Scaling Software Development Teams Faster with the Help of AI

Run-through

pull requests and code reviews

Pull Requests And Code Reviews

There are two words that will help open a lot of doors in life. Push and Pull. Here, we’re going to talk about Pull Requests. Pull requests have since become a standard part of the software development process and are closely associated with code reviews. Code reviews involve teamwork and collaboration on code and serve as one of the four main drivers of software development, alongside speed, quality, and efficiency.

Read More »
The Gitential Guide on How to Reduce the Size of Your Git Repository

The Gitential Guide on How to Reduce the Size of Your Git Repository

Unfortunately, there are no easy ways on how to reduce the size of your git repositories. However, there are quite a few ways to keep your repositories down to an efficient and easily manageable size. Many of them are fairly simple and can be set up fairly quickly. But, they probably won’t help you if you are already exceeding your repository cap on Bitbucket, Github, Gitlab, or Azure DevOps. For this scenario, we can help you identify what to target when it becomes necessary to deal with a large repository.

Read More »

By GITENTIAL TEAM

How can AI help you scale up your software development team faster? While we’re at it, let’s look at how AI can help make the process more efficient and cost-effective, too. Growing teams has always been a challenge. It plays a part in why 70% of software projects are challenged in meeting specifications, schedules, and budgets. Moreover, high-growth companies have average turnover rates of over 25%! So, let’s take a look at how AI can help Startups, SMBs, and Enterprises sidestep a lot of growing pains.

How AI Helps You Scale Teams

While Startups, SMBs and Enterprises can tap into all of the benefits of AI, each is likely to focus on specific challenges when they want to ramp up development fast. We’ll address each business case shortly. Leastwise, most challenges relate directly to your development team with respect to their skill, experience, capacity for teamwork, and wages. 

As many companies have hybrid teams, AI’s benefits extend equally to vendor management when outsourcing development for specific tasks and the performance of augmented teams via IT staffing agencies.  

The following table defines the specific benefits AI brings as relates to scaling challenges. Some of these benefits overlap and apply to software delivery in general terms.

Challenge

How AI Helps with Scaling Teams

Project Specifications

  • Validate the skill of developers.

  • Identify skill gaps by team, project, and/or organization.

  • Better assess the skills you’ll need to hire for or if additional training will be sufficient to meet future requirements.

Delivery Schedule

  • Improved cross-functionality of team members with Next Best Actions.

  • Insights On-Demand to improve developer and team performance.

  • Benchmarking allows comparisons by developer, team, project, even by company and industry standards to set realistic goals and facilitate knowledge sharing.

  • Enhanced risk management to predict, proactively mitigate, or avoid threats to successful delivery.

  • A 360° view plus real-time alerts for faster responses should performance metrics deviate from standards.

Budgeting

  • Find and plan for the best mix of junior, mid-level, and senior developers.

  • Optimize developers and vendors for each project on the basis of cost, performance, and responsiveness.

  • Ability to prioritize projects based on cost, performance, and value (ROI).

Faster Growth

  • Identify team members best suited for promotions to team lead and management roles based per teamwork metrics.

  • Improved cost-performance can allow for more developers or vendors while staying within budget

  • Use behavioral analytics to identify developers at risk of burning out or leaving to reduce turnover

  • Align your entire company, its teams, and projects to organizational objectives and KPIs (see also OKRs). 

  • Investors and C-levels can better direct resources to support management teams in meeting specific growth challenges.

A Quick Look at Super-Sized Scaling

Some of today’s tech giants literally started with a handful of people working out of a garage. It’s hard to get too much smaller than that! Even Microsoft started out small.   

  • Microsoft started in the Bronze Age and now has around 47,000 developers.
  • Amazon started in 1994 and now has about 36,000 developers.
  • Google started in 1998 and has ~27,000 developers with about as many contractors.
  • Facebook started in 2004 and has nearly 9,000 developers.
  • Uber started in 2009 and has 2,000 developers.

For the most part, all of these companies went from being a startup like any other to be the Big Tech Giants without the benefit of AI. AI can help big companies get bigger. Going from $500 billion in market cap to $1 trillion is impressive… but not nearly so impressive as going from $1 million to $1 trillion! The process, however, is essentially the same, suffice that companies today have the potential to do this faster.

AI for Scaling Projects for SMBs and Enterprises

AI’s real-dollar value increases in relative proportion to the size of your organization for its ability to rapidly analyze Big Data. One of our clients with over 500 developers has noted that even a 1% improvement in their delivery equates to $20 million in savings. In this regard, cost and performance are critical factors. Planning for and optimizing overall organizational capabilities is also a prominent issue with the growing global demand and shortage of IT talent.

Large companies with existing software development teams may seek to aggressively scale up a successful pilot. Here, AI can help you scale up with recommendations for the best developers for the project from across your entire organization. AI factors in their programming language expertise, propensity for teamwork, and performance. As noted previously, this also extends similarly to vendor management. 

Knowing everyone with the requisite skills for project specifications also helps identify organizational weaknesses. Will you need to hire more programmers strong in Python, R, or Julia? Perhaps some of your existing developers could get up to speed with some extra in-house training? These points apply equally if you know your company has plans for engaging in new technologies – you can see where your teams are at compared to where you wish to go.

AI for Scaling Startups

With startups, we get to the real fun in helping startups scale their development teams faster while also making it a smoother process. Startups have a lot of challenges though, not least of which are funding, leadership and turnover. There are plenty of other challenges inherent to software development generally which the table above covers in part. Here, though, we’re focused on scaling up fast, so we’ll focus on these three points.

How AI Helps with Turnover

A lot of factors play into turnover and we discuss them at length in Why Developers Leave and important steps to prevent it with a 90-Day Onboarding Program. Developers leaving midstream in a project is disruptive and costly, suffice that it’s hard to grow your team if 25% are dropping out every year. Gitential’s AI uses behavioral analytics that can help spot if a developer is about to leave your company or is starting to burnout.

AI to Help Team Intelligence and Leadership

Leadership and Team Intelligence are major issues for scaling up. AI can help here by helping you identify which of your developers is objectively well-suited to be promoted as a team lead or engineering manager based upon teamwork metrics. This, of course, needs to be accompanied with proper one-on-one meetings, direct observation and interviews. AI always serves as an advisor, humans remain the decision makers. 

 

The added benefit of AI, as referenced in the table, is its ability to evaluate enormous amounts of data to suggest Next Best Actions (NBAs). These allow developers and managers alike to make Google-like queries and receive “Insights On-Demand” – everything they need to know to make an informed decision and how to execute it. We cover this at greater length on whether AI-Assistants are Worth It? and AI Project Management.

The goal here is to use your existing developers to provide “continuity of leadership” at each stage of your development. While a lot of companies hire brand new managers as their team expands, promoting from within has benefits of its own. The more familiar team leads and managers are with the code base, specifications, existing team, etc., the less disruptive growth will be. Checkout Team Intelligence and AI for more details.

Funding and Scaling Go Hand in Hand

Your funding can vary wildly whether you just won $50-500k from a startup incubator or accelerator or won $2-5 million from an Angel or VC investor. This funding puts you on the path to Series A, B, and C funding rounds. The expectations that come with each round may require you to 2-10x your team. 

There’s no hard-set standard or even limits for scaling up tech companies. It’s recommended that startups plan for 18-24 months between funding rounds. Some go faster, others slower.  Amazon did a Round A and jumped straight into an IPO. Uber had over 30 funding rounds before its IPO. So, while startup value and funding levels vary widely, they generally look something like:

 

A “Quick” Overview of Tech-First Startup Stages

StageValuationFunding AmountStage
Seed/AngelVariable$50k to $5MMIdea
Series A$15MM$10MMProof of Concept
Series B$50MM$20MM but over $40MM from 2020Build
Series C$100MM$50+MM Scale

Scaling Up and Funding Runways

Your funding runway matters most. That’s your available cash divided by your net burn rate. If you have $1.2 million but spend $120k monthly, and have no income, your runway is 10 months. It’s how long you can continue to operate without additional funding (or revenue).

You have only three ways to extend your funding runway – a) seek more funding earlier, b) generate revenue faster, c) optimize your expenses. If you’re looking to scale up fast, you’ll want to look at all three. 

Your funding runway is most important because developer wages are the largest expense in software development. The most recent BLS data pegs the average base wage for US developers at $110k annually. The fully loaded cost for in-house employees can add 25-40%. Of course, developer wages also vary internationally, ranging to the extremes of $20k (SE Asia) to $200k (Silicon Valley), with some specialized positions running $400k or more. 

Fully-loaded, an “average” US-based Agile Team with 7 developers costs about $1 million per year. Average is not synonymous with typical, and it’d be hard to pinpoint a typical team especially for so many different types of startups. But, it’s a nice round number that helps to provide context as sales, marketing, customer support, operations, and other business elements also weigh in on your funding runway.

AI for Faster Revenue and Investments

There are two ways that AI can help companies extend their funding runways – get to market faster and impress investors.

In the first case, AI can help improve Cycle Times leading to faster releases for MVPs and continuous improvements that can start generating useful customer data and revenue in the near term. Discussing MVPs is beyond the scope of our present discussion (if closely related). Even so, the MVP process is very important for helping to find product-market fit early on, and before investing time and money into features that customers don’t want. 

The case for investors is three-fold:

  1. Being able to provide objective cost-performance metrics and development trends to investors can impress investors. They want to know that you will treat their money with the same care and attention as your own. 
  2. Investors are increasingly wanting a greater voice in the management of  their startups. This extends to the hiring and firing of personnel. Investors are justified in wanting to see that a team’s performance is improving over time. 
  3. In the event that teams are not improving, AI can identify the specific challenges holding them back on a per metric basis so that investors (and C-levels) can better allocate resources to help their teams get up to speed.

It’s worth noting that an engineering manager for an early-stage startup may encounter challenges with subsequent funding rounds. Each round adds complexity to team management, and AI can help a lot with this. But also, most investment groups have a hand in numerous startups. They are already well connected to companies and engineers who have already faced and overcome the challenges your startup or managers are going through. Mentoring plays an important part in all stages and aspects of a company’s development.

About Gitential

Gitential is an Analytics and Engineering Intelligence service provider bringing visibility and optimization highlights on teams’ productivity. Our mission is to enable faster, data-driven decisions to continuously improve software delivery team cost performance and proactive risk management.

Ready to explore different ways to improve your software projects’ efficiency? Schedule a meeting and we will be happy to listen to and discuss your needs.

Have a project but are not quite ready to contact us? See if Gitential is a fit for you!

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Post created: February 16, 2022

pull requests and code reviews

Pull Requests And Code Reviews

There are two words that will help open a lot of doors in life. Push and Pull. Here, we’re going to talk about Pull Requests. Pull requests have since become a standard part of the software development process and are closely associated with code reviews. Code reviews involve teamwork and collaboration on code and serve as one of the four main drivers of software development, alongside speed, quality, and efficiency.

Read More »
The Gitential Guide on How to Reduce the Size of Your Git Repository

The Gitential Guide on How to Reduce the Size of Your Git Repository

Unfortunately, there are no easy ways on how to reduce the size of your git repositories. However, there are quite a few ways to keep your repositories down to an efficient and easily manageable size. Many of them are fairly simple and can be set up fairly quickly. But, they probably won’t help you if you are already exceeding your repository cap on Bitbucket, Github, Gitlab, or Azure DevOps. For this scenario, we can help you identify what to target when it becomes necessary to deal with a large repository.

Read More »

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Don't miss our latest updates. All About Software Engineering Best Practices, Productivity Measurement, Performance Analytics, Software Team Management and more.