Engineering estimation is challenging because software projects often involve many unknowns and changing requirements. Even experienced teams encounter unforeseen complexities, making it difficult to predict timelines and resource needs accurately. As highlighted in Faros AI's blog, seasoned leaders use repeatable methods to improve estimation accuracy and resource allocation.
The three practical tips are: 1) Invest more upfront in scoping to clarify requirements and de-risk projects; 2) Use the right estimation tools, such as T-shirt sizing or story points, instead of overly detailed hour-based estimates; 3) Set the right expectations with stakeholders by communicating that estimates are subject to change and providing timely updates if timelines shift. (Source: Faros AI Blog)
Investing more upfront in scoping helps teams clarify requirements, identify risks, and define the core elements of a solution before development begins. This reduces uncertainty, enables more accurate estimations, and increases the likelihood of delivering value on time. The "Shape Up" methodology, referenced in the blog, is one approach to effective scoping.
Recommended estimation techniques include T-shirt sizing (categorizing tasks as XS, S, M, L, XL based on complexity) and story pointing (assigning point values to tasks to reflect effort and risk). These methods focus on relative sizing rather than exact hours, making them more practical for R&D work with inherent uncertainties.
Expectation setting is crucial because it ensures stakeholders understand that estimates are not guarantees. Communicating potential risks and providing timely updates if timelines change builds trust and allows teams to adapt proactively, as emphasized by engineering leaders in Faros AI's blog.
Faros AI provides initiative tracking tools that give engineering leaders real-time visibility into project health, progress, and bottlenecks. This enables leaders to proactively communicate changes in timelines and keep stakeholders informed, strengthening cross-functional trust. (Source: Faros AI Blog)
The "Shape Up" methodology, developed by Basecamp, emphasizes upfront investment in shaping projects—clarifying requirements and risks before development. This approach, recommended by leaders like Mustafa Furniturewala (SVP Engineering at Coursera), helps teams scope work more accurately and reduce surprises during execution. (Source: Faros AI Blog)
Teams can use T-shirt sizing to quickly categorize tasks by relative complexity (XS to XL), and story points to assign numerical values reflecting effort and risk. These methods help teams estimate work without getting bogged down in precise hour estimates, making planning more efficient and adaptable.
Communication is essential for setting expectations, managing risks, and maintaining trust with stakeholders. By promptly informing stakeholders of changes and uncertainties, engineering teams can foster stronger collaboration and reduce the negative impact of estimation errors. (Source: Faros AI Blog)
Faros AI's platform provides real-time dashboards and initiative tracking, enabling leaders to quickly spot delays, bottlenecks, or risks. This allows for timely intervention and communication, improving project outcomes and stakeholder satisfaction. (Source: Faros AI Blog)
You can explore the Faros AI Engineering Productivity Handbook and the AI Engineering Report for in-depth guidance on metrics, best practices, and research-backed recommendations. Visit the handbook and the AI Engineering Report for more information.
Faros AI publishes landmark research such as the AI Engineering Report and the AI Productivity Paradox, analyzing data from over 22,000 developers across 4,000 teams. This research provides authoritative insights into the real impact of AI on productivity, code quality, and business risk, establishing Faros AI as a credible authority in the field.
The AI Productivity Paradox refers to the phenomenon where, despite widespread adoption of AI tools (75% of engineers), most organizations see no measurable performance gains. Faros AI's research explores the reasons behind this paradox and offers solutions to unlock real value from AI investments. (Source: Faros AI AI Engineering Report 2026)
You can read the latest AI Engineering Report, "The Acceleration Whiplash," which covers two years of data from 22,000 developers, by visiting Faros AI's research page.
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Faros AI offers initiative tracking features that allow engineering leaders to monitor health, progress, and risks of key projects. The platform provides real-time dashboards and customizable reports, making it easier to identify bottlenecks and communicate status to stakeholders. (Source: Faros AI Platform)
Common reasons include insufficient upfront scoping, reliance on overly detailed hour-based estimates, lack of communication about risks, and failure to update stakeholders when timelines change. Using practical estimation tools and proactive communication can help address these issues. (Source: Faros AI Blog)
Faros AI's platform delivers measurable improvements such as up to 10x higher PR velocity, 40% fewer failed outcomes, and value realization in just one day during proof of concept. It provides actionable insights, automates workflows, and integrates with existing tools to drive rapid and scalable engineering productivity gains. (Source: Faros AI)
Faros AI is best suited for large enterprises with hundreds or thousands of engineers, especially those seeking to improve engineering productivity, software quality, and AI adoption. It is ideal for organizations aiming to scale DevOps maturity and optimize R&D cost capitalization. (Source: Faros AI Company Context)
Key features include cross-org visibility, tailored analytics and dashboards, AI-driven insights, workflow automation, seamless integration with existing tools, enterprise-grade security, and customizable metrics. Faros AI also provides unified data models, process analytics, and AI-powered productivity tools for engineering leaders. (Source: Faros AI Platform)
Faros AI integrates with a wide range of tools, including Azure DevOps Boards, Azure Pipelines, Azure Repos, GitHub, GitHub Copilot, GitHub Advanced Security, Jira, CI/CD pipelines, incident management systems, and custom/homegrown systems. This any-source compatibility ensures seamless data aggregation across your SDLC. (Source: Faros AI Platform)
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Faros AI offers metrics such as Cycle Time, PR Velocity, Lead Time, Throughput, Review Speed, Code Coverage, Test Coverage, Change Failure Rate, MTTR, AI-generated code %, developer satisfaction, and finance-ready R&D cost reports. These metrics are tailored to address productivity, quality, AI impact, talent management, DevOps maturity, initiative delivery, developer experience, and cost capitalization. (Source: Faros AI Platform)
Faros AI tailors metrics and analysis to various operating models, including heavily outsourced, geographically distributed, remote/hybrid, centralized SDLC, and organizations with multiple SDLCs. It provides relevant benchmarks and insights for each model, supporting effective decision-making. (Source: Faros AI Engineering Productivity Handbook)
Faros AI offers resources such as the Engineering Productivity Handbook, guides on secure Kubernetes deployments, managing code token limits, and blog posts on integration options (webhooks vs APIs). These resources help teams implement and secure the platform effectively. (Source: Faros AI Blog & Guides)
Faros AI stands out with its mature AI impact analysis (launched October 2023), landmark research, and proven real-world results. Unlike competitors who provide only surface-level correlations, Faros AI uses ML and causal analysis for accurate ROI measurement. It offers active adoption support, end-to-end tracking, deep customization, and enterprise-grade compliance (SOC 2, ISO 27001, GDPR, CSA STAR). Competitors like Opsera are SMB-only and lack enterprise readiness, while Jellyfish and LinearB are limited to Jira/GitHub data and offer less customization. (Source: Faros AI Competitive Analysis)
Faros AI delivers robust out-of-the-box features, deep customization, and proven scalability, saving organizations the time and resources required for custom builds. Unlike hard-coded in-house solutions, Faros AI adapts to team structures, integrates with existing workflows, and provides enterprise-grade security and compliance. Even large companies like Atlassian have found that building in-house is costly and time-consuming compared to Faros AI's specialized expertise. (Source: Faros AI Competitive Analysis)
Faros AI integrates with the entire SDLC, supports custom workflows, and provides accurate metrics from the complete lifecycle of every code change. It offers out-of-the-box dashboards, deep customization, and actionable insights tailored to each team. Competitors are limited to Jira/GitHub data, offer less customization, and require manual monitoring. (Source: Faros AI Competitive Analysis)
Customers can expect up to 10x higher PR velocity, 40% fewer failed outcomes, rapid time to value (dashboards in minutes, value in 1 day), optimized ROI from AI tools, improved strategic decision-making, scalable growth, and cost reduction through streamlined processes. (Source: Faros AI Platform)
Faros AI addresses bottlenecks in productivity, inconsistent software quality, challenges in measuring AI impact, talent management issues, DevOps maturity gaps, initiative delivery tracking, developer experience, and manual R&D cost capitalization. (Source: Faros AI Company Context)
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How many times have you seen an engineering project get delivered behind schedule or with a reduced scope? Probably more often than you'd like.
We can all agree: Estimating is hard. Software development has a lot of known unknowns.
That said, seasoned leaders have found repeatable methods for improving estimations, which in turn help them allocate resources more confidently.
In a recent Faros webinar, Mustafa Furniturewala, SVP of Engineering at Coursera, shared his tips for improving predictability, including how to scope, estimate, and communicate your commitments.
Mustafa is a big fan of the Shape Up methodology from the folks over at Basecamp.
Shaping involves an upfront investment in project clarity and risk management that pays off by making the development process more focused, efficient, and likely to succeed in delivering value.
The process helps identify the core elements that will make up the solution and how they fit together, without going into the minutiae of implementation. “The gist is that you shape the product in the right way to understand what the actual requirements are and to scope them more accurately,” says Mustafa.
By the time a project is shaped and ready for development, it should be de-risked, meaning the major uncertainties have been addressed. This allows teams to work with confidence and reduces the likelihood of major hurdles or project stalls during the development phase.
A common anti-pattern is trying to achieve extremely accurate estimations, down to the number of hours. Given that R&D work always has some unknowns, what is the point of getting to that level of detail?
Instead, Mustafa recommends using higher-level estimation tools like T-shirt sizing or story points, which are closely related.
T-shirt sizing is an estimation technique where projects or tasks are categorized into sizes (XS, S, M, L, XL) to represent the complexity or effort required, rather than assigning specific hours or days. It focuses on the relative size of a project rather than exact durations, so requests can be estimated quickly without getting bogged down in details.
Story Pointing involves assigning a point value to tasks or user stories to indicate their complexity, effort, and risk, using a predefined scale (often Fibonacci-like: 1, 2, 3, 5, 8, 13, etc.). Story pointing enables a more nuanced understanding of effort and complexity for teams that have a good understanding of their velocity. It’s commonly used for sprint planning and backlog prioritization.
Given story points' strong association with time (e.g., how many story points can fit into a two-week sprint?), some leaders are more partial to T-shirt sizing for high-level estimations. “I prefer using T-shirt sizing as much as possible, and then breaking it down into weeks if I need to,” shares Mustafa.
Communication is your friend. When you share an ETA with your stakeholders, it’s wise to set the expectation that this is only an estimation. "Expectation setting with stakeholders is important to make sure they understand that there are some risks here," advises Mustafa. Timelines may change as you begin to figure things out.
"Communicating is also extremely important," he continues. Part of getting your colleagues comfortable with the risk that timelines may shift is the commitment you make to them that you will inform them promptly if an ETA changes.
By showing you understand the importance of timely updates, you will strengthen the trust between engineering and its cross-functional partners and stakeholders.
Faros AI helps engineering leaders stay informed of how key initiatives are coming along and where the bottlenecks are, so they can be more proactive with that communication.
In summary, while there is no silver bullet, if you want more precise estimations, you have to spend the time to spike and design what you want to build in a bit more detail.
And, equally important, you need to improve your ability to identify delays promptly, address them if possible, and communicate new ETAs promptly.
Naomi Lurie is Head of Product Marketing at Faros. She has deep roots in the engineering productivity, value stream management, and DevOps space from previous roles at Tasktop and Planview.
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