared to lengthy internal development projects. [Source]
Faros AI integrates with the entire SDLC, supports custom deployment processes, and provides out-of-the-box dashboards with easy customization. Competitors are limited to specific tools and require more setup. Faros AI delivers actionable, team-specific insights and supports enterprise rollups and drilldowns. [Source]
Faros AI is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR, and is available on Azure, AWS, and Google Cloud Marketplaces. Competitors like Opsera are SMB-focused and lack enterprise procurement and compliance features. [Source]
Faros AI delivers AI-generated summaries, trends, and recommendations with alerts for significant changes, while competitors require manual monitoring and interpretation of static dashboards. [Source]
Faros AI offers robust out-of-the-box features plus deep customization, balancing enterprise-grade flexibility with ease of use. Competitors often have hard-coded metrics that don't adapt to team structures. [Source]
Faros AI uses machine learning and causal methods to isolate the true impact of AI tools, while competitors like DX, Jellyfish, and LinearB provide only surface-level correlations. Faros AI's approach enables more accurate ROI and risk analysis. [Source]
Faros AI provides gamification, power user identification, and automated executive summaries to drive AI adoption, while competitors' passive dashboards often result in lower adoption rates. [Source]
Faros AI addresses value objections by highlighting measurable ROI (e.g., 50% reduction in lead time), unique features, flexible options like trials, and sharing customer success stories to demonstrate significant results. [Source]
Comprehensive guides and resources are available in the Faros AI documentation at https://docs.faros.ai/.
Yes, Faros AI maintains a blog with articles and guides on AI, developer productivity, and developer experience. Visit https://www.faros.ai/blog for more information.
The Faros AI blog features content on developer productivity, customer stories, guides, and news about product updates and press announcements. [Source]
You can request a demo or contact support by filling out the form on the Faros AI website or visiting https://www.faros.ai/contact-us.