As an engineering leader, you’re constantly faced with the challenge of ensuring your team’s productivity while balancing various priorities. How can you be sure that time is spent on high-impact work rather than just “keeping the lights on”?

It’s well known in the software industry that you can and should measure your teams’ productivity to optimize work distribution and ensure that they’re working towards strategic goals. However, this is reliant on having the right data, which is nearly impossible to gather without overburdening your engineers with extra training and admin work.

Why you need engineering team data

At both the individual and team levels, it’s challenging to get a full-picture view of how work is distributed among the four key categories of work: Admin & IT, Bugs, Operations & Maintenance, and Strategic Work. At Zenhub, we call these the “Big 4.” By breaking work into these four key categories, engineering leaders can pinpoint areas where strategic work productivity is diluted by non-strategic tasks, helping to reallocate resources for maximum impact. In discussions with Zenhub customers, we’ve found that these losses make it difficult to determine the impact of non-strategic work on team capacity and can hinder R&D investment decisions.

This raises the question: What is the best way to get the data you need to optimize your engineering teams? Should you build, buy, or do something else entirely?

Previously, there were two main options: time-consuming and process-heavy manual reports or expensive and difficult-to-set-up tools like Jellyfish and LinearB. However, with the help of AI, Zenhub has built a third solution: Zenhub’s Engineering Investment Report.

Let’s dive into each of these solutions.

Solution 1: Building your own reports

The need for visibility into team productivity has led many organizations to take matters into their own hands, manually building custom reports to track team distribution and workload. However, this approach comes with some major challenges:

1. Manual, Labor-Intensive Efforts: Categorizing and processing data manually requires constant updates, with team members tracking every task and categorizing work in real-time—a process that’s both inconsistent and prone to human error. The result is often incomplete or outdated data that fails to reflect the true workload.”
2. Team Dependence: Achieving accurate data depends on every team member’s input, making the process vulnerable to inconsistencies and requiring ongoing reminders to maintain data accuracy.
3. Timeliness Issues: Since manual reporting takes time, leaders often end up working with outdated data, which can have far-reaching impacts on an organization. For example, for a CTO, even minor reporting delays or inconsistencies can affect high-stakes decisions on resource allocation, R&D investments, and sprint planning.

This manual process leaves plenty of space for inaccurate data. As Ev Haus, Zenhub’s VP of Technology, puts it, when engineering teams are large, “A lot of it is driven by guesswork.” Building a custom solution might seem like a cost-saving approach, but investing in new processes and maintenance can quickly get out of control.

Solution 2: Traditional pre-built solutions

For companies seeking out-of-the-box solutions, enterprise tools like LinearB and Jellyfish offer reporting functionality but come with significant drawbacks:

1. High Cost: These tools can cost hundreds of thousands of dollars annually for even medium-sized teams, which can be prohibitive for many organizations, especially at scale.
2. Complex Setup: Implementing tools like LinearB or Jellyfish may require weeks or months to fully integrate with existing workflows. They demand intensive setup, IT resources, and training, costing valuable engineering time. They both also require new processes to be implemented, which can be a major disruption to otherwise productive teams.
3. Unreliable data: LinearB and Jellyfish rely primarily on Jira to generate data. This is a major flaw, as large amounts of work done by engineering teams aren’t captured in Jira. Even in a LinearB report, they found that 30% of work is unaccounted for in Jira. The only real workaround in these tools is manual cross-checking Jira data with GitHub to find these missing pieces since they don’t offer native GitHub support.
4. Lack of Personalization and Insight: While they may offer dashboards, these tools typically lack tailored support to help teams translate data into actionable steps that align with unique team structures and organizational goals. This limits leaders’ ability to derive actionable insights. Putting it simply, George Champlin-Scharff, Zenhub’s VP of Product, explains that solutions like Jellyfish and Linear B “give you a lot of charts and dashboards, but absolutely no help in what to do with it.”

Let AI do the work: Zenhub’s Engineering Investment Report

Zenhub’s Engineering Investment Report is an AI-powered solution that solves the above issues by automating work categorization and providing actionable insights with minimal manual input. It provides data on the individual and team levels so that leaders can see where time is being disproportionately spent on non-strategic work. We run these reports regularly to help leaders measure progress and provide one-on-one consultation to ensure that the data is being used to make measurable improvements to your team.

Here’s what this means for leaders:

1. Automatic Work Categorization: Zenhub’s EIR uses AI to automatically organize work into four key categories: Admin & IT, Bugs, Operations & Maintenance, and Strategic Work. This high-level view allows leaders to see where time is spent and identify areas for improvement—enabling a more strategic allocation of team resources.
2. No Need for Manual Updates and New Processes: We use AI to eliminate the manual work that these types of reports would typically require. Thanks to AI, your data doesn’t need to be perfect to generate a categorized report of work being done, helping to avoid cumbersome new processes. This saves significant time and removes the risk of human error associated with manual data entry.
3. Personalized Support & Actionable Insights: Zenhub’s EIR doesn’t just deliver raw data. Through one-on-one consultations, teams receive personalized guidance on how to interpret the findings and develop an actionable strategy that takes into account their team’s intricacies.
4. Cost-Effective: Unlike traditional tools, Zenhub’s EIR is designed to be an affordable solution, making advanced team productivity insights accessible.
5. Data Reliability: By connecting directly to GitHub, EIR gathers data from the platform most engineering teams already use. This ensures that the information is up-to-date and reflects real activities without needing any additional input from the team, making implementation a quick and painless process. GitHub data also ensures that you capture all the work your teams do, not just the work in your project management tool. Offering regular, automated reports allows leaders to monitor progress over time and make continuous improvements. Of course, we are also sure to incorporate the stringent security protocols that our customers have come to expect from Zenhub and have consistently earned us SOC 2 Type 2 certifications over the years.

Don’t just take it from us. We’ve built EIR alongside our customers. In running reports for one of our customers, we validated their major concerns that something was off with their teams. Using EIR, we found that certain high-productivity teams and individuals were under-utilized, and they made adjustments to get those individuals focused on more strategic work. Want to see what this looked like? Check out our EIR customer story here.

Drive Strategic Decisions with AI-Powered Reporting

As George Champlin-Scharff notes, EIR’s insights have been transformative for engineering teams. “Before EIR, you may not have an established way to effectively distribute work across your team. You may not know who is best suited for what kinds of work…after EIR, you will know.” By automatically categorizing your team’s GitHub, EIR allows engineering leaders to answer fundamental questions about workload, capacity, and task prioritization, replacing guesswork with precise, actionable insights.

In a landscape where productivity data is both essential and often elusive, Zenhub’s Engineering Investment Report offers a powerful, efficient, and affordable solution. By leveraging AI, EIR gives engineering leaders real-time visibility into work distribution without adding more processes to teams. From optimizing workload distribution to informing R&D investments, EIR supports better decision-making and maximizes team productivity.

Curious about the impact EIR can have on your team? Schedule some time to receive a personalized report and discover new optimization opportunities tailored to your needs.