If you have a team of software engineers and want to move them to Mac, you will need to consider a number of things before you do so.
Here’s what’s new in our January 2023 Release Notes:
* Tables Columns Sorting Improved
* Reconcile Commits Count Between KPI Card and the Table
* Efficiency Tab Improvements and Efficiency KPI Cards Align
Here’s what’s new in our December 2022 Release Notes:
* Developing Improvements for On-Prem Data Processing;
* Improving Jira Data Connection;
* Aligning Metrics throughout the Application.
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.
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 |
|
Delivery Schedule |
|
Budgeting |
|
Faster Growth |
|
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.
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’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.
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.
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.
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.
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:
| Stage | Valuation | Funding Amount | Stage |
| Seed/Angel | Variable | $50k to $5MM | Idea |
| Series A | $15MM | $10MM | Proof of Concept |
| Series B | $50MM | $20MM but over $40MM from 2020 | Build |
| Series C | $100MM | $50+MM | Scale |
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.
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:
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.
Post created: February 16, 2022
If you have a team of software engineers and want to move them to Mac, you will need to consider a number of things before you do so.
Here’s what’s new in our January 2023 Release Notes:
* Tables Columns Sorting Improved
* Reconcile Commits Count Between KPI Card and the Table
* Efficiency Tab Improvements and Efficiency KPI Cards Align
Here’s what’s new in our December 2022 Release Notes:
* Developing Improvements for On-Prem Data Processing;
* Improving Jira Data Connection;
* Aligning Metrics throughout the Application.
